Cities and towns in several other African nations including Mozambique, Zimbabwe and Côte d'Ivoire have been plagued by similar water shortages in recent months, manifestations of a global supply squeeze brought on by drought, population growth, urbanization and insufficient investment in dams and other infrastructure.
Water use has risen about 1% a year since the 1980s and more than two-billion people now live in countries experiencing high water stress, the United Nations said in its World Water Development Report released in Geneva on Tuesday. It projects demand will grow as much as 30% by 2050.
The scourge is set to become exponentially worse in Africa – the UN expects the population of the world's poorest continent to almost double to 2.5-billion by 2050, and that 59% will reside in urban areas by then, up from 43% now. A massive increase in agricultural production will be required to feed everyone, compounding the pressures caused by surging household demand for water.
A study published by the African Development Bank last year found the continent needs to spend at least $130-billion to address an infrastructure backlog, including as much as $66-billion on providing universal access to water and sanitation, but faces a financing gap of $68-billion to $108-billion.
Joyce Ayitey, 42, who sells plastic cups, tissues and sweets from a wooden stall in front of the home she shares with her husband and three daughters in Diabolo in the eastern part of the city, has experienced the extremity of the problem. When her taps ran dry more than two years ago, the water utility company blamed road builders who destroyed its main pipelines and said the outage would be addressed within weeks, but nothing was done.
Ghana Water, the State-owned utility, is aware that some Accra neighborhoods have erratic water supply or are struggling with low pressure, but faces an uphill battle to tackle the problems because new neighborhoods are being developed faster than it can provide the infrastructure, spokesperson Stanley Martey said by phone.


CURITIBA: Boos and Jeers from the Brazilian fans highlighted the goalless FIFA World Cup match played between Islamic Republic of Iran and Nigeria at Baixada Arena at Curitiba, ARY News reports.
Iran filled their line up with several defenders whereas Nigeria was looking favorites to win the match due to the fact that two of their players were playing the English Premier League.
The two sides were clearly a case of misfits rather than World Cup stars. They did not capitalize in any of their plays and their passes went to players in the opposite teams.
However, the Nigerian team had managed to score but referee denied the goal whereas Nigerian defender denied Iran the chance to take the lead when goal keeper captain Vincent Enyeama saved a header on target by Iranian striker Reza Ghoochannejhad.


text:
\section{INTRODUCTION}

The need for dexterous manipulation capabilities in robotic teleoperation has been rising in recent years, especially beyond classical industrial applications, for example in  remote surgery \cite{Choi2018}, assistive robotic limbs for impaired users \cite{Herlant2016}, spatial missions \cite{Artigas2016}, or nuclear waste disposal \cite{Petereit2019}. As the recent pandemic has shown, whenever a flexible and skilled manual action is required but human physical presence is not possible, teleoperation could provide a means to remedy the situation, may it be with telepresence for helping distant relatives, in healthcare to safely provide medical assistance to contagious patients, in industrial productions requiring sterile environments, or in assistive applications restoring arm mobility to impaired users. In such cases, even when no specific tool is used and motion input control could be in principle delivered by natural limb movements (unlike in some assistive scenarios using joysticks or brain-computer interfaces), it might still be difficult to operate a robot with little to no training.
\begin{figure}[t]
    \centering
\includegraphics[width=0.45\textwidth]{Figures/setup.png}
\caption{Experimental setup for the data collection and model testing. A robotic hand is operated in virtual reality to execute pick and place tasks. The participant's head and hands movements are tracked via the Vive Lighthouse system, the eyes are tracked inside the Vive headset. The screen on the background visualizes the scene as perceived by the participant's point of view.}
\label{fig:layout}
\end{figure}
Since perception and action are in this case both mediated by technical systems, they are also possibly affected by delays. Further, usually no or limited haptic feedback is available. Therefore, these applications would also benefit from shared control manipulation \cite{Griffin2005, Rakita2019}. In such cases, the user could interact with objects more naturally, exploiting their natural eye-hand coordination, while the robotic systems translates their intentions into the real scene and relieves them of low-level kinematic control. We recently proposed an intention estimation framework relying on gaze features to infer pick and place intentions and related targets in a rather simplified scene with just 4 objects and 6 intentions \cite{Fuchs2021}. Here, that scenario is extended to work with a more cluttered scene with partially occluded objects that can be grasped with either hand. Hence, here the following contributions are made:
\begin{itemize}
\item A new dataset was collected in virtual reality, with multiple objects purposely placed close together to create visual occlusions. This configuration elicits complex gaze behaviors and different grasping movements, depending on the distractors around the target object and on the used hand.
\item The second hand was also introduced in the virtual environment to evaluate the generalization of the intention recognition model across effectors.
\item The feature set for the model is expanded to take into account the current motion trajectory of the arm, which can be suggestive of the target of the intended interaction.
\item We propose that in this way a single-hand framework can be straightforwardly extended to bimanual manipulations, combining intentions from two hands.
\end{itemize}

The experimental setup for data collection and the feature extraction procedures are presented in Section \ref{sec:exp}. The system was tested on different users and across actions performed with different hands, to demonstrate its robustness and to evaluate the predictive capability of different feature combinations. The new model and the test results are presented in Sections \ref{sec:model}, \ref{sec:bim}, and \ref{sec:results}.  In section \ref{sec:conclusions} we conclude with a discussion on current developments and possible future extensions of the bimanual architecture.

\section{RELATED WORK}

Within shared autonomy, first approaches to intention estimation and several recent ones  have made use of the user control input in driving the robotic movement as behavioral cue for prediction \cite{Hauser2013,Dragan2013,Javdani2015, Aarno08,Tanwani2017}. This could be given via more or less intuitive input modalities, such as mouse pointers \cite{Hauser2013}, joysticks \cite{Javdani2015}, another robot arm \cite{Tanwani2017}, or the user's movements as tracked by a camera \cite{Dragan2013}. Most of these works rely on motion features such as effector pose, velocity, arm joints, or whole gestemes, to predict a distribution over the different action targets.

On the other hand, a number of more recent approaches have exploited last decades' advances in eye tracking capabilities to infer intentions from head-mounted eye trackers. A large body of literature, indeed, has shown how during daily manipulations the eyes anticipate the hand, landing on the targeted object about 0.5 s before making contact with it \cite{Hayhoe2003,Johansson2001,Belardinelli2016}.
Many gaze-based frameworks are used in assistive applications where natural movements might not be an option and control input is provided by joystick or rehabilitation robotic arms \cite{Shafti2019,Aronson2021}. In these cases, the eyes indeed offer a much earlier and less noisy cue for intention estimation, while the effector movements are controlled in a non-usual fashion.
Considering intention estimation from egocentric vision in natural eye-hand coordination, \cite{Fathaliyan2018} and more recently \cite{Wang2020} proposed a method for intention recognition in manipulation sequences, relying on time series of gazed objects and angular velocities of the gaze.
Still, in principle, a number of behavioral cues can be used for intention estimation, leveraging the different predictive power. This was already postulated for instance in \cite{Admoni2016} and \cite{Jain2019}, where general frameworks for intent inference from any number of cues were put forward.
In recent years, some studies have looked at the effective benefit of feeding both gaze and motion features into the prediction system. In \cite{Razin2017}, numerous prediction techniques were benchmarked in the context of teleoperating a robotic claw by means of three levers and considering just 'move', 'hold', and 'stop' intentions. In this simplified scenario, motion features such as the claw position and velocity were enough to reach a good prediction accuracy, with gaze position and velocity \textit{per se} also demonstrating a good predictive power but not improving the performance when combined with motion features. Hidden Markov Models were further among the best scoring classification methods, as shown by their large use in different forms in the literature altogether (e.g., \cite{Aarno08,Tanwani2017,Fuchs2021,Aronson2021}). In \cite{Li2020}, yet, more complex manipulations were considered (handover, transfer, usage) and both the eyes and the hands were tracked for intent recognition also from grasp modeling. In this scenario, the authors found that gaze  helped in improving performance in teleoperation in the case of ambiguous grasps. For these reasons, and with the ultimate goal of prospectively moving  to full bimanual intention recognition, the HMM-based framework presented in \cite{Fuchs2021} is extended with motion features, a richer dataset is collected, and relative challenges and improvements are investigated. For congruence with the gaze features, we used motion features that, as the Areas-Of-Interest recalled below, denote not just an effector pose or motion characteristics, but already entail the likelihood of each object being the target of the current movement trajectories, as detailed in the next section.

\section{DATA COLLECTION AND PROCESSING }
\label{sec:exp}
\begin{figure}[bt]
    \centering
\includegraphics[width=0.48\textwidth]{Figures/AOI_TPA_vis.png}
\caption{Explanatory visualizations of the Areas-Of-Interest (AOIs, left) and Target-Path-Analysis  features (TPAs, right). For the AOIs, the bounding box of the currently gazed object is highlighted in green along with the corresponding normalized likelihood. For the TPAs, the most likely trajectory to each object is shown and the most close to the current predicted hand trajectory is highlighted in green (i.e., to the purple cylinder with approach point on top). The other trajectories are depicted in magenta with line width proportional to their likelihood. In white, also the location labels are depicted: $a_i$ labels refer to 'pickable' cylinders, $b_i$ to placing locations. Features and labels were not visible to the participants performing the tasks. The left hand is not visible in the field of view here, yet a TPA value for it was still computed.}
\label{fig:AOI_TPA_vis}
\end{figure}
\subsection{Experimental setup}
Pick-and-place sequences were collected from human users in a virtual scene, with physics simulated in the Gazebo simulator and rendered to the user via Rviz. This was displayed in the HTC Vive Pro Eye headset, featuring a 1440 x 1600 pixels screen per eye (2880 x 1600 pixels combined, 110$^{\circ}$ Field-Of-View), and a binocular Tobii eyetracker working at 120 Hz with a stated accuracy of 0.5$^{\circ}$ to 1.1$^{\circ}$ (see Fig. \ref{fig:layout} for the physical setup).
The virtual scene consisted of a table with 6 cylinders (20\,cm high and with a radius of 5\,cm) arranged in two lines in front of the participant. The cylinders were spaced 15\,cm apart on the horizontal axis and 10\,cm apart along the vertical axis, on the table plane. The cylinders are conventionally labeled as $\{a0,..,a5\}$, with even numbers for the furthest line and odd numbers for the line closest to the participant (see Fig.~\ref{fig:AOI_TPA_vis}). Further, 6 coasters were presented, aligned along the vertical edges of the table (3 by each side), spaced 20 cm from one another vertically and 66 cm apart horizontally. These discs defined the placing target positions, named as $\{b0,..,b5\}$. For each participant the height of the table was adjusted so that from their egocentric perspective only about the upper half size of the cylinders on the second line was visible. In this way, we wanted to assess the visual behavior in terms of gaze data and the ensuing robustness of the recognition system in case of occluded objects. We decided to work with cylinders  since we were interested here in investigating the gaze and grasping behavior of generic objects in the presence of crowding and occlusions, independently of their identity. The visuomotor behavior with real objects indeed is often influenced by their goal-driven or habitual use \cite{Herbort2011,Belardinelli2016}.\par
In each trial, the participant would first see an occluding screen where the word of the color defining the pick and place tasks was presented. Upon disappearance of this screen (after 3 seconds), they were requested to grasp the cylinder in the given color and place it on the coaster of the same color. Each participant carried out 4 blocks of trials: one with the right hand, one with the left, two with hand-overs (picking up with the right and placing down with the left hand and vice versa). Each block contained 36 trials, i.e., each combination of picking and placing targets. The Vive controllers, tracked by the Vive Lighthouse system, were used to grasp objects. With their movements participants could operate two Kinova robotic grippers and grasp an object by pressing the button on the controller. Only the grippers, i.e. no arms, were visible in the scene and their position and orientation reflected the hand pose of the participants (see \cite{Fuchs2021} for more details on a similar setup). Colors were randomized across both pick and place positions in each trial.  Data were collected from 10 non-naive Honda Research Institute associates (9 males, 1 female) working on a related project, all right-handed.

\subsection{Gaze-based features: Areas-Of-Interest}
Each object in the scene (cylinders, coasters and robot hands) is considered as an Area-Of-Interest (AOI), that is, a stimulus that can be the target of visual attention. As a proxy to measure visual attention on each object, we consider the current gaze position $\bf{g}_t$ at any time as the center of a Gaussian distribution $\mathcal{N}(\bf{g_t},\Sigma)$, with variance defined by a radius of 1$^\circ$ (accounting for eyetracker accuracy and human fovea size \cite{Strasburger2011}).  The surface integral of the density function over the visible faces of each object's bounding box represents the likelihood that this area is the current focus of attention \cite{Fuchs2021}. A depth buffer is used to determine occlusions w.r.t. the participants' point of view and to compute the likelihood only on the portions of the object actually visible to them. The likelihood was normalized across AOIs so to consider a distribution only on those (the table was not relevant to the task and was not considered since it would collect incidental gaze points).

\subsection{Hand motion features: Grasping state and Target Path Analysis}
Besides gaze features, two other types of features were logged in every trial.
The state of the grasping trigger (as a binary feature) and a similarity measure between the predicted trajectory of the acting hand and an ideal trajectory to each object in the scene (as for the AOIs) and to the other hand (see Fig.~\ref{fig:AOI_TPA_vis}, right). We term this latter Target Path Analysis (TPA) and it is inspired by the prediction of risk collision between two trajectories sketched in \cite{Muehlig2014} and formalized in \cite{Iba2019}. TPA features are computed as follows.

The current acting hand pose is taken as input along with each object pose. For each object a number of approach points are computed, by considering a location on the object as destination of the hand movement and the direction of approach of the hand. Possible approach points are: 1) the highest location on top of the object with the hand coming from above (e.g. when grasping an object from above or placing it down); 2) center of the object with the hand current direction along the current $x$ and $y$ as computed from the current velocity vector (e.g., when grasping from the side); 3) the object center and the direction along a straight line from the hand to the object. These points are used to compute the most likely trajectory to the object given the current hand pose and velocity.
At any time step, a Hobby spline \cite{Hobby1986} is computed to approximate the trajectory between the hand with its current direction and any of these approach points. These splines fit Bezier curves passing through the given start and end points and following the given tangent direction at these points. $n$ control points are then computed by the algorithm, along with covariances growing backward from the destination point. Here, to keep the computation time steady, $n$ = 8 for each trajectory was used, independently of the distance to the object\footnote{Considering a reaching distance $<1.5\ m$, this resolution is high enough to approximate the arm trajectories well.}.
From the current hand position and velocity, the hand trajectory is predicted via Kalman filtering with the same number of points as used for the splines, and with increasing variance for each further point, since the uncertainty about future positions of the hand grows with each time step. For each point $\pmb{\mu}_i$ in the predicted hand trajectory and in the estimated trajectories to the objects' approach points, hence a distribution $\mathcal{N}(\pmb{\mu}_i,\pmb{\Sigma}_i)$ can be defined. A measure of distance between two trajectories can thus be computed considering the weighted sum of the Bhattacharyya distances \cite{Kailath1967} between corresponding points on the trajectories. For each object, the approach point trajectory with closest distance to the current trajectory is chosen. To obtain features that express the likelihood of each object to be the target of the current movement, the sign of the sum of Bhattacharyya distances is changed and these are fed to a softmax function with temperature $0.05$. That is, for each object $o_i$ the corresponding TPA feature is computed as:
\begin{equation}
TPA(o_i) = \pmb{\sigma}(-\pmb{z}_i)
\end{equation}
where
\begin{equation}
\pmb{z}_i = \min\limits_{p\in \mathcal{AP}_{i}}(\sum_{j=0}^{n}w_j\cdot D_B[\mathcal{N}(\pmb{\mu_h}_j,\pmb{\Sigma_h}_j),\mathcal{N}(\pmb{\mu_{p}}_j,\pmb{\Sigma_{p}}_j)]) \ .
\end{equation}
Here $\pmb{\sigma}$ denotes the softmax function, $D_B[p(x),q(x)]$ the Bhattacharyya distance between two distributions, and $\mathcal{AP}_{i}$ the set of approach points for $o_i$. The weight $w_j$ is a function of the cosine between the normalized velocity vectors at point $j$ on the predicted hand trajectory and on the trajectory to object $i$, so the larger the difference in direction at the two points the larger the distance between those distributions is weighted.
\begin{figure}[t]
    \centering
\includegraphics[width=0.47\textwidth]{Figures/Model.png}
\caption{Schematic representation of the Gaussian Hidden Markov Models used for intention estimation along with the rearranged features. The four hidden states are fully connected, here only connections between adjacent states are shown for readability. The place GHMM has a similar structure as the pick GHMM, but in this case place targets are considered.}
\label{fig:model}
\end{figure}
\section{GAUSSIAN HIDDEN MARKOV MODELS FOR INTENTION ESTIMATION}
\label{sec:model}
The inference regarding each one-handed action is done by two Gaussian Hidden Markov Models (GHMM), trained on pick and place sequences, respectively. Pick sequences are defined from the start of each trial until pressing of the grasping trigger and labeled with the corresponding pick target, while place sequences were defined from the pressing to the releasing of the trigger and labeled with the corresponding place target. This yields a multiclass classification problem with 12 classes (6 pick and 6 place intentions).

Each of the observation vectors in a sequence consisted of 26 features: 1 binary grasping state, 13 AOI features (6 pick, 6 place targets, the acting robot hand), 12 TPA features (same as for the AOIs but no robot hand)\footnote{Even if the other hand was in the scene, this in not used and never looked at for picking or placing with one hand. We consider here a system capable to work with just one hand first, addressing the extension to a bimanual scenario in the next section.}. AOI and TPA features were normalized to express the probability that each object is the current target of the gaze $\mathbf{g}_t$ and of the hand motion $\mathbf{h}_t$, i.e., $\mathrm{P}(\mathrm{AOI}=o_i|\mathbf{g}_t)$ and $\mathrm{P}(\mathrm{TPA}=o_i|\mathbf{h}_t)$, respectively. Still, this is a large number of features, which would change as soon as an object disappears or appears, or a new scene is presented. In order to make the approach scalable to other scenes than those seen in training and to limit the input to the model to semantically relevant cues describing the current sensorimotor state in a compact form, features were rearranged in the following form to entail just 8 features:
\begin{multline}
    \mathbf{F_{pick}}_t = \{grasping\_state_t,\mathrm{P}(\mathrm{AOI}=a_i|\mathbf{g}_t),\\
    \sum_{j\neq i}\mathrm{P}(\mathrm{AOI}=a_j|\mathbf{g}_t),  \sum_{k=1}^{m}\mathrm{P}(\mathrm{AOI}=b_k|\mathbf{g}_t),\\
    \mathrm{P}(\mathrm{AOI}=Robot|\mathbf{g}_t),
    \mathrm{P}(\mathrm{TPA}=a_i|\mathbf{h}_t),\\
    \sum_{j\neq i}\mathrm{P}(\mathrm{TPA}=a_j|\mathbf{h}_t),  \sum_{k=1}^{m}\mathrm{P}(\mathrm{TPA}=b_k|\mathbf{h}_t)\} \quad .
\end{multline}

This represents the input vector for the 'pick' GHMM, while for the 'place' GHMM a similarly arranged vector is obtained by replacing in the above equation the $a$ terms (picking targets) with the $b$ and viceversa. For the place rearrangement, the TPA feature of the object in hand was forced to be slightly lower than the furthest object, so that it would not dominate the sum of distractors and the place target TPA feature would increase steadily as the hand approaches it.\par
These rearrangements allow us on the one hand to train just one model for the 'pick' and one for the 'place' intention, and on the other hand to have a system independent of the number of objects in the scene. The multiclass classification problem is then tackled in a one-vs-all fashion.
Each GHMM is defined with 4 states that might represent moving/looking toward the action target or the distractors in either action, or looking at the own moving hand.

Training is done by rearranging the sequences according to the current ground truth target (e.g. for "pick at $a_0$", by considering the AOI/TPA for $a_0$ as single features and summing other $a_i$ and $b_j$ together in the other two features) and feeding each GHMM the corresponding sequences. Testing and online prediction is done considering a window of $\Delta t = 0.45s$ and rearranging features in this window according to each possible 'pickable' or 'placeable' target candidate and computing as many likelihood scores (i.e., 12, in our scene). The score with highest log-probability and exceeding a threshold of 0 is taken as prediction of the respective intention. Otherwise, the confidence in the prediction is not large enough and no prediction is output. Exploration of the predicted log-likelihood scores indeed show that these are distributed bimodally, with very negative values when no intention is confidently recognized and sudden positive values as soon as an action is recognized (see Fig. \ref{fig:time_evol}).  Offline training and online recognition are implemented in Python by means of the
hmmlearn library \footnote{\url{https://hmmlearn.readthedocs.io}}. The model with its input is sketched in Fig. \ref{fig:model}.

\section{Bimanual architecture}\label{sec:bim}
Upon assessment of generalization of the models across hands, we investigated a bimanual architecture considering the combination of intentions coming from two effectors.
Both the AOIs and TPAs features as formulated here are not adapted for a specific effector, thus we reasoned that, in principle, each model could be used interchangeably for the left or right hand.  This means also that the same model could be instantiated for each hand: each instantiation receives the same gaze data (up to the 'robot hand' AOI) but different grasping states and TPA features.
In this way, concurrent but independent picking and placing intentions for both hands can be recognized or more coordinated bimanual actions can be considered.
This can be done by learning how the two outputs from the one-hand models relate to labeled one-hand or bimanual intentions but in a simple pick-and-place scenario a rule-based system can suffice.
In this way, a hand-over or other more complex manipulations can be broken down in the intentions assigned to each effector.

Such a rule-based system was implemented to handle some basic bimanual tasks by the following rules: 1) if for both hands 'pick object x' is recognized then a bimanual pick is recognized ('Multihand Pick'); 2) if holding and placing down the same object is recognized for both hands then a bimanual place is recognized at bimanual level ('Multihand Place'); 3) by considering each hand as a possible target surface, if for one hand a placing to the other hand is recognized and for the other hand picking the object is recognized, then a handover from the first hand to the second is output as bimanual intention ('Hand Over').
As mentioned, these rules combine the information provided by the intention modules from each hand, i.e., the predicted one-hand intention for each hand ('Pick' or 'Place'), and additionally the target object of each hand.

\section{RESULTS}
\label{sec:results}

\begin{figure}[t]
    \centering
\includegraphics[width=0.47\textwidth]{Figures/F1_10users_t0.45.png}
\caption{F1 and predictability scores for each intention across 5-fold cross-validation over 10 participants. Here, predictability represents the portion of observations for which a confident prediction is available.}
\label{fig:F1_pred}
\end{figure}
\begin{figure}[t]
    \centering
\includegraphics[width=0.47\textwidth]{Figures/conf_mat_10users_t0.45.png}
\caption{Normalized confusion matrix: on the $x$ axis the predictions, on the $y$ the ground truth.  Numbers represents the frequency with which samples of a certain class (row) were classified with the label on the corresponding column. Some confusion is present between neighboring targets/location, but no confusion between pick and place intentions.}
\label{fig:cm}
\end{figure}

\subsection{Cross-validation across users}
To assess generalizability across users  a 5-fold cross-validation was conducted, by training each time on sequences by 8 participants and testing on sequences by 2 unseen participants. Training was conducted for the one-handed blocks considering the TPA features from the acting hand. For the hand-over blocks, we considered the picking hand for the picking sequences and the placing hand for the placing sequences.
F1 scores across folds for each action are summarized in the boxplot in Fig.~\ref{fig:F1_pred}. Here, it must be noted that these scores are computed considering only samples for which a prediction is indeed available (i.e., log-likelihood score over 0 for at least one of the classes). It can be seen that for each intention the F1 score, which combines precision and recall, is over 70\%. Yet, to put this information in context, in the same plot also the predictability is shown, defined as the ratio of each action for which a prediction is available, independently of its accuracy. For each action, on average at least 60\% of the time an intention is recognized with sufficient confidence. This relates to earliness of prediction, considering the complementary view that a prediction is first available after 40\% of the action has unfolded. Moreover, place intentions appear to be recognized earlier and better than pick intentions: the place locations indeed have less distractors in the vicinity and are directly targeted by the gaze, while to pick a cylinder in the two-lines configuration the eyes check first other cylinders close by to prevent the hand from colliding with them.
Looking at the confusion matrix in Fig.~\ref{fig:cm}, it is apparent that wrong predictions happen mostly between adjacent target locations, but the two general intentions (pick and place) are never confused. As in \cite{Fuchs2021}, this happens because the models learn that the grasping state is the most predictive feature for the type of intention.

\begin{figure}[t]
    \centering
\includegraphics[width=0.40\textwidth]{Figures/Acc_vs_pred.png}
\caption{F1 and predictability as functions of the window size for the sequence in input to the GHMMs. The longer the sequence observed the better the prediction, yet this becomes available only toward the end of the action, causing the predictability (portion of observations with a confident prediction) to drop dramatically.}
\label{fig:wind_size}
\end{figure}

\subsection{Earliness of prediction}
The dependency of the performance and predictability on the window size is further shown in Fig. \ref{fig:wind_size}. We chose $\Delta t$= 0.45\,s since for that value the overall predictability is above 60\% and the overall F1 score above 80\%.

The earliness of prediction is indeed a critical feature, since once an intention is recognized confidently, the robot needs time to plan an appropriate assisting action. To get a feeling about the absolute times when first a prediction is available, instead of a relative portion of the action, accuracy and predictability were computed over all trials at 100\,ms intervals before the end of the task. These were computed separately for pick and place intentions, since they strongly differ in their predictability, as shown in Fig.~\ref{fig:F1_pred}. Median execution times were between 1.1\,s and 1.5\,s for pick intentions and between 2.2 and 2.6 s for place intentions. As it can be noted in Fig.~\ref{fig:acc_evol}, left, for pick intentions accuracy is over 80\% between -900 ms and 0 ms, but predictability steeply increases as the model accumulates evidence for the target over distractors. In the last half second, predictability is then above 70\%. In natural eye-hand coordination,  500 ms ahead is also typically the time when people consistently fixate on the object target of the manipulation before their hand reaches it  \cite{Johansson2001,Hayhoe2003}. For place intentions on the other hand, accuracy and predictability are constantly high with some lower accuracy in the beginning and in the end. In these two moments, indeed, the gaze is more focused on the grasped object, rather than on the place target: in the beginning because after grasping without haptic feedback the user makes sure that the object moves along with the hand by visually monitoring it; at the end because when placing down from above the object finally occludes the coaster. This behavior is exemplified on the top panel right in Fig.~\ref{fig:time_evol}, where it appears that the AOI likelihood of the grasped object is highest at the beginning and end of the placing phase.

\begin{figure}[t]
    \centering
\includegraphics[width=0.47\textwidth]{Figures/Acc_pred_evol.png}
\caption{Evolution in time of the accuracy and predictability from 900 ms until the end of the task (right: the pick task, left: the place task).}
\label{fig:acc_evol}
\end{figure}

\begin{table*}[t]
\caption{Accuracy and predictability for different combinations of features}
\label{tab:ablation}
\begin{center}
\begin{tabular}{|c||c|c|c|c|c|c|c|c|}
\hline
 \bf{Feature combination} & \multicolumn{2}{c|}{\bf{AOI + TPA + GS}} & \multicolumn{2}{c|}{\bf{AOI + GS}} & \multicolumn{2}{c|}{\bf{TPA + GS}} & \multicolumn{2}{c|}{\bf{AOI + TPA}}  \\
\hline
\underline{cross-validation} & \underline{users} &
\underline{hands} & \underline{users} &
\underline{hands} & \underline{users} &
\underline{hands} & \underline{users} &
\underline{hands}\\
\hline
F1 & \bf{82\%} & 80\% & 80\% & 81\% & 38\% & 45\% & 48\% & 50\% \\
\hline
Predictability & 71\% & 73\% & 71\% & 73\% & 71\% & 73\% & \bf{99}\% & \bf{99}\% \\
\hline
\end{tabular}
\end{center}
\end{table*}

\begin{figure*}[t]
    \centering
\includegraphics[width=0.89\textwidth]{Figures/AOI_TPA_Int.png}
\caption{Evolution in time of the AOI and TPA features and of the intention log-likelihoods during an exemplary pick (on the left) and place (on the right) task with the right hand. The intention log-likelihoods are plotted before normalization to appreciate the difference between different intentions. In these plots, also the AOI and TPA likelihoods for the table and the non-acting hand are shown for reference, but they were not considered as input to the GHMMs.}
\label{fig:time_evol}
\end{figure*}

\subsection{Generalization across effectors and ablation study}
With the prospect of a bimanual scenario, we were also interested in assessing whether the models would work equally well when using the non-dominant hand, when perhaps movements are less fast and dexterous and eye movements could display a slightly different behavior.  Moreover, we also wanted to ascertain how reliable the different kinds of features are, and which are the most effective combinations. Hence, we conducted a kind of ablation analysis, training each time on a different combination of features and comparing it to the full-fledged system.
The results of this analyses are presented in Table~\ref{tab:ablation}. For each combination the cross-validation was conducted across users (5-fold) and across hands (2-fold, training on trials performed with the right hand and testing on left hand trials, and vice versa). In general, the models generalize well across hands, that is,  even when training with 50\% of the trials, F1 scores are generally on par with those in the 5-fold cross-validation.\par
The best performance across users is obtained when all features are considered (AOI, TPA and grasping state), although the improvement is small w.r.t. only considering gaze and grasping state (AOI + GS). This is the case because, as illustrated for an exemplary task in Fig.~\ref{fig:time_evol}, the gaze is faster than the hand, hence provides more punctual and directed cues, landing on the target object early on, both in the pick and place phase. The TPA feature of the target, on the other hand, slowly rises to be the winning one as the hand approaches the target. For most part of the action the hand trajectory is compatible with multiple objects in the cluttered scene. Still, TPA features provide some predictive information, considering the performance of TPA + GS across 12 possible intentions, and in scenarios where more complex intentions need to be recognized they might complement gaze predictivity earlier, for example by discriminating between two possible actions on the same object.

Considering other systems, intention estimation frameworks for shared control are usually built on specific setups and datasets, making a direct comparison difficult. Results presented in \cite{Fuchs2021} showed that simply considering fixations on objects (i.e. the winning AOI in a certain time window, without GHMM) performs worse than the generative model approach, since this method deals worse with spurious glances to nearby objects. This is even more the case here where often distractors are glanced for obstacle avoidance. In  the ablation study still, the complete system shows an improvement with respect to our older model (AOI+GS), suggesting that the use of all features with the GHMM yields an even better performance.

\section{DISCUSSION AND CONCLUSIONS}\label{sec:conclusions}

In this study, an intention estimation system was presented, relying on gaze and hand motion features and using GHMMs for inference. The framework was trained on a dataset purposely collected in VR and containing both hands executing pick and place and handover tasks. The accuracy of the intention recognition stays high across different users and across hands, even in a rather cluttered scene, with occlusions and objects presented close together. A reliable prediction is available early on, before the movement is executed halfway and in general 0.5\,s before the end of the action, when a grasping or placing assistance could be provided by the robotic partner. The system works best when using all features together, yet the most decisive are the gaze and the grasping state. Still, motion features are relevant in distinguishing one-hand intentions during bimanual actions. The simulated scenario presented here is indeed intended for bimanual manipulations and data were collected for both hands in each block.
A simple rule-based system was proposed in the described setup and with simple bimanual intentions, but it is prone to the stability and correctness of the learned one-hand intention modules.
More complex bimanual manipulations and the learning of an explicit bimanual intention module are part of current and future work, where the rule-based system will provide a basic baseline in addition.

Moreover, although hands can act in parallel, the eyes can subserve the information and supervising needs of only one effector at a time. This is an open problem since most current gaze-based systems, to our knowledge, deal with the control of just one arm, while most recent bimanual frameworks in shared control rely on learning directly both hand motion patterns (as in \cite{Rakita2019}) or the second robotic arm is autonomously coordinated with the motion of the operator's right arm \cite{Laghi2018}. In natural bimanual eye-hand coordination, we usually visually monitor more closely the effector executing the most difficult action and rely on covert attention, proprioception and haptic feedback to control the other limb \cite{Srinivasan2010}. Alternatively, we shift our eyes between the two targets in an interleaved fashion \cite{Riek2003}. Current and future work is therefore focused on investigating how further actions can be introduced in our framework and how with the same gaze features and different hand features independent and coordinated bimanual actions can be effectively recognized.

\addtolength{\textheight}{-1cm}

\section*{ACKNOWLEDGMENT}
 We acknowledge and thank for the collaboration and the fruitful discussions the colleagues Soshi Iba, Karankumar Patel, Nanami Tsukamoto, and Naoki Hosomi. We further thank Niels Einecke for helping with the AOI occlusion handling.

\bibliographystyle{IEEETran.bst}

redpajama_set_name: RedPajamaArXiv


text:
U.S. soldier accused of wanting to plot ISIS strike on 9/11 Memorial in New York City
Cole James Bridges, 20, allegedly went into an online forum and chatted with a person he thought was with ISIS, but was in fact an undercover FBI agent.
The "Tribute in Light" shines in the sky over Manhattan's skyline on Sept. 11, 2019 in New York.Johannes Eisele / AFP - Getty Images file
Jan. 19, 2021, 7:18 PM UTC
By Jonathan Dienst, Tom Winter and Elisha Fieldstadt
A U.S. soldier was arrested Tuesday for allegedly saying he wanted to help ISIS strike the 9/11 Memorial in New York City and attempting to help ISIS to target U.S. soldiers in the Middle East, according to senior law enforcement officials.
The officials say Cole James Bridges, 20, from Stow, Ohio, allegedly went into an online forum and chatted with a person he thought was with ISIS, but was in fact an FBI undercover agent.
Bridges is currently assigned to Fort Stewart in Georgia with the 3rd infantry division of the Army. He was arrested on charges of attempting to provide material support to a designated foreign terrorist organization and attempting to murder U.S. military service members. He faces up to 40 years in prison.
"When any Soldier attempts to provide sensitive or classified information to an adversary, it is a betrayal of the oath they swore to the United States and the duty owed to their fellow Soldiers," an Army spokesperson said in a statement.
A criminal complaint alleges that Bridges started "researching and consuming online propaganda promoting jihadists and their violent ideology" and expressing his support for ISIS on social media beginning in at least 2019. He joined the Army around September of that year.
In October, he started talking with an undercover FBI agent posing as an ISIS supporter in contact with ISIS fighters in the Middle East.
Bridges expressed his frustration with the Army and gave the undercover agent guidance for attacking U.S. soil, "including advice about potential targets in New York City, such as the 9/11 Memorial," according to a statement from the U.S. Attorney's Office in the Southern District of New York.
He also handed over U.S. Army training material and "guidance about military combat tactics, for use by ISIS," the statement said.
In December of 2020, Bridges started supplying the agent with instructions for ISIS fighters to attack U.S. forces in the Middle East. In one instance, he told the agent how to wire some ISIS buildings with explosives to kill U.S. troops, according to the statement. Bridges also "diagrammed specific military maneuvers intended to help ISIS fighters maximize the lethality of attacks on U.S."
Earlier this month, Bridges sent the agent a video of himself in body armor, standing in front of an ISIS flag and making a gesture symbolic of ISIS support.
Acting U.S. Attorney Audrey Strauss said in a statement, "Cole Bridges betrayed the oath he swore to defend the United States by attempting to provide ISIS with tactical military advice to ambush and kill his fellow service members. Our troops risk their lives for our country, but they should never face such peril at the hands of one of their own."
FBI Assistant Director William F. Sweeney Jr. said, "Bridges could have chosen a life of honorable service, but instead he traded it for the possibility of a lengthy prison sentence."
Attempts to reach a possible attorney for Bridges were unsuccessful Tuesday afternoon.
He is expected to appear in federal court in Georgia on Thursday.
Jonathan Dienst
Jonathan Dienst is a reporter for WNBC-TV in New York, leading its investigative reporting team and covering justice and law enforcement issues.
Tom Winter
Tom Winter is a New York-based correspondent covering crime, courts, terrorism and financial fraud on the East Coast for the NBC News Investigative Unit.
Elisha Fieldstadt
Elisha Fieldstadt is a breaking news reporter for NBC News.

redpajama_set_name: RedPajamaCommonCrawl


Ed Contaldi received a Bachelor of Science in Special, Early Childhood and Elementary Education and a Bachelor's in Music Therapy from Temple University. He is a certified Music Therapist and a Special Education Teacher. He is a special education teacher at Holy Merciful Savior School for Children with Cerebral Palsy and provides individual music therapy at the Kardon Northeast branch. Ed specializes in the use of computer technology to enhance our clients' self-expression through music.


text:
2. Practica\Section H
Section H (current)
Section H. Areas of land
Australia's Defence Force Manual (1994) includes among military objectives "areas of land which are of direct use to defending or attacking forces, eg land through which an adversary is likely to move its forces or which may be used as a forming up point preceding an attack".
Australia, Manual on Law of Armed Conflict, Australian Defence Force Publication, Operations Series, ADFP 37 – Interim Edition, 1994, § 527(h); see also § 916(b) ("areas of land which armed forces use or which have military significance such as hills and bridgeheads").
Australia's LOAC Manual (2006) provides as an example of military objectives "areas of land which are of direct use to defending or attacking forces, eg land through which an adversary is likely to move its forces or which may be used as a forming up point preceding an attack".
Australia, The Manual of the Law of Armed Conflict, Australian Defence Doctrine Publication 06.4, Australian Defence Headquarters, 11 May 2006, § 5.31.


Just got done custom painting 3 sets of SEGWAY fenders for the Monster Cable man himself, Mr. Noel Lee. I flamed the fenders in 24K gold Leaf to match the custom made gold plated rims. I pinstriped the flames in red to make the black and gold pop. I have 3 more sets to customize in "red leaf" just to mix things up in Mr. Lee's stable of Exotic cars and all his Segways. Here are some pics. I will try to get more better pics, but as soon as the Segways are done, Mr. Lee is on and cruising.
Im doing more and more custom work on these SEGWAYS for a very special client. I can't say who this person is just yet, but I can tell you that this person and his company have revolutionized the way we hear music through headphones all over the world. He continues to be the leader in the industry and has alot of big entertainers and professional athletes who use these headphones. See more at the "Projects Page".
Got more Segway fenders to customize for a read big special client. Got to get these done right. Gold Leafing is a messy job but someone got to do it. I can't really say who this person is just yet. I'll put itin the "Projects " page when I'm done.


FTI was BC's first hedge fund. My securities lawyer, Paul MacNeill, had a big challenge getting it approved by the BC Securities Commission because they had never seen one before. After I sold the company I started in grad school, I wanted to invest some of my liquid assets short against the tech market. My thinking was that my portfolio would always be over-weight illiquid tech stocks. The ideal portfolio strategy was to offset that with short exposure to the tech market.
I worked on some of the investments in FTI with the legendary Bob Disbrow. He taught me a lot about investing on the short side. It was tremendous fun and FTI performed very well; but I learned why so few funds invest short - it is much more work than going long. The entire market structure and psychology is biased toward the upside. Going short requires dogged determination, nerves of steel and lots of capital for margin calls and short squeezes. Borrowing stock to short was also becoming harder and harder. As much fun as it was, I decided there were much easier ways to invest.
FTI was about 60% short for the first three years. Shortly after I decided to go 100% long, the market became extremely overvalued and I couldn't find anything to invest in at prices that made sense to me. I thought about reconsidering my strategy and going short again, but decided against it because I was stunned by the upside momentum in 1998 and 1999. Anyone going short against a trend like that would probably have gotten killed before being proven right.
It was a frustrating time because I couldn't go short and I couldn't go long. The markets just didn't make any sense to me, so I went entirely to cash and waited to see what would happen.
Note: These results were prepared by an external accountant but not audited.


Topics: Training & Advice, Recruiter Area, Receptionist, Dispenser, Class Teacher, and Property Lettings Assistant.
jobstoday.co.uk jobstoday - Start your Recruitment & Job Search Now!
Its 2 nameservers are ns2.johnstonpress.co.uk, and ns1.johnstonpress.co.uk. It is hosted by Johnston Press Plc (England, Leeds,) using httpserver web server. Sunderlandjobstoday.co.uk's server IP number is 195.59.103.168.


text:
Q: destroying a binary tree iteratively in java The task is commonly done during recursive post order traversal, and there are few examples online. One of them is here, but I wonder if it's correct because it seems that the _deleteTree() method only does a BFS and does no operation to nodes, and the deletion is done by simply set the root of the tree to null. It will no doubt return an empty tree. But is it the correct way to remove references to all the tree nodes?
Also, for iterative post order traversal, say, like below
public TreeNode postorderTraversal(TreeNode root) {
    if(root==null) return null;
    Stack<TreeNode> stack1=new Stack<>();
    Stack<TreeNode> stack2=new Stack<>();
    TreeNode cur=root;
    stack1.push(cur);
    while(!stack1.isEmpty()){
        cur=stack1.pop();

        if(cur!=null){
            stack2.push(cur);
        }

        if(cur.left!=null){
            stack1.push(cur.left);
        }
        if(cur.right!=null){
            stack1.push(cur.right);
        }
    }

    while(!stack2.isEmpty()){
        //elements poped will be in post order sequence
        }
    return root;
}

How to destroy a binary tree iteratively? Can someone give a sample code (java)?Thanks!

A: Usually when you assign a node to null you technically get rid of all data. As with a linked list when you break its connection by setting its "next" per say to null you delete the list and java garbage collector takes care of the rest. So in the Java code you linked they are doing the same thing once the root is determined to have no children.

A: There's a better solution, which uses the tree nodes themselves to store the queue. Something like this:
static void clear(Node root) {
    while (root != null) {
        Node left = root.left;
        if (left == null) {
            Node right = root.right;
            root.right = null;
            root = right;
        } else {
            // Rotate the left child up.
            root.left = left.right;
            left.right = root;
            root = left;
        }
    }
}

redpajama_set_name: RedPajamaStackExchange


Issue #3187 has been updated by Tomoyuki Chikanaga.
I've written a patch. This patch adds some methods Fiber.default_vm_stacksize, Fiber.default_vm_stacksize=, Fiber#vm_stacksize and add an optional Hash argument to Fiber#initialize, and tests for them.
You can specify default VM stack size of Fiber created afterward and/or specify individually when create a Fiber.
Note that the size of VM stack is number of Objects and memsize of stack is vm_stacksize * sizeof(VALUE) bytes.
Also note that this patch enable to change only VM stack size, but not machine stack size. I think when Fiber is implemented based on makecontext/swapcontext (FIBER_USE_NATIVE=1), machine stack size (default: 64KB) can be configurable. I wonder if procedures like `parse_some_big_xml' in Mike's example also need larger machine stack size? Does anyone have such a testcase?
I'd like a way to increase the size of the Fiber stack dynamically so when my program starts, I can set it to whatever value I need for the code I'm running.  4KB is too easy to run into problems when running recursive code but settling on any arbitrary static value seems pointless to me.


The event will kick off at the Harley-Davidson Museum and will travel through Milwaukee for several hours.
Staff are busy with cleanup as they await word from investigators on suspects and motives.
The district has directed staff to monitor his restroom usage, forcing him and other transgender students to wear green wristbands to help staff recognize them.
Tonight, Hillary Clinton and Bernie Sanders are taking their heated fight for the Democratic presidential nomination back to the debate stage.


Is Good Advice Worth It To You?
Have you ever had an experience where you regret what you said? Perhaps you had wished to say what you were thinking? Do you have ways to change your reactions to others?
As often as possible, I like to share what I learn. Self-development is important for me to learn new ways in relating with others. As a yoga teacher, I've learned to listen more with compassion. However, there are times, where it's extremely difficult to do. In unpleasant discussions, you might encounter harsh words from a family member, colleague or friend. No matter how old you are, words hurt.
What do you want yours to be?
I can't change other people's action. I can only change my reaction.
Through many life challenges, I have created ways to deal with difficult situations. I've dealt with it in a clear way for me to walk away undamaged. And I want the same for you.
Changing your reaction to others will empower you to be more self-aware. It'll enable to be more conscious of your words and actions. Therefore, I'll be offering a free online class on this topic.
When you join the Live Your Giant Life Facebook Community, you'll be able to attend this class. I will offer authentic and mindful techniques in relating better to others behaviors.
Join us, Wednesday, February 28th at 8:30 pm EDT for this important discussion. Please feel free to share this with friends, family, and colleagues.
Social Media Overload = Frustration, Anxiety, and Depression?
Next post: Social Media Overload = Frustration, Anxiety, and Depression?


A new bankruptcy law in the UAE promises to reduce the risks of doing business in the country by allowing businessmen and executives to avoid time in jail if their companies fail to pay debts.
The long-awaited law, approved by the cabinet this week, is expected to take effect by the beginning of Y 2017 after it is promulgated, Obaid Humaid al-Tayer, Minister of State for Financial Affairs, told reporters Tuesday.
"This law will add value to the business community and will be positive for both foreign and local investors. This is an important step for the UAE," he said.
At present the UAE does not have modern bankruptcy regulations, making it difficult for companies to restructure or wind themselves up.
Under existing legislation, unpaid debt or the issue of a bounced cheque can land businessmen in jail.
country, leaving behind bad debts.
Such debts, known as "skips", totaled over $1.4-B last year.
The new law will establish a regulatory body, the Committee of Financial Restructuring, that will oversee restructuring cases and appoint experts to handle them. It provides for companies to receive new loans under terms set by the law.
Businessmen will face a prison sentence of up to 5 years and a fine of up to Dhs1m ($272,000) if their companies fail to pay debts and deliberately avoid filing for bankruptcy.
The new law will not apply retroactively to businessmen who are already facing criminal cases over unpaid debt. It will cover both state-owned and private companies with some exceptions, such as firms based in special free zones.
The UAE was also working on a new personal insolvency law that would apply to individuals. "It will take around 12 months to draft the law," he said.


text:
On this day in 2008: Virat Kohli made his international debut
Cricket, Top News Updates
New Delhi [India]: Thirteen years ago on this day (August 18), skipper Virat Kohli took his first steps on the international stage as he made his ODI debut for India.
However, his stint at the crease was short-lived as Kohli scored just 12 runs in his first international match. Kohli had opened the batting India, and he faced 22 balls and batted for 33 minutes at the crease before Sri Lankan bowler Nuwan Kulasekara dismissed him in the eight over.
The MS Dhoni-led side was bundled out for 146 in this particular match as Ajantha Mendis and Muttiah Muralitharan both picked three wickets each.
Sri Lanka chased down the target with 91 balls to spare and defeated India by eight wickets. Kohli had to wait for 14 matches for his first century, and he finally breached the 100-run mark against Sri Lanka at Kolkata in 2009.
He went on to play a knock of 109 and this century was the first of many. To date, Kohli has registered 43 tons in ODIs and has established his image as one of the finest run-chasers in the history of cricket.
The India skipper has played 254 ODIs so far, in which he has scored 12,169 runs at an average of 59.07 with his highest score being 183 against Pakistan. The right-handed batsman is in second place, only behind legendary cricketer Sachin Tendulkar in the list for most hundreds in ODI cricket.
Delhi HC grants bail to 2008 serial blasts accused after 12…
38 sentenced to death, 11 get life imprisonment in 2008…
Virat Kohli becomes fastest to register 23,000 international…
Virat Kohli becomes first player to win 50 international…
Virat as leader made everyone believe that we can do special…
IPL 2021: Chahal is back to his best after a layoff, reckons…
Cricket fraternity wishes 'run machine' Virat Kohli on his…
Anushka Sharma, Virat Kohli Celebrate Their 4th Wedding…
ENG vs IND: We are the team that everyone wants to beat,…
Virat Kohli will remain Captain


text:
Q: Asynchronously running long running tasks in Elixir I have a module that saves data in csv format which takes a relative long time depending on the data size. What is the Elixir way to accomplish this asynchronously? I tried using Agent, but the process times out.

defmodule FinReporting.Export_CSV do
    alias FinReporting.DistributeRepo
    alias FinReporting.InterfaceMdl
    import Ecto.Query

    def start_link do
        Agent.start_link(fn -> HashDict.new end, name: __MODULE__)
    end

    def export do
        Agent.update(__MODULE__, fn dict ->

                                    export_sub()
                                end)
    end

    defp export_sub do
        file = File.open!("test.csv",[:write, :utf8])
        IO.puts("===> CSV export of NGInterface file started.")
        DistributeRepo.all(from entry in InterfaceMdl, limit: 100000, select: %{ field1: entry.field1, amount: entry.amount})
                |>Enum.map(fn(entry)-> %{entry|amount: Decimal.to_string(entry.amount)}end)
                |> Enum.map(fn(m) -> [m.field1, m.amount] end)
                |> CSV.encode
                |> Enum.each(&IO.write(file, &1))

        IO.puts("===> CSV export of NGInterface file completed.")
        _ = File.close(file)
    end
end

A: You can specify a custom timeout using the third argument to Agent.update. You can pass an integer specifying the number of milliseconds, e.g. 60000 for one minute, or :infinity for infinite timeout.
Agent.update(__MODULE__, fn dict -> export_sub() end, 60000)

But, Agent.update waits for the function to complete executing, which is not what you want.
You want Task and specifically Task.async/1.
Task.async(fn -> export_sub() end)

This will return a Task struct that you can wait on later in your application using Task.await or ask for its status using Task.yield. All this and more is explained in great detail in the documentation of Task.


text:
Bus Stop Buddy
'Digest' Newsletter
Kelly's Classroom
Ya Gotta Try This
The ClassH-Room
The Feed AT Night
Newscasts & Replays
FOX Soul
Bizzy Mamas
Dr. Mike
Philly City Council
FOX 29 Originals
For Goodness' Sake
FurEver Home
Hank's Take
Momologue
Our Race Reality
Man whose info found on Jersey City shooter denied bail
By David Porter
Ahmed A-Hady's attorney termed attempts to connect him to the attacks "ridiculous."
NEWARK, N.J. - A New Jersey man whose number was found in the pocket of one of the perpetrators of last week's fatal attack on a Jewish market in New Jersey was denied bail on an unrelated weapons charge Monday, as his attorney termed attempts to connect him to the attacks "ridiculous."
Ahmed A-Hady appeared in U.S. District Court shackled and wearing a t-shirt and jeans. His parents, brother and fiancee were in court, according to his attorney, K. Anthony Thomas. They declined comment afterward.
The judge gave A-Hady a chance to supplement his request for bail at a hearing Tuesday afternoon.
Federal agents last week searched A-Hady's home and a pawnshop in Keyport, New Jersey, operated by his father. Those searches turned up weapons including three AR-15-style assault rifles, three handguns and one shotgun as well as more than 400 rounds of ammunition.
RELATED COVERAGE:
Official: Deadly Jersey City attack was 'fueled' by anti-Semitism
Timeline of events that left 5 victims, 2 attackers dead in Jersey City shooting
Authorities tie killers in New Jersey attack to fringe group with Philadelphia ties
6 killed in Jersey City gun battle, including police officer
(Photo courtesy of The Office of Gov. Phil Murphy)
On Monday, agents in FBI flak jackets were at a pawnshop in South Amboy that A-Hady's father, Alaa Hady, told The Associated Press was his son's.
The criminal complaint lists the defendant's last name as A-Hady but he said in court the correct spelling is Hady. U.S. Attorney Craig Carpenito said the spelling was taken from an earlier criminal case against A-Hady.
A-Hady hasn't been charged with providing any of the weapons used in the bloody rampage in Jersey City that left four people and the two attackers dead on Dec. 10. But he faces one charge of being a felon in possession of a weapon, which carries a potential maximum sentence of 10 years.
Thomas, who also didn't comment to reporters, criticized the government during the hearing for using the Jersey City slayings to justify seeking to keep A-Hady detained.
"My client is a responsible businessman," he said. "I am pretty sure you can go on the Internet and you will find his name and cellphone number. It's public. So to add some nefarious reasons why my client's phone number is in someone's pocket is ridiculous."
Carpenito didn't say whether investigators had found any further connection between A-Hady and attacker David Anderson, or whether Anderson and accomplice Francine Graham had ever visited A-Hady's pawn shop.
The ties to the shootings "are not being created by the government," he said. "The defendant is connected to the incidents in Jersey City by David Anderson, in the carrying of a note with personal cell phone number and the address of his business in his back pocket."
Anderson and Graham killed Jersey City Police Det. Joseph Seals in a cemetery before storming the JC Kosher Supermarket, killing three people inside before they were fatally shot in a standoff with police. The attack is being investigated as domestic terrorism.
Left to right: David N. Anderson and Francine Graham (New Jersey Office of the Attorney General)
Anderson and Graham re also prime suspects in the slaying of a livery driver found dead in a car trunk in nearby Bayonne the previous weekend, authorities have said.
Anderson and Graham were armed with multiple weapons including an AR-15-style rifle and a shotgun, and a pipe bomb was also found in the stolen U-Haul van they drove to the market. Two of the weapons used by Anderson and Graham were bought by Graham in Ohio last year, police have said. It's not known where they got the three other guns.
Records indicated that A-Hady bought two handguns in 2007 before being convicted of a felony in 2012 that made him ineligible to own firearms. Authorities say he acknowledged still owning the handguns.
Frederick reported from Keyport, New Jersey, and South Amboy, New Jersey.
Pa. counties experience low vaccine supply as state expands groups eligible
Philadelphia launches COVID-19 vaccine interest sign-up website
Natalia Bryant, eldest daughter of Kobe and Vanessa, celebrates her eighteenth birthday
Long lines seen outside Burlington County COVID-19 vaccine megasite
Inauguration Day 2021: Biden placed hand on same family Bible used during his VP, Senate swearing-in
1 man dead, another critically injured after Southwest Philly accident
3 $1M winning Powerball tickets sold in New Jersey
Get Location-Based Weather Alerts!


text:
require 'spec_helper'

describe ILoveSudoku::CellMatrixCreator do
  include TestPuzzlesAndSolutions

  describe "#cell_matrix" do

    let(:creator){ ILoveSudoku::CellMatrixCreator.new(easy_solution) }

    it "is an array" do
      expect(creator.cell_matrix).to be_a Array
    end

    it "is an array with nine items" do
      expect(creator.cell_matrix.length).to eq 9
    end

    it "all nine items are arrays" do
      cell_matrix = creator.cell_matrix
      all_arrays = cell_matrix.all? { |row| row.is_a?(Array) }
      expect(all_arrays).to eq true
    end

    it "all subarrays have nine members" do
      cell_matrix = creator.cell_matrix
      arrays_of_nine = cell_matrix.all? { |row| row.length == 9 }
      expect(arrays_of_nine).to eq true
    end

    it "contains only Cell objects" do
      cell_matrix = creator.cell_matrix
      all_cells = cell_matrix.flatten.all? { |cell| cell.is_a? ILoveSudoku::Cell}
      expect(all_cells).to eq true
    end
  end
end

redpajama_set_name: RedPajamaGithub


text:
Neotoma martinensis es una especie de roedor de la familia Cricetidae.

Distribución geográfica
Se encuentra sólo en México.

Referencias

  Baillie, J. 1996.  Neotoma martinensis.   2006 IUCN Red List of Threatened Species.

Musser, G. G. and M. D. Carleton. 2005. Superfamily Muroidea. Pp. 894-1531 in Mammal Species of the World. A Taxonomic and Geographic Reference. D. E. Wilson and D. M. Reeder eds. Johns Hopkins University Press, Baltimore.

martinensis
Mamíferos endémicos de México
Roedores de México

redpajama_set_name: RedPajamaWikipedia


God made everything, including those who live with disabilities.
Human beings foolishly devise evil plans against God that won't stand.
God can do and will do everything he has always intended to do.
We can trust God to respond to threats (of all kinds) and we can continue to speak the word with boldness.
God is purposeful and glorious in all his ways. Thank you, Lord, for Jesus!


"Paris Can Wait," directed by yet another Coppola, shows the continued strength of movies aimed at older audiences.
As specialized distributors head to Cannes, Eleanor Coppola's French valentine "Paris Can Wait" (Sony Pictures Classics) scored with arthouse moviegoers. It's only the fourth 2017 limited release to break the increasingly rare $20,000 per-theater-average mark.
These days, movies with older audience appeal are sustaining the market — and will likely form the core demo for similar available new films at Cannes. Eleanor Coppola ("Apocalypse Now" documentary "Heart of Darkness") makes her narrative film debut at 81 with her semi-autobiographical first screenplay, starring Diane Lane as the wife of a self-involved film producer (Alec Baldwin).
New York also saw a handful of other small but still promising initial results, led by Cate Blanchett stunt-theater piece "Manifesto" (Film Rise), Israeli marriage story "The Wedding Plan" (Roadside Attractions) and "Stefan Zweig: Farewell to Europe" (First Run).
East LA racing drama "Lowriders" (BH Tilt) managed to place #8 for the weekend with $2.4 million on only 245 theaters — more details in Top Ten Takeaways.
What comes next: This looks to have a faster than usual SPC release with more than ten more cities next week.
Director Doug Liman has moved to studio fare ("The Bourne Identity," "Mr. & Mrs. Smith" and "Edge of Tomorrow") from his indie roots (Miramax's "Swingers"). He teamed with Amazon for this intense low-budget Iraq sniper battle starring Aaron Taylor-Johnson and John Cena.
Roadside took the limited national release route (with no festival play) to minor results of under $2,000 per screen. Roadside has enjoyed stellar success with Amazon's "Love and Friendship" and Oscar-winner "Manchester By the Sea," by far its biggest success. But this one looks like a minor detour for both.
What comes next: It will be a stretch to get to Memorial Day weekend play.
In the tricky world of subtitled movies, Israeli films have a better chance of gaining a foothold than those of most countries. This comedy/drama about a prospective Orthodox bride having her wedding date arranged with no prospective groom opened in two Manhattan theaters to above average results. This is a rare foreign language release for Roadside, but it shows some promise for some success ahead.
What comes next: Los Angeles and nine other cities open this Friday.
A biopic about the Austrian novelist from pre-war Vienna to his exile during World War II scored a date at the New York's Lincoln Plaza. That was enough along with positive reviews to propel this to a respectable initial gross.
What comes next: This already has had significant domestic festival play, with more and other niche runs part of its future. A conventional Los Angeles run opens mid-June.
With a compelling topic (a depressed, suicidal young man quests to find the healing powers of a hallucinogenic tea) and an exotic Peru, this unheralded documentary found a receptive home at New York's Sunshine Theater.
What comes next: Los Angeles opens next this Friday.
This tour-de-force one-woman show by Cate Blanchett — playing 13 roles representing a range of 20th-century art movements — opened at New York's Film Forum. Her draw led to a respectable gross over its initial five days with no reason to suggest this might not also appeal to a wider specialized audience.
What comes next: Los Angeles comes aboard on May 26.
Decent numbers for this reissue of French master Jean-Pierre Melville's study of a priest with Jean-Paul Belmondo, which marked a turn away from gangster and other genre films for which he was best known. It plays as part of a two- year retrospective of his work at New York's Film Forum.
What comes next: This will be included among Melville titles in the ongoing series across the country.
The second weekend for this quirky comedy/drama about a long-married couple coming to terms with their parallel affairs found some positive response in major city dates nationwide. The grosses fall somewhat short of the third-week expansion of A24's older-audience-friendly "20th Century Women." This in-house A24 production will likely will get a significant push to play wider, but it's playing more specialized than mainstream.
The second weekend for this ensemble drama led by Richard Gere about a pair of parents discussing a mutual crisis fell hard from its already minimal opening results last weekend.
Laura Poitras' much talked-about documentary on Wikileaks' Julian Assange went nationwide to big cities. But it also suddenly appeared on iTunes in its second week, a significant factor in weak grosses. The lack of response through in its initial dates suggests that irrespective of the alternative availability there was little theatrical interest for this film — along with other movies about the widely disliked Assange, from Alex Gibney's documentary "We Steal Secrets" to Bill Condon's "The Fifth Estate" starring Benedict Cumberbatch.
Liev Schreiber as working class boxer Chuck Wepner expanded quickly to modest results in new dates.
The second weekend expansion for this family tale focusing on the reaction to transgender transition got minimal interest consistent with its initial dates.
A decent hold for the second weekend of this reissue of Andrei Tarkovsky's 1979 masterpiece.
James Gray's tale of South American discovery starring King Arthur Charlie Hunnam is winding down its run as it heads toward ranking as the second best gross in Bleecker Street's short history.
The slow, steady increase of theaters for this American-made drama from leading Israeli director Joseph Cedar is turning it into a longer-playing film that still could have half or more of its gross still to come.
Still at a large number of theaters though at a low gross for that total, this Holocaust-themed film looks to get to around $17.5 million before it's done.
This World War II London drama starring Gemma Atherton as a government agent continues steady at a level below "Gifted" and "Zookeeper's Wife" and other recent older audience appeal wide releases.
The gross so far for Terence Davies' biopic on Emily Dickinson has grossed about what Music Box saw at the same point of its run for his earlier "The Deep Blue Sea," although with more theaters so far. This looks like it could ultimately exceed the $1.1 million total for that film. Further expansion is planned this week.
Holding at some remaining theaters, Anne Hathaway's foray into genre indie film looks to have made some impression but fell short of its its initial promise as the best platform opener this year.


Barco, the worldwide market share leader in digital cinema projection, announces its public debut today of Barco Escape, the new ultra-wide, multi-screen movie format, on the new Samsung Gear VR powered by Oculus device. Barco Escape uses multiple projectors and screens to create a panoramic viewing experience.
The Escape content running on the Samsung Gear VR simulates what moviegoers will experience when they go to the cinema to see a feature in Barco Escape format. It allows the user to look around during certain scenes of the movie and see what it feels like to view not just what's on the main screen, but peripheral visual content on the side screens that further immerse the user into the movie.
Barco worked with 20th Century Fox and director Wes Ball to re-envision his short movie "Ruin, " which is currently in development with the studio to become a feature film, into Escape format as one of the featured content pieces for the Samsung Gear VR release. While Barco Escape is still in its early stages of deployment, the technology company saw the strong connection points between the "all around you" visual wonderland it is creating in theaters and the Virtual Reality experience Oculus offers.
Ball's upcoming movie with 20th Century Fox, "The Maze Runner", will be the first feature film released in Barco Escape format in select theaters on September 19th. In addition to the wide domestic and international release of "The Maze Runner" in large format and traditional theaters, fans can watch the special Escape version in the following locations: Los Angeles, California; Davie, Florida; Plano, Texas; Woodridge, Illinois; and Redwood City, California; and Belgium. "The Maze Runner" will be released in Auro 11.1 immersive sound by Barco, the latest in this year's series of top productions to offer 3D immersive sound to its audiences.
For those fans outside of the cities currently equipped with Barco Escape theaters, the Samsung Gear VR allows them to get a glimpse of this new movie going experience through Virtual Reality.
When Thomas wakes up trapped in a massive maze with a group of other boys, he has no memory of the outside world other than strange dreams about a mysterious organization known as W.C.K.D. Only by piecing together fragments of his past with clues he discovers in the maze can Thomas hope to uncover his true purpose and a way to escape. Based upon the best-selling novel by James Dashner, the film stars Dylan O'Brien, Kaya Scodelario, Aml Ameen, Thomas Brodie-Sangster, Will Poulter.


Hot Cross Buns - new flavours!
It's time for Easter Holidays and what can be more traditional than indulging in chocolate and or course Hot Cross Buns!
Although hot cross buns are traditionally eaten around Good Friday and Easter, these delicious buns are now available in supermarkets all year round. Major supermarkets now produce variations on the traditional recipe such as Toffee, Orange-Cranberry and Apple-Cinnamon.
To cater to the evolving taste and trends in flavours, ITS have been developing new and exciting twists using our seasonings and natural flavourings. Coconut and Caramel, Maple and Honeycomb and Ginger and Honey are just a few of the many exciting flavour combinations that have been tried and tested in our test bakery.
Our expert panel have also been busy trying out some popular supermarket premium brands of Hot Cross Buns and have some interesting reviews.


Aedeagus chitinised male sexual organ of Coleoptera, often used in taxonomy for separating closely related species.
Anthropochorous distributed as a result of human disturbance or influence.
Anthropogenic arising as a result of human influence or interference.
Basalt(ic) outcropping rocks of high heavy metal and zeolite content comprising the coastal hills of Cos. Antrim and Londonderry; drainage waters and derivative soils are of high base status.
Biogeography the scientific study of the geographic distribution of organisms.
Biome a major category of habitat in a particular region of the world, such as the Boreal coniferous forest zone of northern Europe.
Biotope a habitat which is uniform in its main climatic, soil and biotic conditions.
Boreal of the Boreal biome; the coniferous forest zone in northern Europe and/or the coniferous forest zone in mountains within central Europe.
Boreo-British confined to Scandinavia and the British Isles within Europe (Lindroth 1935) i.e. a Boreal pattern of distribution circumscribed within northern and western Europe and excluding the central European mountains.
Brachypterous with shortened, non-functional wings.
Calcareous containing base-rich materials or calcium.
Calciphilous with an affinity for calcareous conditions.
Calp soils in Fermanagh derived from Carboniferous strata with very impeded drainage and often low base status.
Carboniferous strata of Carboniferous age, mainly the hard limestones of Fermanagh in our area; derivative soils may be relatively shallow or well-drained and therefore of high base status, or of impeded drainage and of low base status e.g. the calp soils of east Fermanagh.
Cretaceous strata of Cretaceous age, mainly soft chalk in our area.
Dorsum (dorsal adj.) the upper surface of the insect body.
Drumlin a glacial feature; specifically small, oval, asymmetric hills from 100 to 2000m long, formed inside end moraines; drumlin landscapes are especially common in Down.
Ecotope a particular kind of habitat within a region or biome.
Edaphic the chemical, physical and biological characteristics of the soil which affect an ecosystem.
Elytra (elytron sing.) the chitinised (horny) front wings of Coleoptera which encase the hind wings protectively.
Epigeic living on the surface of the soil, or at least not below the surface.
Eurytopic with a catholic (broad) tolerance of ground conditions.
Eutrophic have a high trophic status; usually applied to water bodies or to soil water with high concentrations of dissolved minerals, particularly phosphate.
Fluvioglacial formed by the action of both glaciation and river turbulence; fluvioglacial sands are characteristic of many river margins in the west of the Province and around Lough Neagh.
Halobiontic showing an obligate dependence upon salty (littoral) habitats.
Halophilous showing a peak in distibution in salty (littoral) habitats but the association is not obligate.
Helophilous with an affinity for sunny places.
Holarctic with a geographical distribution encompassing the Palaearctic and Nearctic Regions.
Hygrophilous with an affinity for wet places.
Karstic heavily eroded and channelled outcropping limestone rocks.
Littoral belonging to the sea shore.
Mesotrophic having an intermediate trophic status, usually applied to water bodies or to soil water with moderate concentrations of dissolved minerals.
Nearctic with a geographical distribution confined to North America.
Oligophagous (specialised) feeder on a few species of prey organisms.
Oligotrophic having a low trophic status; usually applied to water bodies or to soil water in peaty or hill areas where the underlying rocks are of low base status.
Ombrogenous of a type produced by the action of rain rather than ground water.
Ombrotrophic trophic status largely produced by rainwater rather than groundwater.
Oxyteline with a physical resemblance to rove-beetles (Staphylinidae) in the subfamily Oxytelinae i.e. flattened, narrow and parallel-sided.
Palaearctic with a geographical distribution confined to the northern Eurasian land mass.
Philochthus A subgenus of Bembidion of characteristic appearance (small, with broadly sinuate hind-angles to the pronotum) common in wet water margin habitats e.g. B. aeneum, guttula, lunulatum, mannerheimi.
Piceous a dark brown colour.
Pronotum the dorsal plate of the mid body, or thorax, in Coleoptera.
Psammophilous with an affinity for sandy ground.
Prosternum the main ventral plate of the mid body or thorax in Coleoptera.
Relict indicative of formerly existing conditions; survivor(s) from a previous geological era or from earlier (different) climatic conditions.
Riparian with an affinity for water margin habitats.
Rufinistic/rufinism with a tendency to exhibit lighter, reddish, rather than piceous (darker), colouration.
Silurian rocks of Silurian age, represented in our area primarily by the slates of Down; the best derivative soils are acid brown earths, widely distributed in the Down area.
Silvicolous with an affinity for shaded (woodland) habitats.
Stridulatory file an uneven area of (usually) the wing cases across which is drawn a stridulatory peg on the abdomen to produce sound (stridulation).
Stenotopic fussy or restricted with regard to preferred ground conditions.
Synanthropic associated with man or with human dwellings.
Taiga Boreal coniferous forest occurring at the interface between Eurasian steppe and tundra.
Temperate of mild climatic regime; a zonobiome intermediate between the tropics and the Arctic.
Testaceous a light yellow-red colour.
Thermophilous with an affinity for warm places, specifically sunny and sheltered ground conditions.
Turlough seasonal water bodies found in the karstic limestone areas of the west of Ireland.
Tyrphophilous with an affinity for organic (peat) soils.
Ventrum (ventral adj.) the lower surface of the insect body.
Xerophilous with an affinity for dry ground conditions.


Segment 1: In our first segment we're talking with business development expert Mary Cravets. Mary knows just how challenging it can be to grow your own business. Two years ago, she was diagnosed with extreme adrenal exhaustion and at that moment knew that something had to change. She quickly realized that she never truly ran her business. Instead, her business was running her! She reimagined her business and the results have been amazing. Mary shares her strategies for putting you in charge of ... See More your own business!
Segment 2: In our Business Builder Segment, we talk why creating a unique branded system is so important to your business. We will show you how having one can reinforce and extend your current brand, and how it can establish you as an expert in your market.


text:
By CARE Staff · October 1, 2020
'My Name is Chrissy'
'My Name is Marwa'
'My Name is Sheila'
Cash Transfers for Education Support Needs of Adolescents and Families
By Holly Welcome Radice and Nour Al Saaideh · October 6, 2020
Cash and voucher assistance has transformed humanitarian aid.
'My Name is Maryam'
This International Day of the Girl, Nina Lu raises her voice to tell the story of a girl's unyielding determination.
This International Day of the Girl, we must support and empower girls everywhere to reach their potential.
'My Name is Damary'
Haylie Duff raises her voice alongside Damary to tell the story of a young girl's determination and resilience in Damary's own words.
This International Day of the Girl, Christina Ochoa raises her voice alongside Chrissy to tell the story of a young girl's hope and resilience in Chrissy's own words.
Sheila Shah became a refugee after her family fled incredible violence in Iran. We celebrate the resilience and determination of women and girls around the world like Sheila.
Nia Sioux raises her voice alongside Marwa to tell the story of a girl's dedication and perseverance in Marwa's own words.
'My Name is Mikre'
Alexandra Daddario raises her voice alongside Mikre to tell the story of a young girl's emancipation and empowerment in Mikre's own words.


CLEVELAND INDIANS—Optioned RHP Zach McAllister to Columbus (IL).
KANSAS CITY ROYALS—Recalled RHP Vin Mazzaro from Omaha (PCL). Optioned LHP Tommy Hottovy to Omaha.
COLORADO ROCKIES—Recalled RHP Alex White from Colorado Springs (PCL). Optioned RHP Edgmer Escalona to Colorado Springs.
MILWAUKEE BREWERS—Extended the contract of general manager Doug Melvin by three years through 2015 and promoted him from executive vice president to president of baseball operations. Extended the contract of manager Ron Roenicke by two years through 2014.
NEW YORK METS—Placed C Josh Thole on the seven-day DL. Selected the contract of C Rob Johnson from Buffalo (IL). Transferred RHP Pedro Beato to the 60-day DL.
SAN DIEGO PADRES—Selected the contract of LHP Alex Hinshaw from Tucson (PCL). Optioned OF Blake Tekotte to Tucson. Transferred RHP Micah Owings from the 15- to 60-day DL.
WASHINGTON NATIONALS—Activated 3B Ryan Zimmerman from the 15-Day DL. Placed OF Jayson Werth on the 15-Day DL, retroactive to May 7. Agreed to terms with LHP Michael Gonzalez on a minor-league contract.
ATLANTA FALCONS—Released FB Ovie Mughelli.
CAROLINA PANTHERS—Signed P Nick Harris to a one-year contract.
CHICAGO BEARS—Agree to terms With TE Evan Rodriguez on a four-year contract.
CINCINNATI BENGALS—Claimed G Chris Riley off waivers from Tampa Bay and DT Vaughan Meatoga off waivers from Washington.
HOUSTON TEXANS—Signed K Shayne Graham.
NEW YORK JETS—Signed S Josh Bush, OL Fred Koloto, WR Royce Pollard and QB Matt Simms. Waived C Kris O'Dowd.
OAKLAND RAIDERS—Signed C Colin Miller. Fired college scouting director Jon Kingdon and scout Bruce Kebric. Announced the retirements of scout George Karras and scout Kent McCloughan.
PHILADELPHIA EAGLES—Agreed to terms with LB Mychal Kendricks on a four-year contract.
PITTSBURGH STEELERS—Agreed to terms with WR Toney Clemons.
SAN DIEGO CHARGERS—Signed LB Melvin Ingram, DE Kendall Reyes and C David Molk to four-year contracts.
ST. LOUIS RAMS—Signed FB Todd Anderson, LB Sammy Brown, CB Jeremy Caldwell, LB Derrick Choice, DT Matt Conrath, S Matt Daniels, QB Austin Davis, TE Cory Harkey, G Michael Hay, C T. Bob Hebert, P John Hekker, LB Alex Hoffman-Ellis, DE Jamaar Jarrett, WR Nicholas Johnson, LB Noah Keller, OT Joe Long, S Rodney McLeod, RB Calvin Middleton, TE DeAngelo Peterson, S Quinton Pointer, RB Nick Schwieger, DE Scott Smith and LS Travis Tripuka.
CHICAGO BLACKHAWKS—Fired assistant coach Mike Haviland.
SUN BELT—From Western Kentucky, named junior C Karavin Dew to the All-Conference Softball First Team, and freshman 3B Preslie Cruce and sophomore OF Kelsie Mattox to the All-Conference Softball Second Team.
BUFFALO—Named Danny White athletic director.
ILLINOIS STATE—Named Dan Muller men's basketball coach.
MCMURRY—Announced the resignation of men's basketball coach J.D. Isler.
TEXAS A&M—Announced the retirement of athletic director Bill Byrne and will become a special adviser to A&M President R. Bowen Loftin.
Columbus at Scranton-WB, 7:05 p.m.
Buffalo at Gwinnett, 10:35 a.m.
Lehigh Valley at Indianapolis, 11:05 a.m.
Rochester at Pawtucket, 12:05 p.m.
Columbus at Scranton-WB, 1:05 p.m.
Asheville at Rome, 10:30 a.m.
Greensboro at Lakewood, 6:35 p.m.
Savannah at Hickory, 7 p.m.
Charleston, S.C. at West Virginia, 7:05 p.m.
Bozman, Greene (3), Rexroat (5), Bottoms (6), Terry (8), Bazzani (9) and Hagen; Sagerman, Konrad (2), Miller (8) and Jeffery. W—Bozman (2-0). L—Sagerman (1-2). 2B—Rodriguez (E), Daniel (E). HR—Grisham (E), Soloman (E), Hundley (D).
Records—Eastern Ky. 25-20, Dayton 22-27.
McIntosh, Springer (4) and Lewis; Miller and Fergerson. W—Miller. L—McIntosh.
Starnes and Lynch; Greer, Carroll (6), Peters (6) and Oaks. W—Greer. L—Starnes. 2B—Howard (S), Starnes (S), James (W), Pasch (W).
Atlanta at Boston, 6 or 8 p.m.
x-L.A. Lakers at Denver, 9 or 10:30 p.m.
x-Miami at New York, 8 or 8:30 p.m.
x-Memphis at L.A. Clippers, 9 or 10:30 p.m.
ORLANDO (87) — Turkoglu 1-6 5-7 7, Anderson 5-13 0-0 14, Davis 6-17 3-3 15, Nelson 11-21 0-0 27, J.Richardson 3-7 1-5 8, Redick 4-7 3-4 12, Orton 0-0 0-0 0, Clark 1-1 0-0 2, Duhon 0-3 0-0 0, Q.Richardson 1-1 0-0 2. Totals 32-76 12-19 87.
INDIANA (105) — Granger 8-20 5-5 25, West 6-13 0-0 12, Hibbert 5-10 1-3 11, Hill 5-11 3-4 15, George 3-7 0-1 8, Barbosa 3-4 1-1 7, Hansbrough 1-3 0-0 2, Collison 9-10 0-0 19, Amundson 3-4 0-0 6, Pendergraph 0-0 0-0 0, Price 0-0 0-0 0, Stephenson 0-0 0-0 0. Totals 43-82 10-14 105.
Three-point goals—Orlando 11-30 (Nelson 5-8, Anderson 4-9, J.Richardson 1-3, Redick 1-4, Duhon 0-2, Turkoglu 0-4), Indiana 9-17 (Granger 4-9, Hill 2-2, George 2-4, Collison 1-1, Barbosa 0-1). Fouled out—None. Rebounds—Orlando 41 (Davis 8), Indiana 53 (West 8). Assists—Orlando 14 (Nelson 5), Indiana 16 (Collison 6). Total fouls—Orlando 17, Indiana 16. A—18,165 (18,165).
BOSTON (86) — Pierce 7-17 0-0 16, Bass 4-10 6-6 14, Garnett 7-12 2-2 16, Rondo 6-17 0-0 13, Bradley 1-2 0-0 2, Stiemsma 0-0 0-0 0, Allen 5-10 3-5 15, Pietrus 1-4 0-0 2, Hollins 2-4 1-2 5, Dooling 1-1 0-0 3. Totals 34-77 12-15 86.
ATLANTA (87) — M.Williams 5-9 2-2 15, Smith 6-16 1-2 13, Horford 8-15 3-4 19, Teague 5-11 4-4 16, Jo.Johnson 6-17 2-2 15, McGrady 0-0 0-0 0, Dampier 1-1 0-0 2, Hinrich 3-4 0-0 7, Green 0-0 0-0 0, Collins 0-0 0-0 0, I.Johnson 0-0 0-0 0. Totals 34-73 12-14 87.
Three-point goals—Boston 6-14 (Allen 2-4, Pierce 2-4, Dooling 1-1, Rondo 1-2, Pietrus 0-3), Atlanta 7-16 (M.Williams 3-6, Teague 2-3, Hinrich 1-1, Jo.Johnson 1-5, Smith 0-1). Fouled out—None. Rebounds—Boston 41 (Garnett, Bass 7), Atlanta 46 (Smith 16). Assists—Boston 23 (Rondo 12), Atlanta 20 (Smith 6). Total fouls—Boston 13, Atlanta 19. A—19,319 (18,729).
MEMPHIS (97) — Gay 8-25 6-7 23, Randolph 5-11 2-2 12, Gasol 1-4 6-10 8, Conley 10-15 3-3 25, Allen 3-7 0-0 6, Mayo 1-6 2-2 5, Cunningham 2-5 0-0 4, Arenas 0-0 0-0 0, Pondexter 1-2 0-0 2, Speights 4-7 1-1 9, Haddadi 1-1 1-2 3. Totals 36-83 21-27 97.
L.A. CLIPPERS (101) — Butler 5-10 3-4 14, Griffin 10-15 10-17 30, Jordan 3-5 0-1 6, Paul 10-22 6-6 27, Foye 1-5 0-0 3, Williams 3-10 2-2 9, Martin 1-4 0-0 2, Young 1-4 2-2 5, Evans 0-1 2-4 2, Bledsoe 0-0 3-4 3. Totals 34-76 28-40 101.
Three-point goals—Memphis 4-13 (Conley 2-2, Mayo 1-4, Gay 1-6, Allen 0-1), L.A. Clippers 5-16 (Butler 1-2, Paul 1-3, Young 1-3, Williams 1-4, Foye 1-4). Fouled out—Griffin. Rebounds—Memphis 58 (Randolph 9), L.A. Clippers 49 (Paul 9). Assists—Memphis 18 (Conley 8), L.A. Clippers 18 (Paul, Griffin 7). Total fouls—Memphis 34, L.A. Clippers 26. Technicals—Cunningham, Gay, Randolph, Jordan, Paul. A—19,167 (19,060).
First flight, gross—Maddy Moser 91, Roz Tender 91; net—Helen Hamilton 73, Kathy McArdle 73, Mary Ann Ambroziak 75, Suzanne Adams 75, Brenda Buntin 75.
Second flight, gross—Mary Fangman 97; net—Shirley Cox 73, Shari Gatewood 74, Becky Hudson 74.
Third flight, gross—Sandi Morgan 95; net—Daphna Epperson 70; Leslie Mattmiller 73.
Fourth flight, gross—Sharon Fegley 103; net—Leo Moore 77, Kathy Haag 77, Greer Klesk 78.
Gross—Pat Carter 48, Cantrell 53; net—Jane Burton 38, Lou Brown 41.
First flight, gross—Carol Puckett 37.
Gross—Bettie Lou Evans 81; net—Penny Allen 71; Best Side—Claire Colliver 65.
Gross—Melinda Hendricks 78; net—Liz Jacobson 69; Special—Melinda Hendricks.
Gross—Chris Franklin 46; net—Marsha Stark 31; Special—Chris Franklin.
Kazakhstan vs. Slovakia, 9:15 a.m.
Canada vs. Switzerland, 1:15 p.m.
Norway vs. Italy, 10:15 a.m.
Sweden vs. Germany, 2:15 p.m.
Goals—LC: Mesmer 5, Duncan 3, Yorthey 3, Hawkridge, Gilbert; S: McCullough 2, Courtney, Van Meter 2, Solomon.
Mitchell and Walden; Foster and Cook. W—Foster (18-5). L—Mitchell (3-2). HR—Foster (Cr).
Records—Louisville Christian 27-5, Lexington Catholic 13-11.
Others receiving votes: Bullitt East 44, Pleasure Ridge Park 39, Oldham Co. 36, Eastern 33, Calloway Co. 30, Butler 26, Meade Co. 22, Marshall Co. 21, Henderson Co. 19, Bell Co. 18, Madisonville 16, Union Co. 14, Holy Cross 10, Butler Co. 8, Walton-Verona 7, Taylor Co. 2, Bullitt Central 1, Franklin Co. 1, Harrison Co. 1, South Laurel 1.
#4 Meikle, West Jessamine. def. #8 Least. Woodford Co., 6-1, 6-0.
#2 Benson, Model, def. #6 Wermeling, Henry Clay, 6-1, 6-0.
#1 Gillespie-Harris, Lafayette, def. #7 Puckett-Haley, Lexington Christian, 6-0, 6-0.
#1 Mok, Sayre, def. #7 Green, Lexington Catholic, 6-0, 6-1.
#3 Frazier, Lexington Christian, def. #5 Miracle, Scott Co., 6-0, 6-0.
#6 Tudor, Model, def. #4 Kibler, East Jessamine, 6-2, 6-1.
#2 Rizer, Lexington Catholic, def. #8 Harris, Lexington Christian, 6-1, 6-0.
a-Kentucky Classic; b-Ohio Tournament at Athens, Ohio; c-Ameritas Players Challenge at Lincoln, Neb.


text:
search filter All ContentAll JournalsThe Journal of Interdisciplinary History
JIH at JSTOR
Private Life under Socialism: Love, Intimacy, and Family Change in a Chinese Village, 1949–1999
. By
Yunxiang
) 289 pp. $55.00 cloth $19.95 paper
Beatrice S. Bartlett
© 2004 Massachusetts Institute of Technology and The Journal of Interdisciplinary History, Inc.
The Journal of Interdisciplinary History (2004) 35 (1): 180–181.
Beatrice S. Bartlett; Private Life under Socialism: Love, Intimacy, and Family Change in a Chinese Village, 1949–1999. The Journal of Interdisciplinary History 2004; 35 (1): 180–181. doi: https://doi.org/10.1162/002219504323091711
Shipwrecked: Coastal Disasters and the Making of the American Beach by Jamin Wells
An Early Power-Sharing Regime: The Alternativa System in Spanish Colonial America
Survival in a Nazi Concentration Camp: The Spanish Prisoners of Mauthausen
Kept in the Family: Remarriage, Siblings, and Consanguinity in the Netherlands
Creating a Chinese Harbin: Nationalism in an International City, 1916–1932
The Journal of Interdisciplinary History (July,2004)
Human, Also Human
October (November,2018)
African Arts (July,1968)
Performing Loss, Elegy, and Transcendental Friendship
The New England Quarterly (June,2008)
Source and Substance
Writing on Water
Design to Live: Everyday Inventions from a Refugee Camp
Watershed Governance
Constructing a Confluence


via Michael Geist: Ten Reasons Canada's Wireless Market Is Woefully Uncompetitive.
How Older Parenthood Will Upend American Society.
Of course, the issue is not particular to American professional class parents. It affects all in a similar bind. The solution, interestingly, if not surprisingly, lies outside the US boundaries, in polities that seek to arrange work schedules to accommodate child bearers before (epi-)genetic risks become too high–and thus more costly to the society.


Scaling Builds on 4 Years of Research and Demonstration Efforts in California.
SAN FRANCISCO--(BUSINESS WIRE)--Today, Google and Aclima, a San Francisco company that measures and maps hyperlocal air quality and climate emissions at block-by-block resolution, announced plans to scale and integrate Aclima's mobile sensing platform into Google's global fleet of Street View vehicles starting with 50 cars. Today's announcement builds on years of research and demonstration efforts with Aclima-equipped Google Street View cars in California to support new science that shows the power of mobile monitoring to make air quality visible at scales not previously possible.
Google Maps Street View cars will be equipped with Aclima's mobile sensor node which will generate snapshots of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5) while the cars routinely collect Street View imagery in cities and regions around the world. This snapshot data will be aggregated and designated with a representativeness indicator and will be made available as a public dataset on Google BigQuery. The complete dataset will be available upon request to advance air quality science and research. The first Aclima-equipped Street View cars will hit the road this Fall in the United States, as well as outside the U.S.
"We've been working with Aclima to test the technology for years, and we're excited that we are ready to take the next steps to begin this new phase: expanding to more places around the world with the Street View fleet. These measurements can provide cities with new neighborhood-level insights to help cities accelerate efforts in their transition to smarter, healthier cities," said Karin Tuxen-Bettman, Program Manager for Google Earth Outreach.
Google and Aclima to scale and integrate Aclima's air quality and emissions sensing platform into Google's global fleet of Street View vehicles.


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IC 3109 — галактика типу Sc (компактна спіральна галактика) у сузір'ї Діва.

Цей об'єкт міститься в оригінальній редакції індексного каталогу.

Посилання

 IC 3109 в оригінальному новому загальному каталозі
 IC 3109 в оригінальному новому загальному каталозі
 Перевірена інформація про IC 3109
 IC 3109 в базі SIMBAD
 IC 3109 в базі Vizier
 IC 3109 в базі NASA Extragalactic  Database
 Бази даних про об'єкти NGC/IC

IC 3109
IC 3109
IC 3109


Reflective essay writing is primarily based on reflecting an individual's personal experience.
Clearly, the several types of reports regularly trigger a great lot of distress amongst the students who must write them. The verdict of an analytic record shouldn't leave the reader dangling. An analytic essay is a really specific sort of homework that's created as a means to assist the audience gain better knowledge of a certain thing.
Intimidation contains behaviors that are focused on making another person feel inadequate, or concentrate on shaming somebody else. Bullying is quite an essential subject, clearly. School bullying are available during elementaryschool, middle college, and greater school in the Us. There are plenty of ideas on how you can aid always check or cease cyber bullying.


The distance between Asuncion, Paraguay and Danmarkshavn, Greenland is approximately 11,648 km or 7,238 mi.
To reach Asuncion in this distance one would set out from Danmarkshavn bearing 216° or SW and follow the great circles arc to approach Asuncion bearing 188.6° or S .
Asuncion has a humid subtropical climate (Cfa) whereas Danmarkshavn has a tundra climate (ET).
Asuncion is in or near the subtropical moist forest biome whereas Danmarkshavn is in or near the polar desert biome.
The annual average temperature is 35 °C (63°F) warmer.
Total annual precipitation averages 1194 mm (47 in) more which is equivalent to 1194 l/m² (29.3 US gal/ft²) or 11,940,000 l/ha (1,276,465 US gal/ac) more. About 9 3/5 as much.
The altitude of the sun at midday is overall 50.6° higher in Asuncion than in Danmarkshavn.
The mean dew point temperature is 32.7°C (58.9°F) higher.
The table shows values for Asuncion relative to Danmarkshavn. You can also view this comparison the other way around from the perspective of Danmarkshavn vs Asuncion.


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Schaltungstechnik Vertiefungs VU WS2013
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Lab 1, Lab2
Task 1,2,4,5

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