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Found 11 collections related to Handel, George Frideric, 1685-1759
New York City Opera
Plot summaries, recorded by the New York City Opera, 1994-1998
21 sound cassettes, analog stereo; 21 sound cassettes, analog stereo
Audio cassettes with descriptions and plot summaries for various operas in the New York City Opera's productions of the 1994-1998 seasons. They were originally intended for use by the visually impaired. Operas included: Wonderful town, Carmen,... more
Audio cassettes with descriptions and plot summaries for various operas in the New York City Opera's productions of the 1994-1998 seasons. They were originally intended for use by the visually impaired. Operas included: Wonderful town, Carmen, Prince Igor, Mefistofele, Paul Bunyan, Lakmé, Don Pasquale, Visit of the old lady, Xerxes, The magic flute, Carmina Burana, Emmeline, La bohème, Madama Butterfly, The barber of Seville, L'Italiana in Algieri, Der Rosenkavalier, H.M.S. Pinafore, Voyage of Marco Polo, Seven deadly sins, and Iphigénie en Tauride. less
Peerce, Jan, 1904-1984
Jan Peerce collection of sound recordings, 1932-1983
The collection contains non-commercial recordings of live and recorded performances and interviews of Jan Peerce spanning the years 1938-1983. The collection also features performers such as: singers Zinka Milanov, Roberta Peters, Leonard Warren,... more
The collection contains non-commercial recordings of live and recorded performances and interviews of Jan Peerce spanning the years 1938-1983. The collection also features performers such as: singers Zinka Milanov, Roberta Peters, Leonard Warren, Cesare Siepi, Robert Merrill, Lawrence Tibbett, Virginia Raye and Viola Philo ; conductors Arturo Toscanini, Fausto Cleva, Harry Horlick and David Rubinoff ; and interviewers Ed Sullivan, Johnny Carson and Mike Douglas. Radio and television (audio only) programs included are: The A & P Gypsies, Radio City Music Hall On the Air, Carnegie Hall, The Chevrolet Hour, The Ed Sullivan Show, The Mike Douglas Show, and The Tonight Show. Works featured are selections from operas such as "La boheme," "I pagliacci," "Don Giovanni," "Cavalleria Rusticana," "La traviata," "Lucia di Lammermoor" and "Rigoletto" ; musicals such as "Fiddler on the Roof" ; and songs by Handel and Schubert, among others. less
Sound recordings » Performances » Hanukkah celebration performance
Sound recordings » Performances » Jan Peerce & Roberta Peters at Carnegie Hall
Sound recordings » Performances » Carnegie Hall performance
Sound recordings » Performances » Jan Peerce performances
Sound recordings » Performances » Jan Peerce recital
See all 25 matches in this collection
Series 17: Printed Scores » Subseries 2: Bound Scores » Handel, George Frideric, 1685-1759. [Judas Maccabaeus. Vocal score. German] Judas Maccabaeus / Haendel's Oratorium ; nach Mozarts Bearbeitung ; im Clavier-Auszuge von Ludwig Hellwig. -- Hamburg : Johann August Böhme, [pref.
Silent film music collection, ca. 1884-1931
This collection contains music intended to accompany silent films.
Leigh, Norman. Chanson Argentine; largo / Händel; [Serse. Ombra mai fu; arr.]. Boston: Walter Jacobs Inc.
Hempel, Frieda, 1885-1955
The Frieda Hempel collection of manuscripts, cadenzas, and annotated publications, ca. 1910-ca. 1940
The Frieda Hempel Collection of Manuscripts, Cadenzas, and Annotated Publications contains performing materials used by Frieda Hempel.
Handel, George Frideric, 1685-1759 [Allegro, il Penseroso, ed il Moderato. Sweet bird; arr.] Scene. [Leipzig]: [Leuckart],.
Let's Go to the Opera (Radio program).
Let's go to the opera radio broadcast collection [sound recording]
Let's Go to the Opera was a 1946 radio series that featured Metropolitan Opera principals and other well known singers of the time in selections from popular operas and operettas. All selections are sung in English.
Radio broadcast,.
Jacobs, Paul
Paul Jacobs collection of noncommercial recordings [sound recording], 1967-1983
Paul Jacobs, 1930-1983, was an American pianist and harpsichordist. The collection consists of noncommercial sound and video recordings collected by Paul Jacobs consisting primarily of his own piano performances.
Series I: Jacobs Materials. » Sub-series 2: Performances,. » Recital / Paul Jacobs,.
Rosa Ponselle collection of performances, recorded interviews, and films, 1926-1987
The collection contains commercial and noncommercial sound recordings and noncommercial films and videos collected by Rosa Ponselle from 1926 to 1981, covering her career and retirement; recordings of memorial tributes to Ponselle were added to... more
The collection contains commercial and noncommercial sound recordings and noncommercial films and videos collected by Rosa Ponselle from 1926 to 1981, covering her career and retirement; recordings of memorial tributes to Ponselle were added to the collection from 1982 to 1987. less
Series VII: Films/video recordings,. » Hollywood 1940's,.
Series I: Concert recordings » Concert / National Orchestral Association (U.S.)
Little Orchestra Society
Little Orchestra Society concert recordings collection [sound recording], 1947-1957
The collection consists of concert recordings, made for Mr. Scherman, from late 1947 to late 1957.
See all 6 matches in this collection
The Bell Telephone Hour collection of sound recordings [sound recording], 1940-1968
1579 sound discs
Weekly 30-minute NBC radio broadcasts, featuring classical and light classical music, which began on Apr. 29, 1940 and continued until 1958. In September, 1959, the Bell Telephone Hour made its television debut as a monthly musical special. It... more
Weekly 30-minute NBC radio broadcasts, featuring classical and light classical music, which began on Apr. 29, 1940 and continued until 1958. In September, 1959, the Bell Telephone Hour made its television debut as a monthly musical special. It remained a regular television program until April 26, 1968. Library's holdings incomplete. Broadcasts chiefly originated from New York City, sponsored by the Bell Telephone Company (American Telephone and Telegraph). Each 30-minute program generally features a guest singer or instrumentalist, with the Bell Telephone Hour Orchestra, conducted by Donald Voorhees, and the Bell Telephone Hour Chorus. Frequent guest artists include Marian Anderson, Robert Casadesus, Nelson Eddy, Josef Hofmann, Fritz Kreisler, Oscar Levant, James Melton, Grace Moore, Ezio Pinza, Lily Pons, Bidú Sayão, Gladys Swarthout, John Charles Thomas, Helen Traubel, and Eileen Farrell. Chief announcer: Floyd Mack. less
Radio program » Great Artist series » Radio broadcast


Welcome to the Cullinan Holdings Limited website.
Cullinan Holdings is a Proudly South African Company and a leading company within the Tourism, Leisure and Financial Services sector. The Company owns some of the premier Tourism, Travel & Leisure Brands in South Africa. The Tourism and Travel arena is well established and has been operating successfully for over 35 years through brands such as Thompsons, Pentravel and Hylton Ross, while the recently acquired Springbok Atlas group has been established for 65 years.
The Leisure segment includes the marine and boating business which focus mostly on yachting and water sports.
In 2012, the Company expanded into the Financial Services arena through the acquisition of Glacier Enterprises and has gone on to develop of various financial service products, including bridging finance, marine finance and project finance.
The group has major offices in Johannesburg, Cape Town and Durban and satellite offices and branches throughout the country and within Namibia, Botswana and Zimbabwe. The group employs approximately 1200 staff.


I am an artist living in Barnes, South West London. I draw on many influences when painting, including the unspoilt landscape and dark and untouched areas – real and imaginary – of Richmond Park.
And on my journey, landscape hasn't always meant pure greenery, I have also been stimulated by more urban scenes too.
Pylons, cables and industrially scenic wasteland, painted in more vibrant colours than reality suggests. And in a tilt towards natural inquisitiveness and playfulness, my work, largely in acrylics, has also led to painting gumball machines, Many of which would not be out of place on the streets of New York. Another major love.
There are so many directions to explore and even now I am creating many experimental pieces, blending collage, paint and wire mesh for new and challenging work.


Root canal therapy is a tooth-saving procedure with a remarkably high success rate. Even if the important inner structures of a tooth become damaged, you can save your natural tooth with a root canal in Kelowna. Also serving West Kelowna.
Your teeth contain narrow passageways called root canals, and the root canals contain a substance called pulp. The pulp contains blood vessels, nerves, and soft tissues. When there is a crack or chip in the tooth, bacteria can leak through and damage the pulp, leading to tooth pain and potential tooth loss.
If you suspect you are suffering from an inner tooth infection, come see Dr. Malfair in Kelowna for a thorough examination. He may also discover an infection during your twice-yearly general health checkups.
When Dr. Malfair recommends a root canal, he is committed to saving your natural smile with a comfortable and effective procedure. With modern technology, root canals are more comfortable and efficient than ever. Many patients actually compare root canals to ordinary dental fillings, because the discomfort level is about the same for either procedure.
You will be given a numbing anesthetic to remain pain-free during the process, and you can explore our sedation dentistry options for additional relaxation. Dr. Malfair will remove the damaged pulp from the root canals and gently clean the area. He will fill the canals with a rubbery material and seal the tooth with a dental filling or crown.
If tooth pain is affecting your life, don't let a fear of root canals keep you away from our office. Choose Your Smile is proud to provide stress-free and successful root canals to our patients who want to save their natural smiles. Call us to schedule your consultation for a diagnosis from our dentist in Kelowna today.


The new virtual money options may cause bankruptcy for the ANC government!!
South Africa's government is well aware of technological developments regarding virtual or crypto money and has already created an interministerial working group that monitors it in conjunction with private sector experts with a view to possible regulation. It appears from written questions by adv. Anton Alberts, chairman of the FF Plus, to the Minister of..


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Coronavirus Explainers
Coronavirus Explained: What Are Super-Spreaders And How Are They Responsible For COVID-19 Transmission?
In the context of a human-borne illness, like COVID-19, a super spreader is an individual who is more likely to infect others, compared with other typically infected people
- in Coronavirus Explainers
Some patients are super-spreaders as they have a virus which is slightly different and more severe: Expert
Super spreaders can infect a large number of people
Super spreaders are that way due to their immune system: Expert
There is no way to detect who is a super spreader: Expert
New Delhi: Among all the questions about the origin and transmission of the virus, lies the role of super spreaders and how are they responsible for continuing the spread of COVID-19. Dr Samay Bharadwaj, explains to NDTV, that a super-spreader is an unusually contagious organism infected with a disease. He added,
"In the context of a human-borne illness, like COVID-19, a super spreader is an individual who is more likely to infect others, compared with other typically infected people. Super spreaders transmit the infection to a large number of people and are of particular concern in epidemiology."
As per Centre of Disease Control, Epidemiology is the branch of medicine which deals with the incidence, distribution, and possible control of diseases and other factors relating to health.
Also Read: Experts Explain The Different COVID-19 Tests: Rapid Antigen Vs RT-PCR Test, Which Is Better?
Dr Bharadwaj further said that some patients are super-spreaders as they have a virus which is slightly different and more severe. And when they come in contact with many people without taking any precautions like wearing a face mask, sanitisation and social distancing, they are spreading the virus, at a high rate. He said,
Everyone's body and immune system reacts differently when it comes to the transmission of infectious diseases. It is a known fact in the medical field, that at least one in five people, transmit infections to far more people than the rest. Some studies show that super-spreaders are also that way due to their immune system, which may not be good at suppressing the virus or, on the other hand, maybe so good, that they remain asymptomatic but continue transmitting the virus to others. But this is likely to be caused by factors such as contracting a higher dose of the virus in the first place or being infected with more than one pathogen.
However, Dr Bharadwaj says that there is no way to detect who is a super spreader.
Also Read: Coronavirus Outbreak Explained: What Is A Coronavirus And COVID-19?
Dr Bharadwaj also explained that certain events and gathering may also be referred to as 'super spreader events', these include usually large gatherings or birthday parties, where social distance may not be possible. He says,
At such events, the number of cases transmitted may be disproportionately high as compared to general transmission. This could be due to the fact that multiple super spreaders may be present at the event, who may be in close contact with several people at once.
If any gathering – indoor or outdoor, that is crowded, and people are without personal protective equipment like masks, those are high-risk areas that can be super spreader events, Dr Bharadwaj added.
He says that while there are super-spreaders for all infectious disease, COVID-19 is known to be extremely contagious and thus it is a more threatening situation at present.
The world has been in the grip of COVID-19 pandemic since January, due to the highly contagious nature of the virus. We all know that the disease is primarily spreading due to humans, which is why we have norms like masks, social distance in place. While dealing with such a crisis, one super spreader can infect tens of numbers of people.
Which is why, Dr Bhardwaj says, the need to wear masks, washing or sanitising hands frequently and ensuring a physical distance of at least 6 feet is recommended across the globe.
Also Read: Coronavirus Outbreak Explained: What Is Convalescent Plasma Therapy And How Effective Is It In Treating COVID-19 Patients?
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Coronavirus Explainer: What Are Adverse Events Post COVID-19 Immunisation
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COVID-19 Outbreak Explained: Can COVID-19 Vaccines Fight Against Virus Mutations?

redpajama_set_name: RedPajamaCommonCrawl


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\section{Introduction}
\looseness=-1
With the need to deploy Machine Learning (ML) models in real-life systems such as autonomous vehicles, medical diagnosis, online fraud detection etc., building safe and reliable ML pipelines has become more critical than ever. However, there exist three well-known cases of changes to the in-distribution data -- or anomalies -- to which models are known to be brittle: adversarial attacks (AA), which are intentionally crafted but imperceptible perturbations that can fool a classifier \cite{goodfellow2014explaining}; distribution shifts (DS), which are naturally occurring perturbations that can also change the model's output \cite{hendrycks2019benchmarking}; and out-of-distribution (OOD) samples which come from an entirely different distribution, and that can surprisingly lead to highly confident but wrong predictions \cite{hendrycks2016baseline}. This latter case is notoriously known for generative models, which tend to assign higher likelihoods to samples coming from a distribution that they have not been trained on \cite{nalisnick2018deep}.

\looseness=-1
In an orthogonal line of work, Neural Networks (NN) are also known to be highly over-parameterized, which is made evident by the existence of sparse subnetworks that achieve the same accuracy performance as their dense counter-part \cite{han2015deep}. To find these subnetworks, many works have derived pruning criteria that assign scores to weights, and prune those with the lowest values \cite{lee2018snip, Wang2020picking}. However, the algorithms that attempt to find these subnetworks rarely evaluate them on other challenging tasks such as robustness and anomaly detection.

\looseness=-1
In this paper, we show that pruning can benefit robustness and anomaly detection by tackling the following four questions: 1) How does sparsity affect various properties of networks, beyond test accuracy? (Sec. \ref{section:properties}), 2) Can we find sparse subnetworks within pre-trained dense networks that achieve better robustness and detection? (Sec. \ref{section:finding}), 3) Can sparsity benefit tasks beyond deterministic single-network classification such as ensembles, Bayesian NN and generative models? (Sec. \ref{section:beyond}), and 4) Are pruning criteria useful for the detection of anomalies in the input? (Sec. \ref{section:detecting}) \footnote{Project page including code at https://www.cs.cit.tum.de/daml/uncertainty-snn/}
\section{Related Work}
\label{section:related}
\looseness=-1
\textbf{Sparse Networks.}
Pruning can be performed either before training a model \cite{lee2018snip, Wang2020picking}, early in training \cite{rachwan2022winning, You2020Drawing}, or after the model has been fully trained \cite{frankle2020linear, frankle2020stabilizing, lecun, gohil2020one}.
It can be done either in an unstructured \cite{nalisnick2018deep, Wang2020picking, frankle2020linear} or a structured manner \cite{rachwan2022winning, li2016pruning, you2019gate}, where unstructured pruning removes weights by setting them to 0 whereas structured pruning removes entire neurons or filters by reducing the size of the weight matrices involved. Additionally, pruning can be done globally \cite{rachwan2022winning, lee2018snip, Wang2020picking, frankle2020linear} or locally \cite{ramanujan2020s}, where global pruning automatically decides how much to prune from each layer when given a desired total sparsity, whereas local pruning prunes the same percentage of weights at every layer. Unless otherwise specified, we use global pruning throughout the paper. We briefly introduce the 8 pruning algorithms we use in this paper:

\looseness=-1
\textit{\textbf{Pruning Before Training.}}
    \underline{SNIP \cite{lee2018snip}}: A sensitivity-based pruning method (i.e. it scores weights based on their sensitivity to a certain metric) that aims at preserving the loss function. The score used is: \smash{$s_i = |w_i \frac{\partial L}{\partial w_i}|$}, where $L$ is the loss and $w_i$ the weight.\\
    \underline{GRASP \cite{Wang2020picking}}: A sensitivity-based pruning method that aims at increasing the gradient flow. The final score is: \smash{$s_i = -|w_i(\textbf{H}\frac{\partial L}{\partial \textbf{w}})_i|$}, where \textbf{H} denotes the Hessian.\\
    \underline{CroPit \cite{rachwan2022winning}}: A sensitivity-based pruning method that aims at preserving the gradient flow. The final score is: $s_i = |w_i(\textbf{H}\frac{\partial L}{\partial \textbf{w}})_i|$, where \textbf{H} denotes the Hessian. We also use its structured version CroPit-S.\\
    \underline{Edge-popup \cite{ramanujan2020s}}: An optimization-based method that aims at finding sparse network by optimizing the mask directly instead of the weights. The sparse model is not trained further.

\looseness=-1
\textit{\textbf{Pruning During Training.}}
    \underline{IMP \cite{frankle2020linear}}: An iterative-based pruning method that prunes the smallest weights first. It goes through multiple train-prune-rewind cycles until it reaches its desired sparsity, where "rewind" corresponds to resetting the values of the remaining weights after each pruning phase to their value at an early point in training.\\
    \underline{EarlyCroP}: CroPit performed early in training instead of before training.

\looseness=-1
\textit{\textbf{Pruning After Training.}}
    \underline{SNIP-After}: SNIP applied on a fully trained network.\\
    \underline{Edge-popup-After}: Edge-popup applied on a fully trained network instead of randomly initialized one.

\looseness=-1
\textbf{Robustness and Anomaly Detection of Networks.} While NN are constantly improving in terms of accuracy or generative capability, their weak robustness and anomaly detection pose challenges to their safe deployment in the real world \cite{shafaei2018less}. Classifiers have shown to be un-calibrated, i.e. assign high confidence to wrong predictions, and works have tried to improve existing models \cite{hendrycks2018deep, liu2020simple, huang2021importance} or come up with new methods that focus solely on the detection of anomalous inputs \cite{tack2020csi}. Generative models that can compute the log-likelihood of inputs have also been shown to assign \textit{higher} likelihood to \textit{anomalous} samples \cite{nalisnick2018deep}, leading to another body of works that attempts to understand and solve this problem \cite{schirrmeister2020understanding}. Note that, unless otherwise specified, we use the common baseline Maximum-Softmax-Probability (MSP) \cite{hendrycks2016baseline} to compute the AUC-ROC scores throughout the paper, which uses the predicted probability of an input as the confidence.

\looseness=-1
\textbf{Robustness and Anomaly Detection of Sparse Networks.}
With the growing interest in sparsity of deep NN, the literature started addressing the question of whether sparse models would have different properties than their dense counterparts, beyond their test accuracy.
Some works argue that an appropriate level of sparsity can improve robustness of models either to adversarial attacks \cite{guo2018sparse} or to distribution shifts \cite{diffenderfer2021winning}, while others argue the contrary \cite{verdenius2020pruning, liebenwein2021lost}. Another body of works focuses on simultaneously optimizing for high accuracy, robustness and sparsity by incorporating objectives that promote the latter two during training \cite{madaan2020adversarial, zhang2021can, sehwag2020hydra}. In contrast, we do not directly optimize weights for the tasks of robustness and detection, but evaluate the effect of existing sparsity methods on these tasks and propose new ways to take advantage of them.
\section{Properties of Sparse Classification Networks}
\label{section:properties}
\looseness=-1
We start by addressing the question: \textit{How does sparsity affect various properties of networks, beyond test accuracy?}
Our goal is to give a comprehensive view on the effect of pruning on robustness to and detection of anomalies. We therefore evaluate models pruned with 8 different pruning algorithms, and make the distinction between pruning before, during or after training. Indeed, we expect the pruning time to affect the set of weights being preserved, and therefore their final performance on new tasks.

\looseness=-1
\textbf{Setup.} We train ResNet18 models on the CIFAR10 dataset. We use L-$\infty$ and L-2 FGSM attacks with $\epsilon=8$, CIFAR10C as the DS dataset, and SVHN and CIFAR100 as the OOD datasets. We report relative accuracy (i.e. normalized with respect to the dense model) and relative AUC-ROC on attacks and shifts, and relative Brier score and relative AUC-ROC on OOD datasets. All models are trained with 3 different seeds and their average performance is reported.

\begin{figure*}[h]
    \centering
    \includegraphics[width=\textwidth]{fig/legend.png}
        \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/resnet18_properties_acc_fgsm8.png}}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/resnet18_properties_acc_l2_fgsm8.png}}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/resnet18_properties_acc_cifar10c.png}}
    \caption{Comparison between the relative accuracy (higher is better) of a dense ResNet18 model and sparse ResNet18 models on (a) L-$\infty$ FGSM adversarial attacks with $\epsilon=8$, (b) L-2 FGSM adversarial attacks with $\epsilon=8$ and (c) CIFAR10C corruptions.}
    \label{fig:properties_robustness}
\end{figure*}

\begin{figure*}
    \centering
    \includegraphics[width=\textwidth]{fig/legend.png}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/resnet18_properties_auroc_fgsm8.png}}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/resnet18_properties_auroc_l2_fgsm8.png}}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/resnet18_properties_auroc_cifar10c.png}}
    \caption{Comparison between the relative AUC-ROC (higher is better) of a dense ResNet18 model and sparse ResNet18 models on (a) L-$\infty$ FGSM adversarial attacks with $\epsilon=8$, (b) L-2 FGSM adversarial attacks with $\epsilon=8$ and (c) CIFAR10C corruptions.}
    \label{fig:properties_detection}
\end{figure*}

\begin{figure*}
    \centering
    \includegraphics[width=\textwidth]{fig/legend.png}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/resnet18_properties_brier.png}}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/resnet18_properties_auroc_svhn.png}}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/resnet18_properties_auroc_cifar100.png}}
    \caption{Comparison between a dense ResNet18 and sparse ResNet18 models of (a) the relative Brier score (lower is better), and the relative AUC-ROC on (b) SVHN and (c) CIFAR100.}
    \label{fig:properties_uncertainty}
\end{figure*}

\textbf{Discussion.}
\looseness=-1
Fig. \ref{fig:properties_robustness} clearly shows that almost all pruning methods improve the robustness to L-$\infty$ FGSM attacks (a), but not to L-2 FGSM attacks (b). Indeed, the relation between L-$\infty$ and sparsity has already been discussed in \cite{guo2018sparse}, and works such as \cite{verdenius2020pruning} have reported a \textit{decrease} of robustness on L-2 based attacks such as Carlini-Wagner \cite{carlini2017towards}. We also observe that most methods \textit{maintain} the robustness to DS (c), with During-Training methods performing slightly better than Before-Training methods at high sparsity.

\looseness=-1
In terms of detection, Fig. \ref{fig:properties_detection} shows that generally all methods except Edge-popup can maintain the AUC-ROC on DS. SNIP-After also appears to provide better improvements at an appropriate sparsity than the remaining methods.

\looseness=-1
Finally, from Fig. \ref{fig:properties_uncertainty} we can see that During or After-Training pruning methods perform better than the dense model in terms of Brier score (a) up until 90\% sparsity, but only IMP does so at even higher sparsity. Generally all methods, except Edge-popup, can also maintain the AUC-ROC on the OOD datasets SVHN (b) and CIFAR100 (c). We report additional metrics and results on LeNet5 in App. \ref{appendix:properties}.

\section{Finding robust subnetworks within non-robust dense networks}
\label{section:finding}
\looseness=-1
Next, we address the question: \textit{Can we find  (sparse) sub-networks within dense pre-trained networks that achieve better robustness and detection?} This question has been briefly tackled by \cite{sehwag2020hydra}, but they only evaluated the robustness of the sparse models to adversarial attacks, while we aim to evaluate their robustness to both adversarial attacks and distribution shifts, as well as their OOD detection capability.

\looseness=-1
\textbf{Setup.} We train ResNet18 models on CIFAR10, and report the accuracy and AUC-ROC on FGSM attacks with $\epsilon=8$ and on CIFAR10C, as well as the AUC-ROC on SVHN. Similarly to \cite{sehwag2020hydra}, we use the Edge-popup algorithm, which trains a \textit{mask over the weights} instead of the weights themselves (App. \ref{app:algorithms}). However instead of using the standard cross-entropy objective $\mathcal{H}$, we augment the objective depending on the task we are trying to solve. In the following, $X$ refers to a clean batch, $Y$ to its ground-truth labels, and $f$ to the model:\\
\looseness=-1
\underline{Adversarial Attacks}: $\mathcal{L}(X) = \mathcal{H}(f(X), Y) + \lambda * \mathcal{KL}(f(X), f(X'))$, with $X$with $X'$ the adversarial attack of $X$, and $\mathcal{KL}$ the KL divergence between the predictions on $X$ and $X'$. The second term in $\mathcal{L}$ ensures that the predictions on $X$ and $X'$ do not deviate too much. We use $\lambda = 6$.\\
\underline{Distribution Shifts}: $\mathcal{L}(X) = \mathcal{H}(f(X'), Y)$, with $X'$ being a clean batch perturbed with gaussian noise. Indeed, training a model on clean inputs perturbed with gaussian noise can lead to a model more robust to natural perturbations \cite{diffenderfer2021winning}.\\
\underline{OOD Detection}: $\mathcal{L}(X) = \mathcal{H}(f(X), Y) + \mathcal{H}(f(X'), U)$, with $X'$ being an OOD batch and $U$ a matrix of uniform distributions. The second term in $\mathcal{L}$ ensures the predictions on OOD samples are as close as possible to $U$ (maximum uncertainty).

\begin{table*}[h!]
\centering
\small
\begin{tabular}{m{3.7cm}|m{1.3cm}|m{1.4cm}|m{1.4cm}|m{1.5cm}|m{1.5cm}}
 \toprule
  & Accuracy FGSM 8 & AUC-ROC FGSM 8 & AUC-ROC SVHN & Accuracy CIFAR10C & AUC-ROC CIFAR10C \\
 \midrule
 Dense & 19.9\% & \textbf{79.5\%} & 90.3\% & 66.2\% & \textbf{68.4\%} \\
 \midrule
 Edge-popup - AA Objective & \textbf{72.9\%} & 53.8\% & 77.6\% & 68.8\% & 57.3\% \\
 \midrule
 Edge-popup - OOD Objective & 60.2\% & 64.7\% & \textbf{99.7\%} & 64.5\% & 67.3\% \\
 \midrule
 Edge-popup - DS Objective & 64.4\% & 56.7\% & 78.0\% & \textbf{70.0\%} &  54.5\% \\
\bottomrule
\end{tabular}
\caption{Accuracy and AUC-ROC of ResNet18 models pruned with Edge-popup to 50\% sparsity based on AA, OOD or DS objectives. }
\label{table:finding}
\end{table*}

\looseness=-1
\textbf{Discussion.} In Tab. \ref{table:finding}, we report the metrics of interest for the three objectives on Edge-popup and compare to the original dense model. Each objective improves its corresponding metric the most, but sometimes also benefits others. For example, training on the OOD objective significantly improves the accuracy on FGSM attacks, a correlation which has already been noted in the literature \cite{lee2020removing}. This experiment shows that subnetworks that achieve much better performance on a certain objective do exist wihtin a pre-trained dense network. This suggests that dense networks focus on non-robust features as was shown in \cite{ilyas2019adversarial}, possibly due to their over-parameterization, and pruning eliminates some weights that focus on these features.
\section{The Effect of Sparsity Beyond Single-Network Classification}
\label{section:beyond}
\looseness=-1
In this section, we investigate the effect of sparsity on existing methods that improve the robustness or detection capability of networks, and try to answer the question: \textit{Can sparsity benefit tasks beyond deterministic single-network classification?} We examine ensembles, Bayesian neural networks and generative models.

\textbf{Ensembles.}
\looseness=-1
One popular method to improve robustness and calibration of classifiers is ensembles. It consists of training multiple networks independently, and then averaging their outputs to obtain more calibrated predictions \cite{lakshminarayanan2016simple}. Ensembles have been shown to be among the most calibrated methods against distribution shifts \cite{ovadia2019can}. However, one major drawback is the expensive nature of this method, both in terms of memory and time. Structured pruning helps in overcoming both of these drawbacks, since it removes entire neurons and channels from the architecture, thereby reducing the training and inference time, but also the space complexity. For this experiment, we use the large model AlexNet trained on CIFAR10, and compare a single dense model and an ensemble of 5 dense models to an ensemble of 5 models pruned to 80\% node sparsity each. We can see from Tab. \ref{table:ensembles} that the sparse ensemble reaches almost the same test accuracy as the dense ensemble, matches it in terms of AUC-ROC on SVHN, and is much more efficient than the dense ensemble in terms of training and inference time, and GPU RAM usage.

\begin{table*}[h!]
\centering
\small
\begin{tabular}{m{2.8cm}|m{1.5cm}|m{1.5cm}|m{1.3cm}|m{1.3cm}|m{0.8cm}}
 \toprule
  & Test \newline Accuracy & AUC-ROC SVHN & Training Time & Inference Time & RAM \\
 \midrule
 Dense - Deterministic & 85\% & 83\% & x1 & x1 & x1 \\
 \midrule
 Dense - Ensemble & \textbf{90\%} & \textbf{89\%} & x5 & x4.8 & x5 \\
 \midrule
 CroPit-S - Ensemble & 88.4\% & \textbf{89\%} & \textbf{x2.7} & \textbf{x3.8} & \textbf{x0.42} \\
\bottomrule
\end{tabular}
\caption{Comparison between a dense AlexNet model, an ensemble of 5 dense AlexNet models annd an ensemble of 5 AlexNet models pruned with CroPit-S to 80\% node sparsity trained on CIFAR10.}
\label{table:ensembles}
\end{table*}

\textbf{Bayesian Neural Networks.}
\looseness=-1
Bayesian NN also fall into the category of expensive uncertainty methods. They are both slower to train and infer on, and more memory intensive since they often require to learn more parameters than a dense model. To illustrate the potential of sparsity in such tasks, we turn our attention to the popular method presented in \cite{blundell2015weight}. It consists of learning both a mean $w_i$ and a standard deviation $t_i$ for each weight $i$ in the network. At test time, we sample a value for each weight when doing an inference, and repeat the process multiple times to obtain multiple predictions, similar to ensembles. This work suggests to remove weights with a low Signal-to-Noise Ratio (SNR) $s_i = |\frac{w_i}{t_i}|$, but only evaluates the pruned model on the final accuracy. SNR is an intuitive pruning criterion, since it encourages the removal of weights with small means (similar to IMP which removes weights close to 0), and high standard deviation which indicates the uncertainty of the model and therefore the vulnerability of this weight to changes in the input. We extend this criterion to a structured one by simply summing the scores at each activation, which we call SNR-S, to take advantage of the removal of structures to achieve faster training and inference time. We train a dense Conv6 model on CIFAR10, a deterministic one, and two SNR-S models where pruning is done either during or after training. From Tab. \ref{table:bayesian}, we observe that both versions of Bayesian SNR-S achieve better accuracy than the dense Bayesian, and SNR-S After achieves even better AUC-ROC than both dense models, while being more efficient than the dense Bayesian model.

\begin{table*}[h]
\centering
\small
\begin{tabular}{m{3.4cm}|m{1cm}|m{1cm}|m{1.1cm}}
 \toprule
  & Test Accuracy & AUROC SVHN & Inference Time \\
 \midrule
 Dense - Deterministic & \textbf{0.874} & 0.834 & \textbf{x1} \\
 \midrule
 Dense - Bayesian & 0.864 & 0.873 & x2.6 \\
 \midrule
 SNR-S (After) - Bayesian & 0.865 & \textbf{0.883} & \underline{x1.6} \\
 \midrule
 SNR-S (During) - Bayesian & \underline{0.871} & \underline{0.882} & \underline{x1.6}  \\
\bottomrule
\end{tabular}
\caption{Comparison of Conv6 trained on CIFAR10 in a deterministic, Bayesian or sparse Bayesian setting. For the Bayesian results we use 5 inferences and the sparse models are pruned to 50\% node sparsity.}
\label{table:bayesian}
\end{table*}

\textbf{Generative Models.}
\looseness=-1
Sparsity in generative models has recently started to gain attention in the literature \cite{kalibhat2020winning}. However, to the best of our knowledge, its effect on OOD detection has not been investigated yet. Generative models, some of which are capable of computing the log-likelihood of input samples, are known for their counter-intuitive behavior on OOD samples: they assign \textit{higher} likelihood to OOD samples than to their own training/testing in-distribution samples.
\looseness=-1
In this experiment, starting with the pre-trained generative model Glow \cite{kingma2018glow}, we prune it using EarlyCroP \textit{locally}. We observe that, in the FashionMNIST-MNIST combination, pruning greatly helps in better distinguishing the two types of datasets and correctly assign \textit{lower} likelihoods to OOD samples. However, when moving to more complex cases (CIFAR10-SVHN or CELEBA-SVHN), we do not observe the same behavior. Nevertheless, the success on the FashionMNIST-MNIST combination encourages further exploration of the benefits of sparsity in generative models. Note that local pruning plays a crucial role here, as global pruning did not result in the same behavior on FashionMNIST. One possible explanation for this is that local pruning creates more bottlenecks in the network, which has been shown to help in OOD detection \cite{kirichenko2020normalizing}.

\begin{figure}[h]
    \centering
    \subfigure[]{\includegraphics[width=0.23\textwidth]{fig/mnist_glow_dense.png}}
    \subfigure[]{\includegraphics[width=0.23\textwidth]{fig/mnist_glow_crop.png}}
    \hfill
    \subfigure[]{\includegraphics[width=0.23\textwidth]{fig/celeba_glow_dense.png}}
    \subfigure[]{\includegraphics[width=0.23\textwidth]{fig/celeba_glow_crop.png}}
    \caption{Left: Count vs input log-density of Glow trained on FashionMNIST, tested on the OOD dataset MNIST with (a) no pruning and (b) local pruning to 90\% using EarlyCroP. Right: Count vs input log-density of Glow trained on CELEBA, tested on the OOD dataset SVHN with (c) no pruning and (d) local pruning to 90\% using EarlyCroP.}
    \label{fig:glow_fashion}
\end{figure}

\section{SensNorm - Detecting Anomalies Through Weight Sensitivity}
\label{section:detecting}
\looseness=-1
We finally address the question: \textit{Are pruning criteria useful for the detection of anomalies in the inputs?}
Some pruning methods that operate \textit{before} training have been shown to be independent of the batch being used to score the weights \cite{su2020sanity}. This means that the weights being pruned and the final structure of the network do not depend much on the current task/dataset of interest. However, methods operating \textit{during} training are believed to use information from the batch to keep the most important weights for the given task \cite{su2020sanity}. Note that this applies to methods that use a sensitivity criterion, i.e. that assign a score to each weight (unlike IMP). Since pruning based on one or more batches will preserve the weights that are relevant for these batches, pruning based on batches that contain anomalies will attempt to preserve a different set of weights. Based on these observations, our key idea is to detect anomalies by measuring the distance between the sensitivity of weights on in-distribution (training) data, and that of each incoming test batch. Intuitively, the larger the distance the more likely the input batch contains anomalous samples.

\looseness=-1
\textbf{Method.} More formally, we are given a pre-trained model with weights $\mathbf{w}$, a train dataset \smash{$D_{train}=\{d_i\}_{i=0}^{m}$}, a test dataset \smash{$D_{test}=\{d'_j\}_{j=0}^{n}$}, and a sensitivity scoring function $f_{B}$ where $B$ indicates one or more batches used to get the weights' sensitivity. We first compute the sensitivity of weights on the training data $f_{D_{train}}(\mathbf{w})$ by going through all training batches and accumulating the gradients. We also save the element-wise standard deviation over all batches $std(f_{D_{train}}(\mathbf{w}_i))$. At test time, for every incoming batch $d'_j$, we compute $f_{d'_j}(\mathbf{w})$. Finally, we compute the distance \smash{$S_{d'_j} = \|f_{D_{train}}(\mathbf{w}) - f_{d'_j}(\mathbf{w})\|_p$}, where $\|.\|_p$ is the L$p$-norm (in practice we use $p=5$). If $S_{d'_j} \geq \tau$, where $\tau$ is some pre-defined threshold, then $d'_j$ is an anomalous batch; otherwise, it is an in-distribution batch. We refer to our method as SensNorm.

\looseness=-1
We rely on the SNIP score as our sensitivity score, since its purpose is to preserve the loss of the task at hand. However, we find that dropping the absolute value helps in achieving better performance. Indeed, the  sign of a score in SNIP indicates whether a certain weight was increasing or decreasing the loss, which can help distinguish in-distribution from anomalous batches. We also standardize the test batch scores by their mean and standard deviation computed on the training data, so as to reduce the contribution of the weights that are highly variable on the in-distribution data itself. Our weight sensitivity function is therefore:

\begin{equation}
f_B(\mathbf{w}_i)=\frac{{}\mathbf{w}_i \frac{\partial L}{\partial \mathbf{w}_i} - f_{D_{train}}(\mathbf{w}_i)}{std(f_{D_{train}}(\mathbf{w}_i))}
\end{equation}

\looseness=-1
\textbf{Discussion.} Our method naturally lends itself to batch detection, but can also be applied for single-input prediction. Tab. \ref{table:wi_batches} shows that, with 100 samples, our method can detect all types of inputs easily, and their detection remains high until a batch of size 5. The challenge arises for batches of size 2 and 1, since gradients will be very noisy for these cases. We therefore rely on augmentations to gain more information from the input sample (App. \ref{app:experimental}). Note that, while originally conceived for detecting batches, SensNorm still outperforms MSP on batch size 1. We also compare our results to GradNorm, which uses gradient norms to detect anomalies, and find that SensNorm matches it or even outperforms it on small batch sizes.

\begin{table*}[h!]
\centering
\resizebox{0.6\columnwidth}{!}{
\begin{tabular}{m{0.05cm}|m{1.3cm}|m{0.9cm}|m{1.3cm}|m{0.9cm}|m{0.8cm}|m{1cm}}
 \toprule
 & & \small SVHN & \small CIFAR100 & \small FGSM & \small DS & \small OODom. \\
 \midrule
 \multirow{3}{*}{\rotatebox[origin=c]{90}{\tiny Batch size 100} } & \small MSP  & \textbf{100\%} & \textbf{100\%} & \textbf{100\%} & \underline{98.7\%} & \underline{51.3\%} \\
 \cmidrule{2-7}
   & \small GradNorm & \textbf{100\%} & \textbf{100\%} & \underline{99.8\%} & 91.9\% & 33.3\% \\
  \cmidrule{2-7}
  & \small SensNorm & \textbf{100\%} & \textbf{100\%} & \textbf{100\%} & \textbf{99.9\%} & \textbf{100\%}\\

 \midrule\midrule
 \multirow{3}{*}{\rotatebox[origin=c]{90}{\tiny Batch size 5  } } & \small MSP & 98.5\% & 92.7\% & 87.7\% & 84.5\% & \underline{50.8\%} \\
  \cmidrule{2-7}
  & \small GradNorm & \underline{99.8\%} & \underline{98.6\%} & \underline{94.9\%} & \underline{92.3\%} & 32.9\% \\
  \cmidrule{2-7}
  & \small SensNorm & \textbf{99.9\%} & \textbf{99.2\%} &\textbf{98.8\%} & \textbf{96.5\%} & \textbf{100\%} \\
 \midrule\midrule
 \multirow{3}{*}{\rotatebox[origin=c]{90}{\tiny Batch size 2} } & MSP & 92.2\% & 85.5\% & 81.2\% & 79.1\% & \underline{51.6\%} \\
 \cmidrule{2-7}
   & \small GradNorm & \textbf{98.8\%} & \textbf{92.9\%} & \textbf{88.3\%} & \underline{87.0\%} & 33.1\% \\
  \cmidrule{2-7}
   & \small SensNorm & \underline{97.3\%} & \underline{91.0\%} & \underline{87.7\%} & \textbf{87.3\%} & \textbf{100\%} \\
   \midrule\midrule
   \multirow{3}{*}{\rotatebox[origin=c]{90}{\tiny Batch size 1} }& \small MSP & 90.3\% & \underline{83.3\%} & 79.5\% & 77.1\% & \underline{53.4\%}\\
    \cmidrule{2-7}
    & \small GradNorm & \textbf{96.7\%} & \textbf{85.2\%} & \underline{80.4\%} & \textbf{81.0\%} & 33.0\%\\
    \cmidrule{2-7}
    & \small SensNorm & \underline{93.7\%} & \textbf{85.2\%} & \textbf{80.6\%} & \underline{79.0\%} & \textbf{100\%}\\
\bottomrule
\end{tabular}
}
\caption{AUC-ROC of SensNorm compared to MSP and GradNorm on multiple batch sizes and anomalies. Best result is in bold, second best result is underlined.}
\label{table:wi_batches}
\end{table*}

\section{Conclusion}
\looseness=-1
In conclusion, we show that the topics of pruning, robustness and detection are closely intertwined. First, most pruning algorithms can maintain the original uncertainty of the dense model on AA, DS and OOD. All pruning methods also greatly improve the accuracy to L-$\infty$ FGSM attacks, but not to the L-$2$ based attacks. Pruning methods that operate during training (i.e. EarlyCroP and IMP) often offer the best trade-off between good test performance and maintaining or even improving robustness and detection metrics. Second, we show that it is possible to find subnetworks within pre-trained dense networks that perform better than the original models in terms of robustness and detection. Third, we find that pruning can be beneficial in tasks such as ensembles, Bayesian NN and generative models, especially in terms of efficiency for the expensive uncertainty estimation methods. Finally, we introduce SensNorm, a new method for detecting batches of anomalous samples. SensNorm shows that weight sensitivity is a good indicator of the presence of anomalies in the input.
\section{Experimental Setup}
\label{app:experimental}

\textbf{Architecture-Dataset Combinations}
For classification tasks, we focus on two popular architecture-dataset combinations: LeNet5 trained on MNIST, and ResNet18 trained on CIFAR10. For the Ensembles experiment, we use AlexNet trained on CIFAR10. For the Bayesian experiment, we use Conv6 trained on CIFAR10. For generative models, we use Glow trained on MNIST or CELEBA.

\textbf{Train-Test Dataset Combinations}
We evaluate the robustness of classification models on two types of dataset shifts:\\
\textit{Distribution Shifts (DS)}: we use the CIFAR10-C dataset on the ResNet18-CIFAR10 combination only.\\
\textit{Adversarial Attacks (AA)}: we use the Fast Gradient Sign Method (FGSM) with $\epsilon = 8$ in the [0, 255] scale.

We also evaluate the uncertainty of classification models on the previous two dataset types, and the two following additional dataset types for all models:\\
\textit{Out-of-Distribution (OOD)}: For FashionMNIST, we use MNIST. For CIFAR10, we use SVHN and CIFAR100. For CELEBA, we use SVHN. \\
\textit{Out-of-Domain (OODom)}: this corresponds to the any OOD dataset but scaled in the [0, 255] range instead of the [0, 1] range.

\textbf{Metrics}
We report the clean test accuracy as well as the accuracy on DS and AA (Acc.), the Area Under the Receiver Operating Characteristic curve (AUROC) or Area Under the Precision Recall curve (AUPR) for detection, the Brier score as an uncertainty measure (Brier), and a lower bound on the Lipschitz constant of models (Lip.).

\textbf{Training Details}
Classifiers are trained using Adam optimizer, learning rate 2e-3  and batch size 512. ResNet18 and AlexNet are trained for 50 epochs, while LeNet5 is trained for 30. Glow is trained for 100k iterations, using a batch size of 64 and a learning rate of 1e-5.

\textbf{Agumentations used in SensNorm} We empirically find that rotation transformations in ${90, 180, 270}$, horizontal and vertical flips, as well as other affine transformations using \cite{benton2020learning}, help improve the detection.

\section{Algorithms}
\label{app:algorithms}

\begin{algorithm}[H]
Inputs: $\{X_n, Y_n\}_{n=1}^{N}$, $F$ pretrained network, x percentage to prune\\

Freeze F's weights\\
$S \leftarrow$ kaiming\_normal\\

\textbf{for} $n \in [1,N]$\\
\hspace*{3ex} Create subnetwork of (1-x)\% highest scores in S\\
\hspace*{3ex} Do forward pass\\
\hspace*{3ex} Compute loss $\mathcal{L}(x)$ with desired objective \hspace*{9ex}  \textit{\% Adversarial, OOD or DS}\\
\hspace*{3ex} Do backward pass through \textit{all} scores and update them\\

$F \leftarrow$ subnetwork of (1-x)\% highest scores in S\\
\caption{Finding Better Subnetworks within Dense Networks}
\label{method}
\end{algorithm}

\section{Additional Results}
\label{appendix:properties}

We report additional information concerning the properties of sparse neural networks. Tab. \ref{table:lip} shows the Lipschitz constant \cite{scaman2018lipschitz} of dense and sparse ResNet18 models. Note that, at the exception of Edgepop, all pruning methods result in a \textit{higher} Lipschitz constant than the dense model. In Tab. \ref{table:cifar10c_5}, we look in details at the accuracy of pruned ResNet18 models on CIFAR10C, and whether or not it increased compared to the dense model. We note that the "saturate", "fog" and "contrast" corruptions are the most affected negatively among sparse models. Finally, Fig. \ref{fig:properties_robustness_lenet}, \ref{fig:properties_detection_lenet} and Fig. \ref{fig:properties_detection_ood_lenet} show the robustness, detection and uncertainty of AA, DS and OOD of LeNet5 models trained on MNIST. Performance on robustness and detection of FGSM attacks resembles the results on ResNet18. However, there is a much much noticeable improvement of certain pruning methods on the MNIST dataset, and a much more noticeable drop in detection of the Omniglot dataset. Indeed, the latter consists of a white background with black symbols, instead of black background with white digits for MNIST.

\begin{table*}[h!]
\centering
\begin{tabular}{ m{3cm}|m{1.6cm}}
 \toprule
  & Lipschitz Constant \\
 \midrule
 Dense & 7.27e+18 \\

 Edgepop (50\%) & 2.92e+11 \\

 CROP (50\%) & 6.62e+27 \\

 EarlyCROP (50\%) & 8.95e+20 \\
 \bottomrule
\end{tabular}
\caption{Lipschitz Constant of dense and pruned ResNet18 models trained on CIFAR10}
\label{table:lip}
\end{table*}

\begin{table}[h]
\centering
\tiny
\begin{tabular}{m{1.0cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}|m{0.3cm}}
\toprule
    \rotatebox[origin=c]{90}{criterion} &  \rotatebox[origin=c]{90}{gaussian noise} &  \rotatebox[origin=c]{90}{impulse noise} &  \rotatebox[origin=c]{90}{defocus blur} &  \rotatebox[origin=c]{90}{frosted glass blur} &  \rotatebox[origin=c]{90}{saturate} &  \rotatebox[origin=c]{90}{shot noise} &  \rotatebox[origin=c]{90}{elastic} &  \rotatebox[origin=c]{90}{snow} &  \rotatebox[origin=c]{90}{spatter} &  \rotatebox[origin=c]{90}{fog} &  \rotatebox[origin=c]{90}{frost} &  \rotatebox[origin=c]{90}{gaussian blur} &  \rotatebox[origin=c]{90}{speckle noise} &  \rotatebox[origin=c]{90}{brightness} &  \rotatebox[origin=c]{90}{pixelate} &  \rotatebox[origin=c]{90}{jpeg compression} &  \rotatebox[origin=c]{90}{contrast} &  \rotatebox[origin=c]{90}{motion blur} &  \rotatebox[origin=c]{90}{zoom blur} \\
\midrule
                    Dense &                        0.227 &                       0.243 &                      0.520 &                            0.360 &                  0.812 &                    0.264 &                 0.642 &              0.633 &                 0.707 &             0.700 &               0.521 &                       0.410 &                       0.313 &                    0.850 &                  0.491 &                          0.658 &                  0.292 &                     0.569 &                   0.576 \\

                    GRASP &                        \textbf{0.356} &                       \textbf{0.367} &                      \textbf{0.585} &                            \textbf{0.523} &                  \textbf{0.814} &                    \textbf{0.403} &                 \textbf{0.712} &              \textbf{0.695} &                 \textbf{0.735} &             \textbf{0.673 }&               \textbf{0.611} &                       \textbf{0.436} &                       \textbf{0.447} &                    \textbf{0.849} &                  \textbf{0.520} &                          \textbf{0.721} &                  0.288 &                     \textbf{0.591} &                   \textbf{0.607} \\
                    SNIP &                       \textbf{0.360} &                       \textbf{0.316} &                      \textbf{0.526} &                            \textbf{0.473} &                  \textbf{0.804} &                    \textbf{0.431} &                 \textbf{0.677} &              \textbf{0.680} &                 \textbf{0.730} &             \textbf{0.667} &               \textbf{0.581} &                       0.387 &                       \textbf{0.464} &                    0.847 &                  \textbf{0.505} &                          \textbf{0.713} &                  0.277 &                    \textbf{0.584} &                   \textbf{0.579} \\
                    CROP &                        \textbf{0.324} &                       \textbf{0.305} &                      \textbf{0.597} &                            \textbf{0.489} &                  \textbf{0.821} &                    \textbf{0.372} &                 \textbf{0.713} &             \textbf{0.703} &                 \textbf{0.724} &             0.678 &               \textbf{0.598} &                       \textbf{0.469} &                       \textbf{0.409} &                    \textbf{0.855} &                  \textbf{0.513} &                          \textbf{0.701} &                  0.273 &                     \textbf{0.623} &                   \textbf{0.646} \\
                    Edgepop &                        \textbf{0.437} &                       \textbf{0.333} &                      0.515 &                            \textbf{0.475} &                  0.775 &                    \textbf{0.477} &                 \textbf{0.698} &              \textbf{0.686} &                 \textbf{0.747} &             0.576 &               \textbf{0.618} &                       \textbf{0.423} &                       \textbf{0.489} &                    0.832 &                  \textbf{0.494} &                          \textbf{0.730} &                  0.269 &                     \textbf{0.577} &                   \textbf{0.582} \\
                        EarlyCROP &                        \textbf{0.323} &                       \textbf{0.292} &                      \textbf{0.527} &                            \textbf{0.455} &                  \textbf{0.822} &                    \textbf{0.387} &                 \textbf{0.704} &              \textbf{0.690} &                 \textbf{0.735} &             0.688 &               \textbf{0.607} &                       0.405 &                       \textbf{0.433} &                    \textbf{0.853} &                  \textbf{0.527} &                          \textbf{0.702} &                  \textbf{0.298} &                     \textbf{0.606} &                   \textbf{0.584} \\
                        IMP &                        \textbf{0.278} &                       \textbf{0.272} &                      \textbf{0.590} &                            \textbf{0.442} &                  \textbf{0.832} &                    \textbf{0.337} &                 \textbf{0.693} &              \textbf{0.703} &                 \textbf{0.726} &             0.665 &               \textbf{0.614} &                       \textbf{0.507} &                       \textbf{0.370} &                    \textbf{0.869} &                  \textbf{0.544} &                          \textbf{0.704} &                  0.265 &                     \textbf{0.586} &                   \textbf{0.645} \\

                           Edgepop-A &                        \textbf{0.445} &                       \textbf{0.352} &                      \textbf{0.585} &                            \textbf{0.475} &                  0.807 &                    \textbf{0.464} &                 \textbf{0.705} &              \textbf{0.715} &                 \textbf{0.735} &             \textbf{0.724} &               \textbf{0.642} &                       \textbf{0.476} &                       \textbf{0.466} &                    \textbf{0.880} &                  0.432 &                          \textbf{0.710} &                  \textbf{0.387} &                     \textbf{0.613} &                   \textbf{0.634} \\

                       SNIP-A &                        \textbf{0.241} &                       \textbf{0.255} &                      \textbf{0.614} &                            \textbf{0.414} &                  0.764 &                    \textbf{0.276} &                 \textbf{0.680} &              \textbf{0.686} &                 0.698 &             \textbf{0.734} &               \textbf{0.601} &                      \textbf{0.511} &                       0.310 &                    \textbf{0.861} &                  0.417 &                          0.658 &                  \textbf{0.439} &                     \textbf{0.633} &                   \textbf{0.655} \\

\bottomrule
\end{tabular}
\caption{AUROC of CIFAR10C corruptions (severity 5) on dense and pruned ResNet18 models. We report the best result across sparsity. Bold indicates an improvement compared to the dense model.}
\label{table:cifar10c_5}
\end{table}

\begin{figure}[h]
    \centering
    \includegraphics[width=\textwidth]{fig/legend.png}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/lenet5_acc_fgsm8.png}}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/lenet5_acc_l2_fgm8.png}}
    \caption{Robustness of the dense LeNet5 vs. pruned at different levels of sparsity to (a) L-$\infty$ FGSM adversarial attack with $\epsilon=8$, and (b) L-2 FGSM adversarial attack with $\epsilon=8$.}
    \label{fig:properties_robustness_lenet}
\end{figure}

\begin{figure}[h]
    \centering
    \includegraphics[width=\textwidth]{fig/legend.png}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/lenet5_auroc_fgsm8.png}}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/lenet5_auroc_l2_fgsm8.png}}
    \caption{AUROC of the dense LeNet5 vs. pruned at different levels of sparsity to (a) L-$\infty$ FGSM adversarial attack with $\epsilon=8$, and (b) L-2 FGSM adversarial attack with $\epsilon=8$.}
    \label{fig:properties_detection_lenet}
\end{figure}

\begin{figure}[h]
    \centering
    \includegraphics[width=\textwidth]{fig/legend.png}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/lenet5_auroc_mnist.png}}
    \subfigure[]{\includegraphics[width=0.3\textwidth]{fig/lenet5_auroc_omniglot.png}}
    \caption{AUROC on (a) MNIST and (b) Omniglot of the dense LeNet5 vs. pruned at different levels of sparsity.}
    \label{fig:properties_detection_ood_lenet}
\end{figure}

redpajama_set_name: RedPajamaArXiv


text:
\section{Introduction}

Tornadoes are today one of the most destructive and frightening natural phenomena in the planet. On average, the economic damage caused by tornadoes in the US only is estimated at USD 3.1 billion per year \footnote{https://www.statista.com/statistics/237409/economic-damage-caused-by-tornadoes-in-us/}. Getting worse the situation is climate change. Some studies predict that it may generate conditions for more severe thunderstorms by increasing the opportunities for tornadoes to form \footnote{https://education.nationalgeographic.org/resource/tornadoes-and-climate-change}. Fighting the causes that generate natural disasters with increasing frequency and intensity undoubtedly is the best strategy, but doing that requires international agreements which are often not easy to conclude. Thus, the most realistic and immediate goal at the moment is to predict a tornado sufficiently in advance to be able to save lives and drastically reduce economic damage. The tornadogenesis, however, is a very chaotic process \cite{https://doi.org/10.1002/met.1437} which makes prediction very hard, also because of an incomplete understanding of it \cite{Markowski2009TornadogenesisOC}. Nowadays, their prediction is mainly carried out by specialized meteorologists who observe the weather variations in a given area and try to early catch the tornadogenesis \cite{NSSL_TF}. However, this is a very limited approach, which can't be used on a large scale and that is not always enough accurate. It would be therefore desirable to develop automated systems capable of effectively analysing meteorological data and predicting the formation of a tornado as early as possible. Today such systems can be developed by exploiting modern machine learning (ML) techniques which can learn the weather conditions leading to a tornado from an amount of historical examples of meteorological data. Although ML techniques stand out for their effectiveness in countless sectors, they require large amounts of data which unfortunately are not available for the tornadogenesis.
In this paper, we therefore present a system for the tornadogenesis early detection based on existing and widespread climate metrics collection systems. This allowed us to validate multiple ML techniques on a large amount and realistic data and to produce a proposal for a more sustainable and economically applicable real-world system.

\section{Related Works}
In the last few years, a lot of effort has been made by researchers to tackle the problem of predicting tornadoes. Ji Qiang \cite{qiang2020proposal} for example, proposed a system based on the use of clusters of balloons able to monitor wind speed and also counteract tornadogenesis by reducing the wind speed of the tornado, blocking it and destroying the convective flow of air. However, such methods require a large number of very specific physical instrumentations, which need to be deployed in the risk areasa and this is very expensive and not always feasible. Further, it would also require periodic maintenance or replacement costs.

Gradually, more and more methods related to ML have been proposed, mainly using Support Vector Machines (SVMs) as in Adrianto et al. \cite{adrianto2009support} where ML has been exploited for predicting the location and time of tornadoes using a set of 33 storm days. Also Trafalis et al. \cite{trafalis2011linear} proposed a rule-based classifier trained with meteorological data collected from 1562 observations (half of them representing tornadoes).

Available datasets for tornado prediction are generally small and unbalanced. This is a rather common situation when working with tasks trying to identify a rare event \cite{Pang_2021}. Trafalis et al. \cite{trafalis2014machine} proposed the most significant dataset in the literature composed by 10816 observed circulations, with 721 of them representing tornadoes. A circulation is a particular weather condition that can lead to the formation of a tornado. For each observation there are 83 attributes such as temperature and wind speed, which are metrics also taken into account in our research. Although in this dataset the number of tornado circulations is quite small, thus making it difficult to train and validate a suitable model. Recently, Aleskerov et al. \cite{aleskerov2020superposition} have proposed, using the same dataset, the application of superposition principle, stating that for all linear systems the net response caused by two or more stimuli is the sum of the responses that would have been caused by each stimulus individually, to tornado prediction, obtaining good results compared to other more traditional solutions.

Compared to what has been presented in the literature, we propose a more accurate ML-based system capable of performing tornado prediction earlier and also exploiting data-streams from satellites that are already well-distributed around the planet, thus lowering deployment costs.

\section{Method}
 As shown in Figure \ref{figure:system}, the system exploits meteorological data collected daily from deployed instrumentation which monitors vast geographical areas.
 In particular, we used as data source the instrumentations of Copernicus\footnote{https://www.copernicus.eu/it}, an initiative of the European Space Agency and the European Commission that makes publicly available the data collected by a series of satellites monitoring the entire surface of the Earth. The data considered by our system are collected over a specific geographical area which is represented by a grid.

\begin{figure}[H]
    \centering
    \includegraphics[width=1\textwidth]{system.png}
    \caption{Proposed Tornado Early Detection System overview.}
    \label{figure:system}
\end{figure}

\begin{table}[h]
\centering

\begin{tabular}{@{}lrr@{}}
\toprule
\midrule
Name & Unit & Range \\
\midrule
 Temperature          & $K$ &  $[0, +\infty]$ \\
 Wind Components & $m*s^{-1}$ &  $[0, +\infty]$ \\
 Precipitation          &  $m$ & $[0, +\infty]$ \\
 Column Rain Water          & $kg*m^{-2}$ &  $[0, +\infty]$ \\
 Large scale rain rate          &  $kg*m^{-2}*s^{-1}$ &   $[0,
+\infty]$\\
 Cloud Coverage        &  N/A & $[0, 1]$ \\

\bottomrule
\bottomrule
\end{tabular}
\caption{Meteorological metrics present in the dataset indicating units of measurement and range.}
\label{tab:metrics}
\end{table}

 For each cell of this grid, the meteorological information listed in Table \ref{tab:metrics} are considered. Each grid covers an area of 5 latitude/longitude degrees per side. The considered area is deliberately very large because the process of tornadogenesis can be influenced or triggered by large-scale meteorological phenomena. Further, to predict tornado formation several days in advance is important to monitor areas that are not necessarily close to where the tornadogenesis will occur.  Subsequently, the grid is divided into four quadrants and for each of them, we compute the mean and standard deviation of the metrics values collected for each cell, from now on called features.  Data are daily stored on a database and then the collected meteorological information of several days are used to obtain the outcome from a ML classification model. In order to achieve the earliest possible detection, the system is designed to estimate the tornadogenesis, considering the meteorological metrics in a specific geographical area, up to five days before.

\section{Experiments}
\subsection{Dataset}
In order to compare the performance of various classification techniques and to choose the most suitable one to include in the proposed system, we created a specific dataset. It contains two types of event instances, tornadoes and non-tornadoes. The considered meteorological data source used is the ERA5 dataset \cite{ERA5_DB} representing a worldwide grid with resolution of $0.25\times0.25$ latitude/longitude degrees.
To label the event instances we used the historical occurrences of tornadoes in the United States collected by the National Centers for Environmental Information \cite{NOAA_DB}. Our dataset consists of 2470 tornadic and 2633 non-tornadic instances and the time period in which these events occur is between 1990 and 2017. Non-tornadic instances are selected by considering areas where tornado events are occurred but in a time period at least 10 days away from any tornadic event in the dataset. For each event, there is weather information for five days. These information are represented as five grids containing all the metrics shown in Table \ref{tab:metrics} and each grid has a size of 19x19 cells. For training the classifiers, the grids values were transformed into features, as explained in Section 2.

\subsection{Results}
In a real-world context, the classifier could be trained on historical tornado data and then have to make predictions on data collected in real time. To simulate this situation, the dataset was divided into training set containing all events collected from 1990 to 2016 and test set composed of 2017 events only. The first one is then composed of 2360 tornadic events and 2633 non-tornadic events while the test set contains 110 tornadic events and 90 non-tornadic events.
Furthermore, in order to investigate the influence of the number of days considered, tests were carried out using as input data to the classifier to make the prediction at day $i$, only the data at day $i-1$, then those at day $i-1$ and $i-2$ and so on until all five daily data available in the dataset were considered. Classifiers, evaluated using two quality metrics commonly used in this field,  Probability Of Detection, $POD = \frac{tp}{(tp+fn)}$ and False Alarm Rate, $FAR =\frac{fp}{(tp+fp)}$ are listed in Table \ref{tab:classifiers}. These metrics are indicative for our system as they measure its ability to generate an alert in the presence of a real tornado and on the other hand not to issue false alarms too frequently.

\begin{table}[]
\resizebox{\textwidth}{!}{
\begin{tabular}{l|cc|cc|ll|ll|ll}
\toprule \midrule
\multicolumn{1}{c|}{Classifier} & \multicolumn{2}{c|}{5 Days} & \multicolumn{2}{c|}{4 Days} & \multicolumn{2}{c|}{3 Days} & \multicolumn{2}{c|}{2 Days} & \multicolumn{2}{c}{1 Day} \\ \midrule
                                 & POD     & FAR         & POD     & FAR         & POD     & FAR     & POD     & FAR     & POD    & FAR    \\ \midrule
Gaussian Classifier                 & 0.79    & 0.10       & 0.83    & 0.05      & 0.80    & 0.06      & 0.75    & 0.07      & 0.61   & 0.07   \\
Decision Tree                       & 0.59    & 0.31       & 0.55    & 0.41        & 0.58    & 0.32       & 0.59    & 0.34    & 0.51   & 0.55   \\
\textbf{Random Forest}                      & \textbf{0.84}    & \textbf{0.06}       & \textbf{0.84}    & \textbf{0.07}      & \textbf{0.77}    & \textbf{0.09}     & \textbf{0.62}    & \textbf{0.20}     & \textbf{0.76}   & \textbf{0.10}   \\
SVM                                & 0.70    & 0.25     & 0.72    & 0.21     & 0.63    & 0.32      & 0.60    & 0.38       & 0.54   & 0.32   \\
K-nearest Neighors Classifier     & 0.62    & 0.28       & 0.60    & 0.24       & 0.59    & 0.36       & 0.61    & 0.31      & 0.62   & 0.27   \\
AdaBoost Classifier                & 0.64    & 0.29       & 0.68    & 0.24       & 0.63    & 0.36      & 0.61    & 0.25      & 0.62   & 0.25   \\ \bottomrule \bottomrule
\end{tabular}
}
\caption{Comparison of different classifiers on our dataset}
\label{tab:classifiers}
\end{table}

As can be seen in the table, using four- or five-days data almost always leads to a higher probability of tornado prediction. The Random Forest classifier stands out, which obtains a 84\% POD and a 0.06\% FAR, five days before the tornadogenesis.

By comparing our system, based on Random Forest, with other works in the literature, it can be seen that with this approach we are able to predict a tornado with greater probability and anticipation and with fewer false alarms, as can be seen in the table \ref{tab:sota}

\begin{table}[]
\centering
\begin{tabular}{@{}llll@{}}
\toprule
\midrule
Approach & POD & FAR & Advance  \\
\midrule
Logistic regression \cite{aleskerov2016constructing} & 0.7 & 0.25 & 20 minutes \\
Random forest \cite{aleskerov2016constructing} & 0.58 & 0.17 & 20 minutes \\
Superposition \cite{aleskerov2016constructing} & 0.68 & 0.16 & 20 minutes \\
SVM with threshold adj. \cite{trafalis2014machine} & 0.73 & 0.18 & Present \\
Rotation Forest \cite{trafalis2014machine} & 0.76 & 0.08 & Present \\
\textbf{Random Forest (Our)} & \textbf{0.84} & \textbf{0.06} & \textbf{5 days}  \\
\bottomrule
\bottomrule
\end{tabular}
\caption{Comparison of our best model with previous works. The "Advance" column indicates how far in advance tornadogenesis is predicted.}
\label{tab:sota}
\end{table}

\section{Conclusions}
In this research, we presented a novel early-detection tornado system that, according to our experiments, would be able to identify a tornado with a probability of 84\% and up to five days in advance, validated in a real-world context. Our system can be used to monitor weather conditions in at-risk areas in an automated way and warn authorities early so that action can be taken to stem the massive damage that such disasters can cause. Also of central importance is the sustainability and applicability of the proposed system, which makes use of satelittes that are already widely spread throughout the planet and doesn't require any significant additional expenditure to put it into operation.
The contribution made by this research was also to create a class-balanced and large dataset containing tornadoes and other weathers, paving the way for the development of other tornado forecasting approaches. Thanks to the continued enlargement of chosen data sources, the dataset can also be easily expanded in the future.
It is essential for the scientific community to continue research in this area by proposing novel tornadogenesis early-detection systems to obtain increasingly accurate predictions of these phenomena, thus enabling society to be prepared to tackle this serious and pressing problem, which will be increasingly aggravated by climate change, and prevent disasters.

\printbibliography

\end{document}


There is a point in backing horses before Sandown or Punchestown but after that, keep your powder dry until your fancy has an entry.
It's particularly a rookie error to back a horse because the trainer says it's come in from the field looking magnificent (see Jessie re: Our Duke and Sizing John). If a horse has done that well out at grass it will take that much longer to get it to racing condition.
It's actually more a comment anti to those who spent the better part of the season calculating how Nicky was going to carve up the novice hurdles between Angels Breath, Mr Fisher, Champ, Birchdale, Dicky Diver etc etc.
Nicky is unfortunate that expectations are usually unfairly high. The same can be said of Mullins, Elliott and, this season, Nicholls.
All I'm saying is that this time last year, people were saying that Santini was a good thing for the RSA based on a fair run in the AB. Most people would accept that Champ's hurdle form is better than Santini's and he must be on the shortlist for next season. The rest have a lot to prove.
Santini was vastly more inexperienced than Champ ever was.
7 runs to Santini's 3 (including a ptp).
For reference so will Kemboy!
Would really question how much he will improve and also how many will improve past him?
Not sure what thread to post this on but is anyone showing an interest in tomorrow's card at Cheltenham?
I took some 4/1 for Mister Whittaker.
I've put 2 Lucky 31's combining Fergal O'Briens and Nicky Henderson horses. I'm intrigued by Wenyerreadyfreddies first run at Cheltenham and Rather Be back over hurdles.
Had a £5 free bet to use so have put on a comically speculative 400/1 double of Le Richebourg & Delta Work. Equally, I think there are worse 400/1 shots out there!


text:
Cougar Sailors Win Top National Honors
June 5, 2018 1 0 Dan Dickison
When you're on a roll, you're on a roll and the momentum is sweet. That's how it is for the College of Charleston Varsity Sailing Team these days. For the second year in a row, the Cougars have won the prestigious Leonard M. Fowle Trophy recognizing them as the overall top performing collegiate sailing team in the U.S.
"It's definitely an impressive accomplishment," says Greg Fisher, director of the College's sailing program. "Our team sailed so well in all the events this year. And we were so close in the women's championship. Our team staged a strong comeback on the final day in that event and only lost first place by virtue of the tiebreaker. For everybody involved, winning the Fowle Trophy again is amazing. It just shows how hard everyone on the team – coaches and sailors – works to be competitive."
Senior Stefano Peschiera won the LaserPerformance Singlehanded National Championship last fall. (Photo by Grace Adams)
This year's win marks the eighth time the Cougars have been awarded the Fowle Trophy and it's the third time they've achieved this distinction in the last four seasons. The Fowle Trophy recognizes genuine supremacy in the sport of sailing because each year it's awarded to the team that performs best in collegiate sailing's five disciplines – singlehanded racing, team racing, match racing, women's dinghy competition and coed dinghy competition.
Last fall, senior Stefano Peschiera won the LaserPerformance Singlehanded National Championship with a commanding display of skill. Sophomore Gerald Williams finished second at that event. On the heels of that regatta, the Cougars finished an impressive second at the ICSA Match Race National Championship held in their home waters.
Late this spring, the Cougars traveled to Old Dominion University in Virginia for the back-to-back-to-back national championship regattas, which began with the Sperry Women's National Championship. The women's team finished a very close second in that event.
Immediately afterward was the LaserPerformance Team Race National Championship, which the Cougars dominated, led by skippers Peschiera, Augie Dale and Christophe Killian, with Grace McCarthy, Katherine Lounsbury and Elizabeth Pemberton crewing.
The Women's Sailing Team came in second at this year's Sperry Women's National Championship. (Photo by Colin Grey Voight)
The culminating event was the Gill Coed Dinghy National Championships, and the Cougars sailed well enough to finish second, but couldn't unseat the eventual champions from the Massachusetts Institute of Technology.
The upshot of all these top finishes was winning the Fowle Trophy. In addition, Peschiera was honored as College Sailor of the Year, and he, Dale, Lounsbury and McCarthy each earned recognition as collegiate All-Americans. Annabel Carrington and Pemberton were selected as All-America Honorable Mention crew as well.
For the Varsity Sailing Team, winning the Fowle Trophy is tremendously gratifying. It vindicates hours and hours spent training and practicing each week. During the season, the team travels to compete nearly every weekend. Collectively, this represents a huge effort.
"What's special," Fisher says, "is that we're the only team in collegiate sailing that had a podium finish in all six national events (including the women's singlehanded championship where Kelly-Ann Arrindell finished third). That's really rare."
He adds, "We've always been proud of the depth of talent we have on this team. But the greatest thing is how much fun everyone had. We probably had more sailors at the nationals than every other team and all our guys and gals gave 200 percent, whether they were competing or not. That was the best part. These student-athletes just supported each other so well, it makes for a winning formula."
For full scores from the national championship regattas, visit collegesailing.org.
Featured image: Members of the Varsity Sailing Team celebrate their individual awards as well as the Fowle Trophy win. (Photo by Colin Grey Voight)
Tags: College of Charleston Sailing Programsailing teamtoday-featured
The College Joins Collegiate Consortium Studying Slavery
Library Acquires Images of Robert Kennedy's Final Hours


Directions: This cemetery is located in Blissville Twp., Illinois, just east of Ashley, Illinois. Take Rt. 15 east out of Ashley, for approx. 2.10 miles. Take Old State Road (Rt 15 turns to the left, and Old State Rd is straight ahead). Old State Rd. turns into Seville Rd. as soon as you cross the county border. Take Seville Rd. for approx. 1.7 miles. The cemetery is on the left (north) side of the road.


We are pleased to present below all posts archived in 'January 1993'. If you still can't find what you are looking for, try using the search box.
A delightful romantic comedy at its surface, Groundhog Day is one of these rare movies that allows different audiences to enjoy the film on various levels of depth and perspective. Ultimately Groundhog Day is a fun film with a serious message, that director Harold Ramis manages to serve without overburdening its lightness.


text:
TNZ 1999/802: Report on The Assessment of Training Programmes Supported Through the Government of Tanzania/UNICEF Country Programme of Cooperation 1997-2001
Author: Muro, A.; Mwalemba, G.
The Government of Tanzania and UNICEF Country Programme (CP) of Cooperation 1997 - 2001 aims at improving the living conditions of children and women in the country. The key strategy of the Programme is community-based. To attain the overall objective of the CP, it was planned to assist communities and Local Authorities to enhance their capacity in planning and managing social development as well as in maintaining information management systems; support to Basic Education; enhance efforts for HIV/AIDS prevention and care and on Malaria prevention and control; increase efforts and awareness for social mobilization for the human rights of children and women including development of a framework for implementing CRC and CEDAW; and strengthening UNICEF's contribution to macro-level policies through a stronger role in social policy formulation and analysis, monitoring and evaluation. The Country Programme employs three basic interventions strategies: delivery of specific social services: capacity building and advocacy. The choice of intervention strategy expresses the priority given to different programme objectives and concerns and how they relate to human rights of women and children. The three strategies are alternative, but not mutually exclusive. It is expected that the application of these strategies should be for the ultimate purpose of improving the situation of children and women. Capacity building becomes a number one strategy in the Country Programme because if effectively implemented, it provides the progamme duty bearers and partners ability to move ahead on their own, and therefore sustaining social development.
The overall objective of the training assessment was to generate ideas towards the development of a framework to guide formation of training programs, and how to monitor and evaluate the programmes in order to increase efficiency and effectiveness of CP training within the context of capacity building objectives. The specific objects of the Training Assessment were to:
- Review the formulation of training packages/manuals and the training process in order to determine issues of need and demand; involvement of target groups; and other partners, linkages and coordination
- Undertake a capacity gap analysis to find out whether training helped the actors and the beneficiaries resolve community problems
- Find out, in particular, whether training processes had taken into account the key recommendations of the Training Review of 1996
- Make recommendations that will contribute to improving future training programmes particularly with regard to relevance, purpose, monitoring and evaluation of impact
Two UNICEF National Project Officers from the Community-based Capacity Building and Communications Section and from the Monitoring and Evaluation Section conducted a rapid assessment on some of the trainings supported by UNICEF at different levels. Several methodologies were involved. Desk review on training packages/manuals and the training review report; in-house consultations and discussions with the professionals and relevant project officers who have been directly involved with the development of the training packages; and field visits in four districts - Unguja South in Zanzibar, Kibaha, Morogoro Rural and Morogoro Urban in Mainland Tanzania. The authors observed participants during training workshops. Simple tools were developed to guide interviews and discussions at national, district, ward and village levels.
Pre-training assessment is meant to appraise and set out a baseline situation and, from that, plan appropriate and feasible response. The Training Review of 1996 found that most of the packages were prepared without doing a proper needs assessment. The current training assessment revealed that not much effort has been made to implement the recommendations. In most of the cases, the packages were prepared nationally and developed in response to needs expressed at national levels by sector ministries and supporting organizations. Such short consultations cannot tap the real needs of the beneficiaries.
The assessment revealed that the problem of coordination was not only with UNICEF-supported programs, but also other UN and Development Agents were duplicating training packages similar to those developed by UNICEF. There are also several training packages that are sector specific, targeted to the same target groups in a series of uncoordinated interventions.
Some training programmes are still following a traditional 'cascade' training process -- prepare national trainers to train district trainers who, in turn, train ward trainers who then train the beneficiaries at the community level. Most of the Training of Trainers (TOTs) conducted at regional and district levels have not been disseminated to the beneficiaries at ward and village level as expected. This is due to lack of regular follow-up and supervision of the training by those responsible.
Often, due to the weak capacity of district TOTs in training and facilitation, the community beneficiaries receive diluted knowledge and skills. The few training seminars/workshops conducted at ward and community levels last a shorter time than at district and national levels. There was a feeling expressed by community members that training at lower levels were rushed, leaving the village actors with very limited real understanding of the issue and only with a set of instructions to follow. In some cases, key actors who matter in addressing problems for children and women in the community were left out of important training.
Different methodologies and techniques were applied during the training sessions. These included lecture, group work discussions, role playing, films and participation of children. The interviewees reported that they gained a lot where practical and participatory methods were applied.
Except for few training programmes conducted, it was revealed that most training did not conduct performance evaluation. The study revealed that follow up and supervision were the weakest points of the training supported by the program.
There is a need to carry out a systematic needs assessment prior all future training, using the framework provided and proposed in 1996 by the Training Review Team.
Each training should develop a performance evaluation for pre- and post-training, and the outcome should be integrated in the end-of-training evaluation. It is also important to develop a monitoring and evaluation plan.
Ensure that each training comes up with action plans to translate what was learnt into actions to improve the situation of women and children.
ESARO


text:
.ProductTable {
    background-color: cornflowerblue;
}

redpajama_set_name: RedPajamaGithub


text:
Q: how can i get returned value with double quotes in javascript i want to get a variable value with double quoted in javascript. i have an input field. i get the value from there but it is single quoted.
i try the below code already
let id=document.querySelector("#v_id");   //v_id is input field ID
let stringID= id.value;

var vObj = {id:stringID};

result is like that { id: '1'}  see single quoted
i cannot use this value in JSON object.
i need a result like { id: "1"}   see double quoted
i am expecting your precious answer

A: Use JSON.stringify

console.log(JSON.stringify('123123'))

A: You actually don't need to convert it. Rather you can use bracket notation while creating the key
jsonObj[id.value] = 'someValue';

redpajama_set_name: RedPajamaStackExchange


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text:
Bob Dylan awarded Nobel Prize and changes the Literary world forever
Dylan was called 'a great poet in the English-speaking tradition.'
In 1993, author Toni Morrison received the Nobel Prize in Literature. Since then, no American has been awarded the honor - until now.
By Nikki Crawford (CALIFORNIA NETWORK)
CALIFORNIA NETWORK	(https://www.youtube.com/c/californianetwork)
10/13/2016 (4 years ago)
Published in Music
Keywords: Bob Dylan, Nobel Prize, Literature, award
LOS ANGELES, CA (Catholic Online) - Famous singer and songwriter Bob Dylan is known as one of the world's most influential musicians.
Songs such as "Like a Rolling Stone" and "Knockin' on Heaven's Door" were instant classics - but songwriting isn't exactly why he won the award.
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At 75-years-old, Dylan has not once been considered an author. Poet, perhaps, but his works fail to fit within the confines of the exclusive "literature" genre.
Many scholars have long argued what separates an average story from a piece of literature and the simplest explanation they agree upon remains controversial.
A mainstream novel serves the purpose all books should - it is entertaining, well-written and need not be either fiction or nonfiction.
Literature is also exempt from limiting itself to either fiction or nonfiction; however, these works offer deliberate parallels to larger social, political, existential or individual issues. What differentiates a work of literature from mainstream novels is the exploration of some part of the human condition.
Of course, whether a book can be considered literature remains subjective, which brings us back to Bob Dylan.
Three years ago a journalist for The New York Times argued: "Mr. Dylan's work remains utterly lacking in conventionality, moral sleight of hand, pop pabulum or sops to his audience.
"His lyricism is exquisite; his concerns and subjects are demonstrably timeless; and few poets of any era have seen their work bear more influence."
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Sara Danius, a literary scholar and the permanent secretary of the Swedish Academy, which awards the prize, agrees Dylan is "a great poet in the English-speaking tradition."
She compared him to Homer and Sappho, both of whom remain famous storytellers and poets, whose works were delivered both orally and in print.
25;;08 *1 45;;6*
When asked if the decision to award a musician with the Nobel Prize in Literature is an indication the term "literature" is broadening, she jokingly replied, "The times they are a changing, perhaps," in reference to one of Dylan's songs.
The Swedish Academy said, in a biographical note released with the announcement of his award, described Dylan's albums had "a tremendous impact on popular music...Dylan has recorded a large number of albums revolving around topics like the social conditions of man, religion, politics and love.
"The lyrics have continuously been published in new editions, under the title 'Lyrics.' As an artist, he is strikingly versatile; he has been active as painter, actor and scriptwriter.
Second Grade Catechism FREE Online Classes
"Since the late 1980s, Bob Dylan has toured persistently, an undertaking called the 'Never-Ending Tour.' Dylan has the status of an icon. his influence on contemporary music is profound, and he is the object of a steady stream of secondary literature."
Many, including David Hajdu, a music critic for The Nation agree with the Swedish Academy and believe the prize for Dylan is long overdue.
In an interview Thursday, Hajdu stated: "It's partly a recognition of the whole tradition that Bob Dylan represents, so it's partly a retroactive award for Robert Johnson and Hank Williams and Smokey Robinson and the Beatles.
"It should have been taken seriously as an art form a long time ago...It's literature but it's music, it's performance, it's art, it's also highly commercial. The old categories of high and low art, they've been collapsing for a long time, but this is it being made official."
The California Network is the Next Wave in delivery of information and entertainment on pop culture, social trends, lifestyle, entertainment, news, politics and economics. We are hyper-focused on one audience, YOU, the connected generation. JOIN US AS WE REDEFINE AND REVOLUTIONIZE THE EVER-CHANGING MEDIA LANDSCAPE.


text:
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 50 років від дня народження ланкової колгоспу імені XXII з'їзду КПРС Борщівського району, двічі Герой Соціалістичної Праці Євгенії Долинюк

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Народилися
 3 квітня — український журналіст, редактор, науковець Ярослав Буяк
 16 серпня — український вчений у галузі педіатрії Наталія Банадига
 29 вересня — український вчений у галузі медицини Арсен Гудима

Померли

Призначено, звільнено

Див. також
 1964 у Тернопільській області

Примітки

Джерела

Посилання

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