A Paris : Chez Iean Dhourry, au bout du Pont-Neuf, près les Augustins, à l'Image S. Iean, M. DC. LXI. [1661]

München [Munich] : Jos. Albert K.B. Hof-Photograph, [1860]

[London], [between 1820 and 1829?]

Liverpool (82, Duke Street) ; And in London (13 St. James's Place, Hampstead Road) : Published ... by the artist, April 10th. 1854.

Liverpool (82, Duke Street) ; And in London (13 St. James's Place, Hampstead Road) : Published ... by the artist, April 10th. 1854.

London (No. 90, Cheapside, & 8, Pall Mall) : Published ... by Hurst, Robinson & Co., March 1st. 1824.

London (No. 90, Cheapside, & 8, Pall Mall) : Published ... by Hurst, Robinson & Co., March 1st. 1824.

[London], [1824]

[Paris] : [de l'Imprimerie de Clousier], [between 1780 and 1789]

[London] : [Wellcome Trust], [2009]

How to tune in to Tom Wilson's best games on Sunday.

Alex Ovechkin can lay claim as the best Capitals player to wear No. 8, but what about the rest of the jersey numbers? Who are the best Caps for every number?

Alex Ovechkin can lay claim as the best Capitals player to wear No. 8, but what about the rest of the jersey numbers? Who are the best Caps for every number?

The Washington Capitals on Friday placed Brendan Leipsic on unconditional waivers to terminate his contract after he made disparaging comments about women and teammates in a private social media chat. In a conversation involving his brother and Florida Panthers minor leaguer Jack Rodewald, Leipsic commented on the physical appearances of Vancouver forward Tanner Pearson's wife and Edmonton captain Connor McDavid's girlfriend. The NHL called it ''inexcusable conduct'' and said it would address the matter with the Capitals and Panthers.

The Washington Capitals have placed former Winterhawks wing Brendan Leipsic on unconditional waivers with the intention of his contract being terminated after private messages revealed misogynistic comments. > The Washington Capitals have placed Brendan Leipsic on unconditional waivers for purposes of terminating his contract.https://t.co/UnADibu2yQ

The Washington Capitals decided to part ways with forward Brendan Leipsic on Friday after he made vulgar and disparaging comments on an social media chat group. The 25-year old Canadian was placed on waivers by the NHL club after his Instagram group chat messages were leaked earlier this week. The chat included inappropriate remarks about Edmonton Oilers forward Connor McDavid's girlfriend and the wife of Vancouver Canucks player Tanner Pearson.

Troy Brouwer scored the game-winning goal as the Capitals beat the Blackhawks at Nationals Park on Jan. 1, 2015. Relive the excitement with Monday night's replay.

Washington announced it is terminating the contract of forward Brendan Leipsic after his mysogynistic comments on Instagram were leaked.

2019

Jersey City, NJ : Monsters In My Head, LLC, 2011.

Rockville, Maryland : National Institute on Drug Abuse, 1985.

Springfield, Virginia : National Technical Information Service, 1974.

Springfield, Virginia : National Technical Information Service, 1973.

Washington, D.C. : Wynne Associates, 1974.

Washington, D.C. : Wynne Associates, [1974]

Oregon State basketball player Destiny Slocum has opted to enter the transfer portal for her final season of eligibility. Slocum, a 5-foot-7 guard, averaged a team-best 14.9 points and had 4.7 assists a game this past season with the Beavers, who finished the season ranked No. 14 with a 23-9 record. In a statement released by the university on Thursday, Slocum thanked everyone who supported her in the decision.

Hear how former Oregon State guard and current member of the WNBA's LA Sparks Sydney Wiese is recovering from a COVID-19 diagnosis, seeing friends and family show support and love during a trying time.

Pac-12 Networks' Mike Yam interviews former Oregon State guard Sydney Wiese to hear how she's recovering from contracting COVID-19. Wiese recounts her recent travel and how she's been lifted up by steadfast support from friends, family and fellow WNBA players. See more from Wiese during "Pac-12 Playlist" on Monday, April 6 at 7 p.m. PT/ 8 p.m. MT on Pac-12 Network.

South Carolina coach Dawn Staley believes her top-ranked Gamecocks are the women's basketball national champions, even without an NCAA Tournament trophy to put in their display case due to the pandemic-shortened season. The NCAA decided against officially crowning champions after its signature tournaments were called off due to the coronavirus pandemic that has sent much of the world into lock down. Staley spoke from her home where she's spent the past month managing her program and ensuring her players don't linger too much on what they missed.

Oregon State guard Mikayla Pivec is the epitome of a versatile player. Her 1,030 career rebounds were the most in school history, and she finished just one assist shy of becoming the first in OSU history to tally 1,500 points, 1,000 rebounds and 500 assists. She'll head to the WNBA looking to showcase her talents at the next level following the 2020 WNBA Draft.

Pac-12 Networks' Yogi Roth and Ashley Adamson talk with Hall of Fame player and Pac-12 Networks talent Bill Walton during Thursday's Pac-12 Perspective podcast.

During Wednesday's "Pac-12 Perspective" podcast, Natalie Chou shared why she is using her platform to speak out against racism she sees in her community related to the novel coronavirus.

Pac-12 Networks' Mike Yam has a conversation with UCLA's Natalie Chou during Wednesday's "Pac-12 Perspective" podcast. Chou reflects on her role models, passion for basketball and how her mom has made a big impact on her hoops career.

Watch the debut of "Our Stories - Natalie Chou" on Sunday, May 10 at 12:30 p.m. PT/ 1:30 p.m. MT on Pac-12 Network.

Great attention has been paid to Big Data in recent years. Such data hold promise for scientific discoveries but also pose challenges to analyses. One potential challenge is noise accumulation. In this paper, we explore noise accumulation in high dimensional two-group classification. First, we revisit a previous assessment of noise accumulation with principal component analyses, which yields a different threshold for discriminative ability than originally identified. Then we extend our scope to its impact on classifiers developed with three common machine learning approaches---random forest, support vector machine, and boosted classification trees. We simulate four scenarios with differing amounts of signal strength to evaluate each method. After determining noise accumulation may affect the performance of these classifiers, we assess factors that impact it. We conduct simulations by varying sample size, signal strength, signal strength proportional to the number predictors, and signal magnitude with random forest classifiers. These simulations suggest that noise accumulation affects the discriminative ability of high-dimensional classifiers developed using common machine learning methods, which can be modified by sample size, signal strength, and signal magnitude. We developed the measure total signal index (TSI) to track the trends of total signal and noise accumulation.

John J. Dziak, Donna L. Coffman, Matthew Reimherr, Justin Petrovich, Runze Li, Saul Shiffman, Mariya P. Shiyko.

Source: Statistics Surveys, Volume 13, 150--180.

Abstract:
Researchers are sometimes interested in predicting a distal or external outcome (such as smoking cessation at follow-up) from the trajectory of an intensively recorded longitudinal variable (such as urge to smoke). This can be done in a semiparametric way via scalar-on-function regression. However, the resulting fitted coefficient regression function requires special care for correct interpretation, as it represents the joint relationship of time points to the outcome, rather than a marginal or cross-sectional relationship. We provide practical guidelines, based on experience with scientific applications, for helping practitioners interpret their results and illustrate these ideas using data from a smoking cessation study.

Mariella Dimiccoli.

Source: Statistics Surveys, Volume 10, 53--99.

Abstract:
Cone regression is a particular case of quadratic programming that minimizes a weighted sum of squared residuals under a set of linear inequality constraints. Several important statistical problems such as isotonic, concave regression or ANOVA under partial orderings, just to name a few, can be considered as particular instances of the cone regression problem. Given its relevance in Statistics, this paper aims to address the fundamentals of cone regression from a theoretical and practical point of view. Several formulations of the cone regression problem are considered and, focusing on the particular case of concave regression as an example, several algorithms are analyzed and compared both qualitatively and quantitatively through numerical simulations. Several improvements to enhance numerical stability and bound the computational cost are proposed. For each analyzed algorithm, the pseudo-code and its corresponding code in Matlab are provided. The results from this study demonstrate that the choice of the optimization approach strongly impacts the numerical performances. It is also shown that methods are not currently available to solve efficiently cone regression problems with large dimension (more than many thousands of points). We suggest further research to fill this gap by exploiting and adapting classical multi-scale strategy to compute an approximate solution.

Multi-Class Incremental Learning (MCIL) aims to learn new concepts by incrementally updating a model trained on previous concepts. However, there is an inherent trade-off to effectively learning new concepts without catastrophic forgetting of previous ones. To alleviate this issue, it has been proposed to keep around a few examples of the previous concepts but the effectiveness of this approach heavily depends on the representativeness of these examples. This paper proposes a novel and automatic framework we call mnemonics, where we parameterize exemplars and make them optimizable in an end-to-end manner. We train the framework through bilevel optimizations, i.e., model-level and exemplar-level. We conduct extensive experiments on three MCIL benchmarks, CIFAR-100, ImageNet-Subset and ImageNet, and show that using mnemonics exemplars can surpass the state-of-the-art by a large margin. Interestingly and quite intriguingly, the mnemonics exemplars tend to be on the boundaries between different classes.

Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered in the domain of high-energy physics, using a well-studied dataset: the 2014 Higgs ML Kaggle dataset. The advantages are evaluated in terms of both performance metrics and the time required to train and apply the resulting models. Techniques examined include domain-specific data-augmentation, learning rate and momentum scheduling, (advanced) ensembling in both model-space and weight-space, and alternative architectures and connection methods.

Following the investigation, we arrive at a model which achieves equal performance to the winning solution of the original Kaggle challenge, whilst being significantly quicker to train and apply, and being suitable for use with both GPU and CPU hardware setups. These reductions in timing and hardware requirements potentially allow the use of more powerful algorithms in HEP analyses, where models must be retrained frequently, sometimes at short notice, by small groups of researchers with limited hardware resources. Additionally, a new wrapper library for PyTorch called LUMINis presented, which incorporates all of the techniques studied.

The notion of incremental learning is to train an ANN algorithm in stages, as and when newer training data arrives. Incremental learning is becoming widespread in recent times with the advent of deep learning. Noise in the training data reduces the accuracy of the algorithm. In this paper, we make an empirical study of the effect of noise in the training phase. We numerically show that the accuracy of the algorithm is dependent more on the location of the error than the percentage of error. Using Perceptron, Feed Forward Neural Network and Radial Basis Function Neural Network, we show that for the same percentage of error, the accuracy of the algorithm significantly varies with the location of error. Furthermore, our results show that the dependence of the accuracy with the location of error is independent of the algorithm. However, the slope of the degradation curve decreases with more sophisticated algorithms

I study the minimax-optimal design for a two-arm controlled experiment where conditional mean outcomes may vary in a given set. When this set is permutation symmetric, the optimal design is complete randomization, and using a single partition (i.e., the design that only randomizes the treatment labels for each side of the partition) has minimax risk larger by a factor of $n-1$. More generally, the optimal design is shown to be the mixed-strategy optimal design (MSOD) of Kallus (2018). Notably, even when the set of conditional mean outcomes has structure (i.e., is not permutation symmetric), being minimax-optimal for variance still requires randomization beyond a single partition. Nonetheless, since this targets precision, it may still not ensure sufficient uniformity in randomization to enable randomization (i.e., design-based) inference by Fisher's exact test to appropriately detect violations of null. I therefore propose the inference-constrained MSOD, which is minimax-optimal among all designs subject to such uniformity constraints. On the way, I discuss Johansson et al. (2020) who recently compared rerandomization of Morgan and Rubin (2012) and the pure-strategy optimal design (PSOD) of Kallus (2018). I point out some errors therein and set straight that randomization is minimax-optimal and that the "no free lunch" theorem and example in Kallus (2018) are correct.

The next seminar in the 2017–18 History of Pre-Modern Medicine seminar series takes place on Tuesday 30 January. Speaker: Professor Guido Alfani (Bocconi University, Milan) Plague in Italy and Europe during the 17th century: epidemiology and impact Abstract: After many years of relative… Continue reading