cssi/SciSciGPT-SciSciNet huggingface dataset:
Enhanced Energy Management System With Corrective Transmission Switching Strategy—Part I: Methodology

The flexibility in transmission networks is not fully utilized in existing energy management systems (EMSs). Corrective transmission switching (CTS) is proposed in this two-part paper to enable EMS to take advantage of the flexibility in transmission systems in a practical way. This paper proposes the following two EMS procedures: first, Procedure-A connects real-time security-constrained economic dispatch (RT SCED) with real-time contingency analysis (RTCA), which is consistent with industrial practice; and second, Procedure-B, an enhanced version of Procedure-A, includes CTS in EMS with the proposed concept of branch pseudo limit used in RT SCED. Part-I of this paper presents the methodology, whereas Part-II includes detailed results analysis. It is demonstrated that Procedure-A can effectively eliminate the base case overloads and the potential post-contingency overloads identified by RTCA, and Procedure-B can achieve significant congestion cost reduction with consideration of CTS in RT SCED. Numerical simulations also illustrate that integrating CTS into RT SCED would improve social welfare.
On critical index coding problems

The question of under what condition some side information for index coding can be removed without affecting the capacity region is studied, which was originally posed by Tahmasbi, Shahrasbi, and Gohari. To answer this question, the notion of unicycle for the side information graph is introduced and it is shown that any edge that belongs to a unicycle is critical, namely, it cannot be removed without reducing the capacity region. Although this sufficient condition for criticality is not necessary in general, a partial converse is established, which elucidates the connection between the notion of unicycle and the maximal acylic induced subgraph outer bound on the capacity region by Bar-Yossef, Birk, Jayram, and Kol.
Asymptotic expansions of the Helmholtz equation solutions using approximations of the Dirichlet to Neumann operator

Abstract This paper is concerned with the asymptotic expansions of the amplitude of the solution of the Helmholtz equation. The original expansions were obtained using a pseudo-differential decomposition of the Dirichlet to Neumann operator. This work uses first and second order approximations of this operator to derive new asymptotic expressions of the normal derivative of the total field. The resulting expansions can be used to appropriately choose the ansatz in the design of high-frequency numerical solvers, such as those based on integral equations, in order to produce more accurate approximation of the solutions around the shadow and the deep shadow regions than the ones based on the usual ansatz.
Graphene-based Bolometers

This article reviews recent research for development of sensitive graphene photon detectors in the infrared/far infrared/THz range. For this range, graphene has promising potential in thermal photon detectors. Graphene has ultra-small volume and low electron density, which gives relatively large heating per absorbed photon and fast response. At low temperatures the electron-phonon energy loss is small, so ultrasensitive power detection is possible. We review recent research on the science base of such detectors, and outline the major design challenges. Important factors that must be considered in making useful detectors include the photon and readout coupling efficiency, the method of electron temperature readout, and thermal isolation of the hot electrons.
THE INFLUENCE OF MERGERS ON SCATTER AND EVOLUTION IN SUNYAEV–ZEL’DOVICH EFFECT SCALING RELATIONS

The Sunyaev?Zel?dovich effect (SZE) observable-mass (Y?M) scaling relation is a promising technique for obtaining mass estimates for large samples of galaxy clusters and holds a key to studying the nature of dark matter and dark energy. However, cosmological inference based on SZE cluster surveys is limited by our incomplete knowledge of scatter, and evolution in the Y?M relation. In this work, we investigate the effects of galaxy cluster mergers on the scaling relation using the Omega500 high-resolution cosmological hydrodynamic simulation. We show that the non-thermal pressure associated with merger-induced gas motions contributes significantly to the scatter, and evolution of the scaling relation. After the merger, the kinetic energy of merging systems is slowly converted into thermal energy through dissipation of turbulent gas motions, which causes the thermal SZE signal to increase steadily with time. This post-merger evolution is one of the primary source of scatter in the Y?M relation. However, we show that when the missing non-thermal energy is accounted for, the resulting relation exhibits little to no redshift evolution and the scatter around the scaling relation is ?20%?30% smaller than that of the thermal SZE signal alone. Our work opens up a possibility to further improve the current robust mass proxy, Y, by accounting for the missing non-thermal pressure component. We discuss the future prospect of measuring internal gas motions in galaxy clusters and its implication for cluster-based cosmological tests.
Conditional central limit theorems for Gaussian projections

This paper addresses the question of when projections of a high-dimensional random vector are approximately Gaussian. This problem has been studied previously in the context of high-dimensional data analysis, where the focus is on low-dimensional projections of high-dimensional point clouds. The focus of this paper is on the typical behavior when the projections are generated by an i.i.d. Gaussian projection matrix. The main results are bounds on the deviation between the conditional distribution of the projections and a Gaussian approximation, where the conditioning is on the projection matrix. The bounds are given in terms of the quadratic Wasserstein distance and relative entropy and are stated explicitly as a function of the number of projections and certain key properties of the random vector. The proof uses Talagrand's transportation inequality and a general integral-moment inequality for mutual information. Applications to random linear estimation and compressed sensing are discussed.
A note on error estimation for hypothesis testing problems for some linear SPDEs

The aim of the present paper is to estimate and control the Type I and Type II errors of a simple hypothesis testing problem of the drift/viscosity coefficient for stochastic fractional heat equation driven by additive noise. Assuming that one path of the first $$N$$ Fourier modes of the solution is observed continuously over a finite time interval $$[0,T]$$ , we propose a new class of rejection regions and provide computable thresholds for $$T$$ , and $$N$$ , that guarantee that the statistical errors are smaller than a given upper bound. The considered tests are of likelihood ratio type. The main ideas, and the proofs, are based on sharp large deviation bounds. Finally, we illustrate the theoretical results by numerical simulations.
Contextuality, Pigeonholes, Cheshire Cats, Mean Kings, and Weak Values

The Kochen–Specker (KS) theorem shows that noncontextual hidden variable models of reality that allow random choice are inconsistent with quantum mechanics. Such noncontextual models predict certain outcomes for specific experiments that are not observed in practice, and this is how the theorem is proved. A realist hidden-variable model suggested by the Aharonov–Bergmann–Lebowitz reformulation of quantum mechanics is introduced to explain why those outcomes are never observed. Just as the KS theorem requires them due to noncontextuality, this model requires independent truth-value assignments for each observable, but now allows that the entire set of assignments depends on both the pre-selected and post-selected quantum states in a time-symmetric manner. Using sets that prove the KS theorem, along with pre- and post-selected states, we find that particular projectors within the set cannot be assigned logically consistent truth-values, and furthermore that the weak values of these projectors have a corresponding signature. The contextual behavior has effectively been confined to a particular context by the pre- and post-selection. This inconsistency is never observed because it is never in the pre-selected or post-selected quantum state, but its signature can be experimentally revealed through weak measurements. We also show that for specific cases where the logical inconsistency is in a ‘classical basis,’ this gives rise to the quantum pigeonhole effect. As a related issue, we show using weak values that any KS set can be used to ensure that the Mean King always wins the game.
On cocycle superrigidity for Gaussian actions

We present a general setting to investigate 𝒰fin-cocycle superrigidity for Gaussian actions in terms of closable derivations on von Neumann algebras. In this setting we give new proofs to some 𝒰fin-cocycle superrigidity results of S. Popa and we produce new examples of this phenomenon. We also use a result of K. R. Parthasarathy and K. Schmidt to give a necessary cohomological condition on a group representation in order for the resulting Gaussian action to be 𝒰fin-cocycle superrigid.
Group-invariant Subspace Clustering

In this paper we consider the problem of group-invariant subspace clustering where the data is assumed to come from a union of group-invariant subspaces of a vector space, i.e. subspaces which are invariant with respect to action of a given group. Algebraically, such group-invariant subspaces are also referred to as submodules. Similar to the well known Sparse Subspace Clustering approach where the data is assumed to come from a union of subspaces, we analyze an algorithm which, following a recent work [1], we refer to as Sparse Sub-module Clustering (SSmC). The method is based on finding group-sparse self-representation of data points. In this paper we primarily derive general conditions under which such a group-invariant subspace identification is possible. In particular we extend the geometric analysis in [2] and in the process we identify a related problem in geometric functional analysis.
An information theoretic approach to sample acquisition and perception in planetary robotics

An important and emerging component of planetary exploration is sample retrieval and return to Earth. Obtaining and analyzing rock samples can provide unprecedented insight into the geology, geo-history and prospects for finding past life and water. Current methods of exploration rely on mission scientists to identify objects of interests and this presents major operational challenges. Finding objects of interests will require systematic and efficient methods to quickly and correctly evaluate the importance of hundreds if not thousands of samples so that the most interesting are saved for further analysis by the mission scientists. In this paper, we propose an automated information theoretic approach to identify shapes of interests using a library of predefined interesting shapes. These predefined shapes maybe human input or samples that are then extrapolated by the shape matching system using the Superformula to judge the importance of newly obtained objects. Shape samples are matched to a library of shapes using the eigenfaces approach enabling categorization and prioritization of the sample. The approach shows robustness to simulated sensor noise of up to 20%. The effect of shape parameters and rotational angle on shape matching accuracy has been analyzed. The approach shows significant promise and efforts are underway in testing the algorithm with real rock samples.
Classification of Household Materials via Spectroscopy

Recognizing an object's material can inform a robot on the object's fragility or appropriate use. To estimate an object's material during manipulation, many prior works have explored the use of haptic sensing. In this letter, we explore a technique for robots to estimate the materials of objects using spectroscopy. We demonstrate that spectrometers provide several benefits for material recognition, including fast response times and accurate measurements with low noise. Furthermore, spectrometers do not require direct contact with an object. To explore this, we collected a dataset of spectral measurements from two commercially available spectrometers during which a robotic platform interacted with 50 flat material objects, and we show that a neural network model can accurately analyze these measurements. Due to the similarity between consecutive spectral measurements, our model achieved a material classification accuracy of 94.6% when given only one spectral sample per object. Similar to prior works with haptic sensors, we found that generalizing material recognition to new objects posed a greater challenge, for which we achieved an accuracy of 79.1% via leave-one-object-out cross validation. Finally, we demonstrate how a PR2 robot can leverage spectrometers to estimate the materials of everyday objects found in the home. From this letter, we find that spectroscopy poses a promising approach for material classification during robotic manipulation.
Scattering for the Klein-Gordon equation with quadratic and variable coefficient cubic nonlinearities

We study the 1D Klein-Gordon equation with variable coefficient cubic nonlinearity. This problem exhibits a striking resonant interaction between the spatial frequencies of the nonlinear coefficients and the temporal oscillations of the solutions. In the case where the worst of this resonant behavior is absent, we prove L-Infinity scattering as well as a certain kind of strong smoothness for the solution at time-like infinity with the help of several new normal-forms transformations. Some explicit examples are also given which suggest qualitatively different behavior in the case where the strongest cubic resonances are present.
Differential Analysis of Directed Networks.

We developed a novel statistical method to identify structural differences between networks characterized by structural equation models. We propose to reparameterize the model to separate the differential structures from common structures, and then design an algorithm with calibration and construction stages to identify these differential structures. The calibration stage serves to obtain consistent prediction by building the L2 regularized regression of each endogenous variables against pre-screened exogenous variables, correcting for potential endogeneity issue. The construction stage consistently selects and estimates both common and differential effects by undertaking L1 regularized regression of each endogenous variable against the predicts of other endogenous variables as well as its anchoring exogenous variables. Our method allows easy parallel computation at each stage. Theoretical results are obtained to establish nonasymptotic error bounds of predictions and estimates at both stages, as well as the consistency of identified common and differential effects. Our studies on synthetic data demonstrated that our proposed method performed much better than independently constructing the networks. A real data set is analyzed to illustrate the applicability of our method.
Multi-class classification without multi-class labels.

This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weaker form of annotation. The proposed method, meta classification learning, optimizes a binary classifier for pairwise similarity prediction and through this process learns a multi-class classifier as a submodule. We formulate this approach, present a probabilistic graphical model for it, and derive a surprisingly simple loss function that can be used to learn neural network-based models. We then demonstrate that this same framework generalizes to the supervised, unsupervised cross-task, and semi-supervised settings. Our method is evaluated against state of the art in all three learning paradigms and shows a superior or comparable accuracy, providing evidence that learning multi-class classification without multi-class labels is a viable learning option.
A Practical Searchable Symmetric Encryption Scheme for Smart Grid Data

Outsourcing data storage to the remote cloud can be an economical solution to enhance data management in the smart grid ecosystem. To protect the privacy of data, the utility company may choose to encrypt the data before uploading them to the cloud. However, while encryption provides confidentiality to data, it also sacrifices the data owners' ability to query a special segment in their data. Searchable symmetric encryption is a technology that enables users to store documents in ciphertext form while keeping the functionality to search keywords in the documents. However, most state-of-the-art SSE algorithms are only focusing on general document storage, which may become unsuitable for smart grid applications. In this paper, we propose a simple, practical SSE scheme that aims to protect the privacy of data generated in the smart grid. Our scheme achieves high space complexity with small information disclosure that was acceptable for practical smart grid application. We also implement a prototype over the statistical data of advanced meter infrastructure to show the effectiveness of our approach.
Learning to Authenticate with Deep Multibiometric Hashing and Neural Network Decoding

In this paper, we propose a novel multimodal deep hashing neural decoder (MDHND) architecture, which integrates a deep hashing framework with a neural network decoder (NND) to create an effective multibiometric authentication system. The MDHND consists of two separate modules: a multimodal deep hashing (MDH) module, which is used for feature-level fusion and binarization of multiple biometrics, and a neural network decoder (NND) module, which is used to refine the intermediate binary codes generated by the MDH and compensate for the difference between enrollment and probe biometrics (variations in pose, illumination, etc.). Use of NND helps to improve the performance of the overall multimodal authentication system. The MDHND framework is trained in 3 steps using joint optimization of the two modules. In Step 1, the MDH parameters are trained and learned to generate a shared multimodal latent code; in Step 2, the latent codes from Step 1 are passed through a conventional error-correcting code (ECC) decoder to generate the ground truth to train a neural network decoder (NND); in Step 3, the NND decoder is trained using the ground truth from Step 2 and the MDH and NND are jointly optimized. Experimental results on a standard multimodal dataset demonstrate the superiority of our method relative to other current multimodal authentication systems.
Partially Replicated Causally Consistent Shared Memory: Lower Bounds and An Algorithm

The focus of this paper is on causal consistency in a partially replicated distributed shared memory (DSM) system that provides the abstraction of shared read/write registers. Maintaining causal consistency in distributed shared memory systems has received significant attention in the past, mostly on full replication wherein each replica stores a copy of all the registers in the shared memory. To ensure causal consistency, all causally preceding updates must be performed before an update is performed at any given replica. Therefore, some mechanism for tracking causal dependencies is required, such as vector timestamps with the number of vector elements being equal to the number of replicas in the context of full replication. In this paper, we investigate causal consistency in partially replicated systems, wherein each replica may store only a subset of the shared registers. Building on the past work, this paper makes three key contributions: present a necessary condition on the metadata (which we refer as a timestamp) that must be maintained by each replica to be able to track causality accurately. The necessary condition identifies a set of directed edges in a share graph that a replica's timestamp must keep track of.We present an algorithm for achieving causal consistency using a timestamp that matches the above necessary condition, thus showing that the condition is necessary and sufficient.We define a measurement of timestamp space size and present a lower bound (in bits) on the size of the timestamps. The lower bound matches our algorithm in several special cases.
A self-supervised terrain roughness estimator for off-road autonomous driving

Accurate perception is a principal challenge of autonomous off-road driving. Perceptive technologies generally focus on obstacle avoidance. However, at high speed, terrain roughness is also important to control shock the vehicle experiences. The accuracy required to detect rough terrain is significantly greater than that necessary for obstacle avoidance. We present a self-supervised machine learning approach for estimating terrain roughness from laser range data. Our approach compares sets of nearby surface points acquired with a laser. This comparison is challenging due to uncertainty. For example, at range, laser readings may be so sparse that significant information about the surface is missing. Also, a high degree of precision is required when projecting laser readings. This precision may be unavailable due to latency or error in pose estimation. We model these sources of error as a multivariate polynomial. The coefficients of this polynomial are obtained through a self-supervised learning process. The "labels" of terrain roughness are automatically generated from actual shock, measured when driving over the target terrain. In this way, the approach provides its own training labels. It "transfers" the ability to measure terrain roughness from the vehicle's inertial sensors to its range sensors. Thus, the vehicle can slow before hitting rough terrain. Our experiments use data from the 2005 DARPA Grand Challenge. We find our approach is substantially more effective at identifying rough surfaces and assuring vehicle safety than previous methods - often by as much as 50%.
Quantum cohomology and toric minimal model programs

Abstract We give a quantum version of the Danilov-Jurkiewicz presentation of the cohomology of a compact toric orbifold with projective coarse moduli space. More precisely, we construct a canonical isomorphism from a formal version of the Batyrev ring from [4] to the quantum orbifold cohomology at a canonical bulk deformation. This isomorphism generalizes results of Givental [23] , Iritani [34] and Fukaya-Oh-Ohta-Ono [21] for toric manifolds and Coates-Lee-Corti-Tseng [11] for weighted projective spaces. The proof uses a quantum version of Kirwan surjectivity ( Theorem 2.6 below) and an equality of dimensions ( Theorem 4.19 below) deduced using a toric minimal model program (tmmp). We show that there is a natural decomposition of the quantum cohomology where summands correspond to singularities in the tmmp, each of which gives rise to a collection of Hamiltonian non-displaceable Lagrangian tori.
The “triradiate Bump”: A Novel Radiographic Sign That May Confound Assessment of Acetabular Retroversion

Background: Radiographic evaluation of femoroacetabular impingement (FAI) involves assessment for signs of acetabular retroversion, including the ischial spine sign. We identified a novel radiographic sign wherein the triradiate cartilage transiently projects medially within the pelvic brim around the time of triradiate closure, mimicking the ischial spine sign. The purpose of this study is to characterize this newly-identified radiographic sign using a longitudinal radiographic study. Methods: We identified 73 subjects from a longitudinal radiographic study of healthy adolescents, each …
Multi-Task Networks With Universe, Group, and Task Feature Learning

We present methods for multi-task learning that take advantage of natural groupings of related tasks. Task groups may be defined along known properties of the tasks, such as task domain or language. Such task groups represent supervised information at the inter-task level and can be encoded into the model. We investigate two variants of neural network architectures that accomplish this, learning different feature spaces at the levels of individual tasks, task groups, as well as the universe of all tasks: (1) parallel architectures encode each input simultaneously into feature spaces at different levels; (2) serial architectures encode each input successively into feature spaces at different levels in the task hierarchy. We demonstrate the methods on natural language understanding (NLU) tasks, where a grouping of tasks into different task domains leads to improved performance on ATIS, Snips, and a large in-house dataset.
Movement Improves the Quality of Temporal Perception and Decision-Making

A critical aspect of behavior is that mobile organisms must be able to precisely determine where and when to move. A better understanding of the mechanisms underlying precise movement timing and action planning is therefore crucial to understanding how we interact with the world around us. Recent evidence suggests that our experience of time is directly and intrinsically computed within the motor system, consistent with the theory of embodied cognition. To investigate the role of the motor system, we tested human subjects (n = 40) on a novel task combining reaching and time estimation. In this task, subjects were required to move a robotic manipulandum to one of two physical locations to categorize a concurrently timed suprasecond. Critically, subjects were divided into two groups: one in which movement during the interval was unrestricted and one in which they were restricted from moving until the stimulus interval had elapsed. Our results revealed a higher degree of precision for subjects in the free-moving group. A further experiment (n = 14) verified that these findings were not due to proximity to the target, counting strategies, bias, or movement length. A final experiment (n = 10) replicated these findings using a within-subjects design, performing a time reproduction task, in which movement during encoding of the interval led to more precise performance. Our findings suggest that time estimation may be instantiated within the motor system as an ongoing readout of timing judgment and confidence.
Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings.

The recent success of neural machine translation models relies on the availability of high quality, in-domain data. Domain adaptation is required when domain-specific data is scarce or nonexistent. Previous unsupervised domain adaptation strategies include training the model with in-domain copied monolingual or back-translated data. However, these methods use generic representations for text regardless of domain shift, which makes it infeasible for translation models to control outputs conditional on a specific domain. In this work, we propose an approach that adapts models with domain-aware feature embeddings, which are learned via an auxiliary language modeling task. Our approach allows the model to assign domain-specific representations to words and output sentences in the desired domain. Our empirical results demonstrate the effectiveness of the proposed strategy, achieving consistent improvements in multiple experimental settings. In addition, we show that combining our method with back translation can further improve the performance of the model.
Locally connected spiking neural networks for unsupervised feature learning.

Abstract In recent years, spiking neural networks (SNNs) have demonstrated great success in completing various machine learning tasks. We introduce a method for learning image features with locally connected layers in SNNs using a spike-timing-dependent plasticity (STDP) rule. In our approach, sub-networks compete via inhibitory interactions to learn features from different locations of the input space. These locally-connected SNNs (LC-SNNs) manifest key topological features of the spatial interaction of biological neurons. We explore a biologically inspired n -gram classification approach allowing parallel processing over various patches of the image space. We report the classification accuracy of simple two-layer LC-SNNs on two image datasets, which respectively match state-of-art performance and are the first results to date. LC-SNNs have the advantage of fast convergence to a dataset representation, and they require fewer learnable parameters than other SNN approaches with unsupervised learning. Robustness tests demonstrate that LC-SNNs exhibit graceful degradation of performance despite the random deletion of large numbers of synapses and neurons. Our results have been obtained using the BindsNET library, which allows efficient machine learning implementations of spiking neural networks.
Large Scale Markov Decision Processes with Changing Rewards

We consider Markov Decision Processes (MDPs) where the rewards are unknown and may change in an adversarial manner. We provide an algorithm that achieves a regret bound of $O( \sqrt{\tau (\ln|S|+\ln|A|)T}\ln(T))$, where $S$ is the state space, $A$ is the action space, $\tau$ is the mixing time of the MDP, and $T$ is the number of periods. The algorithm's computational complexity is polynomial in $|S|$ and $|A|$. We then consider a setting often encountered in practice, where the state space of the MDP is too large to allow for exact solutions. By approximating the state-action occupancy measures with a linear architecture of dimension $d\ll|S|$, we propose a modified algorithm with a computational complexity polynomial in $d$ and independent of $|S|$. We also prove a regret bound for this modified algorithm, which to the best of our knowledge, is the first $\tilde{O}(\sqrt{T})$ regret bound in the large-scale MDP setting with adversarially changing rewards.
Integrating patterns of thermal tolerance and phenotypic plasticity with population genetics to improve understanding of vulnerability to warming in a widespread copepod

Differences in population vulnerability to warming are defined by spatial patterns in thermal adaptation. These patterns may be driven by natural selection over spatial environmental gradients, but can also be shaped by gene flow, especially in marine taxa with high dispersal potential. Understanding and predicting organismal responses to warming requires disentangling the opposing effects of selection and gene flow. We begin by documenting genetic divergence of thermal tolerance and developmental phenotypic plasticity. Ten populations of the widespread copepod Acartia tonsa were collected from sites across a large thermal gradient, ranging from the Florida Keys to Northern New Brunswick, Canada (spanning over 20° latitude). Thermal performance curves (TPCs) from common garden experiments revealed local adaptation at the sampling range extremes, with thermal tolerance increasing at low latitudes and decreasing at high latitudes. The opposite pattern was observed in phenotypic plasticity, which was strongest at high latitudes. No relationship was observed between phenotypic plasticity and environmental variables. Instead, the results are consistent with the hypothesis of a trade-off between thermal tolerance and the strength of phenotypic plasticity. Over a large portion of the sampled range, however, we observed a remarkable lack of differentiation of TPCs. To examine whether this lack of divergence is the result of selection for a generalist performance curve or constraint by gene flow, we analyzed cytochrome oxidase I mtDNA sequences, which revealed four distinct genetic clades, abundant genetic diversity, and widely distributed haplotypes. Strong divergence in thermal performance within genetic clades, however, suggests that the pace of thermal adaptation can be relatively rapid. The combined insight from the laboratory physiological experiments and genetic data indicate that gene flow constrains differentiation of TPCs. This balance between gene flow and selection has implications for patterns of vulnerability to warming. Taking both genetic differentiation and phenotypic plasticity into account, our results suggest that local adaptation does not increase vulnerability to warming, and that low-latitude populations in general may be more vulnerable to predicted temperature change over the next century.
Environmental influences on placental programming and offspring outcomes following maternal immune activation.

Abstract Adverse experiences during pregnancy induce placental programming, affecting the fetus and its developmental trajectory. However, the influence of ‘positive’ maternal experiences on the placenta and fetus remain unclear. In animal models of early life stress, environmental enrichment (EE) has ameliorated and even prevented associated impairments in brain and behavior. Here, using a maternal immune activation (MIA) model in rats, we test whether EE attenuates maternal, placental and/or fetal responses to an inflammatory challenge, thereby offering a mechanism by which fetal programming may be prevented. Moreover, we evaluate life-long EE exposure on offspring development and examine a constellation of genes and epigenetic writers that may protect against MIA challenges. In our model, maternal plasma corticosterone and interleukin-1β were elevated 3 h after MIA, validating the maternal inflammatory response. Evidence for developmental programming was demonstrated by a simultaneous decrease in the placental enzymes Hsd11b2 and Hsd11b2/Hsd11b1, suggesting disturbances in glucocorticoid metabolism. Reductions of Hsd11b2 in response to challenge is thought to result in excess glucocorticoid exposure to the fetus and altered glucocorticoid receptor expression, increasing susceptibility to behavioral impairments later in life. The placental, but not maternal, glucocorticoid implications of MIA were attenuated by EE. There were also sustained changes in epigenetic writers in both placenta and fetal brain as a consequence of environmental experience and sex. Following MIA, both male and female juvenile animals were impaired in social discrimination ability. Life-long EE mitigated these impairments, in addition to the sex specific MIA associated disruptions in central Fkbp5 and Oprm1. These data provide the first evidence that EE protects placental functioning during stressor exposure, underscoring the importance of addressing maternal health and well-being throughout pregnancy. Future work must evaluate critical periods of EE use to determine if postnatal EE experience is necessary, or if prenatal exposure alone is sufficient to confer protection.
Faithful Multimodal Explanation for Visual Question Answering

AI systems’ ability to explain their reasoning is critical to their utility and trustworthiness. Deep neural networks have enabled significant progress on many challenging problems such as visual question answering (VQA). However, most of them are opaque black boxes with limited explanatory capability. This paper presents a novel approach to developing a high-performing VQA system that can elucidate its answers with integrated textual and visual explanations that faithfully reflect important aspects of its underlying reasoning while capturing the style of comprehensible human explanations. Extensive experimental evaluation demonstrates the advantages of this approach compared to competing methods using both automated metrics and human evaluation.
A Riesz-Thorin type interpolation theorem in Euclidean Jordan algebras

Abstract In a Euclidean Jordan algebra V of rank n which carries the trace inner product, to each element a we associate the eigenvalue vector λ ( a ) in R n whose components are the eigenvalues of a written in the decreasing order. For any p ∈ [ 1 , ∞ ] , we define the spectral p-norm of a to be the p-norm of λ ( a ) in R n . In a recent paper, based on the K-method of real interpolation theory and a majorization technique, we described an interpolation theorem for a linear transformation on V relative to the same spectral p-norm. In this paper, using complex function theory methods, we describe a Riesz-Thorin type interpolation theorem relative to two spectral p-norms. We illustrate the result by estimating the norms of certain special linear transformations such as Lyapunov transformations, quadratic representations, and positive transformations.
On the Most Informative Boolean Functions of the Very Noisy Channel

Let Xn be a uniformly distributed n-dimensional binary vector, and Yn be the result of passing Xn through a binary symmetric channel (BSC) with crossover probability α. A recent conjecture postulated by Courtade and Kumar states that I(f(Xn); Yn) ≤ 1 − H(α). Although the conjecture has been proved to be true in the dimension-free high noise regime by Samorodnitsky, here we present a calculus-based approach to show a dimension-dependent result by examining the second derivative of H(α) − H(f(Xn)|Yn) at α = 1/2. Along the way, we show that the dictator function is the most informative function in the high noise regime.
Semiparametric Estimation in Continuous-Time: Asymptotics for Integrated Volatility Functionals with Small and Large Bandwidths

This article studies the estimation of integrated volatility functionals, which is a semiparametric two-step estimation problem in the nonstationary continuous-time setting. We generalize the asymp...
Making information flow explicit in HiStar

HiStar is a new operating system designed to minimize the amount of code that must be trusted. HiStar provides strict information flow control, which allows users to specify precise data security policies without unduly limiting the structure of applications. HiStar's security features make it possible to implement a Unix-like environment with acceptable performance almost entirely in an untrusted user-level library. The system has no notion of superuser and no fully trusted code other than the kernel. HiStar's features permit several novel applications, including privacy-preserving, untrusted virus scanners and a dynamic Web server with only a few thousand lines of trusted code.
Constitutive expression of a fluorescent protein reports the size of live human cells

Cell size is important for cell physiology because it sets the geometric scale of organelles and biosynthesis. A number of methods exist to measure different aspects of cell size, but each has significant drawbacks. Here, we present an alternative method to measure the size of single human cells using a nuclear localized fluorescent protein expressed from a constitutive promoter. We validate this method by comparing it to several established cell size measurement strategies, including flow cytometry optical scatter, total protein dyes, and quantitative phase microscopy. We directly compare our fluorescent protein measurement with the commonly used measurement of nuclear volume and show that our measurements are more robust and less dependent on image segmentation. We apply our method to examine how cell size impacts the cell division cycle and reaffirm that there is a negative correlation between size at cell birth and G1 duration. Importantly, combining our size reporter with fluorescent labeling of a different protein in a different color channel allows measurement of concentration dynamics using simple wide-field fluorescence imaging. Thus, we expect our method will be of use to researchers interested in how dynamically changing protein concentrations control cell fates.
Throughput and resolution with a next-generation direct electron detector.

Direct electron detectors (DEDs) have revolutionized cryo-electron microscopy (cryo-EM) by facilitating the correction of beam-induced motion and radiation damage, and also by providing high-resolution image capture. A new-generation DED, the DE64, has been developed by Direct Electron that has good performance in both integrating and counting modes. The camera has been characterized in both modes in terms of image quality, throughput and resolution of cryo-EM reconstructions. The modulation transfer function, noise power spectrum and detective quantum efficiency (DQE) were determined for both modes, as well as the number of images per unit time. Although the DQE for counting mode was superior to that for integrating mode, the data-collection throughput for this mode was more than ten times slower. Since throughput and resolution are related in single-particle cryo-EM, data for apoferritin were collected and reconstructed using integrating mode, integrating mode in conjunction with a Volta phase plate (VPP) and counting mode. Only the counting-mode data resulted in a better than 3 A resolution reconstruction with similar numbers of particles, and this increased performance could not be compensated for by the increased throughput of integrating mode or by the increased low-frequency contrast of integrating mode with the VPP. These data show that the superior image quality provided by counting mode is more important for high-resolution cryo-EM reconstructions than the superior throughput of integrating mode.
Multi-low-rank Approximation for Traffic Matrices.

With the Internet applications become more complex and diverse, simple network traffic matrix estimation or approximation methods such as gravity model are no longer adequate. In this paper, we advocate a novel approach of approximating traffic matrices with multiple low-rank matrices. We build the theory behind the MULTI-LOW-RANK approximation and discuss the conditions under which it is better than Low-Rank SVD in terms of both matrix approximation and preserving the local (and global) structure of the traffic matrices. Further, we develop an effective technique based on spectral clustering of column/row feature vectors for decomposing traffic matrices into multiple low rank matrices. We perform a series of experiments on traffic matrices extracted from a synthetic dataset and two real world datasets – one that represents nationwide cellular traffic and another taken from a tier-1 ISP. The results thus obtained show that: 1) MULTI-LOW-RANK approximation is superior for traffic classification; 2) it can be used to predict complete or missing entries of traffic matrices over time; 3) show it's robustness against noise; and 4) demonstrate that it closely follows the optimal solution (i.e., low-rank SVD solution).
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification.

New conversation topics and functionalities are constantly being added to conversational AI agents like Amazon Alexa and Apple Siri. As data collection and annotation is not scalable and is often costly, only a handful of examples for the new functionalities are available, which results in poor generalization performance. We formulate it as a Few-Shot Integration (FSI) problem where a few examples are used to introduce a new intent. In this paper, we study six feature space data augmentation methods to improve classification performance in FSI setting in combination with both supervised and unsupervised representation learning methods such as BERT. Through realistic experiments on two public conversational datasets, SNIPS, and the Facebook Dialog corpus, we show that data augmentation in feature space provides an effective way to improve intent classification performance in few-shot setting beyond traditional transfer learning approaches. In particular, we show that (a) upsampling in latent space is a competitive baseline for feature space augmentation (b) adding the difference between two examples to a new example is a simple yet effective data augmentation method.
Effects of speaking rate on the vowel length distinction in Korean

This study investigated the effect of speaking rate on vowel duration in Korean, a language traditionally described as having phonemic vowel length. Results showed that the long and short members of the vowel length contrast were similarly affected across speaking rates, that the durations of short vowels spoken at a slow rate were always longer than short vowels spoken at a fast rate, and that short vowels spoken at a slow rate almost always overlapped in duration with long vowels spoken at a fast rate. These results contrast with findings for temporal dimensions in consonants which show asymmetries between short and long members of the phonetic contrast, with the short member serving as a phonetic anchor. Results also showed that the distinction between short and long vowels emerged less consistently in sentential context than in isolation. Finally, the data suggest that the vowel length distinction is being lost in Korean and is no longer a productive contrast.
Co-Separating Sounds of Visual Objects

Learning how objects sound from video is challenging, since they often heavily overlap in a single audio channel. Current methods for visually-guided audio source separation sidestep the issue by training with artificially mixed video clips, but this puts unwieldy restrictions on training data collection and may even prevent learning the properties of "true" mixed sounds. We introduce a co-separation training paradigm that permits learning object-level sounds from unlabeled multi-source videos. Our novel training objective requires that the deep neural network's separated audio for similar-looking objects be consistently identifiable, while simultaneously reproducing accurate video-level audio tracks for each source training pair. Our approach disentangles sounds in realistic test videos, even in cases where an object was not observed individually during training. We obtain state-of-the-art results on visually-guided audio source separation and audio denoising for the MUSIC, AudioSet, and AV-Bench datasets.
Episodic Source Memory over Distribution by Quantum-Like Dynamics - A Model Exploration.

In source memory studies, a decision-maker is concerned with identifying the context in which a given episodic experience occurred. A common paradigm for studying source memory is the ‘three-list’ experimental paradigm, where a subject studies three lists of words and is later asked whether a given word appeared on one or more of the studied lists. Surprisingly, the sum total of the acceptance probabilities generated by asking for the source of a word separately for each list (‘list 1?’, ‘list 2?’, ‘list 3?’) exceeds the acceptance probability generated by asking whether that word occurred on the union of the lists (‘list 1 or 2 or 3?’). The episodic memory for a given word therefore appears over distributed on the disjoint contexts of the lists. A quantum episodic memory model [QEM] was proposed by Brainerd, Wang and Reyna [8] to explain this type of result. In this paper, we apply a Hamiltonian dynamical extension of QEM for over distribution of source memory. The Hamiltonian operators are simultaneously driven by parameters for re-allocation of gist-based and verbatim-based acceptance support as subjects are exposed to the cue word in the first temporal stage, and are attenuated for description-dependence by the querying probe in the second temporal stage. Overall, the model predicts well the choice proportions in both separate list and union list queries and the over distribution effect, suggesting that a Hamiltonian dynamics for QEM can provide a good account of the acceptance processes involved in episodic memory tasks.
Towards an Understanding of Hole Superconductivity

From the very beginning K. Alex Muller emphasized that the materials he and George Bednorz discovered in 1986 were hole superconductors. Here I would like to share with Alex and others what I believe to be the key reason for why high T c cuprates as well as all other superconductors are hole superconductors, which I only came to understand a few months ago.