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Discovering Traveling Companions using Autoencoders
07/23/2020 ∙ by Xiaochang Li, et al. ∙ East China Normal University ∙ 0 ∙ share
With the wide adoption of mobile devices, today's location tracking systems such as satellites, cellular base stations and wireless access points are continuously producing tremendous amounts of location data of moving objects. The ability to discover moving objects that travel together, i.e., traveling companions, from their trajectories is desired by many applications such as intelligent transportation systems and location-based services. Existing algorithms are either based on pattern mining methods that define a particular pattern of traveling companions or based on representation learning methods that learn similar representations for similar trajectories. The former methods suffer from the pairwise point-matching problem and the latter often ignore the temporal proximity between trajectories. In this work, we propose a generic deep representation learning model using autoencoders, namely, ATTN-MEAN, for the discovery of traveling companions. ATTN-MEAN collectively injects spatial and temporal information into its input embeddings using skip-gram, positional encoding techniques, respectively. Besides, our model further encourages trajectories to learn from their neighbours by leveraging the Sort-Tile-Recursive algorithm, mean operation and global attention mechanism. After obtaining the representations from the encoders, we run DBSCAN to cluster the representations to find travelling companion. The corresponding trajectories in the same cluster are considered as traveling companions. Experimental results suggest that ATTN-MEAN performs better than the state-of-the-art algorithms on finding traveling companions.
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Xiaochang Li
Bei Chen
Xuesong Lu
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Discovering moving objects that travel together, i.e., traveling companions, is an interesting problem in many real-world applications. For example, in mobile advertising it is proven that consumers who occurred at the same location at the same time generally exhibit commonalities in their taste (Ghose et al., 2019) . Thus detecting groups of consumers who walk together and advertising to them using coupons in the same product category might increase the advertising response. Other applications concerning with finding traveling companions include intelligent transportation systems, animal migration monitoring and public procession management.
Traveling companions can be discovered from the trajectories of moving objects. An object's trajectory is a sequence of its location points sorted chronologically. Traveling companions are thus moving objects with trajectories that are both spatially and temporally close. Existing methods for mining traveling companions can be broadly divided into two categories: methods based on pattern mining (Jeung et al., 2008; Li et al., 2010; Zheng et al., 2013) and methods based on representation learning (Yao et al., 2017; Li et al., 2018; Yao et al., 2019)
. Pattern mining based methods often first define a particular movement pattern pertaining to traveling companions based on some similarity measurement of their trajectories, and then develop specific algorithms to extract the predefined patterns. The similarity measurements are often based on point-wise Euclidean distance and thereby require trajectories are aligned along timestamp. However, real trajectories often contain many missing location points and have to be interpolated, which introduces additional measurement errors. Representation learning based methods, on the other hand, do not rely on pattern definition or point-wise comparison. Instead, they learn the representations of the trajectories using machine/deep learning models and then cluster the representations to discover similar ones, which in turn represent similar trajectories. Existing models either require certain amount of feature engineering, or specific labels for supervised learning. But in practice, feature engineering is often problem dependent and time consuming, which introduces extra overhead to representation learning itself. Yet trajectory labels are usually unavailable as they are difficult to collect and often have ethics issues. Moreover, existing models focus more on learning spatial proximity between trajectories, therefore they can only extract objects with similar spatial features, which are not necessarily traveling companions.
To tackle the aforementioned issues, we develop in this paper an unsupervised model using autoencoders, which learns the representations directly from original trajectories. The original trajectories are not necessarily aligned along timestamp, and can be of various lengths, i.e., having different numbers of location points. The learned representations contain both spatial and temporal features of the original trajectories, and thereby can be clustered to discover traveling companions.
The model is inspired by the work of text summarization
(Chu and Liu, 2019) , where paragraphs of similar topics are grouped and summarized. Thus we propose to first group the original trajectories using the Sort-Tile-Recursive (STR) algorithm (Leutenegger et al., 1997) . STR is originally used to group spatial data for the bulk-loading construction of R-tree. We use it to group the trajectories so that the trajectories within the same group already have certain spatial and temporal proximity. Then we feed each group of trajectories independently into an attention-based encoder. The idea behind is to encourage closer trajectories to learn more similar representations from each other. Then we use an encoder-decoder structure to reconstruct the input trajectories, and use a decoder-encoder structure to learn the similarity between the input trajectories, as we will show in Section 1. The similarity is computed between the mean encodings of the first encoder and the intermediate encodings of the second encoder. As we use global attentions to produce the encoding and a mean operation to aggregate a group of encodings, we call our model ATTN-MEAN Autoencoder. Once we obtain the trajectory encodings using the trained model, we use DBSCAN (as always used in literature (Yao et al., 2017; Li et al., 2018) ) to cluster the encodings. The corresponding trajectories in the same cluster are therefore considered as traveling companions.
2. Related Work
The problem of finding moving objects that travel together has been extensively studied over the past decade using data mining algorithms. Some representative studies include Flock (Al-Naymat et al., 2007) , Convoy (Jeung et al., 2008) , Swarm (Li et al., 2010) and Gathering (Zheng et al., 2013) . The authors in (Jeung et al., 2008) define convoy to describe a generic pattern of traveling companions of any shape. A convoy is a group of at least m moving objects that are density-connected with respect to a distance e during at least k consecutive time points. A simple algorithm for discovering convoys is to perform a density-connected cluster algorithm at each time point and maintain convoy candidates who have at least k clusters during consecutive time points. Then for each candidate, an intersection of its clusters is conducted to test whether there are at least m objects shared by all the clusters. To overcome the high computational complexity, the authors propose to first extract candidate convoys based on simplified trajectories and then decide whether each candidate is indeed qualified in the refinement step. The pattern of swarms (Li et al., 2010) further relaxes the constraint of convoys by defining traveling companions as moving objects that move within density-connected clusters for at least k time points that are possibly non-consecutive. The goal of the work is to discover all closed swarms, such that neither the object set nor the number of time points of the swarms can be enlarged. As the number of candidate closed swarms is prohibitively huge, the authors propose two pruning strategies to effectively reduce the search space.
Recently, representation learning of trajectories has drawn lots of attention because it requires very little feature engineering and similarity computation, since using representations naturally avoids the problem of point matching. Studies mostly related to our work include trajectory clustering (Yao et al., 2017) and trajectory similarity computation (Li et al., 2018; Yao et al., 2019; Zhang et al., 2019) via representation learning. The authors in (Yao et al., 2017) use a seq2seq autoencoder to learn trajectory representations for clustering tasks. They first extract trajectory features such as speed and rate of turn and transform them into a feature sequence that describes the movement pattern of the corresponding object. Then, they feed the feature sequences into their model to learn fixed-length representations. Later the work in (Li et al., 2018)
proposes an RNN based model to learn trajectory representations for similarity computation. The model does not require feature extraction and directly learns from original trajectories represented by trajectory tokens. However, the model training is supervised and requires to construct training pairs by sampling from the original trajectories. Both studies in
(Yao et al., 2017; Li et al., 2018) focus on finding trajectories with similar shapes, regardless of their actual timestamps. Other studies like (Zhang et al., 2019) propose to inject additional semantic information such as environmental constraints and trajectory activities into deep models, in order to obtain more accurate trajectory representations for similarity computation. Nonetheless, none of the existing models is designed to learn simultaneously spatial and temporal proximity between trajectories. As such they cannot be directly applied on discovering traveling companions that are both spatially and temporally close.
3. The models
3.1. Representation of Trajectory Points
Firstly, we divide the entire space into square cells of the same size. Then, following the idea in (Li et al., 2018) , we use the skip-gram technique (Mikolov et al., 2013) to pre-train the cell representations (or token) such that spatially close cells have similar representations. So, each location point of a trajectory can be represented using the token of the corresponding cell the point falls in.
Secondly, to capture temporal characteristics, we propose to use the positional encoding (Gehring et al., 2017)
technique to inject time information into the above trajectory tokens. Positional encodings have been used in the self-attention model to capture information about token positions in a sequence. As a trajectory is an ordered sequence of tokens, we could use the same mechanism to model the sequence. We compute the positional encodings as Equation
1 and 2:
(1) PE(ti,2p)=sin(ti100002p/dcell)
(2) PE(ti,2p+1)=cos(ti100002p/dcell)
where ti is the ith timestamp of the entire time duration in the data, dcell indicates the embedding size of a cell, and p∈[0,dcell/2−1]. Each positional encoding has the same size with the above token, and is therefore added directly to it.
3.2. ATTN-MEAN Autoencoder
In order to encourage trajectories to learn more from their neighbours, we propose to first roughly divide them into groups w.r.t their spatial-temporal proximity, and then feed each group independently into the other autoencoder. We use the Sort-Tile-Recursive (STR) algorithm (Leutenegger et al., 1997) to group the trajectories. The STR algorithm is originally used to pack spatially-close objects into minimum bounding rectangles (MBRs) and construct a bulk-loading R-tree index of the objects. Since each trajectory can be considered as an object in a three-dimensional space (t,x,y), where t is the time dimension and (x,y) stands for the two spatial dimensions, we apply the idea of STR to pack trajectories. Trajectories in the same MBR or group are more likely to be traveling companions, and thus fed as an independent group into the model.
The entire model is constructed using two autoencoders, as depicted in Figure 1. The encoder and decoder on the left-hand side constitute the first autoencoder, which is an ordinary LSTM autoencoder used to reconstruct the trajectories. On the right-hand side, the encodings are firstly averaged using a mean operation and then fed into a decoder. The decoded intermediate trajectories are again input into an encoder to obtain the encoded intermediate mean trajectories. Then we force the encodings output by the left encoder to learn from each other by computing the similarity between them and the encodings produced by the right encoder. Thus the autoencoder on the right-hand side is indeed a decoder-encoder structure. The two encoders and the two decoders have the same structures and share the same parameters, respectively.
Figure 1. The proposed ATTN-MEAN model
On the two LSTM encoders, we additionally use a global attention mechanism to aggregate the hidden states of each step to form the output encodings. The encoding contains more overall information of the entire trajectory and boosts the model's performance in the experiments. In particular, we firstly initialize a global attention vector
a to calculate an attention score on each trajectory token, as shown in equation 3,
(3) αi=exp(hTi⋅a)∑ltrj=1exphTj⋅a
, where hi
is the hidden representation of point
i, αi is the attention weight of this representation and lTr is the length of each trajectory. Then, we use these attention scores to conduct a weighted sum of the hidden state vectors of each trajectory, denoted by Htr,
(4) Htr=ltr∑i=1αi⋅hi
, where Htr is the final representation of the trajectory.
For optimization, we compute the trajectory reconstruction loss lrec using mean squared error (MSE) for the left component, as shown in Equation 5,
(5) ιrec({(tr1,tr1′),⋯,(trk,trk′)})=k∑i=1lMSE(tri,tri′)
where k is the batch size, tri denotes the ith trajectory and tri′ is the corresponding reconstructed trajectory. Then we compute the representation similarity loss between the trajectory encodings produced by the two encoders using average cosine distance, as shown in Equation 6,
(6) ιsim({(tr1,tr′),⋯,(trk,tr′})=1kk∑i=1dCOS(Htri,H′tr)
where Htri denotes the aggregated encoding of trajectory tri generated by the encoder in the left component and Htr′ denotes the intermediate trajectory encoding generated by the encoder in the right component.
Through minimizing both the reconstruction loss and similarity loss, we force the encodings produced by the encoder to keep the distinctive features of their own original trajectories as well as learn similar features from their neighbours in the same group. As we use global attentions and a mean operation inside and outside the encoder, we call this model ATTN-MEAN Autoencoder.
4. Performance Evaluation
4.1. The Dataset and Overall Settings
We obtain a trajectory dataset of the passengers in our collaboration with an airport in Asia. The dataset contains 14605 trajectories with about 719,507 points, and each trajectory has 20 to 120 location points. We attempt to use this data to find passengers who walk together.
Using the dataset, we compare ATTN-MEAN with an LSTM autoencoder (LSTM-AE), Convoy (Jeung et al., 2008) , Swarm (Li et al., 2010) , T2VEC (Li et al., 2018) , BFEA (Yao et al., 2017) . For two pattern mining based methods Convoy and Swarm, we compare the number of extracted clusters with that discovered using our learned representations. To comply with the two algorithms, we conduct linear interpolations in the dataset, such that we generate a synthetic point every 10 seconds for each trajectory, if necessary. For T2VEC and BFEA, we also inject positional encodings into their original input for a fair comparison. We use DBSCAN to cluster the learned encodings and compare the clustering performance. We only list the main results due to page limit. More experimental results are available upon request.
4.2. Parameter Settings and Training Details
Group size and batch size. We set the group size and batch size to 64 and 8, respectively. The group size is the size of MBR capacity used in the STR algorithm.
Cell Size. We divide the entire airport space into square cells with length 5 meters on each side, and obtain 15,354 cells.
Embedding Size. We set the embedding size of the cell representations to be 256. Therefore the positional encoding for each timestamp also has 256 dimensions.
Training Details. We use Adam stochastic optimization for training with the learning rate 0.001 and weight decay rate 0.00001
. The training process is terminated when trajectory reconstruction loss and representation similarity loss both converge. We observe that all the three models converge after 20 epochs.
4.3. Main Results
We employ three metrics to evaluate the clustering results, namely, Davies-Bouldin Index, Silhouette Coefficient and weighted average entropy. A smaller Davies-Bouldin index and a high Silhouette Coefficient value indicate better clustering performance in general. To calculate the weighted average entropy of each cluster, we use the nearest gate of each trajectory's last point as its label. The idea behind is that traveling companions are more likely to walk to the same gate. For all these measurements, we discard all clusters of size one, i.e., trajectories that are considered as traveling alone. We vary the distance parameter epsilon of DBSCAN from 0 to 0.2 with increments 0.0001 and plot the three metrics for each epsilon. The results are presented in Figure 2(a), 2(b) and 2(c), respectively.
(a) Davies Bouldin Index
(b) Silhouette Coefficient
(c) Weighted Average Entropy
Figure 2. Clustering evaluation using three metrics.
We observe in Figure 2(a) and 2(b) that in general ATTN-MEAN has smaller Davies-Bouldin Index and larger Silhouette Coefficient for varying epsilon, suggesting that ATTN-MEAN performs better than LSTM-AE, T2VEC and BFEA for both internal and external criteria evaluation. We also observe in Figure 2(c) that ATTN-MEAN produces smaller weighted average entropy than LSTM-AE, T2VEC and BFEA for varying epsilon. This means that clusters produced by ATTN-MEAN have fewer different gate labels, which means the clustered trajectories are more likely to be travelling companion.
Besides, We compare the number of clusters that have at least two trajectories found by the two models with that extracted by Convoy and Swarm. The results are presented in Table 1. For Convoy and Swarm, we set k to be 18 (i.e., at least 3 minutes), m to be 2, and vary e in (3m,5m). For example, the first row in the table means that a convoy should have at least two trajectories with distance less than 3 meters in at least 18 consecutive time points, where two consecutive time points have 10-second offset. For our two models, we simply show the results when they discover the largest number of clusters.
Number of Clusters
Single trajectories
k=18,m=2,e=3m 308 4629
k=18,m=2,e=5m 2431 2710
LSTM-AE
ATTN-MEAN
Table 1. The clusters discovered by different algorithms.
We observe that even for these relaxed parameter settings, Convoy and Swarm generate lots of single trajectories that do not belong to any cluster. By contrast, our models can group most trajectories into clusters.
4.4. The Effect of Positional Encodings
We also conduct an ablation study to show the effect of positional encodings. We observe in Figure 3, ATTN-MEAN performs generally better than ATTN-MEAN without positional encodings for varying epsilon on all the three metrics. This proves that the injection of positional encodings helps our model to find trajectories that are both spatially and temporally close. The model without positional encodings would find trajectories with similar shape in different time periods.
Figure 3. The Effect of Positional Encodings.
5. Conclusion and Future Work
In this work, we propose an unsupervised deep representation model ATTN-MEAN for the discovery of traveling companions. We first employ positional encoding and skip-gram techniques to embedding the trajectories. The input trajectory token representations are collectively embedded with spatial and temporal information of the location points. Then we use STR to group original trajectories to encourage them learn from the neighbours. A double autoencoder architecture with global attentions and a mean operation is used to construct the model. Experimental results show that ATTN-MEAN learns overall better trajectory representations than LSTM-AE, T2VEC and BFEA for discovering traveling companions. In future, we plan to explore other mechanisms to fuse the two autoencoders, and further improve ATTN-MEAN.
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Although I am downtrodden, I am not defeated and I desire to testify. I know that God is still and always has been in control and has not changed. I know the healing power that Jesus showed in the Bible has not lessened. He is the same. He is the same as he was in the beginning, and he will never lose that given to him by Father God.
My parents were sick since I was a child. I saw the mighty hand of God so many times when they were sick. My mother saw many, many miracles in her life alone. She was supposed to die young. She had a very bad heart condition diagnosed in her 20's. God gave her many years. God gave her the desires of her heart to bear two more children after this diagnosis.
She suffered many physical attacks by Satan and every time until her day for that final rest was chosen, she was healed. Her doctors saw the many miracles and so did her family. Her congestive heart failure in her 20's did not have a good prognosis. They did not expect her to live. She lived and then they did not give her long to live. She believed in the power of God and desired to see her children grow.
She was encouraged by her doctors to abort her last child to save her life but she refused. God delivered her from the surgery and he allowed safe delivery of her baby. Again, years later, they told her she needed surgery but she persevered and God healed her heart valve saving her years from this surgery.
Her children grew up. Again in 1979 she had to have a second open heart surgery. The artificial heart valve she was given was later recalled because thousands died from it's defect. She never had it removed.
Long after this time, her husband was diagnosed with throat cancer. The burden was terrible because they were going to remove his larynx and a lot of the surrounding tissue. My father was not saved. God sent a minister to my father with the message that he was going to be healed. My father believed and accepted Christ as his saviour.
After the radiation that the doctors said would in no way kill the cancer but maybe make it more operable, my mother asked the doctors to examine his throat again. The cancer was gone. The doctors were astounded.
My mother became ill again. They did not expect her to survive. She had Ovarian Cancer. It was in the 3rd stage of 4 stages. God carried her through severe surgery procedures and the awful following 3 year period of Chemo therapy. Following the chemo, she had strokes, many, many, emergency blood transfusions and ER visits for imbalance of body chemicals. Sick and in the hospital again, they told her to give up and die because the cancer was surely back. She did not feel it was time. They did surgery at her pleas, and she did not have cancer, but a twisted blocked bowel. Again, God intervened. She would have died, if not for the surgery to unblock her bowel.
Once she almost died from a severe blood infection septisymia. She survived by God's hand, I know. Later, as she was told she would need heart surgery again to live she refused it because her husband was dying. She gave up any chance of surviving to see him through his difficult time. My mother told me this time she did not feel she would be healed here on earth.
She never gave up faith though and her life was a testimony to many through the 18+ times she was rushed to the hospital the last years before her death. God heals. His healings were visible to even the most sceptic around her. My mother lived to be almost 70. Now, she is with Jesus.


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Game On! Ponte Vedra Beach Triathlon - Fall
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DRONE VIDEO ST. AUGUSTINE TRIAHTLON
We were in St. Augustine in May but will be back in Ponte Vedra Beach this time. The venues are both awesome and very similar. Thought you would enjoy seeing this drone video of the spring event. CLICK HERE TO VIEW
The 5th Annual Game On! Ponte Vedra Beach Triathlon, Presented by Bowen, Hanes & Company will take place at Mickler's Landing Beach on Sunday, October 15, 2023.
Nestled comfortably between Jacksonville and St. Augustine, Ponte Vedra Beach has a rich and unique history. Ponce de Leon, a Spanish explorer best known for his pursuit of the legendary "fountain of youth," is believed to have landed on what would later become Ponte Vedra Beach on April 2, 1513.Today, this thriving community has come to be considered one of the most luxurious and prestigious recreational and residential destinations in Florida, if not the entire country. It was voted Money Magazine's "Best Place to Live in Florida" and among the top 50 places to live in the United States.
This destination race will be hosted at Mickler's Landing and will include an Olympic distance Triathlon, Duathlon and Aquabike as well as the Sprint Triathlon and Sprint Duathlon. The swim will take place in the Atlantic Ocean, with a scenic bike course taking you South on A1A through the Guana Tolomato Matanzas National Estuarine Research Reserve (GTM Research Reserve). Finish with a run North on Ponte Vedra Blvd taking in the views of the lavish homes and jaw-dropping oceanfront settings. Upon first sight of the meticulously maintained resort-style community, you'll instantly understand why it was ranked one of the "Best Places to Live in United States".
About our Presenting Sponsor Bowen, Hanes & Company, Inc.
Bowen, Hanes & Company Inc., headquartered in Atlanta, GA, with an offices in Ponte Vedra Beach, FL and Winston-Salem, NC, has been providing investment counseling services to individuals and institutions for the past four decades. Since inception, it has been our mission to produce and provide a service in a manner consistent with our high-quality, long-term approach. The exclusive focus will continue to be on adding value by offering a superior product, thus allowing us to establish and maintain mutually beneficial relationships. The firm operates according to a carefully formulated strategy, and is dedicated to our primary mission, meeting the needs and long-term objectives of our clients, including pension funds, profit sharing plans, foundations, endowments, individuals and family groups.
It is our intention to remain a compact organization, enabling us to provide a high level of personal attention by allowing clients to work closely with the senior executives of the firm.
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redpajama_set_name: RedPajamaCommonCrawl


text:
Does Worms Rumble Have Split-Screen Local Multiplayer?
Team17's lovable and deadly worms are back in Worms Rumble. The newest series release shakes up the classic formula by shifting from turn-based combat to real-time action. But does this mean that classic features like local multiplayer are gone? Does Worms Rumble have split-screen multiplayer?
Worms Rumble | Is There Split-Screen Local Multiplayer?
Worms Rumble does not have a split-screen multiplayer mode. In fact, there's no local multiplayer whatsoever. There's no way for two people to play from the same screen, not even against one another.
For classic Worms fans, Rumble will be a fairly major departure from the norm. Previous series releases allowed two players to command a team of worms against one another in turn-based combat. Rumble is not turn-based, being instead designed around real-time combat, and it's played exclusively online. This means that you can't play against a friend in local multiplayer, nor can you play a single-player match against bots.
Thankfully, there are several different modes for players to jump into. Deathmatch is the base mode, with groups of solo worms duking it out against each other for supremacy. Last Worm Standing is the new battle royale mode, where players fight to be the sole survivor of the match. There's also Last Squad Standing, which allows groups of three worms to see who can outlive all the rest.
To keep the action fresh, the crew at Team17 has also implemented limited-time events and challenges. Random challenges can be found on the multiplayer screen, and completing them will unlock rewards in the form of new outfits, skins, and emotes. Players can also take turns in The Lab to try out new weapons and strategies.
Sadly, you can't play Worms Rumble split-screen, nor is there any local multiplayer. If you want to join the fight, you'll have to play online. Luckily, there is cross-play, meaning you can join your friends across PS4, PS5, and PC.
Kevin Tucker
Kevin Tucker is the managing editor of Guide Fall and From Gamers Magazine. When he's not busy exploring or explaining the latest video games, he spends his time riding his motorcycle, reading science fiction, and playing electric bass.


text:
Subroto Ghosh (born 15 July 1987 in Patna, Bihar) is an Indian first-class cricketer who played for Jharkhand cricket team. He was a right-handed wicket-keeper batsman.

References

External links

1987 births
Living people
Indian cricketers
Cricketers from Patna
Jharkhand cricketers
East Zone cricketers

redpajama_set_name: RedPajamaWikipedia


text:
Anti-Republican Culture
BAT LIGHTNING!
Robert Stacy McCain is the reincarnated amalgamation of Hunter S. Thompson and Richard Brautigan.
Republicans Under Siege
So Right and Natural
The Ministry of Truth
Words vs. Pictures
The Conspirator
Michael Ramirez Gets Your Attention.
All Hell Breaks Loose!
Visit "The Cat in the Bag."
(It's a look at the Democratic Party in a way you won't see anywhere else. )
Do you feel it?
You are probably aware of the "Arab Spring" taking place in the Middle East. People in Tunisia, Syria, Egypt, and Libya are fighting for freedom, and at tremendous personal cost. There is bloodshed and death, with the despotic regimes of the region exacting their due.
But there is also an awakening that is happening in America. Maybe you have seen the signs…
--Atlas Shrugged Part I, the new cinematic version of Ayn Rand's 1957 novel, is now in theaters and people are interested in what it has to say. The box office receipts have not been great, but there is definite "buzz" associated with the film.
--Hillary Clinton, Secretary of State, lets us know that Bashar al-Assad, President of Syria, is a "reformer."
These are both instances where words are powerful, but pictures (even fifty years after the words) are more so.
What's that? You don't see the image associated with Mrs. Clinton's viewpoint? Here is a link to a Pajamas Media post that completes the picture. The videos portray the "humanity" of the government forces of President Assad of Syria, and they stand in stark contrast to the words used to characterize President Assad and his wife, the former Asma Akhras of London.
That contrast comes to us because of the Internet. It doesn't exist in the traditional media. NBC News did provide some coverage of the "Syrian unrest" last night (4/26/2011) using a few frames from a YouTube video, but they did so to give the false impression that NBC cameras were on the scene. The video was actually taken by a Syrian citizen with a cell phone camera.
While the "Arab Spring" is enabled by the existence of the public Internet and its related technologies, Americans are experiencing their own awakening, courtesy of that same Internet. Here are some examples:
--Our President tells us that America can tax its way out of the current economic problems. We simply need to get tax revenue up to 23% of GDP. However, because of the Internet (and Hauser's Law), we know that will not happen. The picture of Mr. Hauser's graph is stronger than the President's words.
--Our President also tells us "[his budgeting approach] will lower our interest payments on the [national] debt by $1 trillion." Because we can visualize the simple equation, interest = principal x rate, we know that with principal (national debt) increasing, and the rate (of inflation) going up, this will not happen either.
The Internet helps us see contrasts by accentuating the difference between words and pictures. Here's another instance that caught my attention:
Over the Easter Weekend, PJTV released a video of Dr. Helen Smith interviewing Dr. Barbara Oakley and Amy Alkon (The Advice Goddess). Dr. Oakley is a university professor and is the author of the recently-published Cold-Blooded Kindness. (Thanks for the kind mention of this blog in the interview, Dr. Oakley.) Amy Alkon is the author of I See Rude People.
The interview turned to the subject of political correctness, and Amy advised the viewers what to do if they were terrified of being called a racist: "You must be willing to take on the people who are going to attack you if you say stuff that's not the approved speech." She also emphasized, "You have to be prepared for this to come your way."
By way of example, Amy referenced a personal smear attack she had experienced last year. She characterizes herself as an Atheist and an Independent, but the attack came because she appeared to be acting like a Republican!
Stacy McCain rose to her defense, and suddenly the Internet became an arena for a case study on political correctness run amok. More importantly, the Internet stripped away the anonymity of the smear merchant. The alleged perpetrator has a face.
Amy Alkon, Dr. Oakley and Dr. Smith are ready for the rough-and-tumble of ideological combat and have the scars to prove it. They are "armed and dangerous."
(Note to "new civility" police: That terminology is metaphorical.)
If Americans are "getting ready to rumble," it may be that our ladies will lead the way. They come equipped with blogs, cell phone cameras, Facebook posts, Tweets, and online interviews.
Is their impact creating an "American Awakening?" The 2012 elections will be our first indicator.
In the meantime, here is a question to think about:
If you wished to better understand the world in which we live, would you be better served by listening to Hillary Clinton and Asma Assad, or Barb Oakley and Amy Alkon?
"Golden Goose" photograph posted on WunderPhotos by Bill Meier.
UPDATE 5/8/2011:
James Taranto, editor of Best of the Web Today, interviews Paul Wolfowitz on the "Arab Spring." Mr. Wolfowitz gives context to President Bush's efforts to create a center of Western influence in the Middle East.
UPDATE 5/24/2011:
Martin Peretz, writing at The New Republic characterizes Syria in this way...
Syria is where the new paradigms of Arab history will be made. The brutality of the Assad dictatorship is legendary, and it has gone over 40 years from father to son. No one is willing to predict whether the family will survive or be taken out. If it survives, it will be more dictatorial than anyone imagined possible. If it is overthrown, it will be replaced by a regime equally cruel but more pious, much more pious. It is not easy for outsiders to decide what they want.
But Barack Obama and Hillary Clinton have already more than indicated that they prefer the continued dominion of Bashar Assad. You have to be pretty cold-blooded to make a choice like that. There are other consequences to this decision. Syrian dominion over Lebanon will continue. The Syrian alliance with Iran will continue. Syrian influence over Turkey will continue, perhaps intensify. Syrian intrusion in Iraq will continue. Syria might even get its chance to be on the U.N. Human Rights Council.
John Hinderaker at Power Line posts this story to emphasize the human costs of our Syrian foreign policy.
Roger L. Simon lets us know it is just a matter of style.
Return to Bottom
Posted by Howard Towt at 11:39 AM 0 comments Links to this post
Labels: Republicans Under Siege
Robert Redford's latest film "The Conspirator" opened this past weekend. The reviews are generally positive, with many critics reflecting on the timeliness of the subject.
The movie itself is about the assassination of President Abraham Lincoln and the role played by a boarding house proprietor named Mary Surratt. Mrs. Surratt was accused of being an active conspirator in the Lincoln plot, and was put on trial in a military court along with three other alleged conspirators.
The movie was released on April 15, 2011 to coincide with the 146th anniversary of Abraham Lincoln's death. The tragedy of that time was the American Civil War, which began near Charleston, South Carolina on April 12, 1861 when the Confederacy bombarded Fort Sumter at the mouth of Charleston Harbor. The war ended four years later on April 9, 1965 with General Robert E. Lee surrendering at Appomattox, Virginia. Less than a week later, President Lincoln was dead.
While there was obviously great drama at this time in our nation's history, Mr. Redford chose to focus on the circumstances of Mrs. Surratt. Why is this?
Patrick McDonald from hollywoodchicago.com thinks he knows:
The aim of Robert Redford in the construction of The Conspirator is obviously to parallel the circumstances of the last 10 years, where the fear of terrorism has led to perpetual war and the suspension of highly regarded constitutional beliefs in exchange for an illusion of safety. The debate about that legality in the film consists of the same concerns that takes [sic] regarding the Guantanamo Bay detention camps and the politicians who believe that war powers means the suspension of fair trials, privacy and yes, freedom.
And so we see the relevance. Our Constitution prohibits unlawful imprisonment in the United States except in cases of rebellion or invasion, when "the public safety may require it."
As you can imagine, political leaders like to suspend habeas corpus and declare the public safety in jeopardy whenever it helps them navigate difficult situations. Why get involved in messy civilian trials when you can use military prisons and tribunals?
Suspensions have occurred during the Civil War and World War II. More recently, our leaders have wrestled with the question of whether this constitutional right applies to non-citizens, specifically those persons designated as enemy combatants.
But back to Mary. She is portrayed as a sympathetic figure…
She is a woman.
She is a practicing Catholic.
She is struggling with problems within her family unit.
The people arrayed against her are of a different stripe. Those who testify against her appear to be less than truthful. A military officer is duplicitous in the representations he makes "off the record" versus those under oath. The leader of the military tribunal clearly shows antagonism toward the legal counsel representing Mrs. Surratt. The whole process is "a rush to judgment."
In this context, Mr. Redford contrives for us a quiet display of the theme that "Republicans are shredding the Constitution." The people "wearing the black hats" are associated with a Republican administration, and they come across as shallow and opportunistic. While the film doesn't resolve the guilt or innocence of Mrs. Surratt, we leave the theater knowing that we don't like the people who adjudicated her.
And this brings me to the purpose of this post. People in America are divided on the issue of the Constitution.
Republicans believe they know what our Constitution says. Others believe they know what the Constitution means. To these latter individuals, the Constitution is a document of charity, compassion and forgiveness. It speaks of human yearning, and has but a glancing influence on the rule of law.
These two different perspectives end up dividing us by political philosophy. We are either Founders or Progressives.
Who is right? Robert Redford weighs in with his cinematic effort, showing how Republicans are those who are shredding the Constitution. He lets us know that habeas corpus must be preserved at all costs. The constitutional exceptions noted in Article One, Section 9, Paragraph 2, can be ignored.
Does it seem that I am projecting a little of my own bias onto this gifted actor and director?
Probably, but then why not take the occasional liberty with things like the juxtaposition of the title and photo of a blog post?
Posted by Howard Towt at 3:40 PM 0 comments Links to this post
Labels: The Themes
Investors Business Daily has an editorial today that displays the Michael Ramirez cartoon shown above.
The editorial is a critique of yesterday's speech by President Obama at George Washington University. The President's speech was an effort to counter Paul Ryan's budget proposal.
The speech was political in nature, and incorporated some problematic assumptions. President Obama says that America will save $1 trillion by lowering interest payments on the national debt over the next twelve years. He apparently expects us to believe that interest rates on the national debt can be reduced from the current level of 0.00%.
But that's in the fine print...
If you want to understand the impact of the current budget debate, look no further than Mr. Ramirez's cartoon. Do you recognize the Pied Piper leading our children into a dangerous future?
It's President Obama, and that's devastating.
Standard and Poor's lowered rating on United States debt repayment will increase the interest rate on that debt. The Presiden't assurance that he will lower the amount of interest payments on our national debt is looking more and more like another empty promise.
Mr. Ramirez has another commentary on our debt burden at IBD.
Labels: The Ministry of Truth
Republican Congressman Paul Ryan from Wisconsin is chairman of the House Budget Committee. Today he announced the GOP "Path to Prosperity." It is what House Republicans are offering as the budget of the United States for fiscal year 2012.
The budget acknowledges Hauser's Law, proposing a 20% cap on federal spending rather than the 23% currently being promoted. It proposes welfare and healthcare reforms that acknowledge the motivations behind the choices people make in these programs. It acknowledges the complexity of our current tax law and proposes to simplify it.
Representative Ryan notes in his concluding paragraph in the Wall Street Journal, "We can reform government so that people don't have to reorient their lives for less."
Get ready for the preening of the Democratic Party. This will be a display for the ages!
Readers of this blog will be familiar with the Democratic Party theme that "Republicans are bad people." They will also understand how our current federal executive branch evaluates each national policy action from the perspective of what it does for the power and authority of the Democratic Party.
Republicans have offered a policy proposal, and they will be met with characterizations of them being the worst of the worst. You say you haven't seen much of the theme, "Republicans are turning the economy into a catastrophe"? Get ready for it. How about, "Republicans are stealing from our Seniors"? That will be front and center as well.
The idea of meeting policy proposals with negative characterizations is time-honored politics.
Let the demagoguery begin.
House Minority Leader Nancy Pelosi warns that Republicans are at war with women:
"We have a big fight on our hands, in terms of respect for women..."
"This is the same old ideological turn back the clock for women..."
"Women are predominantly the recipients of Medicare, and they are going to be hurt by this."
"There is actually a war on women."
Representative Pelosi's remarks are not quite as strong ("the new civility") as those delivered by Representative Louise Slaughter who believes that newly elected Republicans are "here to kill women." That disquieting theme of the Democratic Party is being used more frequently.
Labels: Republicans Under Siege, The Themes


FASTA [Pearson et al. 1988] is another commonly used family of programs for sequence database searches. FASTA stands for FAST-All, reflecting the fact that it can be used for a protein or nucleotide comparisons. The program achieves a high level of sensitivity for similarity searching at high speed. The high speed is achieved by using the observed pattern of word hits to identify potential matches before attempting the more time consuming optimized search. The trade-off between speed and sensitivity is controlled by the ktup parameter, which specifies the size of the word. Increasing the ktup decreases the number of background hits. Not every word hit is investigated but instead initially looks for segments containing several nearby hits.
The EBI offers a WWW service for FASTA, the full documentation is also available.
fasta3 Compare a protein sequence to a protein sequence database or a DNA sequence to a DNA sequence database using the FASTA algorithm (Pearson and Lipman, 1988, Pearson, 1996). Search speed and selectivity are controlled with the ktup(wordsize) parameter. For protein comparisons, ktup = 2 by default; ktup =1 is more sensitive but slower. For DNA comparisons, ktup=6 by default; ktup=3 or ktup=4 provides higher sensitivity; ktup=1 should be used for oligonucleotides (DNA query lengths < 20).
ssearch3 Compare a protein sequence to a protein sequence database or a DNA sequence to a DNA sequence database using the Smith-Waterman algorithm (Smith and Waterman, 1981). ssearch3 is about 10-times slower than FASTA3, but is more sensitive for full-length protein sequence comparison.
fastx3/fasty3 Compare a DNA sequence to a protein sequence database, by comparing the translated DNA sequence in three frames and allowing gaps and frameshifts. fastx3 uses a simpler, faster algorithm for alignments that allows frameshifts only between codons; fasty3 is slower but produces better alignments with poor quality sequences because frameshifts are allowed within codons.
tfastx3/tfasty3 Compare a protein sequence to a DNA sequence database, calculating similarities with frameshifts to the forward and reverse orientations.
tfasta3 Compare a protein sequence to a DNA sequence database, calculating similarities (without frameshifts) to the 3 forward and three reverse reading frames. tfastx3 and tfasty3 are preferred because they calculate similarity over frameshifts.


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Brianne Shuller speaks to the media outside her Fishing Creek Valley home where Pennsylvania State Troopers shot and killed her husband, Mitchell James Shuller, on July 20, 2021.
Mark Pynes / PennLive
Mom of 3 deals with agony after police shoot, kill gun-wielding husband in Dauphin County
Matt Miller/PennLive
Matt Miller covers Dauphin County court, state courts and federal court. Reach me at mmiller@pennlive.com or 717-254-3142.
(West Hanover Township) — Brianne Shuller said the nightmare she had been living for months exploded early Tuesday when her husband, Mitchell Shuller, was shot and killed by state police in front of his Dauphin County house.
Police said Mitchell Shuller had been firing a gun, sending bullets into at least two neighboring homes, and troopers shot him after he refused orders to drop the weapon and continued to fire.
For Brianne Shuller, who said she separated from her husband in January for safety reasons, the aftermath echoed with sorrow and tearful disbelief.
Mitchell Shuller had been suffering from mental illness and hallucinations, she said as she spoke to reporters in the driveway of the family's West Hanover Township home on Tuesday afternoon. He was an addict, she said, and had been diagnosed with congestive heart failure.
The COVID-19 pandemic had cost him his construction job as well, she said. He attended rehabilitation in September, but refused out-patient services after spending 60 days in the treatment center, according to pending divorce paperwork.
Brianne Shuller said her husband's volatility prompted her to move out of the house on Fishing Creek Valley Road with her three daughters, ages 8, 10 and 12.
Pennsylvania State Troopers shot and killed Mitchell James Shuller, 34, of Harrisburg, on July 20, 2021.
She said she learned something was wrong when a neighbor, who had reported to police that gunshots were being fired, called to alert her. She said she regrets not making it back to the house in time to possibly try to defuse the situation.
Brianne Shuller said she doesn't know what triggered the fatal encounter with state troopers. "I'm not 100 percent sure of what happened," she said.
Even after more than a year of turmoil, Brianne Shuller said she was still hoping her husband would revert to the man she once knew. She said Mitchell still had visitations with their daughters and that the two of them stayed in communication.
"I wanted him to change so badly, not just for me and for the girls, but for him," she said. "He was a hard worker. He was a great provider."
Perhaps the hardest thing she had to do Tuesday was tell her daughters what had happened.
"I took them to a place we like to hike down the road," Brianne Shuller said. "I told them that Daddy had passed away. And we knew daddy was sick."
"They sat and cried with me," she said.
"I don't know what I'm going to do next," she concluded. "I'll take care of my girls, protect them. Be there for them."
Mitchell Shuller was the only one home at the time of the fatal shooting. He was pronounced dead at the scene.
The only visible evidence that remained of that lethal encounter Tuesday afternoon was found on a towering evergreen tree in the front yard of the hillside house. Police had cut a notch out of it to retrieve bullets that had struck the tree.
This story originally appeared at pennlive.com. The version republished here has been edited by the WITF news team.
Dauphin Countypolice shooting
Philly native who lived through Pulse nightclub shooting to lead Gun Violence Survivors Foundation