Depression, Mental -- Etiology.

Paris : Société d’éditions scientifiques, 1895.

Hamburg : Voss, 1886.

Berlin : G. Reimer, 1899.

Frankfurt a. M. : H.L. Brönner, 1862.

London : Elliot Stock, 1886.

[Place of publication not identified] : [publisher not identified], [1897?]

Paris : Trouvé, 1822.

Paris (rue de la Vannerie a l'image St. Michel) : Dupuis graveur du Roy, [1721]

Romae [Rome] (ad Pasquinum) : Venit ... apud Montagnani-Mirabili, [1805?]

London : Published for the proprietors, [between 1800 and 1899]

[19--?]

London (5 Haymarket) : Published ... by J. Hogarth, June 1 1849.

Leith, 2019

London : Reproductive Health Matters, 1993-2018.

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

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

London : Reproductive Health Matters, 1993-2018.

London : Reproductive Health Matters, 1993-2018.

London : Reproductive Health Matters, 1993-2018.

London : Reproductive Health Matters, 1993-2018.

London : Reproductive Health Matters, 1993-2018.

London : Reproductive Health Matters, 1993-2018.

Ruthy Hebard and Sabrina Ionescu have had a remarkable four years together in Eugene, rewriting the history books and pushing the Ducks into the national spotlight. Catch the debut of "Our Stories Unfinished Business: Sabrina Ionescu and Ruthy Hebard" at Wednesday, April 15 at 7 p.m. PT/ 8 p.m. MT on Pac-12 Network.

Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces important challenges for learning and inference since nodes, attributes, and edges change over time. In this survey, we review the recent advances in representation learning for dynamic graphs, including dynamic knowledge graphs. We describe existing models from an encoder-decoder perspective, categorize these encoders and decoders based on the techniques they employ, and analyze the approaches in each category. We also review several prominent applications and widely used datasets and highlight directions for future research.

Nancy Heckman

Source: Statist. Surv., Volume 6, 113--141.

Abstract:
The popular cubic smoothing spline estimate of a regression function arises as the minimizer of the penalized sum of squares $sum_{j}(Y_{j}-mu(t_{j}))^{2}+lambda int_{a}^{b}[mu''(t)]^{2},dt$, where the data are $t_{j},Y_{j}$, $j=1,ldots,n$. The minimization is taken over an infinite-dimensional function space, the space of all functions with square integrable second derivatives. But the calculations can be carried out in a finite-dimensional space. The reduction from minimizing over an infinite dimensional space to minimizing over a finite dimensional space occurs for more general objective functions: the data may be related to the function $mu$ in another way, the sum of squares may be replaced by a more suitable expression, or the penalty, $int_{a}^{b}[mu''(t)]^{2},dt$, might take a different form. This paper reviews the Reproducing Kernel Hilbert Space structure that provides a finite-dimensional solution for a general minimization problem. Particular attention is paid to the construction and study of the Reproducing Kernel Hilbert Space corresponding to a penalty based on a linear differential operator. In this case, one can often calculate the minimizer explicitly, using Green’s functions.

Johan A.K. Suykens, Carlos Alzate, Kristiaan Pelckmans

Source: Statist. Surv., Volume 4, 148--183.

Abstract:
This paper discusses the role of primal and (Lagrange) dual model representations in problems of supervised and unsupervised learning. The specification of the estimation problem is conceived at the primal level as a constrained optimization problem. The constraints relate to the model which is expressed in terms of the feature map. From the conditions for optimality one jointly finds the optimal model representation and the model estimate. At the dual level the model is expressed in terms of a positive definite kernel function, which is characteristic for a support vector machine methodology. It is discussed how least squares support vector machines are playing a central role as core models across problems of regression, classification, principal component analysis, spectral clustering, canonical correlation analysis, dimensionality reduction and data visualization.

In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning. Plan2vec constructs a weighted graph on an image dataset using near-neighbor distances, and then extrapolates this local metric to a global embedding by distilling path-integral over planned path. When applied to control, plan2vec offers a way to learn goal-conditioned value estimates that are accurate over long horizons that is both compute and sample efficient. We demonstrate the effectiveness of plan2vec on one simulated and two challenging real-world image datasets. Experimental results show that plan2vec successfully amortizes the planning cost, enabling reactive planning that is linear in memory and computation complexity rather than exhaustive over the entire state space.

Emanuele Dolera, Stefano Favaro.

Source: Bernoulli, Volume 26, Number 2, 1294--1322.

Abstract:
Given a sequence ${X_{n}}_{ngeq 1}$ of exchangeable Bernoulli random variables, the celebrated de Finetti representation theorem states that $frac{1}{n}sum_{i=1}^{n}X_{i}stackrel{a.s.}{longrightarrow }Y$ for a suitable random variable $Y:Omega ightarrow [0,1]$ satisfying $mathsf{P}[X_{1}=x_{1},dots ,X_{n}=x_{n}|Y]=Y^{sum_{i=1}^{n}x_{i}}(1-Y)^{n-sum_{i=1}^{n}x_{i}}$. In this paper, we study the rate of convergence in law of $frac{1}{n}sum_{i=1}^{n}X_{i}$ to $Y$ under the Kolmogorov distance. After showing that a rate of the type of $1/n^{alpha }$ can be obtained for any index $alpha in (0,1]$, we find a sufficient condition on the distribution of $Y$ for the achievement of the optimal rate of convergence, that is $1/n$. Besides extending and strengthening recent results under the weaker Wasserstein distance, our main result weakens the regularity hypotheses on $Y$ in the context of the Hausdorff moment problem.

Mathieu Wolff
Jan 2, 2019; 39:3-14
Viewpoints

Vinod Venkatraman
Mar 9, 2011; 31:3712-3718
BehavioralSystemsCognitive

Fatma Deniz
Sep 25, 2019; 39:7722-7736
BehavioralSystemsCognitive

R Shadmehr
May 1, 1994; 14:3208-3224
Articles

Knowledge about objects encompasses not only their prototypical features but also complex, atypical, semantic knowledge (e.g., "Pizza was invented in Naples"). This fMRI study of male and female human participants combines univariate and multivariate analyses to consider the cortical representation of this more complex semantic knowledge. Using the categories of food, people, and places, this study investigates whether access to spatially related geographic semantic knowledge (1) involves the same domain-selective neural representations involved in access to prototypical taste knowledge about food; and (2) elicits activation of neural representations classically linked to places when this geographic knowledge is accessed about food and people. In three experiments using word stimuli, domain-relevant and atypical conceptual access for the categories food, people, and places were assessed. Results uncover two principles of semantic representation: food-selective representations in the left insula continue to be recruited when prototypical taste knowledge is task-irrelevant and under conditions of high cognitive demand; access to geographic knowledge for food and people categories involves the additional recruitment of classically place-selective parahippocampal gyrus, retrosplenial complex, and transverse occipital sulcus. These findings underscore the importance of object category in the representation of a broad range of knowledge, while showing how the cross recruitment of specialized representations may endow the considerable flexibility of our complex semantic knowledge.

SIGNIFICANCE STATEMENT We know not only stereotypical things about objects (an apple is round, graspable, edible) but can also flexibly combine typical and atypical features to form complex concepts (the metaphorical role an apple plays in Judeo-Christian belief). In this fMRI study, we observe that, when atypical geographic knowledge is accessed about food dishes, domain-selective sensorimotor-related cortical representations continue to be recruited, but that regions classically associated with place perception are additionally engaged. This interplay between categorically driven representations, linked to the object being accessed, and the flexible recruitment of semantic stores linked to the content being accessed, provides a potential mechanism for the broad representational repertoire of our semantic system.

Human ventral temporal cortex (VTC) is critical for visual recognition. It is thought that this ability is supported by large-scale patterns of activity across VTC that contain information about visual categories. However, it is unknown how category representations in VTC are organized at the submillimeter scale and across cortical depths. To fill this gap in knowledge, we measured BOLD responses in medial and lateral VTC to images spanning 10 categories from five domains (written characters, bodies, faces, places, and objects) at an ultra-high spatial resolution of 0.8 mm using 7 Tesla fMRI in both male and female participants. Representations in lateral VTC were organized most strongly at the general level of domains (e.g., places), whereas medial VTC was also organized at the level of specific categories (e.g., corridors and houses within the domain of places). In both lateral and medial VTC, domain-level and category-level structure decreased with cortical depth, and downsampling our data to standard resolution (2.4 mm) did not reverse differences in representations between lateral and medial VTC. The functional diversity of representations across VTC partitions may allow downstream regions to read out information in a flexible manner according to task demands. These results bridge an important gap between electrophysiological recordings in single neurons at the micron scale in nonhuman primates and standard-resolution fMRI in humans by elucidating distributed responses at the submillimeter scale with ultra-high-resolution fMRI in humans.

SIGNIFICANCE STATEMENT Visual recognition is a fundamental ability supported by human ventral temporal cortex (VTC). However, the nature of fine-scale, submillimeter distributed representations in VTC is unknown. Using ultra-high-resolution fMRI of human VTC, we found differential distributed visual representations across lateral and medial VTC. Domain representations (e.g., faces, bodies, places, characters) were most salient in lateral VTC, whereas category representations (e.g., corridors/houses within the domain of places) were equally salient in medial VTC. These results bridge an important gap between electrophysiological recordings in single neurons at a micron scale and fMRI measurements at a millimeter scale.

It has been proposed that people can generate probabilistic predictions at multiple levels of representation during language comprehension. We used magnetoencephalography (MEG) and electroencephalography (EEG), in combination with representational similarity analysis, to seek neural evidence for the prediction of animacy features. In two studies, MEG and EEG activity was measured as human participants (both sexes) read three-sentence scenarios. Verbs in the final sentences constrained for either animate or inanimate semantic features of upcoming nouns, and the broader discourse context constrained for either a specific noun or for multiple nouns belonging to the same animacy category. We quantified the similarity between spatial patterns of brain activity following the verbs until just before the presentation of the nouns. The MEG and EEG datasets revealed converging evidence that the similarity between spatial patterns of neural activity following animate-constraining verbs was greater than following inanimate-constraining verbs. This effect could not be explained by lexical-semantic processing of the verbs themselves. We therefore suggest that it reflected the inherent difference in the semantic similarity structure of the predicted animate and inanimate nouns. Moreover, the effect was present regardless of whether a specific word could be predicted, providing strong evidence for the prediction of coarse-grained semantic features that goes beyond the prediction of individual words.

SIGNIFICANCE STATEMENT Language inputs unfold very quickly during real-time communication. By predicting ahead, we can give our brains a "head start," so that language comprehension is faster and more efficient. Although most contexts do not constrain strongly for a specific word, they do allow us to predict some upcoming information. For example, following the context of "they cautioned the...," we can predict that the next word will be animate rather than inanimate (we can caution a person, but not an object). Here, we used EEG and MEG techniques to show that the brain is able to use these contextual constraints to predict the animacy of upcoming words during sentence comprehension, and that these predictions are associated with specific spatial patterns of neural activity.

27-year-old Amiat Ahmed and her two-year-old son live with Amiat’s parents in the South Wollo Zone of Amhara Region, Ethiopia. Like many other young people in her region, Amiat used to feel that there were limited opportunities to earn income in her village, which led to her decision to migrate to Saudi Arabia.  

A new study ties the ousting of women directors, actors, producers and screenwriters to the rise of entertainment studios

<span class="topsy_trackback_comment"><span class="topsy_twitter_username"><span class="topsy_trackback_content">Bebo doesn't fancy Liz Taylor! | RSS Feeds – IMC OnAir, India ...: May not do Madhur Bhandarkar's film which appar... http://bit.ly/9btJW3</span></span>

Newfoundland and Labrador's already fragile economy suffered another gut-punch in April, with Statistics Canada reporting Friday that 29,000 jobs were lost in April alone.

A longstanding battle between an Alberta entrepreneur and a $50-million lottery winner is still raging after a new legal judgment, a securities investigation, allegations of harassment and even duelling ebooks.

For most immunoblots developed with chemiluminescence or with fluorochrome-based detection systems, it is possible to remove the primary and secondary antibodies from the membrane without affecting the bound antigen. This allows you to reuse the membrane for detection of another protein antigen. The blots developed with chromogenic substrates can also be stripped of antibodies and reprobed, but the bands detected in the first round of immunoblotting will remain unaffected. Stripping and reprobing of the membrane are particularly useful when the amount of sample is limited or when it is important to accurately compare the signal between two different protein antigens in the same sample. Examples of such experiments include determining the levels of a protein antigen in a series of samples relative to the loading control and comparison of the phosphorylated form to the total levels of the protein in the sample.

Overcoming scorching heat and little rain, experimental vineyards teach winemakers to cope with climate change

With something like 380 websites being created every minute, building an e-commerce shop can be a daunting task. It's easy to look at all of the success stories out there and think, "How could I possibly be that lucky?" However, the opportunity for growth in e-commerce is not slowing down. E-commerce sales are projected to grow 85 percent from their 2019 totals by 2022.

Kiuna College hopes to play an active role in the emergence of the next generation of Indigenous filmmakers and creators.

Avid curler Dale Kohlenberg, 64, got a lucky break when he was seconded to Iqaluit — and was quickly scouted for Team Nunavut.