cssi/SciSciGPT-SciSciNet huggingface dataset:
A consolidated view of loss functions for supervised deep learning-based speech enhancement

Deep learning-based speech enhancement for real-time applications recently made large advancements. Due to the lack of a tractable perceptual optimization target, many myths around training losses emerged, whereas the contribution to success of the loss functions in many cases has not been investigated isolated from other factors such as network architecture, features, or training procedures. In this work, we investigate a wide variety of loss spectral functions for a recurrent neural network architecture suitable to operate in online frame-by-frame processing. We relate magnitude-only with phase-aware losses, ratios, correlation metrics, and compressed metrics. Our results reveal that combining magnitude-only with phase-aware objectives always leads to improvements, even when the phase is not enhanced. Furthermore, using compressed spectral values also yields a significant improvement. On the other hand, phase-sensitive improvement is best achieved by linear domain losses such as mean absolute error.
Optical N-invariant of graphene’s viscous Hall fluid

We introduce a new topological classification of two-dimensional matter related to its optical properties. Graphene’s viscous Hall fluid is the first candidate of this topological electromagnetic phase of matter with the underlying physical mechanism being Hall viscosity.
A POLYNOMIAL ROTH THEOREM FOR CORNERS IN FINITE FIELDS

We prove a Roth type theorem for polynomial corners in the finite field setting. Let $\phi_1$ and $\phi_2$ be two polynomials of distinct degree. For sufficiently large primes $p$, any subset $ A \subset \mathbb F_p \times \mathbb F_p$ with $ \lvert A\rvert > p ^{2 - \frac1{16}} $ contains three points $ (x_1, x_2) , (x_1 + \phi_1 (y), x_2), (x_1, x_2 + \phi_2 (y))$. The study of these questions on $ \mathbb F_p$ was started by Bourgain and Chang. Our Theorem adapts the argument of Dong, Li and Sawin, in particular relying upon deep Weil type inequalities established by N. Katz.
The effects of ratings disclosure by bank regulators

Abstract I examine how banks change their risk management practices in response to the private disclosure of regulatory ratings that summarize bank risk-taking. Upon ratings disclosure, affected banks increase the timeliness of their loan loss provisioning. These effects are concentrated among banks that lie below key rating thresholds and those headquartered in states with low competition. After ratings disclosure, deficient banks decrease commercial lending while shifting assets into cash. Overall, my findings highlight how performance measures produced and privately disclosed by a third party can influence actions within a firm.
Seroconversion Following SARS-CoV-2 Infection or Vaccination in Pediatric IBD Patients.

ObjectiveInflammatory bowel disease (IBD) patients are commonly treated with immunomodulatory medications, and the effect of these medications on seroconversion to SARS-CoV-2 infection and vaccination are scant, particularly in pediatrics. We sought to determine serologic responses to SARS-CoV-2 infection and vaccination in pediatric IBD patients. DesignWe conducted a single-center, retrospective study of all pediatric ([≤]21 years old) IBD patients in whom a SARS-CoV-2 IgG Antibody Assay was performed between April 2020 and May 2021 at our tertiary care center. This assay measures IgG antibody to the full-length SARS-CoV-2 spike protein and was routinely collected at infusion and outpatient clinic visits. The primary outcome was SARS-CoV-2 seroconversion, and the secondary outcome was titer level, with high titer defined as [≥]960 titer or >40 AU/mL. Clinical characteristics, including demographics, IBD location, behavior, activity, and therapy, SARS-CoV-2 exposures, COVID-19 testing and symptoms, SARS-CoV-2 infection status (WHO COVID-19: Case Definitions, 2020) and COVID-19 vaccination status and type, were gathered, and univariate analyses examined associations between clinical characteristics and outcome measures. ResultsThere were 340 pediatric patients with SARS-CoV-2 Antibody Testing; 15% for confirmed or probable COVID-19, 2% for suspected COVID-19, 16% for asymptomatic exposure to a close contact with SARS-CoV-2 infection, 61% without any prior symptoms or exposures, and 6% for history of COVID-19 vaccination. Patients with confirmed or probable COVID-19 infection had a 90% rate of seroconversion, with 76% of these patients on biologic therapy. Patients post-infection without seroconversion had a significantly longer interval between infection and antibody assay (P=0.03). Within those with asymptomatic SARS-CoV-2 exposure, 43% had seroconversion, and there were no identified clinical characteristics associated with positive titer. All pediatric patients who received vaccination seroconverted, and all who received mRNA vaccinations, including one after a single dose, achieved high titer levels; 100% of those who received vaccination were on biologic or small molecule therapy, including one on combination therapy with ustekinumab and tofacitinib. ConclusionPediatric IBD patients have strong serologic antibody responses to SARS-CoV-2 infection and COVID-19 vaccination despite high rates of immunomodulatory therapy.
Quantification of in-plane flexoelectricity in lipid bilayers

Lipid bilayers behave as 2D dielectric materials that undergo polarization and deformation in the presence of an electric field. This effect has been previously modeled by continuum theories which assume a polarization field oriented normal to the membrane surface. However, the molecular architecture of the lipids reveals that the headgroup dipoles are primarily oriented tangential to the membrane surface. Here, we perform atomistic and coarse-grained molecular dynamics simulations to quantify the in-plane polarization undergone by a flat bilayer and a spherical vesicle in the presence of an applied electric field. We use these predictions to compute an effective in-plane flexoelectric coefficient for four different lipid types. Our findings provide the first molecular proof of the in-plane polarization undergone by lipid bilayers and furnish the material parameter required to quantify membrane-electric field interactions.
Statistical inference attack against PHY-layer key extraction and countermeasures

The establishment of secure secret keys ahead of transmissions is one of the key issues in the field of information security. The security of traditional cryptographic secret key establishment mechanisms is seriously challenged by computing-intensive attacks, with the fast growth of high-performance computing. As an alternative, considerable efforts have been made to develop physical (PHY) layer security measures in recent years, such as link-signature-based (LSB) secret key extraction techniques. Those mechanisms have been believed secure, based on the fundamental assumption that wireless signals received at two locations are uncorrelated when separated by more than half a wavelength. However, this assumption does not hold in some circumstances under latest observations, rendering LSB key extraction mechanisms vulnerable to attacks. To address this problem, the formal theoretical analysis on channel correlations in both real indoor and outdoor environments is provided in this paper. Moreover, this paper proposes empirical statistical inference attacks (SIA) against LSB key extraction, whereby an adversary infers the signature of a target link. Consequently, the secret key extracted from that signature has been recovered by observing the surrounding links. In contrast to prior literature that assumes theoretical link-correlation models for the inference, our study does not make any assumption on link correlation. Instead, we employ machine learning (ML) methods for link inference based on empirically measured link signatures. We further propose a countermeasure against the SIAs, called forward-backward cooperative key extraction protocol with helpers (FBCH). In the FBCH, helpers (other trusted wireless nodes) are introduced to provide more randomness in the key extraction. Our experimental results have shown that the proposed inference methods are still quite effective even without making assumptions on link correlation. Furthermore, the effectiveness of the proposed FBCH protocol is validated by our experiment results.
Individually Conditional Individual Mutual Information Bound on Generalization Error

We propose a new information-theoretic bound on generalization error based on a combination of the error decomposition technique of Bu et al. and the conditional mutual information (CMI) construction of Steinke and Zakynthinou. In a previous work, Haghifam et al. proposed a different bound combining the two aforementioned techniques, which we refer to as the conditional individual mutual information (CIMI) bound. However, in a simple Gaussian setting, both the CMI and the CIMI bounds are order-wise worse than that by Bu et al.. This observation motivated us to propose the new bound, which overcomes this issue by reducing the conditioning terms in the conditional mutual information. In the process of establishing this bound, a conditional decoupling lemma is established, which also leads to a meaningful dichotomy and comparison among these information-theoretic bounds.
Streaming End-to-End ASR Based on Blockwise Non-Autoregressive Models

Non-autoregressive (NAR) modeling has gained more and more attention in speech processing. With recent state-of-the-art attention-based automatic speech recognition (ASR) structure, NAR can realize promising real-time factor (RTF) improvement with only small degradation of accuracy compared to the autoregressive (AR) models. However, the recognition inference needs to wait for the completion of a full speech utterance, which limits their applications on low latency scenarios. To address this issue, we propose a novel end-to-end streaming NAR speech recognition system by combining blockwise-attention and connectionist temporal classification with mask-predict (Mask-CTC) NAR. During inference, the input audio is separated into small blocks and then processed in a blockwise streaming way. To address the insertion and deletion error at the edge of the output of each block, we apply an overlapping decoding strategy with a dynamic mapping trick that can produce more coherent sentences. Experimental results show that the proposed method improves online ASR recognition in low latency conditions compared to vanilla Mask-CTC. Moreover, it can achieve a much faster inference speed compared to the AR attention-based models. All of our codes will be publicly available at this https URL.
A novel machine learning technique to identify and categorize plasma waves in spacecraft measurements

The available magnetic field data from the terrestrial magnetosphere, solar wind and planetary magnetospheres exceeds over $10^6$ hours. Identifying plasma waves in these large data sets is a time consuming and tedious process. In this Paper, we propose a solution to this problem. We demonstrate how Self-Organizing Maps can be used for rapid data reduction and identification of plasma waves in large data sets. We use 72,000 fluxgate and 110,000 search coil magnetic field power spectra from the Magnetospheric Multiscale Mission (MMS$_1$) and show how the Self-Organizing Map sorts the power spectra into groups based on their shape. Organizing the data in this way makes it very straightforward to identify power spectra with similar properties and therefore this technique greatly reduces the need for manual inspection of the data. We suggest that Self-Organizing Maps offer a time effective and robust technique, which can significantly accelerate the processing of magnetic field data and discovery of new wave forms.
Broad cross-reactivity across sarbecoviruses exhibited by a subset of COVID-19 donor-derived neutralizing antibodies.

Summary Many anti-severe acute respiratory syndrome coronavirus 2 (anti-SARS-CoV-2) neutralizing antibodies target the angiotensin-converting enzyme 2 (ACE2) binding site on viral spike receptor-binding domains (RBDs). Potent antibodies recognize exposed variable epitopes, often rendering them ineffective against other sarbecoviruses and SARS-CoV-2 variants. Class 4 anti-RBD antibodies against a less-exposed, but more-conserved, cryptic epitope could recognize newly emergent zoonotic sarbecoviruses and variants, but they usually show only weak neutralization potencies. Here, we characterize two class 4 anti-RBD antibodies derived from coronavirus disease 2019 (COVID-19) donors that exhibit breadth and potent neutralization of zoonotic coronaviruses and SARS-CoV-2 variants. C118-RBD and C022-RBD structures reveal orientations that extend from the cryptic epitope to occlude ACE2 binding and CDRH3-RBD main-chain H-bond interactions that extend an RBD β sheet, thus reducing sensitivity to RBD side-chain changes. A C118-spike trimer structure reveals rotated RBDs that allow access to the cryptic epitope and the potential for intra-spike crosslinking to increase avidity. These studies facilitate vaccine design and illustrate potential advantages of class 4 RBD-binding antibody therapeutics.
RiLACS: Risk Limiting Audits via Confidence Sequences

Accurately determining the outcome of an election is a complex task with many potential sources of error, ranging from software glitches in voting machines to procedural lapses to outright fraud. Risk-limiting audits (RLA) are statistically principled “incremental” hand counts that provide statistical assurance that reported outcomes accurately reflect the validly cast votes. We present a suite of tools for conducting RLAs using confidence sequences—sequences of confidence sets which uniformly capture an electoral parameter of interest from the start of an audit to the point of an exhaustive recount with high probability. Adopting the SHANGRLA [13] framework, we design nonnegative martingales which yield computationally and statistically efficient confidence sequences and RLAs for a wide variety of election types.
A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes

Abstract Uncertainty Quantification (UQ) is an essential step in computational model validation because assessment of the model accuracy requires a concrete, quantifiable measure of uncertainty in the model predictions. The concept of UQ in the nuclear community generally means forward UQ, in which the information flow is from the inputs to the outputs. Inverse UQ, in which the information flow is from the model outputs and experimental data to the inputs, is an equally important component of UQ but has been significantly underrated until recently. Forward UQ requires knowledge in the input uncertainties which has been specified by expert opinion or user self-evaluation. Inverse UQ is defined as the process to inversely quantify the input uncertainties based on experimental data. This review paper aims to provide a comprehensive and comparative discussion of the major aspects of the inverse UQ methodologies that have been used on the physical models in system thermal–hydraulics codes. Inverse UQ methods can be categorized by three main groups: frequentist (deterministic), Bayesian (probabilistic), and empirical (design-of-experiments). We used eight metrics to evaluate an inverse UQ method, including solidity, complexity, accessibility, independence, flexibility, comprehensiveness, transparency, and tractability. Twelve inverse UQ methods are reviewed, compared, and evaluated based on these eight metrics. Such comparative evaluation is intended to provide a good guidance for users to select a proper inverse UQ method based on the problem under investigation.
Distributed Acoustic Sensing Based on Coherent Microwave Photonics Interferometry

A microwave photonics method has been developed for measuring distributed acoustic signals. This method uses microwave-modulated low coherence light as a probe to interrogate distributed in-fiber interferometers, which are used to measure acoustic-induced strain. By sweeping the microwave frequency at a constant rate, the acoustic signals are encoded into the complex microwave spectrum. The microwave spectrum is transformed into the joint time–frequency domain and further processed to obtain the distributed acoustic signals. The method is first evaluated using an intrinsic Fabry Perot interferometer (IFPI). Acoustic signals of frequency up to 15.6 kHz were detected. The method was further demonstrated using an array of in-fiber weak reflectors and an external Michelson interferometer. Two piezoceramic cylinders (PCCs) driven at frequencies of 1700 Hz and 3430 Hz were used as acoustic sources. The experiment results show that the sensing system can locate multiple acoustic sources. The system resolves 20 ne when the spatial resolution is 5 cm. The recovered acoustic signals match the excitation signals in frequency, amplitude, and phase, indicating an excellent potential for distributed acoustic sensing (DAS).
Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping

We present the first fully distributed multi-robot system for dense metric-semantic Simultaneous Localization and Mapping (SLAM). Our system, dubbed Kimera-Multi, is implemented by a team of robots equipped with visual-inertial sensors, and builds a 3D mesh model of the environment in real-time, where each face of the mesh is annotated with a semantic label (e.g., building, road, objects). In Kimera-Multi, each robot builds a local trajectory estimate and a local mesh using Kimera [1]. Then, when two robots are within communication range, they initiate a distributed place recognition and robust pose graph optimization protocol with a novel incremental maximum clique outlier rejection; the protocol allows the robots to improve their local trajectory estimates by leveraging inter-robot loop closures. Finally, each robot uses its improved trajectory estimate to correct the local mesh using mesh deformation techniques. We demonstrate Kimera-Multi in photo-realistic simulations and real data. Kimera-Multi (i) is able to build accurate 3D metric-semantic meshes, (ii) is robust to incorrect loop closures while requiring less computation than state-of-the-art distributed SLAM backends, and (iii) is efficient, both in terms of computation at each robot as well as communication bandwidth.
Control of testes mass by androgen receptor paralogs in a cichlid.

Steroid hormones play numerous important and diverse roles in the differentiation and development of vertebrate primary and secondary reproductive characteristics. However, the exact role of androgen receptors—which bind circulating androgens—in this regulatory pathway is unclear. Teleost fishes further complicate this question by having two paralogs of the androgen receptor (ARα and ARβ) resulting from a duplication of their ancestral genome. We investigated the functional role of these two ARs on adult testes mass, by eliminating receptor function of one or both paralogs using CRISPR/Cas9 genome edited Astatotilapia burtoni, an African cichlid fish. Fish with two or more functional AR alleles were more likely to be male compared to fish with one or fewer, suggesting that the two paralogs may play redundant roles in the A. burtoni sex determination system. We replicated previous work showing that fish lacking functional ARβ possess testes smaller than wild-type fish, while fish lacking ARα possess testes larger than wild-type fish. However, we found novel evidence supporting a complex relationship between the two AR paralogs in the regulation of testes mass. For instance, the effects of ARα mutation on testes mass are eliminated in homozygous ARβ mutants but the reverse is not true. These results suggest a dynamic relationship between these two AR paralogs where ARβ functions may be permissive to ARα functions in the control of testes mass. This mechanism may contribute to the robust physiological plasticity displayed by A. burtoni and other social teleost fishes.
Mice with high FGF21 serum levels had a reduced preference for morphine and an attenuated development of acute antinociceptive tolerance and physical dependence.

Abstract Because of increased opioid misuse, there is a need to identify new targets for minimizing opioid tolerance, and physical and psychological dependence. Previous studies showed that fibroblast growth factor 21 (FGF21) decreased alcohol and sweet preference in mice. In this study, FGF21-transgenic (FGF21-Tg) mice, expressing high FGF21 serum levels, and wildtype (WT) C57BL/6J littermates were treated with morphine and saline to determine if differences exist in their physiological and behavioral responses to opioids. FGF21-Tg mice displayed reduced preference for morphine in the conditioned place preference assay compared to WT littermates. Similarly, FGF21-Tg mice had an attenuation of the magnitude and rate of acute morphine antinociceptive tolerance development, and acute and chronic morphine physical dependence, but exhibited no change in chronic morphine antinociceptive tolerance. The ED50 values for morphine-induced antinociception in the 55 °C hot plate and the 55 °C warm-water tail withdrawal assays were similar in both strains of mice. Likewise, FGF21-Tg and WT littermates had comparable responses to morphine-induced respiratory depression. Overall, FGF21-Tg mice had a decrease in the development of acute analgesic tolerance, and the development of physical dependence, and morphine preference. FGF21 and its receptor have therapeutic potential for reducing opioid withdrawal symptoms and craving, and augmenting opioid therapeutics for acute pain patients to minimize tolerance development.
Bridging convex and nonconvex optimization in robust PCA: Noise, outliers and missing data

This paper delivers improved theoretical guarantees for the convex programming approach in low-rank matrix estimation, in the presence of (1) random noise, (2) gross sparse outliers and (3) missing data. This problem, often dubbed as robust principal component analysis (robust PCA), finds applications in various domains. Despite the wide applicability of convex relaxation, the available statistical support (particularly the stability analysis in the presence of random noise) remains highly suboptimal, which we strengthen in this paper. When the unknown matrix is well conditioned, incoherent and of constant rank, we demonstrate that a principled convex program achieves near-optimal statistical accuracy, in terms of both the Euclidean loss and the l∞ loss. All of this happens even when nearly a constant fraction of observations are corrupted by outliers with arbitrary magnitudes. The key analysis idea lies in bridging the convex program in use and an auxiliary nonconvex optimization algorithm, and hence the title of this paper.
The Difficulty of Passive Learning in Deep Reinforcement Learning

Learning to act from observational data without active environmental interaction is a well-known challenge in Reinforcement Learning (RL). Recent approaches involve constraints on the learned policy or conservative updates, preventing strong deviations from the state-action distribution of the dataset. Although these methods are evaluated using non-linear function approximation, theoretical justifications are mostly limited to the tabular or linear cases. Given the impressive results of deep reinforcement learning, we argue for a need to more clearly understand the challenges in this setting. In the vein of Held & Hein's classic 1963 experiment, we propose the "tandem learning" experimental paradigm which facilitates our empirical analysis of the difficulties in offline reinforcement learning. We identify function approximation in conjunction with fixed data distributions as the strongest factors, thereby extending but also challenging hypotheses stated in past work. Our results provide relevant insights for offline deep reinforcement learning, while also shedding new light on phenomena observed in the online case of learning control.
Language immersion and student achievement

Language immersion education uses a non-English language for a significant portion of instruction. One goal is to develop bilingual students, which many argue provides cognitive benefits. Using dat...
Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration

Deep learning has powered recent successes of artificial intelligence (AI). However, the deep neural network, as the basic model of deep learning, has suffered from issues such as local traps and miscalibration. In this paper, we provide a new framework for sparse deep learning, which has the above issues addressed in a coherent way. In particular, we lay down a theoretical foundation for sparse deep learning and propose prior annealing algorithms for learning sparse neural networks. The former has successfully tamed the sparse deep neural network into the framework of statistical modeling, enabling prediction uncertainty correctly quantified. The latter can be asymptotically guaranteed to converge to the global optimum, enabling the validity of the down-stream statistical inference. Numerical result indicates the superiority of the proposed method compared to the existing ones.
Strong Embeddings for Transitory Queueing Models

In this paper, we establish strong embedding theorems, in the sense of the Komlos-Major-Tusnady framework, for the performance metrics of a general class of transitory queueing models of nonstation...
Econometrics For Decision Making: Building Foundations Sketched By Haavelmo And Wald

In the early 1940s, Haavelmo proposed a probabilistic structure for econometric modeling, aiming to make econometrics useful for public decision making. His fundamental contribution has become thoroughly embedded in subsequent econometric research, yet it could not fully answer all the deep issues that the author raised. Notably, Haavelmo struggled to formalize the implications for decision making of the fact that models can at most approximate actuality. In the same period, Wald initiated his own seminal development of statistical decision theory. Haavelmo favorably cited Wald, but econometrics subsequently did not embrace statistical decision theory. Instead, it focused on study of identification, estimation, and statistical inference. This paper proposes statistical decision theory as a framework for evaluation of the performance of models in decision making. I particularly consider the common practice of as-if optimization: specification of a model, point estimation of its parameters, and use of the point estimate to make a decision that would be optimal if the estimate were accurate. A central theme is that one should evaluate as-if optimization or any other model-based decision rule by its performance across the state space, not the model space. I use prediction and treatment choice to illustrate. Statistical decision theory is conceptually simple, but application is often challenging. Advancement of computation is the primary task to continue building the foundations sketched by Haavelmo and Wald.
Label Disentanglement in Partition-based Extreme Multilabel Classification

Partition-based methods are increasingly-used in extreme multi-label classification (XMC) problems due to their scalability to large output spaces (e.g., millions or more). However, existing methods partition the large label space into mutually exclusive clusters, which is sub-optimal when labels have multi-modality and rich semantics. For instance, the label "Apple" can be the fruit or the brand name, which leads to the following research question: can we disentangle these multi-modal labels with non-exclusive clustering tailored for downstream XMC tasks? In this paper, we show that the label assignment problem in partition-based XMC can be formulated as an optimization problem, with the objective of maximizing precision rates. This leads to an efficient algorithm to form flexible and overlapped label clusters, and a method that can alternatively optimizes the cluster assignments and the model parameters for partition-based XMC. Experimental results on synthetic and real datasets show that our method can successfully disentangle multi-modal labels, leading to state-of-the-art (SOTA) results on four XMC benchmarks.
System-Wide Security for Offline Payment Terminals.

Most self-service payment terminals require network connectivity for processing electronic payments. The necessity to maintain network connectivity increases costs, introduces cybersecurity risks, and significantly limits the number of places where the terminals can be installed. Leading payment service providers have proposed offline payment solutions that rely on algorithmically generated payment tokens. Existing payment token solutions, however, require complex mechanisms for authentication, transaction management, and most importantly, security risk management. In this paper, we present VolgaPay, a blockchain-based system that allows merchants to deploy secure offline payment terminal infrastructure that does not require collection and storage of any sensitive data. We design a novel payment protocol which mitigates security threats for all the participants of VolgaPay, such that the maximum loss from gaining full access to any component by an adversary incurs only a limited scope of harm. We achieve significant enhancements in security, operation efficiency, and cost reduction via a combination of polynomial multi-hash chain micropayment channels and blockchain grafting for off-chain channel state transition. We implement the VolgaPay payment system, and with thorough evaluation and security analysis, we demonstrate that VolgaPay is capable of delivering a fast, secure, and cost-efficient solution for offline payment terminals.
Increased connectivity among sensory and motor regions during visual and audiovisual speech perception

In everyday conversation, we usually process the talker's face as well as the sound of their voice. Access to visual speech information is particularly useful when the auditory signal is degraded. Here we used fMRI to monitor brain activity while adult humans (n = 60) were presented with visual-only, auditory-only, and audiovisual words. The audiovisual words were presented in quiet and several signal-to-noise ratios. As expected, audiovisual speech perception recruited both auditory and visual cortex, with some evidence for increased recruitment of premotor cortex in some conditions (including in substantial background noise). We then investigated neural connectivity using psychophysiological interaction (PPI) analysis with seed regions in both primary auditory cortex and primary visual cortex. Connectivity between auditory and visual cortices was stronger in audiovisual conditions than in unimodal conditions, including a wide network of regions in posterior temporal cortex and prefrontal cortex. In addition to whole-brain analyses, we also conducted a region-of-interest analysis on the left posterior superior temporal sulcus (pSTS), implicated in many previous studies of audiovisual speech perception. We found evidence for both activity and effective connectivity in pSTS for visual-only and audiovisual speech, although these were not significant in whole-brain analyses. Taken together, our results suggest a prominent role for cross-region synchronization in understanding both visual-only and audiovisual speech that complements activity in "integrative" brain regions like pSTS.Significance StatementIn everyday conversation, we usually process the talker's face as well as the sound of their voice. Access to visual speech information is particularly useful when the auditory signal is hard to understand (for example, to background noise). Prior work has suggested that specialized regions of the brain may play a critical role in integrating information from visual and auditory speech. Here we show a complementary mechanism relying on synchronized brain activity between sensory and motor regions may also play a critical role. These findings encourage reconceptualizing audiovisual integration in the context of coordinated network activity.
Truncation-based energy weighting string method for efficiently resolving small energy barriers

The string method is a useful numerical technique for resolving minimum energy paths in rare-event barrier-crossing problems. However, when applied to systems with relatively small energy barriers, the string method becomes inconvenient since many images trace out physically uninteresting regions where the barrier has already been crossed and recrossing is unlikely. Energy weighting alleviates this difficulty to an extent, but typical implementations still require the string’s endpoints to evolve to stable states that may be far from the barrier, and deciding upon a suitable energy weighting scheme can be an iterative process dependent on both the application and the number of images used. A second difficulty arises when treating nucleation problems: for later images along the string, the nucleus grows to fill the computational domain. These later images are unphysical due to confinement effects and must be discarded. In both cases, computational resources associated with unphysical or uninteresting images are wasted. We present a new energy weighting scheme that eliminates all of the above difficulties by actively truncating the string as it evolves and forcing all images, including the endpoints, to remain within and cover uniformly a desired barrier region. The calculation can proceed in one step without iterating on strategy, requiring only an estimate of an energy value below which images become uninteresting.
Law, Bubbles, and Financial Regulation - Chapter 1 - The Regulatory Instability Hypothesis

This introductory chapter of the book "Law, Bubbles, and Financial Regulation" outlines how financial regulation can fail when it is needed the most. The dynamics of asset price bubbles weaken financial regulation just as financial markets begin to overheat and the risk of crisis spikes. At the same time, the failure of financial regulations adds further fuel to a bubble.This book examines the interaction of bubbles and financial regulation through over three centuries of history of financial crises. This perspective reveals that law is crucial to the story of bubbles and that the legal history of the current global crisis has many forerunners. The book explores the ways in which bubbles lead to the failure of financial regulation by outlining five dynamics, which it collectively labels the “Regulatory Instability Hypothesis.” The introductory chapter describes these five dynamics:The regulatory stimulus cycle. As bubbles form and markets boom, policy-makers face increasing pressure to provide regulatory stimulus for financial markets. Policymakers provide this stimulus not only by deregulating financial markets and repealing statutes and regulations, but also by lowering enforcement, providing exemptions to legal rules, and choosing not to apply legal rules to new contexts. In many cases, governments intervene in markets by providing legal preferences, monopolies, and other subsidies to select market participants.Compliance rot. Bubbles undermine the compliance by market participants with antifraud and other financial rules. Regulatory arbitrage frenzies. Bubbles foster increased gamesmanship of financial regulations by market participants. Bubbles sharpen the creativity of these parties and their appetite for legal risk.Procyclical regulation. Certain regulations exacerbate boom and bust cycles in financial markets, without policymakers changing legal rules or market participants varying their level of compliance.Promoting of investment herding. Other legal rules encourage investors and financial institutions to engage in dangerous herding into particular investments. These five dynamics become most dangerous when they undermine financial regulations that govern financial institution leverage and the supply of credit. The feedback between law and markets fuels bubbles, camouflages mistakes in pricing risk, and leaves financial institutions and markets vulnerable to a crash. The current global crisis provided a master class in these five dynamics, as they midwifed the growth of a shadow banking system and shadow banking bubble. The shadow banking bubble, in turn, promoted more regulatory stimulus, compliance rot, regulatory arbitrage frenzies, and investment herding. The book concludes by outlining approaches to make financial regulation more resilient to these dynamics that undermine law.
A monoclonal antibody against HIV Tat attenuates neurotoxicity via glutamate receptors.

HIV dementia is a neurodegenerative disease for which there is no available neuroprotective therapy. Viral proteins, such as Tat, have been implicated as agents of neurotoxicity via multiple mechanisms, including effects on glutamate receptors. We coincubated HIV-1 Tat protein with a monoclonal antibody against the N-terminal of Tat, in human neuronal cultures. The antibody significantly (P < 0.005) attenuated the neurotoxicity caused by Tat alone, as measured by mitochondrial membrane potential. This protection occurred despite previous observations that peptides from the N-terminal of Tat are non-toxic. We hypothesized that the antibodies may have a more indirect neuroprotective function, preventing excitotoxicity at glutamate receptors. Thus, we coincubated Tat and anti-Tat antibody with NMDA and kainate, in our cultures. The Tat-antibody combination attenuated the toxicity seen with NMDA (p < 0.05). There was some protection against kainate (AMPA) excitotoxicity, but it was not statistically significant (p < 0.2). The Tat-antiTat antibody complex appears to prevent excitotoxic effects at glutamate receptors, especially NMDA receptors. Host immune responses may influence host susceptibility to the effects of viral proteins, altering HIV complications, such as onset of HIV dementia.
Identity maps and their extensions on parameter spaces: Applications to anomaly detection in video

We propose an algorithm for detecting anomalies in video sequences. In order to build an appropriate model, video of nominal activity is utilized to construct an anomaly free representation of the data. The resulting model produces alarm notifications when anomalous activity is observed. The approach involves characterizing segments of video as subspaces and invoking the geometric framework of Grassmann manifolds, i.e., the space of k-dimensional subspaces of n-dimensional space, Gr(k, n). With subspaces treated as points on Gr(k, n) together with a suitably chosen Grassmannian metric, one can exploit novel aspects of the geometry of the data for the purpose of anomaly detection. This mathematical framework is used to extend the Multivariate State Estimation Technique to the context of Grassmann manifolds. We present an application to the ETHZ Living Room Data Set for detecting anomalous activities.
Stable Lasers and Optical Frequency Standards for Testing the Postulates of Physics

The fundamental ideas of modern physics have been developed over the past three quarters of a century by imaginative people who have created a beautiful, logical and quantitative structure which is able to account for the oft-time surprising experimental results. Man’s whole view of the world has been changed by such counter-intuitive concepts as quantum-mechanical probability, the uncertainty principle, and the several “paradoxes” of special relativity. It is an awesome — nearly religious — experience to realize how much these microscopic ideas are at variance with our ordinary experience, yet really none of these principles once enunciated and accepted has later been found to be in error.
Recommended Practice for Determining the Effectiveness and Safety of Aquatic Animal Control Agents in Static Water Studies

This recommended practice describes procedures for testing the effectiveness of aquatic animal control agents and for determining the effect of these agents on nontarget organisms under actual-use conditions. The field tests are recommended after basic relative toxicity data have been established by laboratory tests. Federal and state regulations pertaining to field testing of pesticides must be followed. Data obtained using this practice should generally be applicable to some requirements for registration of an aquatic control agent.
Assessing PTSD and resilience for females who during childhood were exposed to domestic violence

This study examined 68 females, who as children were exposed to domestic violence, to explore childhood risk and protective factors and their relationship to adult levels of post-traumatic stress disorder (PTSD) and resilience. Independent sample t-tests indicated significant differences in PTSD levels between participants with and without police involvement during childhood. There were also significant differences in PTSD levels between participants who reported their mothers had mental-health problems with those who did not. Additionally, participants whose mothers had full-time steady employment had significantly higher resilience than those with mothers who did not work or worked inconsistently. Implications include advancing ecological theory and conceptual insights regarding childhood risk and protective factors and their association to adult psychological distress and hardiness for daughters exposed to their mothers' intimate partner violence.
Theory into Practice: Adolescent Brain Development and Implications for Classroom Management

(2012). Theory into Practice: Adolescent Brain Development and Implications for Classroom Management. Strategies: Vol. 25, No. 6, pp. 32-34.
A Change in the Early Growth Rates of Four Larval Marine Fishes1

The growth curves (age on length) of four widely diversified species of fish larvae (Sardinops caerulea, Pleuronichthys verticalis, Tetragonurus cuvieri, and Trachurus symmetricus) were divided into three sections: A, B, and C. Growth in Section A is very rapid but of short duration. Growth in Section B is much slower but of longer duration. Section C follows absorption of yolk materials and chronicles the rate at which the larvae metabolize themselves. The growth curves for several other species of fish derived from data, given in the literature are generally in agreement with those derived by more rigorous analysis. The slopes of Section A were compared with Section B for each fish. These ratios lie between 4 and 10. The effect of non-logarithmic growth on estimates of larval survival is discussed.
Salmonella as Monitors of Fecal Pollution in the Aquatic Environment

Abstract Polluted river water, river bottom sediment, fish body slime, fish gastrointestinal tracts and fresh water mussel gastrointestinal tracts were examined for Salmonella. Eleven serotypes were found among 131 Isolates. S. amsterdam was the predominating Salmonella spp., and was obtained from all specimens. Two serotypes, give and senftenberg. were found only in the fish gastrointestinal tracts. Salmonellae may colonize in fish and serve as an indicator system for current as well as retrospective evidence of fecal pollution of the aquatic environment.
Full-solid-angle photoelectron diffraction from bulk and surface atoms of clean W(110)

The authors have studied the atomic structure of the clean W(110) surface by means of site-resolved scanned-angle W4f{sub 7/2} photoelectron diffraction (PD) data obtained over nearly the full 2{pi} solid angle above the surface. Prior to the availability of high-brightness sources such as the Advanced Light Source, such large high-resolution data sets were prohibitively time consuming to obtain. The well characterized W(110) system was used as a reference case to check the accuracy of structure determinations from such scanned-angle data via R-factor comparisons of experiment with theoretical multiple scattering calculations. The photoelectron kinetic energy of {approximately}40 eV used was also lower than in many prior PD studies, providing further challenges to theory. The influence of various non-structural theoretical input parameters (e.g., scattering phase shifts, electron inelastic attenuation length, and inner potential) was thus also assessed. A final optimized structure is presented, together with comments on the future applications of this method.
Binary fission and endomixis in the Trichodina from tadpoles (Protozoa, Ciliata)

This peritrichous ciliate lives as an ectocommensal on the skin and gills of anuran tadpoles. Its relation to described species of Trichodina is doubtful. This study was made almost exclusively on fixed and stained material. Binary fission is similar to that in other ciliates. The horseshoe-shaped macronucleus condenses, then divides amitotically. The single small micronucleus forms a spindle containing between four and six chromosomes. Endomixis is of high incidence in the free-living Trichodinae. Encystment was not observed. At the onset of endomixis, the macronucleus disintegrates into fragments which persist throughout the process. The micronucleus undergoes three rapidly succeeding mitotic divisions to form eight nuclei. There is no evidence of chromosome reduction during these divisions. Seven of the nuclei differentiate into macronuclear anlagen; the eighth becomes the functional micronucleus. Successive cell divisions—before each of which the micronucleus divides—distribute macronuclei to daughter cells. Variations from the regular process of endomixis may arise, 1) by precocious division of endomictic parents; 2) by extra divisions of the micronucleus; 3) by less than the usual number (three) of divisions of the micronucleus; 4) by hypertrophy and early differentiation of the micronucleus into macronuclei; 5) by unusual segregation of nuclei to daughters, and, 6) from miscellaneous causes. The significance of these variations is discussed in connection with the possible origin of bimicronucleate and amicronucleate races.
Comment on “Communicating government science”

Soroosh Sorooshian's editorial in the 18 April issue of Eos (87(16) 2005) is a timely reminder of the need for unambiguous guidelines governing the interactions between government scientists and the media. His comments implicitly recognize the central role that science plays in a modern democratic society, which includes informing policy at the highest levels of government and educating the general public about the world we inhabit. Federal research scientists, who constitute approximately 15 percent of the AGU's U.S. membership, have a unique public responsibility. They would welcome a consistent policy for the review and approval of publications, oral presentations, and media communications.
Host range determination and functional mapping of the nucleoprotein and matrix genes of influenza viruses using monoclonal antibodies.

Abstract Construction and comparison of phylogenetic trees, the standard approach to determining the host-specific lineage of influenza A virus genes, is tedious and expensive. In this study, panels of monoclonal antibodies (Mabs) produced against the matrix proteins (M1) of A/WSN and A/PR/8/34 and the nucleoprotein (NP) of A/WSN were assessed for their value in identifying the hosts of origin of the M1 and NP genes in influenza virus isolates and in mapping the proteins' functional domains. Using ELISA against a broad spectrum of reference viruses, we found two Mabs against the NP (150/4 and 469/6) to be useful in determining host-specific lineage. Comparative sequence analysis placed five amino acids within the antigenic domains recognized by Mab 150/4 and two amino acids within the domains recognized by 469/6. One Mab against the NP (5/1) recognized a conserved epitope that is present on each of the 36 influenza A viruses tested. This epitope may be a type-specific determinant for influenza A viruses and an RNA binding site. Monoclonal antibodies to M1 did not discriminate among species, but they did contribute information to the construction of a functional map of M1. These results demonstrate that Mabs to defined protein epitopes can provide useful information on the molecular epidemiology of influenza viruses.