cssi/SciSciGPT-SciSciNet huggingface dataset:
Linalool acts as a fast and reversible anesthetic in Hydra

The ability to make transgenic Hydra lines has allowed for quantitative in vivo studies of Hydra regeneration and physiology. These studies commonly include excision, grafting and transplantation experiments along with high-resolution imaging of live animals, which can be challenging due to the animal's response to touch and light stimuli. While various anesthetics have been used in Hydra studies, they tend to be toxic over the course of a few hours or their long-term effects on animal health are unknown. Here, we show that the monoterpenoid alcohol linalool is a useful anesthetic for Hydra. Linalool is easy to use, non-toxic, fast acting, and reversible. It has no detectable long-term effects on cell viability or cell proliferation. We demonstrate that the same animal can be immobilized in linalool multiple times at intervals of several hours for repeated imaging over 2-3 days. This uniquely allows for in vivo imaging of dynamic processes such as head regeneration. We directly compare linalool to currently used anesthetics and show its superior performance. Linalool will be a useful tool for tissue manipulation and imaging in Hydra research in both research and teaching contexts.
Flow-Based Local Graph Clustering with Better Seed Set Inclusion.

Flow-based methods for local graph clustering have received significant recent attention for their theoretical cut improvement and runtime guarantees. In this work we present two improvements for using flow-based methods in real-world semi-supervised clustering problems. Our first contribution is a generalized objective function that allows practitioners to place strict and soft penalties on excluding specific seed nodes from the output set. This feature allows us to avoid the tendency, often exhibited by previous flow-based methods, to contract a large seed set into a small set of nodes that does not contain all or even most of the seed nodes. Our second contribution is a fast algorithm for minimizing our generalized objective function, based on a variant of the push-relabel algorithm for computing preflows. We make our approach very fast in practice by implementing a global relabeling heuristic and employing a warm-start procedure to quickly solve related cut problems. In practice our algorithm is faster than previous related flow-based methods, and is also more robust in detecting ground truth target regions in a graph, thanks to its ability to better incorporate semi-supervised information about target clusters.
Early tetrapodomorph biogeography: Controlling for fossil record bias in macroevolutionary analyses

Abstract The fossil record provides direct empirical data for understanding macroevolutionary patterns and processes. Inherent biases in the fossil record are well known to confound analyses of this data. Sampling bias proxies have been used as covariates in regression models to test for such biases. Proxies, such as formation count, are associated with paleobiodiversity, but are insufficient for explaining species dispersal owing to a lack of geographic context. Here, we develop a sampling bias proxy that incorporates geographic information and test it with a case study on early tetrapodomorph biogeography. We use recently-developed Bayesian phylogeographic models and a new supertree of early tetrapodomorphs to estimate dispersal rates and ancestral habitat locations. We find strong evidence that geographic sampling bias explains supposed radiations in dispersal rate (potential adaptive radiations). Our study highlights the necessity of accounting for geographic sampling bias in macroevolutionary and phylogenetic analyses and provides an approach to test for its effect.
Low-Resource Parsing with Crosslingual Contextualized Representations

Despite advances in dependency parsing, languages with small treebanks still present challenges. We assess recent approaches to multilingual contextual word representations (CWRs), and compare them for crosslingual transfer from a language with a large treebank to a language with a small or nonexistent treebank, by sharing parameters between languages in the parser itself. We experiment with a diverse selection of languages in both simulated and truly low-resource scenarios, and show that multilingual CWRs greatly facilitate low-resource dependency parsing even without crosslingual supervision such as dictionaries or parallel text. Furthermore, we examine the non-contextual part of the learned language models (which we call a “decontextual probe”) to demonstrate that polyglot language models better encode crosslingual lexical correspondence compared to aligned monolingual language models. This analysis provides further evidence that polyglot training is an effective approach to crosslingual transfer.
The spatially conscious machine learning model

Abstract Successfully predicting gentrification could have many social and commercial applications; however, real estate sales are difficult to predict because they belong to a chaotic system compr...
Carcinoma of the prostate.

• The results of palliative treatment of prostatic carcinoma were observed in 1,249 patients followed for five years or more. The high incidence of this disease makes rectal examinations advisable in all men over 50 years of age. The shortness of life expectancy and the amount of suffering seen when the treatment has been palliative argue in favor of radical prostatectomy. The advent of radical retropubic prostatectomy has made total extirpation more generally available; it has also shed new light on the mode of spread of the growth. Such spread was found in 30 out of 100 patients without elevated acid phosphatase levels in the blood or evidence of bone involvement. Interstitial irradiation with a colloidal form of radiogold (Au198) has been done in 389 patients with prostatic carcinoma that had extended too far locally to be operable but had not yet metastasized to the bones. It has been found possible to avoid radiation injury to the bladder and rectum, and of the first 100 patients in this series 22 are alive without clinical evidence of disease. Endocrine therapy has often given dramatic though temporary improvement, and the relief from pain has in some cases been lasting. The modern treatment of prostatic carcinoma requires the combined resources of surgery, chemistry, endocrinology, and radiology.
AC-DC current shunts and system for extended current and frequency ranges

This paper describes a new ac-dc current-shunt calibration system with a current range of 1 mA-100 A and a frequency range from 10 Hz to 100 kHz. The new system consists of an ac-dc comparator controlled via a universal serial bus and a set of reference coaxial shunts with dedicated thermal voltage converters. The design of the custom-built shunts has been optimized through a theoretical modeling. The results of the calibrations of the reference coaxial shunts are presented.
Input–Output Linkages and Sectoral Volatility

Why are some sectors more volatile than others? This paper uncovers evidence of an empirical regularity in the US economy: upstream sectors that are far removed from final consumers have higher levels of output volatility. The relationship between volatility and upstreamness is not driven by sector size, sector concentration, trade openness or the level of aggregation at which sectors are defined. Rather, the paper shows a stronger link between upstreamness and nominal output volatility, than with indexes of real output volatility. Aggregate exports at the national level also reflect the empirical regularity of higher volatility with upstreamness: Export volatility is higher in economies with trade portfolios dominated by upstream sectors. On average, reducing the upstreamness of exports by 1 also reduces aggregate export volatility by about 10%. The pattern of higher volatility for upstream sectors is explained with a model of demand shock transmission between sectors.
Ouroboros: On accelerating training of transformer-based language models

Language models are essential for natural language processing (NLP) tasks, such as machine translation and text summarization. Remarkable performance has been demonstrated recently across many NLP domains via a Transformer-based language model with over a billion parameters, verifying the benefits of model size. Model parallelism is required if a model is too large to fit in a single computing device. Current methods for model parallelism either suffer from backward locking in backpropagation or are not applicable to language models. We propose the first model-parallel algorithm that speeds the training of Transformer-based language models. We also prove that our proposed algorithm is guaranteed to converge to critical points for non-convex problems. Extensive experiments on Transformer and Transformer-XL language models demonstrate that the proposed algorithm obtains a much faster speedup beyond data parallelism, with comparable or better accuracy. Code to reproduce experiments is to be found at \url{https://github.com/LaraQianYang/Ouroboros}.
Theory of the flow-induced deformation of shallow compliant microchannels with thick walls

Long, shallow microchannels embedded in thick, soft materials are widely used in microfluidic devices for lab-on-a-chip applications. However, the bulging effect caused by fluidstructure interactio...
Model-Free Stochastic Reachability Using Kernel Distribution Embeddings

We present a data-driven solution to the terminal-hitting stochastic reachability problem for a Markov control process. We employ a nonparametric representation of the stochastic kernel as a conditional distribution embedding within a reproducing kernel Hilbert space (RKHS). This representation avoids intractable integrals in the dynamic recursion of the stochastic reachability problem since the expectations can be calculated as an inner product within the RKHS. We demonstrate this approach on a high-dimensional chain of integrators and on Clohessy-Wiltshire-Hill dynamics.
Hamiltonian cycles in k‐partite graphs

Chen, Faudree, Gould, Jacobson, and Lesniak determined the minimum degree threshold for which a balanced $k$-partite graph has a Hamiltonian cycle. We give an asymptotically tight minimum degree condition for Hamiltonian cycles in arbitrary $k$-partite graphs in which all parts have at most $n/2$ vertices (a necessary condition). To do this, we first prove a general result which both simplifies the process of checking whether a graph $G$ is a robust expander and gives useful structural information in the case when $G$ is not a robust expander. Then we use this result to prove that any $k$-partite graph satisfying the minimum degree condition is either a robust expander or else contains a Hamiltonian cycle directly.
Fourier multipliers for nonlocal Laplace operators

Fourier multiplier analysis is developed for nonlocal peridynamic-type Laplace operators, which are defined for scalar fields in Rn. The Fourier multipliers are given through an integral representa...
On a conjecture about an analogue of Tokuyama’s theorem for $$G_2$$

We prove the conjecture of Friedlander et al. (J Algebr Comb 41:1089, 2015) about sums over Littelmann patterns for the root system of type $$G_2$$ , which is an analogue of Tokuyama’s theorem Tokuyama (J Math Soc Jpn 40(4):671–685, 1988) for root systems of type $$A_r$$ . We use elementary means to show that the conjecture is implied by a finite set of polynomial identities.
LeafByte: A mobile application that measures leaf area and herbivory quickly and accurately

Abstract In both basic and applied studies, quantification of herbivory on foliage is a key metric in characterizing plant-herbivore interactions, which underpin many ecological, evolutionary, and agricultural processes. Current methods of quantifying herbivory are slow or inaccurate. We present LeafByte, a free iOS application for measuring leaf area and herbivory. LeafByte can save data automatically, read and record barcodes, handle both light and dark colored plant tissue, and be used non-destructively. We evaluate its accuracy and efficiency relative to existing herbivory assessment tools. LeafByte has the same accuracy as ImageJ, the field standard, but is 50% faster. Other tools, such as BioLeaf and grid quantification, are quick and accurate, but limited in the information they can provide. Visual estimation is quickest, but it only provides a coarse measure of leaf damage and tends to overestimate herbivory. LeafByte is a quick and accurate means of measuring leaf area and herbivory, making it a useful tool for research in fields such as ecology, entomology, agronomy, and plant science.
The Effect of Academic Load on Success for New College Students: Is Lighter Better?

Both students and advisers often assume that a lighter academic load during the first year of college will result in greater student success. This article examines that assumption. Academic load is measured in terms of credit load and course difficulty; success is measured in terms of GPA and retention. The experiences of a sample of first-year students at a comprehensive regional university are examined. While the credit loads for which students register are related to academic ability and prior academic success, the difficulty level of courses for which these students register is not. Variation in student credit loads is reduced because weaker students are required to take developmental courses but do not drop a corresponding number of college-credit courses. Contrary to common assumptions, students who register for more credits tend to earn higher GPAs and have greater retention even after controlling for academic ability, prior academic success, on-campus employment hours, and other background characteristics. Students who register for more difficult courses, however, tend to earn lower GPAs and experience lower retention. Any effect of credit load on retention appears to work through GPA. While much of the effect of course difficulty on retention also works through GPA, course difficulty does have a separate negative effect on one-year retention. While the possibilities that weaker students might be more successful with lighter credit loads or that stronger students might be more successful with more difficult courses were investigated, no significant interactions between prior academic success, academic load, and success were found.
A Three-Country Macroeconomic Model for Portugal

This paper outlines a simple three-country macroeconomic model designed to focus on the transmission of external shocks to Portugal. Building on the framework developed by Berg et al (2006), this model differentiates between shocks originating from both inside and outside the euro area, as well as domestic shocks, each of which have different implications for Portugal. This framework is also used to consider the dynamics of the Portuguese economy over recent decades. The model, which is designed to guide forecasts and undertake simulations, can easily be modified for use in other small euro area countries.
Poisson Geometry of Discrete Series Orbits, and Momentum Convexity for Noncompact Group Actions

The main result of this paper is a convexity theorem for momentum mappings of certain Hamiltonian actions of noncompact semisimple Lie groups. The image is required to fall within a certain open subset D of the (dual of the) Lie algebra, and the momentum map itself is required to be proper as a map to D. The set D corresponds roughly, via the orbit method, to the discrete series of representations of the group, Much of the paper is devoted to the study of D itself, which consists of the Lie algebra elements which have compact centralizer. When the group is Sp(2n), these elements are the ones which are called 'strongly stable' in the theory of linear Hamiltonian dynamical systems, and our results may be seen as a generalization of some of that theory to arbitrary semisimple Lie groups. As an application, we prove a new convexity theorem for the frequency sets of sums of positive definite Hamiltonians with prescribed frequencies.
Information-Theoretic Understanding of Population Risk Improvement with Model Compression.

We show that model compression can improve the population risk of a pre-trained model, by studying the tradeoff between the decrease in the generalization error and the increase in the empirical risk with model compression. We first prove that model compression reduces an information-theoretic bound on the generalization error; this allows for an interpretation of model compression as a regularization technique to avoid overfitting. We then characterize the increase in empirical risk with model compression using rate distortion theory. These results imply that the population risk could be improved by model compression if the decrease in generalization error exceeds the increase in empirical risk. We show through a linear regression example that such a decrease in population risk due to model compression is indeed possible. Our theoretical results further suggest that the Hessian-weighted K-means clustering compression approach can be improved by regularizing the distance between the clustering centers. We provide experiments with neural networks to support our theoretical assertions.
Waiting Before Serving: A Companion to Packet Management in Status Update Systems

In this paper, we explore the potential of server waiting before packet transmission in improving the Age of Information (AoI) in status update systems. We consider a non-preemptive queue with Poisson arrivals and independent general service distribution and we incorporate waiting before serving in two packet management schemes: M/GI/1/1 and M/GI/1/2*. In M/GI/1/1 there is no data buffer and a packet is taken to server only when it is idle whereas in M/GI/1/2* there is a single unit buffer that captures the latest arriving status update packet. In both schemes, we introduce “server waiting”: In M/GI/1/1 scheme, the server waits for a deterministic time immediately after a packet enters the server. In M/GI/1/2* scheme, depending on queue state, the server waits for a deterministic time before starting service. In both cases, if a newer arrival is captured, existing packet is discarded with no reset of the waiting period. Different from most existing works, we analyze AoI evolution by indexing the incoming packets, which is enabled by an alternative method of partitioning the area under the evolution of instantaneous AoI to calculate its time average. We obtain expressions for average AoI and average peak AoI for both queueing disciplines with waiting. Our numerical results demonstrate that waiting before service can bring significant improvement in average age, particularly, for heavy-tailed service distributions. This improvement comes at the expense of an increase in average peak AoI. We highlight the trade-off between average and average peak AoI generated by waiting before serving.
Automatic Fact-guided Sentence Modification

Online encyclopediae like Wikipedia contain large amounts of text that need frequent corrections and updates. The new information may contradict existing content in encyclopediae. In this paper, we focus on rewriting such dynamically changing articles. This is a challenging constrained generation task, as the output must be consistent with the new information and fit into the rest of the existing document. To this end, we propose a two-step solution: (1) We identify and remove the contradicting components in a target text for a given claim, using a neutralizing stance model; (2) We expand the remaining text to be consistent with the given claim, using a novel two-encoder sequence-to-sequence model with copy attention. Applied to a Wikipedia fact update dataset, our method successfully generates updated sentences for new claims, achieving the highest SARI score. Furthermore, we demonstrate that generating synthetic data through such rewritten sentences can successfully augment the FEVER fact-checking training dataset, leading to a relative error reduction of 13%.1
Enhanced emission from ultra-thin long wavelength infrared superlattices on epitaxial plasmonic materials

Molecular beam epitaxy allows for the monolithic integration of wavelength-flexible epitaxial infrared plasmonic materials with quantum-engineered infrared optoelectronic active regions. We experimentally demonstrate a sixfold enhancement in photoluminescence from ultrathin (total thickness λ o / 33) long wavelength infrared (LWIR) superlattices grown on highly doped semiconductor “designer metal” virtual substrates when compared to the same superlattice grown on an undoped virtual substrate. Analytical and numerical models of the emission process via a dyadic Green's function formalism are in agreement with experimental results and relate the observed enhancement of emission to a combination of Purcell enhancement due to surface plasmon modes as well as directionality enhancement due to cavity-substrate-emitter interaction. The results presented provide a potential pathway toward efficient, ultrasubwavelength LWIR emitter devices, as well as a monolithic epitaxial architecture offering the opportunity to investigate the ultimate limits of light-matter interaction in coupled plasmonic/optoelectronic materials.
Non-selective beta blocker use is associated with improved short-term survival in patients with cirrhosis referred for liver transplantation

Recent evidence cautions against the use of non-selective beta-blockers (NSBB) in patients with refractory ascites or spontaneous bacterial peritonitis while other data suggests a survival benefit in patients with advanced liver disease. The aim of this study was to describe the use and impact of NSBB in patients with cirrhosis referred for liver transplantation. A single-center cohort of patients with cirrhosis, who were referred and evaluated for liver transplantation between January and June 2012 were studied for baseline characteristics and clinical outcomes. Patients were grouped according to the use of NSBB at initial evaluation, with the endpoint of 90-day mortality. Sixty-five (38%) of 170 consecutive patients evaluated for liver transplantation were taking NSBB. Patients taking NSBB had higher MELD and Child Pugh score. NSBB use was associated with lower 90-day mortality (6% vs. 15%) with a risk adjusted hazard ratio of 0.27 (95%CI .09–0.88, p = .03). Patients taking NSBB developed acute kidney injury (AKI) within 90 days more frequently than patients not taking NSBB (22% vs 11%), p = 0.048). However, this was related to increased stage 1 AKI episodes, all of which resolved. Twelve (27%) of 45 patients with > 90 day follow up discontinued NSBB, most commonly for hypotension and AKI, had increased subsequent MELD and mortality. NSBB use in patients with cirrhosis undergoing liver transplant evaluation is associated with better short-term survival. Nevertheless, ongoing tolerance of NSBB in this population is dynamic and may select a subset of patients with better hemodynamic reserve.
Planning in Stochastic Environments with Goal Uncertainty

We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem — a general framework to model path planning and decision making in stochastic environments with goal uncertainty. The framework extends the stochastic shortest path (SSP) model to dynamic environments in which it is impossible to determine the exact goal states ahead of plan execution. GUSSPs introduce flexibility in goal specification by allowing a belief over possible goal configurations. The unique observations at potential goals helps the agent identify the true goal during plan execution. The partial observability is restricted to goals, facilitating the reduction to an SSP with a modified state space. We formally define a GUSSP and discuss its theoretical properties. We then propose an admissible heuristic that reduces the planning time using FLARES — a start-of-the-art probabilistic planner. We also propose a determinization approach for solving this class of problems. Finally, we present empirical results on a search and rescue mobile robot and three other problem domains in simulation.
Monotonicity Properties of Certain Measures over the Two-Level Quantum Systems

We demonstrate – using the case of the two-dimensional quantum systems – that the 'natural measure in the space of density matrices ρ describing N-dimensional quantum systems' proposed by Życzkowski et al. [Phys. Rev. A58 (1998), 883] does not belong to the class of normalized volume elements of monotone metrics on the quantum systems. Such metrics possess the statistically important property of being decreasing under stochastic mappings (coarse-grainings). We do note that the proposed natural measure (and certain evident variations upon it) exhibit quite specific monotonicity characteristics, but not of the form required for membership in that distinguished class.
Designed nonlocal pseudopotentials for enhanced transferability

A pseudopotential generation method is presented which significantly improves transferability. The method exploits flexibility contained in the separable Kleinman-Bylander form of the nonlocal pseudopotential @Phys. Rev. Lett. 48, 1425 ~1982!#. By adjusting the functional form of the local potential, we are able to improve the agreement with all-electron calculations. Results are presented for the H, Si, Ca, Zr, and Pb atomic pseudopotentials. Configuration testing, logarithmic derivatives, chemical hardness, and structural tests all confirm the accuracy of these pseudopotentials. @S0163-1829~99!14715-5#
Signal Reconstruction From Modulo Observations

We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the under-determined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the inverse problem limited to two modulo periods, inspired by recent advances in algorithms for phase retrieval under sparsity constraints. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal and provides improved performance over other existing algorithms. We also provide experiments validating our approach on both synthetic and real data to depict its superior performance.
Recurrent neural circuits for contour detection

We introduce a deep recurrent neural network architecture that approximates visual cortical circuits (Mely et al., 2018). We show that this architecture, which we refer to as the 𝜸-net, learns to solve contour detection tasks with better sample efficiency than state-of-the-art feedforward networks, while also exhibiting a classic perceptual illusion, known as the orientation-tilt illusion. Correcting this illusion significantly reduces \gnetw contour detection accuracy by driving it to prefer low-level edges over high-level object boundary contours. Overall, our study suggests that the orientation-tilt illusion is a byproduct of neural circuits that help biological visual systems achieve robust and efficient contour detection, and that incorporating these circuits in artificial neural networks can improve computer vision.
Corporate social responsibility, product market perception, and firm value

Abstract We examine whether corporate social responsibility (CSR) is used to signal product quality and whether CSR affects firm value through its positive effect on product market perception. Using a proprietary database, we find that visible CSR, such as environmental and community involvement, positively impacts product market perception, particularly for standardized goods and in competitive industries, and that this impact is more pronounced for product quality attributes. Furthermore, we find that CSR indirectly increases firm value through an improvement in product market perception. We conclude that product market perception is a channel through which CSR creates firm value.
Noncyclic division algebras over fields of Brauer dimension one

Abstract Let K be a complete discretely valued field of rank one, with residue field Q p . It is well known that period equals index in Br ( K ) . We prove that when p = 2 there exist noncyclic K-division algebras of every 2-power degree divisible by four. Otherwise, every K-division algebra is cyclic. This gives the first published example of a field whose Brauer dimension and cyclic length are not equal.
Obesity associated with increased postoperative pulmonary complications and mortality after trauma laparotomy.

Patient-related risk factors for the development of postoperative pulmonary complications (PPCs) include age ≥ 60-years, congestive heart failure, hypoalbuminemia and smoking. The effect of obesity is unclear and has not been shown to independently increase the likelihood of PPCs in trauma patients undergoing trauma laparotomy. We hypothesized the likelihood of mortality and PPCs would increase as body mass index (BMI) increases in trauma patients undergoing trauma laparotomy. The Trauma Quality Improvement Program (2010–2016) was queried to identify trauma patients ≥ 18-years-old undergoing trauma laparotomy within 6-h of presentation. A multivariable logistic regression analysis was used to determine the likelihood of PPCs and mortality when stratified by BMI. From 8,330 patients, 2,810 (33.7%) were overweight (25–29.9 kg/m2), 1444 (17.3%) obese (30–34.9 kg/m2), 580 (7.0%) severely obese (35–39.9 kg/m2), and 401 (4.8%) morbidly obese (≥ 40 kg/m2). After adjusting for covariates including age, injury severity score, chronic obstructive pulmonary disease, smoking, and rib/lung injury, the likelihood of PPCs increased with increasing BMI: overweight (OR = 1.37, CI 1.07–1.74, p = 0.012), obese (OR = 1.44, CI 1.08–1.92, p = 0.014), severely obese (OR = 2.20, CI 1.55–3.14, p < 0.001), morbidly obese (OR = 2.42, CI 1.67–3.51, p < 0.001), compared to those with normal BMI. In addition, the adjusted likelihood of mortality increased for the morbidly obese (OR = 2.60, CI 1.78–3.80, p < 0.001) compared to those with normal BMI. Obese trauma patients undergoing emergent trauma laparotomy have a high likelihood for both PPCs and mortality, with morbidly obese trauma patients having the highest likelihood for both. This suggests obesity should be accounted for in risk prediction models of trauma patients undergoing laparotomy.
Mixed bundling in oligopoly markets

Abstract This paper proposes a framework for studying competitive mixed bundling with an arbitrary number of firms. We examine both a firm's incentive to introduce mixed bundling and equilibrium tariffs when all firms adopt the mixed-bundling strategy. In the duopoly case, relative to separate sales, mixed bundling has ambiguous impacts on prices, profit and consumer surplus; with many firms, however, mixed bundling typically lowers all prices, harms firms and benefits consumers.
Covid-19: control measures must be equitable and inclusive.

Failure to respect the needs of vulnerable groups will seriously undermine response efforts
Optimal topological generators of $U(1)$

Abstract Sarnak's golden mean conjecture states that ( m + 1 ) d φ ( m ) ⩽ 1 + 2 5 for all integers m ⩾ 1 , where φ is the golden mean and d θ is the discrepancy function for m + 1 multiples of θ modulo 1. In this paper, we characterize the set S of values θ that share this property, as well as the set T of those with the property for some lower bound m ⩾ M . Remarkably, S mod 1 has only 16 elements, whereas T is the set of G L 2 ( Z ) -transformations of φ.
On Local Coefficients for Non-generic Representations of Some Classical Groups

This paper is concerned with representations of split orthogonal and quasi-split unitary groups over a nonarchimedean local field which are not generic, but which support a unique model of a different kind, the generalized Bessel model. The properties of the Bessel models under induction are studied, and an analogue of Rodier's theorem concerning the induction of Whittaker models is proved for Bessel models which are minimal in a suitable sense. The holomorphicity in the induction parameter of the Bessel functional is established. Local coefficients are defined for each irreducible supercuspidal representation which carries a Bessel functional and also for a certain component of each representation parabolically induced from such a supercuspidal. The local coefficients are related to the Plancherel measures, and their zeroes are shown to be among the poles of the standard intertwining operators.
Reward-Based Deception with Cognitive Bias

Deception plays a crucial role in adversarial or strategic interactions for self-defense and survival. This paper introduces a general framework and solution to address the deception of adversaries with bounded rationality. Such a problem is commonly encountered in many applications especially involving human adversaries. Leveraging the cognitive bias of humans in reward evaluation under stochastic outcomes, we introduce a framework to optimally assign resources of a limited quantity to optimally defend against human adversaries. Then we formulate the resource allocation problem as a signomial program to minimize the defender’s cost in an environment modeled as a Markov decision process. We use police patrol hour assignment as an illustrative example and provide detailed simulation results based on real-world data.
Can Mentoring Help Female Assistant Professors in Economics? An Evaluation by Randomized Trial

Women continue to be underrepresented in academic ranks in the economics profession. The Committee on the Status of Women in the Economics Profession of the American Economics Association established the CeMENT mentoring workshop to support women in research careers. The program was designed as a randomized controlled trial. This study evaluates differences between the treatment and control groups in career outcomes. Results indicate that relative to women in the control group, treated women are more likely to stay in academia and more likely to have received tenure in an institution ranked in the top 30 or 50 in economics in the world.
Effect of synaptic cell-to-cell transmission and recombination on the evolution of double mutants in HIV

Recombination in HIV infection can impact virus evolution in vivo in complex ways, as has been shown both experimentally and mathematically. The effect of free virus versus synaptic, cell-to-cell transmission on the evolution of double mutants, however, has not been investigated. Here, we do so by using a stochastic agent-based model. Consistent with data, we assume spatial constraints for synaptic but not for free-virus transmission. Two important effects of the viral spread mode are observed: (i) for disadvantageous mutants, synaptic transmission protects against detrimental effects of recombination on double mutant persistence. Under free virus transmission, recombination increases double mutant levels for negative epistasis, but reduces them for positive epistasis. This reduction for positive epistasis is much diminished under predominantly synaptic transmission, and recombination can, in fact, lead to increased mutant levels. (ii) The mode of virus spread also directly influences the evolutionary fate of double mutants. For disadvantageous mutants, double mutant production is the predominant driving force, and hence synaptic transmission leads to highest double mutant levels due to increased transmission efficiency. For advantageous mutants, double mutant spread is the most important force, and hence free virus transmission leads to fastest invasion due to better mixing. For neutral mutants, both production and spread of double mutants are important, and hence an optimal mixture of free virus and synaptic transmission maximizes double mutant fractions. Therefore, both free virus and synaptic transmission can enhance or delay double mutant evolution. Implications for drug resistance in HIV are discussed.
Riding the storm: fiscal sustainability in the Caribbean

Fiscal sustainability remains a paramount challenge for small economies with high debt and greater vulnerability to climate change. This paper applies the model-based fiscal sustainability test in ...
Deep Learning-Based Beam Alignment in Mmwave Vehicular Networks

Millimeter wave channels exhibit structure that allows beam alignment with fewer channel measurements than exhaustive beam search. From a compressed sensing (CS) perspective, the received channel measurements are usually obtained by multiplying a CS matrix with a sparse representation of the channel matrix. Due to the constraints imposed by analog frontends, designing CS matrices that efficiently exploit the channel structure is challenging. In this paper, we propose an end-to-end deep learning technique to design a structured CS matrix that is well suited to the underlying channel distribution, leveraging both sparsity and the particular spatial structure that appears in vehicular channels. The channel measurements acquired with the designed CS matrix are then used to predict the best beam for link configuration. Simulation results for vehicular communication channels indicate that our deep learning-based approach achieves better beam alignment than standard CS techniques that use the random phase shift-based design.
Electrocatalytic CO2 Reduction to Formate with Molecular Fe(III) Complexes Containing Pendent Proton Relays

Previously, we reported an iron(III) complex with 6,6′-([2,2′-bipyridine]-6,6′-diyl)bis(2,4-ditertbutyl-phenol) as a ligand (Fe(tbudhbpy)Cl, 1) as catalytically competent for the electrochemical re...
Generalizing the PAC Model: Sample Size Bounds From Metric Dimension-based Uniform Convergence Results

We consider the problem of learning functions on a domain X that take values in an arbitrary metric space Y. We assume only that the examples are generated by independent draws from an unknown distribution on X × Y. The learner's goal is to find a function in a given hypothesis space F of functions from X into Y that on average gives Y-values that are close to those observed in random examples (i.e. that has small error). We give a theorem on the uniform convergence of empirical error estimates to true error rates for certain hypothesis spaces F (generalizing a result of Pollard's), and show how this implies learnability, disregarding computational complexity. We generalize the notion of VC dimension to classes of functions mapping into a metric space and show that small VC dimension gives rapid uniform convergence for any distribution. We do this by relating the VC dimension of F to the metric dimension of certain embeddings of F, using results of Pollard and Dudley. As an application, we give a distribution-free uniform convergence result for certain classes of functions computed by neural nets. Here we fix a multi-layer feedforward neural net architecture in which each computation node applies a smooth sigmoid function to a weighted sum of its inputs. We then consider the class of functions defined by varying the weights. We also give uniform convergence results for classes of functions that are uniformly continuous on average, a new notion we introduce. These include classes of continuous functions with a uniform Lipschitz bound, and many classes of discontinuous functions, including indicator functions for regions of small boundary in the unit square. Our results are distribution-free in the former case, but distribution-specific in the latter case (we assume a “near uniform” distribution on the unit square). An extended abstract of this work is given in [1].
Learning Spatio-Temporal Features with Two-Stream Deep 3D CNNs for Lipreading.

We focus on the word-level visual lipreading, which requires recognizing the word being spoken, given only the video but not the audio. State-of-the-art methods explore the use of end-to-end neural networks, including a shallow (up to three layers) 3D convolutional neural network (CNN) + a deep 2D CNN (e.g., ResNet) as the front-end to extract visual features, and a recurrent neural network (e.g., bidirectional LSTM) as the back-end for classification. In this work, we propose to replace the shallow 3D CNNs + deep 2D CNNs front-end with recent successful deep 3D CNNs --- two-stream (i.e., grayscale video and optical flow streams) I3D. We evaluate different combinations of front-end and back-end modules with the grayscale video and optical flow inputs on the LRW dataset. The experiments show that, compared to the shallow 3D CNNs + deep 2D CNNs front-end, the deep 3D CNNs front-end with pre-training on the large-scale image and video datasets (e.g., ImageNet and Kinetics) can improve the classification accuracy. Also, we demonstrate that using the optical flow input alone can achieve comparable performance as using the grayscale video as input. Moreover, the two-stream network using both the grayscale video and optical flow inputs can further improve the performance. Overall, our two-stream I3D front-end with a Bi-LSTM back-end results in an absolute improvement of 5.3% over the previous art on the LRW dataset.
Shotgun Drug Repurposing Biotechnology to Tackle Epidemics and Pandemics

In this manuscript we highlight consensus between the list of drugs currently in clinical trials to treat COVID-19, the worldwide pandemic caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), and the list of predictions made using our shotgun drug discovery, repurposing, and design platform known as CANDO (Computational Analysis of Novel Drug Opportunities). We make the argument that increased funding and development for drug repurposing biotechnology like ours will help combat the inevitable pathogenic outbreaks of the future.
Optimal Control of Fractional Elliptic PDEs with State Constraints and Characterization of the Dual of Fractional-Order Sobolev Spaces

This paper introduces the notion of state constraints for optimal control problems governed by fractional elliptic partial differential equations. Several mathematical tools are developed during the process to study these problems, for instance, the characterization of the dual of fractional-order Sobolev spaces and the well-posedness of fractional elliptic equations with measure-valued data. These tools are widely applicable. We show well-posedness of the optimal control problem and derive the first- order optimality conditions. Notice that the adjoint equation is a fractional partial differential equation with a measure as the right-hand-side datum. We use the characterization of the fractional- order dual spaces to study the regularity of solutions of the state and adjoint equations. We emphasize that the classical case was considered by E. Casas, but almost none of the existing results are applicable to our fractional case. As an application of the regularity result of the adjoint equation, we establish the Sobolev regularity of the optimal control. In addition, under this setup, even weaker controls can be used.
Comprehensive palmitoyl-proteomic analysis identifies distinct protein signatures for large and small cancer-derived extracellular vesicles

Extracellular vesicles (EVs) are membrane-enclosed particles that play an important role in cancer progression and have emerged as a promising source of circulating biomarkers. Protein S-acylation, frequently called palmitoylation, has been proposed as a post-translational mechanism that modulates the dynamics of EV biogenesis and protein cargo sorting. However, technical challenges have limited large-scale profiling of the whole palmitoyl-proteins of EVs. We successfully employed a novel approach that combines low-background acyl-biotinyl exchange (LB-ABE) with label-free proteomics to analyse the palmitoyl-proteome of large EVs (L-EVs) and small EVs (S-EVs) from prostate cancer cells. Here we report the first palmitoyl-protein signature of EVs, and demonstrate that L- and S-EVs harbour proteins associated with distinct biological processes and subcellular origin. We identified STEAP1, STEAP2, and ABCC4 as prostate cancer-specific palmitoyl-proteins abundant in both EV populations. Importantly, localization of the above proteins in EVs was reduced upon inhibition of palmitoylation in the producing cells. Our results suggest that this post-translational modification may play a role in the sorting of the EV-bound secretome and possibly enable selective detection of disease biomarkers.
The Karger-Stein algorithm is optimal for k-cut

In the k-cut problem, we are given an edge-weighted graph and want to find the least-weight set of edges whose deletion breaks the graph into k connected components. Algorithms due to Karger-Stein and Thorup showed how to find such a minimum k-cut in time approximately O(n 2k−2). The best lower bounds come from conjectures about the solvability of the k-clique problem and a reduction from k-clique to k-cut, and show that solving k-cut is likely to require time Ω(n k ). Our recent results have given special-purpose algorithms that solve the problem in time n 1.98k + O(1), and ones that have better performance for special classes of graphs (e.g., for small integer weights). In this work, we resolve the problem for general graphs, by showing that for any fixed k ≥ 2, the Karger-Stein algorithm outputs any fixed minimum k-cut with probability at least O(n −k ), where O(·) hides a 2 O(lnlnn)2 factor. This also gives an extremal bound of O(n k ) on the number of minimum k-cuts in an n-vertex graph and an algorithm to compute a minimum k-cut in similar runtime. Both are tight up to O(1) factors. The first main ingredient in our result is a fine-grained analysis of how the graph shrinks—and how the average degree evolves—under the Karger-Stein process. The second ingredient is an extremal result bounding the number of cuts of size at most (2−δ) OPT/k, using the Sunflower lemma.
Cross-Modality Relevance for Reasoning on Language and Vision

This work deals with the challenge of learning and reasoning over language and vision data for the related downstream tasks such as visual question answering (VQA) and natural language for visual reasoning (NLVR). We design a novel cross-modality relevance module that is used in an end-to-end framework to learn the relevance representation between components of various input modalities under the supervision of a target task, which is more generalizable to unobserved data compared to merely reshaping the original representation space. In addition to modeling the relevance between the textual entities and visual entities, we model the higher-order relevance between entity relations in the text and object relations in the image. Our proposed approach shows competitive performance on two different language and vision tasks using public benchmarks and improves the state-of-the-art published results. The learned alignments of input spaces and their relevance representations by NLVR task boost the training efficiency of VQA task.
Boosting for Control of Dynamical Systems

We study the question of how to aggregate controllers for dynamical systems in order to improve their performance. To this end, we propose a framework of boosting for online control. Our main result is an efficient boosting algorithm that combines weak controllers into a provably more accurate one. Empirical evaluation on a host of control settings supports our theoretical findings.