cssi/SciSciGPT-SciSciNet huggingface dataset:
High-dimensional fast convolutional framework (HICU) for calibrationless MRI

Purpose To present a computational procedure for accelerated, calibrationless magnetic resonance image (Cl-MRI) reconstruction that is fast, memory efficient, and scales to high-dimensional imaging. Theory and methods Cl-MRI methods can enable high acceleration rates and flexible sampling patterns, but their clinical application is limited by computational complexity and large memory footprint. The proposed computational procedure, HIgh-dimensional fast convolutional framework (HICU), provides fast, memory-efficient recovery of unsampled k-space points. For demonstration, HICU is applied to 6 2D T2-weighted brain, 7 2D cardiac cine, 5 3D knee, and 1 multi-shot diffusion weighted imaging (MSDWI) datasets. Results The 2D imaging results show that HICU can offer 1-2 orders of magnitude computation speedup compared to other Cl-MRI methods without sacrificing imaging quality. The 2D cine and 3D imaging results show that the computational acceleration techniques included in HICU yield computing time on par with SENSE-based compressed sensing methods with up to 3 dB improvement in signal-to-error ratio and better perceptual quality. The MSDWI results demonstrate the feasibility of HICU for a challenging multi-shot echo-planar imaging application. Conclusions The presented method, HICU, offers efficient computation and scalability as well as extendibility to a wide variety of MRI applications.
Provider engagement in Indiana's opioid use disorder ECHO programme: there is a will but not always a way.

Project Extension for Community Healthcare Outcomes (ECHO) is an innovative means of expanding clinical providers’ knowledge and skills using virtual case-based learning in a ‘hub-and-spoke’ network of providers and specialists.1 Especially in rural and underserved areas, where healthcare needs are great and access to care may be limited, ECHO presents a promising continuing education model and way to facilitate management of complex cases.2–5 It also provides clinicians with digital space for professional collaboration and shared learning, which is valuable in today’s high-pressure practice environment6 and can be rapidly deployed to address crises like the COVID-19 pandemic.7 However, some practitioners have argued that administrative demands on time, rather than access to specialty knowledge, are the barrier to developing networks of providers for mutual education.8 Other providers have noted that ECHO is likely not a ‘panacea for access to specialty care,’ but rather a ‘force multiplier’ for skills transfer.9 It, therefore, is important to capture and disseminate quality improvement data to determine the ways in which ECHO programmes best can serve providers’ specialty healthcare training needs. Our multidisciplinary team started an ECHO for opioid use disorder (OUD) in Indiana, USA, in 2018. Each session was hosted on Zoom and lasted 90 min; the first 15 were a didactic presentation (eg, ‘Neurobiology of OUD and MAT’), and the latter 75 were case-based learning, moderated by a panel of experts, focused on real, deidentified cases submitted in advance by participants. Our ECHO programme has facilitated extensive participation in shared, case-based learning on OUD, producing more than 1771 person-hours of attendance in the first year of operation alone, and has elucidated the need for statewide mentorship related to OUD and psychiatric comorbidities.10 However, not all participants became regular ECHO attenders, so we surveyed low-attendance participants for programmatic quality improvement. The results of this microstudy highlighted barriers to provider engagement in continuing education and digital communities of practice.
Arithmetic progressions of Carmichael numbers in a reduced residue class

Abstract Fix coprime natural numbers a , q . Assuming the Prime k-tuple Conjecture, we show that there exist arbitrarily long arithmetic progressions of Carmichael numbers, each of which lies in the reduced residue class a mod q and is a product of three distinct prime numbers.
An Alternative Cross Entropy Loss for Learning-to-Rank

Listwise learning-to-rank methods form a powerful class of ranking algorithms that are widely adopted in applications such as information retrieval. These algorithms learn to rank a set of items by optimizing a loss that is a function of the entire set—as a surrogate to a typically non-differentiable ranking metric. Despite their empirical success, existing listwise methods are based on heuristics and remain theoretically ill-understood. In particular, none of the empirically successful loss functions are related to ranking metrics. In this work, we propose a cross entropy-based learning-to-rank loss function that is theoretically sound, is a convex bound on NDCG—a popular ranking metric—and is consistent with NDCG under learning scenarios common in information retrieval. Furthermore, empirical evaluation of an implementation of the proposed method with gradient boosting machines on benchmark learning-to-rank datasets demonstrates the superiority of our proposed formulation over existing algorithms in quality and robustness.
Analysis of cell-type-specific chromatin modifications and gene expression in Drosophila neurons that direct reproductive behavior

Examining the role of chromatin modifications and gene expression in neurons is critical for understanding how the potential for behaviors are established and maintained. We investigate this question by examining Drosophila melanogaster fru P1 neurons that underlie reproductive behaviors in both sexes. We developed a method to purify cell-type-specific chromatin (Chromatag), using a tagged histone H2B variant that is expressed using the versatile Gal4/UAS gene expression system. Here, we use Chromatag to evaluate five chromatin modifications, at three life stages in both sexes. We find substantial changes in chromatin modification profiles across development and fewer differences between males and females. Additionally, we find chromatin modifications that persist in different sets of genes from pupal to adult stages, which may point to genes important for cell fate determination in fru P1 neurons. We generated cell-type-specific RNA-seq data sets, using translating ribosome affinity purification (TRAP). We identify actively translated genes in fru P1 neurons, revealing novel stage- and sex-differences in gene expression. We also find chromatin modification enrichment patterns that are associated with gene expression. Next, we use the chromatin modification data to identify cell-type-specific super-enhancer-containing genes. We show that genes with super-enhancers in fru P1 neurons differ across development and between the sexes. We validated that a set of genes are expressed in fru P1 neurons, which were chosen based on having a super-enhancer and TRAP-enriched expression in fru P1 neurons.
Experimental Design for Regret Minimization in Linear Bandits

In this paper we propose a novel experimental design-based algorithm to minimize regret in online stochastic linear and combinatorial bandits. While existing literature tends to focus on optimism-based algorithms--which have been shown to be suboptimal in many cases--our approach carefully plans which action to take by balancing the tradeoff between information gain and reward, overcoming the failures of optimism. In addition, we leverage tools from the theory of suprema of empirical processes to obtain regret guarantees that scale with the Gaussian width of the action set, avoiding wasteful union bounds. We provide state-of-the-art finite time regret guarantees and show that our algorithm can be applied in both the bandit and semi-bandit feedback regime. In the combinatorial semi-bandit setting, we show that our algorithm is computationally efficient and relies only on calls to a linear maximization oracle. In addition, we show that with slight modification our algorithm can be used for pure exploration, obtaining state-of-the-art pure exploration guarantees in the semi-bandit setting. Finally, we provide, to the best of our knowledge, the first example where optimism fails in the semi-bandit regime, and show that in this setting our algorithm succeeds.
On the Manhattan pinball problem

We consider the periodic Manhattan lattice with alternating orientations going north-south and east-west. Place obstructions on vertices independently with probability 0 1 2−e with some e>0.
BW-EDA-EEND: streaming END-TO-END Neural Speaker Diarization for a Variable Number of Speakers

We present a novel online end-to-end neural diarization system, BW-EDA-EEND, that processes data incrementally for a variable number of speakers. The system is based on the Encoder-Decoder-Attractor (EDA) architecture of Horiguchi et al., but utilizes the incremental Transformer encoder, attending only to its left contexts and using block-level recurrence in the hidden states to carry information from block to block, making the algorithm complexity linear in time. We propose two variants: For unlimited-latency BW-EDA-EEND, which processes inputs in linear time, we show only moderate degradation for up to two speakers using a context size of 10 seconds compared to offline EDA-EEND. With more than two speakers, the accuracy gap between online and offline grows, but the algorithm still outperforms a baseline offline clustering diarization system for one to four speakers with unlimited context size, and shows comparable accuracy with context size of 10 seconds. For limited-latency BW-EDA-EEND, which produces diarization outputs block-by-block as audio arrives, we show accuracy comparable to the offline clustering-based system.
Optimal Algebraic Breadth-First Search for Sparse Graphs

There has been a rise in the popularity of algebraic methods for graph algorithms given the development of the GraphBLAS library and other sparse matrix methods. An exemplar for these approaches is Breadth-First Search (BFS). The algebraic BFS algorithm is simply a recurrence of matrix-vector multiplications with the n × n adjacency matrix, but the many redundant operations over nonzeros ultimately lead to suboptimal performance. Therefore an optimal algebraic BFS should be of keen interest especially if it is easily integrated with existing matrix methods. Current methods, notably in the GraphBLAS, use a Sparse Matrix masked-Sparse Vector multiplication in which the input vector is kept in a sparse representation in each step of the BFS, and nonzeros in the vector are masked in subsequent steps. This has been an area of recent research in GraphBLAS and other libraries. While in theory, these masking methods are asymptotically optimal on sparse graphs, many add work that leads to suboptimal runtime. We give a new optimal, algebraic BFS for sparse graphs, thus closing a gap in the literature. Our method multiplies progressively smaller submatrices of the adjacency matrix at each step. Let n and m refer to the number of vertices and edges, respectively. On a sparse graph, our method takes O(n) algebraic operations as opposed to O(m) operations needed by theoretically optimal sparse matrix approaches. Thus, for sparse graphs, it matches the bounds of the best-known sequential algorithm, and on a Parallel Random Access Machine, it is work-optimal. Our result holds for both directed and undirected graphs. Compared to a leading GraphBLAS library, our method achieves up to 24x faster sequential time, and for parallel computation, it can be 17x faster on large graphs and 12x faster on large-diameter graphs.
FairSquare: Probabilistic Verification of Program Fairness

With the range and sensitivity of algorithmic decisions expanding at a break-neck speed, it is imperative that we aggressively investigate fairness and bias in decision-making programs. First, we show that a number of recently proposed formal definitions of fairness can be encoded as probabilistic program properties. Second, with the goal of enabling rigorous reasoning about fairness, we design a novel technique for verifying probabilistic properties that admits a wide class of decision-making programs. Third, we present FairSquare, the first verification tool for automatically certifying that a program meets a given fairness property. We evaluate FairSquare on a range of decision-making programs. Our evaluation demonstrates FairSquare's ability to verify fairness for a range of different programs, which we show are out-of-reach for state-of-the-art program analysis techniques.
Ensure: Ensemble Stein’s Unbiased Risk Estimator for Unsupervised Learning

Deep learning algorithms are emerging as powerful alternatives to compressed sensing methods, offering improved image quality and computational efficiency. Unfortunately, fully sampled training images may not be available or are difficult to acquire in several applications, including high-resolution and dynamic imaging. Previous studies in image reconstruction have utilized Stein’s Unbiased Risk Estimator (SURE) as a mean square error (MSE) estimate for the image denoising step in an unrolled network. Unfortunately, the end-to-end training of a network using SURE remains challenging since the projected SURE loss is a poor approximation to the MSE, especially in the heavily undersampled setting. We propose an ENsemble SURE (ENSURE) approach to train a deep network only from undersampled measurements. In particular, we show that training a network using an ensemble of images, each acquired with a different sampling pattern, can closely approximate the MSE. Our preliminary experimental results show that the proposed ENSURE approach gives comparable reconstruction quality to supervised learning and a recent unsupervised learning method.
On the occasional exactness of the distributional transform approximation for direct Gaussian copula models with discrete margins

Abstract The direct Gaussian copula model with discrete margins is appealing but poses computational challenges due to its intractable likelihood. We show that the distributional transform-based approximate likelihood is essentially exact for some variants of the model, and we propose a quantity that can be used to assess exactness for a given dataset.
A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news

Abstract By applying machine learning to the accurate and cost-effective classification of photos based on sentiment, we introduce a daily market-level investor sentiment index (Photo Pessimism) obtained from a large sample of news photos. Consistent with behavioral models, Photo Pessimism predicts market return reversals and trading volume. The relation is strongest among stocks with high limits to arbitrage and during periods of elevated fear. We examine whether Photo Pessimism and pessimism embedded in news text act as complements or substitutes for each other in predicting stock returns and find evidence that the two are substitutes.
Jacobi–Trudi Identity and Drinfeld Functor for Super Yangian

We show that the quantum Berezinian which gives a generating function of the integrals of motions of XXX spin chains associated to super Yangian $\mathrm{Y}(\mathfrak{gl}_{m|n})$ can be written as a ratio of two difference operators of orders $m$ and $n$ whose coefficients are ratios of transfer matrices corresponding to explicit skew Young diagrams. In the process, we develop several missing parts of the representation theory of $\mathrm{Y}(\mathfrak{gl}_{m|n})$ such as $q$-character theory, Jacobi-Trudi identity, Drinfeld functor, extended T-systems, Harish-Chandra map.
Edge Augmentation With Controllability Constraints in Directed Laplacian Networks

In this letter, we study the maximum edge augmentation problem in directed Laplacian networks to improve their robustness while preserving lower bounds on their strong structural controllability (SSC). Since adding edges could adversely impact network controllability, the main objective is to maximally densify a given network by selectively adding missing edges while ensuring that SSC of the network does not deteriorate beyond certain levels specified by the SSC bounds. We consider two widely used bounds: first is based on the notion of zero forcing (ZF), and the second relies on the distances between nodes in a graph. We provide an edge augmentation algorithm that adds the maximum number of edges in a graph while preserving the ZF-based bound, and also derive a closed-form expression for the exact number of edges added to the graph. Then, we examine the edge augmentation while preserving the distance-based bound and present a randomized algorithm that guarantees an $\alpha $ –approximate solution with high probability. Finally, we numerically evaluate and compare these edge augmentation solutions.
Bayesian joint modeling of chemical structure and dose response curves

Today there are approximately 85,000 chemicals regulated under the Toxic Substances Control Act, with around 2,000 new chemicals introduced each year. It is impossible to screen all of these chemicals for potential toxic effects, either via full organism in vivo studies or in vitro high-throughput screening (HTS) programs. Toxicologists face the challenge of choosing which chemicals to screen, and predicting the toxicity of as yet unscreened chemicals. Our goal is to describe how variation in chemical structure relates to variation in toxicological response to enable in silico toxicity characterization designed to meet both of these challenges. With our Bayesian partially Supervised Sparse and Smooth Factor Analysis (BS3FA) model, we learn a distance between chemicals targeted to toxicity, rather than one based on molecular structure alone. Our model also enables the prediction of chemical dose-response profiles based on chemical structure (i.e., without in vivo or in vitro testing) by taking advantage of a large database of chemicals that have already been tested for toxicity in HTS programs. We show superior simulation performance in distance learning and modest to large gains in predictive ability compared to existing methods. Results from the high-throughput screening data application elucidate the relationship between chemical structure and a toxicity-relevant high-throughput assay. An R package for BS3FA is available online at https://github.com/kelrenmor/bs3fa.
Heterogeneity in individual expectations, sentiment, and constant-gain learning

Abstract This paper uses adaptive learning to understand the heterogeneity of individual-level expectations. We exploit individual Survey of Professional Forecasters data on output and inflation forecasts. We endow all forecasters with the same information set that they would have as economic agents in a benchmark New Keynesian model. Forecasters are, however, allowed to differ in the constant gain values that they use to update their beliefs and in their sentiments. The latter are defined as the degrees of excess optimism or pessimism about the economy that cannot be justified by the learning model. Our results highlight the heterogeneity in the gain coefficients adopted by forecasters. The median values of the gain coefficients occasionally jump to higher values in the 1970-80s, and stabilize in the 1990s and 2000s. Individual sentiment is also persistent and heterogeneous. Differences in sentiment, however, do not simply cancel out in the aggregate: the majority of forecasters exhibit excess optimism, or excess pessimism, at the same time.
Rough isometry between Gromov hyperbolic spaces and uniformization

In this note we show that given two complete geodesic Gromov hyperbolic spaces that are roughly isometric and an arbitrary \(\epsilon>0\) (not necessarily small), either the uniformization of both spaces with parameter \(\epsilon\) results in uniform domains, or else neither uniformized space is a uniform domain. The terminology of "uniformization" is from [BHK], where it is shown that the uniformization, with parameter \(\epsilon>0\), of a complete geodesic Gromov hyperbolic space results in a uniform domain provided \(\epsilon\) is small enough.
List Decoding of Arıkan's PAC Codes.

Polar coding gives rise to the first explicit family of codes that provably achieve capacity with efficient encoding and decoding for a wide range of channels. However, its performance at short blocklengths under standard successive cancellation decoding is far from optimal. A well-known way to improve the performance of polar codes at short blocklengths is CRC precoding followed by successive-cancellation list decoding. This approach, along with various refinements thereof, has largely remained the state of the art in polar coding since it was introduced in 2011. Recently, Arikan presented a new polar coding scheme, which he called polarization-adjusted convolutional (PAC) codes. At short blocklengths, such codes offer a dramatic improvement in performance as compared to CRC-aided list decoding of conventional polar codes. PAC codes are based primarily upon the following main ideas: replacing CRC codes with convolutional precoding (under appropriate rate profiling) and replacing list decoding by sequential decoding. One of our primary goals in this paper is to answer the following question: is sequential decoding essential for the superior performance of PAC codes? We show that similar performance can be achieved using list decoding when the list size L is moderately large (say, L⩾128). List decoding has distinct advantages over sequential decoding in certain scenarios, such as low-SNR regimes or situations where the worst-case complexity/latency is the primary constraint. Another objective is to provide some insights into the remarkable performance of PAC codes. We first observe that both sequential decoding and list decoding of PAC codes closely match ML decoding thereof. We then estimate the number of low weight codewords in PAC codes, and use these estimates to approximate the union bound on their performance. These results indicate that PAC codes are superior to both polar codes and Reed–Muller codes. We also consider random time-varying convolutional precoding for PAC codes, and observe that this scheme achieves the same superior performance with constraint length as low as ν=2.
Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech

Countermeasures to effectively fight the ever increasing hate speech online without blocking freedom of speech is of great social interest. Natural Language Generation (NLG), is uniquely capable of developing scalable solutions. However, off-the-shelf NLG methods are primarily sequence-to-sequence neural models and they are limited in that they generate commonplace, repetitive and safe responses regardless of the hate speech (e.g., "Please refrain from using such language.") or irrelevant responses, making them ineffective for de-escalating hateful conversations. In this paper, we design a three-module pipeline approach to effectively improve the diversity and relevance. Our proposed pipeline first generates various counterspeech candidates by a generative model to promote diversity, then filters the ungrammatical ones using a BERT model, and finally selects the most relevant counterspeech response using a novel retrieval-based method. Extensive Experiments on three representative datasets demonstrate the efficacy of our approach in generating diverse and relevant counterspeech.
Primed to die: an investigation of the genetic mechanisms underlying noise-induced hearing loss and cochlear damage in homozygous Foxo3-knockout mice.

The prevalence of noise-induced hearing loss (NIHL) continues to increase, with limited therapies available for individuals with cochlear damage. We have previously established that the transcription factor FOXO3 is necessary to preserve outer hair cells (OHCs) and hearing thresholds up to two weeks following mild noise exposure in mice. The mechanisms by which FOXO3 preserves cochlear cells and function are unknown. In this study, we analyzed the immediate effects of mild noise exposure on wild-type, Foxo3 heterozygous (Foxo3+/−), and Foxo3 knock-out (Foxo3−/−) mice to better understand FOXO3’s role(s) in the mammalian cochlea. We used confocal and multiphoton microscopy to examine well-characterized components of noise-induced damage including calcium regulators, oxidative stress, necrosis, and caspase-dependent and caspase-independent apoptosis. Lower immunoreactivity of the calcium buffer Oncomodulin in Foxo3−/− OHCs correlated with cell loss beginning 4 h post-noise exposure. Using immunohistochemistry, we identified parthanatos as the cell death pathway for OHCs. Oxidative stress response pathways were not significantly altered in FOXO3’s absence. We used RNA sequencing to identify and RT-qPCR to confirm differentially expressed genes. We further investigated a gene downregulated in the unexposed Foxo3−/− mice that may contribute to OHC noise susceptibility. Glycerophosphodiester phosphodiesterase domain containing 3 (GDPD3), a possible endogenous source of lysophosphatidic acid (LPA), has not previously been described in the cochlea. As LPA reduces OHC loss after severe noise exposure, we treated noise-exposed Foxo3−/− mice with exogenous LPA. LPA treatment delayed immediate damage to OHCs but was insufficient to ultimately prevent their death or prevent hearing loss. These results suggest that FOXO3 acts prior to acoustic insult to maintain cochlear resilience, possibly through sustaining endogenous LPA levels.
A Data-Driven Transcriptional Taxonomy of Adipogenic Chemicals to Identify White and Brite Adipogens

Background: Chemicals in disparate structural classes activate specific subsets of the transcriptional programs of peroxisome proliferator-activated receptor-γ (PPARγ) to generate adipocytes with d...
Employing Altruistic Vehicles at On-ramps to Improve the Social Traffic Conditions

Highway on-ramps are regarded as typical bottlenecks in transportation networks. In previous work, mainline vehicles' selfish lane choice behavior at on-ramps is studied and regarded as one cause leading to on-ramp inefficiency. When on-ramp vehicles plan to merge into the mainline of the highway, mainline vehicles choose to either stay steadfast on the current lane or bypass the merging area by switching to a neighboring lane farther from the on-ramp. Selfish vehicles make the decisions to minimize their own travel delay, which compromises the efficiency of the whole on-ramp. Results in previous work have shown that, if we can encourage a proper portion of mainline vehicles to bypass rather than to stay steadfast, the social traffic conditions can be improved. In this work, we consider employing a proportion of altruistic vehicles among the selfish mainline vehicles to improve the efficiency of the on-ramps. The altruistic vehicles are individual optimizers, making decisions whether to stay steadfast or bypass to minimize their own altruistic cost, which is a weighted sum of the travel delay and their negative impact on other vehicles. We first consider the ideal case that altruistic costs can be perfectly measured by altruistic vehicles. We give the conditions for the proportion of altruistic vehicles and the weight configuration of the altruistic costs, under which the social delay can be decreased or reach the optimal. Subsequently, we consider the impact of uncertainty in the measurement of altruistic costs and we give the optimal weight configuration for altruistic vehicles which minimizes the worst case social delay under such uncertainty.
Exterior square gamma factors for cuspidal representations of $\mathrm{GL}_n$: simple supercuspidal representations

We compute the local twisted exterior square gamma factors for simple supercuspidal representations, using which we prove a local converse theorem for simple supercuspidal representations.
Influence of Chemical Kinetics Models on Plasma Generation in Hypersonic Flight

The accuracy and sensitivity of plasma generation predicted by several different chemical kinetics models is investigated in the context of weakly ionized hypersonic flowfields around the Radio Att...
Sex-specific, pdfr-1-dependent modulation of pheromone avoidance by food abundance enables flexibility in C. elegans foraging behavior.

Summary To make adaptive feeding and foraging decisions, animals must integrate diverse sensory streams with multiple dimensions of internal state. In C. elegans, foraging and dispersal behaviors are influenced by food abundance, population density, and biological sex, but the neural and genetic mechanisms that integrate these signals are poorly understood. Here, by systematically varying food abundance, we find that chronic avoidance of the population-density pheromone ascr#3 is modulated by food thickness, such that hermaphrodites avoid ascr#3 only when food is scarce. The integration of food and pheromone signals requires the conserved neuropeptide receptor PDFR-1, as pdfr-1 mutant hermaphrodites display strong ascr#3 avoidance, even when food is abundant. Conversely, increasing PDFR-1 signaling inhibits ascr#3 aversion when food is sparse, indicating that this signal encodes information about food abundance. In both wild-type and pdfr-1 hermaphrodites, chronic ascr#3 avoidance requires the ASI sensory neurons. In contrast, PDFR-1 acts in interneurons, suggesting that it modulates processing of the ascr#3 signal. Although a sex-shared mechanism mediates ascr#3 avoidance, food thickness modulates this behavior only in hermaphrodites, indicating that PDFR-1 signaling has distinct functions in the two sexes. Supporting the idea that this mechanism modulates foraging behavior, ascr#3 promotes ASI-dependent dispersal of hermaphrodites from food, an effect that is markedly enhanced when food is scarce. Together, these findings identify a neurogenetic mechanism that sex-specifically integrates population and food abundance, two important dimensions of environmental quality, to optimize foraging decisions. Further, they suggest that modulation of attention to sensory signals could be an ancient, conserved function of pdfr-1.
Unconventional light-induced states visualized by ultrafast electron diffraction and microscopy

Exciting electrons in solids with intense light pulses offers the possibility of generating new states of matter through nonthermal means and controlling their macroscopic properties on femto- to picosecond time scales. One way to manipulate a solid is by altering its lattice structure, which often underlies the electronic, magnetic, and other phases. Here, we review how structures of solids are affected by photoexcitation and how their ultrafast dynamics are captured with time-resolved electron diffraction and microscopy. Specifically, we survey how a strong light pulse has been used to tailor the nonequilibrium characteristics to yield on-demand properties in various material classes. In the existing literature, four main routes have been exploited to control material structures: (1) phase competition, (2) electronic correlations, (3) excitation of coherent modes, and (4) defect generation. In this article, we discuss experiments relevant to all four schemes and finish by speculating about future directions.
On Hamiltonian cycles in balanced k-partite graphs

Abstract For all integers k with k ≥ 2 , if G is a balanced k-partite graph on n ≥ 3 vertices with minimum degree at least ⌈ n 2 ⌉ + ⌊ n + 2 2 ⌈ k + 1 2 ⌉ ⌋ − n k = { ⌈ n 2 ⌉ + ⌊ n + 2 k + 1 ⌋ − n k : k odd n 2 + ⌊ n + 2 k + 2 ⌋ − n k : k even , then G has a Hamiltonian cycle unless 4 divides n and k ∈ { 2 , n 2 } . In the case where 4 divides n and k ∈ { 2 , n 2 } , we can characterize the graphs which do not have a Hamiltonian cycle and see that ⌈ n 2 ⌉ + ⌊ n + 2 2 ⌈ k + 1 2 ⌉ ⌋ − n k + 1 suffices. This result is tight for all k ≥ 2 and n ≥ 3 divisible by k.
Primordial obliquities of brown dwarfs and super-Jupiters from fragmenting gravito-turbulent discs

Super-Jupiters, brown dwarfs, and stars can form from the collapse of self-gravitating discs. Such discs are turbulent, with flocculent spiral arms accelerating gas to transonic speeds horizontally and vertically. Objects that fragment from gravito-turbulent discs should spin with a wide range of directions, reflecting the random orientations of their parent eddies. We show by direct numerical simulation that obliquities of newly collapsed fragments can range up to 45$^\circ$. Subsequent collisions between fragments can further broaden the obliquity distribution up to 90$^\circ$. The large obliquities of newly discovered super-Jupiters on wide orbits around young stars may thus be gravito-turbulent in origin. Obliquely spinning fragments are born on orbits that may be inclined relative to their parent discs by up to 20$^\circ$, and gravitationally stir leftover material to many times the pre-fragmentation disc thickness.
Principal regression for high dimensional covariance matrices

This manuscript presents an approach to perform generalized linear regression with multiple high dimensional covariance matrices as the outcome. In many areas of study, such as resting-state functional magnetic resonance imaging (fMRI) studies, this type of regression can be utilized to characterize variation in the covariance matrices across units. Model parameters are estimated by maximizing a likelihood formulation of a generalized linear model, conditioning on a well-conditioned linear shrinkage estimator for multiple covariance matrices, where the shrinkage coefficients are proposed to be shared across matrices. Theoretical studies demonstrate that the proposed covariance matrix estimator is optimal achieving the uniformly minimum quadratic loss asymptotically among all linear combinations of the identity matrix and the sample covariance matrix. Under certain regularity conditions, the proposed estimator of the model parameters is consistent. The superior performance of the proposed approach over existing methods is illustrated through simulation studies. Implemented to a resting-state fMRI study acquired from the Alzheimer’s Disease Neuroimaging Initiative, the proposed approach identified a brain network within which functional connectivity is significantly associated with Apolipoprotein E e4, a strong genetic marker for Alzheimer’s disease.
Demystifying Pythia: A Survey of ChainLink Oracles Usage on Ethereum.

Smart contracts are dependent on oracle systems for their adoption and usability. We perform an empirical study of oracle systems’ usage trends and adoption metrics to provide better insight into the health of the smart contract ecosystem. We collect ChainLink usage data on the Ethereum network using a modified Ethereum client and running a full node. We analyze the collected data and present our findings and insights surrounding the usage trends, adoption metrics, oracle pricing and service quality associated with ChainLink on the Ethereum network. We infer that ChainLink’s usage and growth are dominated by the DeFi ecosystem and for its demand for decentralized price feeds.
Equivariant Filters for Efficient Tracking in 3D Imaging

We demonstrate an object tracking method for 3D images with fixed computational cost and state-of-the-art performance. Previous methods predicted transformation parameters from convolutional layers. We instead propose an architecture that neither flattens convolutional features nor uses fully connected layers, but instead relies on equivariant filters to preserve transformations between inputs and outputs (e.g., rotations/translations of inputs rotate/translate outputs). The transformation is then derived in closed form from the outputs of the filters. This method is useful for applications requiring low latency, such as real-time tracking. We demonstrate our model on synthetically augmented adult brain MRI, as well as fetal brain MRI, which is the intended use-case.
Neuroprediction: New Technology, Old Problems

Neuroprediction is the use of structural or functional brain or nervous system variables to make any type of prediction, including medical prognoses and behavioral forecasts, such as an indicator of future dangerous behavior. This commentary will focus on behavioral predictions, but the analysis applies to any context. The general thesis is that using neurovariables for prediction is a new technology, but that it raises no new ethical issues, at least for now. Only if neuroscience achieves the ability to “read” mental content will genuinely new ethical issues be raised, but that is not possible at present.
A unified view of graph regularity via matrix decompositions

We prove algorithmic weak and \Szemeredi{} regularity lemmas for several classes of sparse graphs in the literature, for which only weak regularity lemmas were previously known. These include core-dense graphs, low threshold rank graphs, and (a version of) $L^p$ upper regular graphs. More precisely, we define \emph{cut pseudorandom graphs}, we prove our regularity lemmas for these graphs, and then we show that cut pseudorandomness captures all of the above graph classes as special cases. The core of our approach is an abstracted matrix decomposition, roughly following Frieze and Kannan [Combinatorica '99] and \Lovasz{} and Szegedy [Geom.\ Func.\ Anal.\ '07], which can be computed by a simple algorithm by Charikar [AAC0 '00]. This gives rise to the class of cut pseudorandom graphs, and using work of Oveis Gharan and Trevisan [TOC '15], it also implies new PTASes for MAX-CUT, MAX-BISECTION, MIN-BISECTION for a significantly expanded class of input graphs. (It is NP Hard to get PTASes for these graphs in general.)
Elevated fear responses to threatening cues in rats with early life stress is associated with greater excitability and loss of gamma oscillations in ventral-medial prefrontal cortex

Stress experienced early in development can have profound influences on developmental trajectories and ultimately behaviors in adulthood. Potent stressors during brain maturation can profoundly disrupt prefrontal cortical areas in particular, which can set the stage for prefrontal-dependent alterations in fear regulation and risk of drug abuse in adulthood. Despite these observations, few studies have investigated in vivo signaling in prefrontal signals in animals with a history of early life stress (ELS). Here, rats with ELS experienced during the first post-natal week were then tested on a conditioned suppression paradigm during adulthood. During conditioned suppression, electrophysiological recordings were made in the ventral medial prefrontal cortex (vmPFC) during presentations of a fear-associated cue that resolved both single-unit activity and local field potentials (LFPs). Relative to unstressed controls, ELS-experienced rats showed greater fear-related suppression of lever pressing. During presentations of the fear-associated cue (CS+), neurons in the vmPFC of ELS animals showed a significant increase in the probability of excitatory encoding relative to controls, and excitatory phasic responses in the ELS animals were reliably of higher magnitude than Controls. In contrast, vmPFC neurons in ELS subjects better discriminated between the shock-associated CS+ and the neutral ("safe") CS- cue than Controls. LFPs recorded in the same locations revealed that high gamma band (65-95 Hz) oscillations were strongly potentiated in Controls during presentation of the fear-associated CS+ cue, but this potentiation was abolished in ELS subjects. Notably, no other LFP spectra differed between ELS and Controls for either the CS+ or CS-. Collectively, these data suggest that ELS experience alters the neurobehavioral functions of PFC in adulthood that are critical for processing fear regulation. As such, these alterations may also provide insight into increased susceptibility to other PFC-dependent processes such as risk-based choice, motivation, and regulation of drug use and relapse in ELS populations.
Robust Pipek-Mezey Orbital Localization in Periodic Solids.

We describe a robust method for determining Pipek-Mezey (PM) Wannier functions (WF), recently introduced by Jonsson et al. (J. Chem. Theor. Chem.2017,13, 460), which provide some formal advantages over the more common Boys (also known as maximally-localized) Wannier functions. The Broyden-Fletcher-Goldfarb-Shanno-based PMWF solver is demonstrated to yield dramatically faster convergence compared to the alternatives (steepest ascent and conjugate gradient) in a variety of one-, two-, and three-dimensional solids (including some with vanishing gaps) and can be used to obtain Wannier functions robustly in supercells with thousands of atoms. Evaluation of the PM functional and its gradient in periodic linear combination of atomic orbital representation used a particularly simple definition of atomic charges obtained by Moore-Penrose pseudoinverse projection onto the minimal atomic orbital basis. An automated "canonicalize phase then randomize" method for generating the initial guess for WFs contributes significantly to the robustness of the solver.
Elastic turbulence generates anomalous flow resistance in porous media.

Many energy, environmental, industrial, and microfluidic processes rely on the flow of polymer solutions through porous media. Unexpectedly, the macroscopic flow resistance often increases above a ...
Quarkyonic stars with isospin-flavor asymmetry

We suggest an extension to isospin asymmetric matter of the quarkyonic model from McLerran and Reddy [Phys. Rev. Lett. 122, 122701 (2019)]. This extension allows us to construct the $\ensuremath{\beta}$ equilibrium between quarks, nucleons, and leptons. The concept of quarkyonic matter originates from the large number of color limit for which nucleons have the correct degrees of freedom near the Fermi surface---reflecting the confining forces---while deep inside the Fermi sea quarks appear naturally. In isospin-asymmetric matter, we suggest to implement this concept within a global isoscalar relation between the shell gaps differentiating the nucleon and the quark sectors. In addition, we impose the conservation of the isospin-flavor asymmetry for the nucleon and the quark components. Within this model, several quarkyonic stars are constructed on top of the SLy4 model for the nucleon sector, producing a bump in the sound speed. As a consequence, quarkyonic stars are systematically bigger and have a larger maximum mass than the associated neutron stars. This model also predicts a lower proton fraction at $\ensuremath{\beta}$ equilibrium, which potentially quenches fast cooling in massive compact stars.
ValNorm Quantifies Semantics to Reveal Consistent Valence Biases Across Languages and Over Centuries

Word embeddings learn implicit biases from linguistic regularities captured by word co-occurrence statistics. By extending methods that quantify human-like biases in word embeddings, we introduce ValNorm, a novel intrinsic evaluation task and method to quantify the valence dimension of affect in human-rated word sets from social psychology. We apply ValNorm on static word embeddings from seven languages (Chinese, English, German, Polish, Portuguese, Spanish, and Turkish) and from historical English text spanning 200 years. ValNorm achieves consistently high accuracy in quantifying the valence of non-discriminatory, non-social group word sets. Specifically, ValNorm achieves a Pearson correlation of r=0.88 for human judgment scores of valence for 399 words collected to establish pleasantness norms in English. In contrast, we measure gender stereotypes using the same set of word embeddings and find that social biases vary across languages. Our results indicate that valence associations of non-discriminatory, non-social group words represent widely-shared associations, in seven languages and over 200 years.
Visual Adversarial Imitation Learning using Variational Models

Reward function specification, which requires considerable human effort and iteration, remains a major impediment for learning behaviors through deep reinforcement learning. In contrast, providing visual demonstrations of desired behaviors often presents an easier and more natural way to teach agents. We consider a setting where an agent is provided a fixed dataset of visual demonstrations illustrating how to perform a task, and must learn to solve the task using the provided demonstrations and unsupervised environment interactions. This setting presents a number of challenges including representation learning for visual observations, sample complexity due to high dimensional spaces, and learning instability due to the lack of a fixed reward or learning signal. Towards addressing these challenges, we develop a variational model-based adversarial imitation learning (V-MAIL) algorithm. The model-based approach provides a strong signal for representation learning, enables sample efficiency, and improves the stability of adversarial training by enabling on-policy learning. Through experiments involving several vision-based locomotion and manipulation tasks, we find that V-MAIL learns successful visuomotor policies in a sample-efficient manner, has better stability compared to prior work, and also achieves higher asymptotic performance. We further find that by transferring the learned models, V-MAIL can learn new tasks from visual demonstrations without any additional environment interactions. All results including videos can be found online at \url{this https URL}.
Reference-Centric Models for Grounded Collaborative Dialogue

We present a grounded neural dialogue model that successfully collaborates with people in a partially-observable reference game. We focus on a setting where two agents each observe an overlapping part of a world context and need to identify and agree on some object they share. Therefore, the agents should pool their information and communicate pragmatically to solve the task. Our dialogue agent accurately grounds referents from the partner’s utterances using a structured reference resolver, conditions on these referents using a recurrent memory, and uses a pragmatic generation procedure to ensure the partner can resolve the references the agent produces. We evaluate on the OneCommon spatial grounding dialogue task (Udagawa and Aizawa 2019), involving a number of dots arranged on a board with continuously varying positions, sizes, and shades. Our agent substantially outperforms the previous state of the art for the task, obtaining a 20% relative improvement in successful task completion in self-play evaluations and a 50% relative improvement in success in human evaluations.
Unraveling Behavioral Ordering: Relative Costs and the Bullwhip Effect

Problem definition: We study the bullwhip effect and analyze the impact of human behavior. We separate rational ordering in response to increasing incoming orders from irrational ordering. Academic...
Corporate social performance and the managerial labor market

This paper examines the impact of a firm’s social performance on the CEO’s employment prospects. We find that CEOs are more (less) likely to leave office when there is a significant recent decline (improvement) in social performance. We then track departing CEOs’ subsequent employment records and find that the social performance of their previous employers improves their labor market outcomes. These CEOs are more likely to find a new executive position, move up to a larger public firm, and receive higher compensation from the new public firm. Using a Cox proportional hazard model, we find that the strong social performance of the previous employer helps CEOs find their next executive positions sooner. Overall, our results suggest that corporate social performance enhances CEOs’ labor market potential.
Sovereign Risk in Macroprudential Solvency Stress Testing

This paper explains the treatment of sovereign risk in macroprudential solvency stress testing, based on the experiences in the Financial Sector Assessment Program (FSAP). We discuss four essential steps in assessing the system-wide impact of sovereign risk: scope, loss estimation, shock calibration, and capital impact calculation. Most importantly, a market-consistent valuation approach lies at the heart of assessing the resilience of the financial sector in a tail risk scenario with sovereign distress. We present a flexible, closed-form approach to calibrating haircuts based on changes in expected sovereign defaults affecting bank solvency during adverse macroeconomic conditions. This paper demonstrates the effectiveness of using extreme value theory (EVT) in this context, with empirical examples from past FSAPs.