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Evaluating drug information programs / Panel on the Impact of Information on Drug Use and Misuse, National Research Council ; prepared for National Institute of Mental Health.

Springfield, Virginia : National Technical Information Service, 1973.




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The university chemical dependency project : final report : November 1 1986 / Steven A. Bloch, Steven Ungerleider.

[Indiana] : Integrated Research Services, Inc., 1986.




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प्रजनन स्वास्थ्य के मामले : Reproductive health matters.

London : Reproductive Health Matters, 1993-2018.




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生殖健康问题 : Reproductive health matters.

London : Reproductive Health Matters, 1993-2018.




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проблемы репродуктивного здоровья : reproductive health matters.

London : Reproductive Health Matters, 1993-2018.




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Temas de salud reproductiva : Reproductive health matters.

London : Reproductive Health Matters, 1993-2018.




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Questions de santé reproductive : Reproductive health matters.

London : Reproductive Health Matters, 1993-2018.




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Questões de saúde reprodutiva : Reproductive health matters.

London : Reproductive Health Matters, 1993-2018.




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Ruthy Hebard, Sabrina Ionescu 'represent everything that is great about basketball'

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.




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Former Alabama prep star Davenport transfers to Georgia

Maori Davenport, who drew national attention over an eligibility dispute during her senior year of high school, is transferring to Georgia after playing sparingly in her lone season at Rutgers. Lady Bulldogs coach Joni Taylor announced Davenport's decision Wednesday. The 6-foot-4 center from Troy, Alabama will have to sit out a season under NCAA transfer rules before she is eligible to join Georgia in 2021-22.




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Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement

We develop ancestral Gumbel-Top-$k$ sampling: a generic and efficient method for sampling without replacement from discrete-valued Bayesian networks, which includes multivariate discrete distributions, Markov chains and sequence models. The method uses an extension of the Gumbel-Max trick to sample without replacement by finding the top $k$ of perturbed log-probabilities among all possible configurations of a Bayesian network. Despite the exponentially large domain, the algorithm has a complexity linear in the number of variables and sample size $k$. Our algorithm allows to set the number of parallel processors $m$, to trade off the number of iterations versus the total cost (iterations times $m$) of running the algorithm. For $m = 1$ the algorithm has minimum total cost, whereas for $m = k$ the number of iterations is minimized, and the resulting algorithm is known as Stochastic Beam Search. We provide extensions of the algorithm and discuss a number of related algorithms. We analyze the properties of ancestral Gumbel-Top-$k$ sampling and compare against alternatives on randomly generated Bayesian networks with different levels of connectivity. In the context of (deep) sequence models, we show its use as a method to generate diverse but high-quality translations and statistical estimates of translation quality and entropy.




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Representation Learning for Dynamic Graphs: A Survey

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.




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Hierarchical modelling of power law processes for the analysis of repairable systems with different truncation times: An empirical Bayes approach

Rodrigo Citton P. dos Reis, Enrico A. Colosimo, Gustavo L. Gilardoni.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 2, 374--396.

Abstract:
In the data analysis from multiple repairable systems, it is usual to observe both different truncation times and heterogeneity among the systems. Among other reasons, the latter is caused by different manufacturing lines and maintenance teams of the systems. In this paper, a hierarchical model is proposed for the statistical analysis of multiple repairable systems under different truncation times. A reparameterization of the power law process is proposed in order to obtain a quasi-conjugate bayesian analysis. An empirical Bayes approach is used to estimate model hyperparameters. The uncertainty in the estimate of these quantities are corrected by using a parametric bootstrap approach. The results are illustrated in a real data set of failure times of power transformers from an electric company in Brazil.




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The theory and application of penalized methods or Reproducing Kernel Hilbert Spaces made easy

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.




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Primal and dual model representations in kernel-based learning

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.




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Plan2Vec: Unsupervised Representation Learning by Latent Plans. (arXiv:2005.03648v1 [cs.LG])

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.




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Generative Feature Replay with Orthogonal Weight Modification for Continual Learning. (arXiv:2005.03490v1 [cs.LG])

The ability of intelligent agents to learn and remember multiple tasks sequentially is crucial to achieving artificial general intelligence. Many continual learning (CL) methods have been proposed to overcome catastrophic forgetting. Catastrophic forgetting notoriously impedes the sequential learning of neural networks as the data of previous tasks are unavailable. In this paper we focus on class incremental learning, a challenging CL scenario, in which classes of each task are disjoint and task identity is unknown during test. For this scenario, generative replay is an effective strategy which generates and replays pseudo data for previous tasks to alleviate catastrophic forgetting. However, it is not trivial to learn a generative model continually for relatively complex data. Based on recently proposed orthogonal weight modification (OWM) algorithm which can keep previously learned input-output mappings invariant approximately when learning new tasks, we propose to directly generate and replay feature. Empirical results on image and text datasets show our method can improve OWM consistently by a significant margin while conventional generative replay always results in a negative effect. Our method also beats a state-of-the-art generative replay method and is competitive with a strong baseline based on real data storage.




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Subdomain Adaptation with Manifolds Discrepancy Alignment. (arXiv:2005.03229v1 [cs.LG])

Reducing domain divergence is a key step in transfer learning problems. Existing works focus on the minimization of global domain divergence. However, two domains may consist of several shared subdomains, and differ from each other in each subdomain. In this paper, we take the local divergence of subdomains into account in transfer. Specifically, we propose to use low-dimensional manifold to represent subdomain, and align the local data distribution discrepancy in each manifold across domains. A Manifold Maximum Mean Discrepancy (M3D) is developed to measure the local distribution discrepancy in each manifold. We then propose a general framework, called Transfer with Manifolds Discrepancy Alignment (TMDA), to couple the discovery of data manifolds with the minimization of M3D. We instantiate TMDA in the subspace learning case considering both the linear and nonlinear mappings. We also instantiate TMDA in the deep learning framework. Extensive experimental studies demonstrate that TMDA is a promising method for various transfer learning tasks.




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Fractional ridge regression: a fast, interpretable reparameterization of ridge regression. (arXiv:2005.03220v1 [stat.ME])

Ridge regression (RR) is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using RR is the need to set a hyperparameter ($alpha$) that controls the amount of regularization. Cross-validation is typically used to select the best $alpha$ from a set of candidates. However, efficient and appropriate selection of $alpha$ can be challenging, particularly where large amounts of data are analyzed. Because the selected $alpha$ depends on the scale of the data and predictors, it is not straightforwardly interpretable. Here, we propose to reparameterize RR in terms of the ratio $gamma$ between the L2-norms of the regularized and unregularized coefficients. This approach, called fractional RR (FRR), has several benefits: the solutions obtained for different $gamma$ are guaranteed to vary, guarding against wasted calculations, and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. We provide an algorithm to solve FRR, as well as open-source software implementations in Python and MATLAB (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems, and delivers results that are straightforward to interpret and compare across models and datasets.




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Public libraries report spike in demand for books in language

Tuesday 17 March 2020
NSW residents are reading more and more books in languages other than English than ever before with the State Library of NSW reporting a 20% increase in requests from public libraries for multicultural material just in the last 12 months.




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Invertebrate embryology and reproduction

El-Bawab, Fatma, author.
9780128141151 (electronic bk.)




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DNA repair disorders

9789811067228 (electronic bk.)




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Scaling limits for super-replication with transient price impact

Peter Bank, Yan Dolinsky.

Source: Bernoulli, Volume 26, Number 3, 2176--2201.

Abstract:
We prove a scaling limit theorem for the super-replication cost of options in a Cox–Ross–Rubinstein binomial model with transient price impact. The correct scaling turns out to keep the market depth parameter constant while resilience over fixed periods of time grows in inverse proportion with the duration between trading times. For vanilla options, the scaling limit is found to coincide with the one obtained by PDE-methods in ( Math. Finance 22 (2012) 250–276) for models with purely temporary price impact. These models are a special case of our framework and so our probabilistic scaling limit argument allows one to expand the scope of the scaling limit result to path-dependent options.




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Rates of convergence in de Finetti’s representation theorem, and Hausdorff moment problem

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.




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Calif. Ed-Tech Consortium Seeks Media Repository Solutions; Saint Paul District Needs Background Check Services

Saint Paul schools are in the market for a vendor to provide background checks, while the Education Technology Joint Powers Authority is seeking media repositories. A Texas district wants quotes on technology for new campuses.

The post Calif. Ed-Tech Consortium Seeks Media Repository Solutions; Saint Paul District Needs Background Check Services appeared first on Market Brief.




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Almost 12,000 meatpacking and food plant workers have reportedly contracted COVID-19. At least 48 have died.

The infections and deaths are spread across roughly two farms and 189 meat and processed food factories.





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Meet the Ohio health expert who has a fan club — and Republicans trying to stop her

Some Buckeyes are not comfortable being told by a "woman in power" to quarantine, one expert said.





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Brazil's Amazon: Surge in deforestation as military prepares to deploy

The military is preparing to deploy to the region to try to stop illegal logging and mining.





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The Cognitive Thalamus as a Gateway to Mental Representations

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




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The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality

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




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Adaptive representation of dynamics during learning of a motor task

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




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Le Rapport trimestriel de la BRI analyse le repli et le rebond des marchés

French translation of the BIS press release about the BIS Quarterly Review, March 2019




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Wintrust Financial Corporation Reports Record Full-Year 2019 Net Income of $355.7 million and Fourth Quarter 2019 Net Income of $86.0 million, up 8% from the Fourth Quarter 2018

To view more press releases, please visit http://www.snl.com/irweblinkx/news.aspx?iid=1024452.





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Academy comments on government support for entrepreneurs




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New Engineering X Pandemic Preparedness programme to support global innovation and knowledge sharing




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Cross Recruitment of Domain-Selective Cortical Representations Enables Flexible Semantic Knowledge

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.




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Ultra-high-resolution fMRI of Human Ventral Temporal Cortex Reveals Differential Representation of Categories and Domains

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.




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Neural Evidence for the Prediction of Animacy Features during Language Comprehension: Evidence from MEG and EEG Representational Similarity Analysis

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.




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Streaming of Repeated Noise in Primary and Secondary Fields of Auditory Cortex

Statistical regularities in natural sounds facilitate the perceptual segregation of auditory sources, or streams. Repetition is one cue that drives stream segregation in humans, but the neural basis of this perceptual phenomenon remains unknown. We demonstrated a similar perceptual ability in animals by training ferrets of both sexes to detect a stream of repeating noise samples (foreground) embedded in a stream of random samples (background). During passive listening, we recorded neural activity in primary auditory cortex (A1) and secondary auditory cortex (posterior ectosylvian gyrus, PEG). We used two context-dependent encoding models to test for evidence of streaming of the repeating stimulus. The first was based on average evoked activity per noise sample and the second on the spectro-temporal receptive field. Both approaches tested whether differences in neural responses to repeating versus random stimuli were better modeled by scaling the response to both streams equally (global gain) or by separately scaling the response to the foreground versus background stream (stream-specific gain). Consistent with previous observations of adaptation, we found an overall reduction in global gain when the stimulus began to repeat. However, when we measured stream-specific changes in gain, responses to the foreground were enhanced relative to the background. This enhancement was stronger in PEG than A1. In A1, enhancement was strongest in units with low sparseness (i.e., broad sensory tuning) and with tuning selective for the repeated sample. Enhancement of responses to the foreground relative to the background provides evidence for stream segregation that emerges in A1 and is refined in PEG.

SIGNIFICANCE STATEMENT To interact with the world successfully, the brain must parse behaviorally important information from a complex sensory environment. Complex mixtures of sounds often arrive at the ears simultaneously or in close succession, yet they are effortlessly segregated into distinct perceptual sources. This process breaks down in hearing-impaired individuals and speech recognition devices. By identifying the underlying neural mechanisms that facilitate perceptual segregation, we can develop strategies for ameliorating hearing loss and improving speech recognition technology in the presence of background noise. Here, we present evidence to support a hierarchical process, present in primary auditory cortex and refined in secondary auditory cortex, in which sound repetition facilitates segregation.




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Report: eradicate hunger and malnutrition

Eradicating hunger must be accompanied by strenuous efforts to end malnutrition and its devastating effects. That was a pivotal message at the launch of FAO’s key publication The State of Food and Agriculture, which this year focuses on Food systems for better nutrition. “FAO’s message is that we must strive for nothing less than the eradication of hunger and malnutrition,” said Director-General [...]




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5 critical things we learned from the latest IPCC report on climate change

Today leading international experts on climate change, the IPCC, presented their latest report on the impacts of climate change on humanity, and what we can do about it. It’s a lengthy report, so we’ve shrunk it down to Oxfam's five key takeaways on climate change and hunger. 1. Climate change: the impacts on crops are worse than we thought Climate change has [...]




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State of the World: Ten reports you need in your library

Every year, FAO publishes a number of major ‘State of the World’ reports containing balanced, science-based reviews of issues related to food, agriculture, forestry, fisheries and natural resources. The reports also cover vital areas such as food production and hunger alleviation, which are at the heart of FAO’s work. With close to 800 million people currently chronically undernourished and an extra [...]




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Ethiopia's youth find hope in agricultural entrepreneurship

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.  




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First report on the SDG indicators under FAO custodianship

Four years into the 2030 Agenda and there is a pressing need to understand where the world stands in eradicating hunger and food insecurity, as well as ensuring sustainable [...]




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Release of FAO's resource mobilization annual report, Resources, Partnerships, Impact – 2019


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Release of FAO + Switzerland Report

The latest FAO + Switzerland partnership report shows the catalytic achievements and innovative solutions of FAO’s collaboration with one of our strongest partners.

From 2008 to 2018, Switzerland supported FAO in [...]




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Release of 2019 Technical Cooperation Programme Report

The 2019 Report of the Technical Cooperation Programme (TCP) examines the role of the TCP to deliver FAO technical assistance for agriculture, food and nutrition in response to countries’ most [...]




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Virus worries K-Town: Local agencies to discuss virus preparedness




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Assembly OKs ‘salmon cans’: Set of policy issue statements that Boro representative will take to D.C. approved