cssi/SciSciGPT-SciSciNet huggingface dataset:
Labor Market Dynamics, Informality and Regulations in Latin America

Labor markets in Latin America and the Caribbean (LAC) are characterized by high levels of informality and relatively rigid regulation. This paper shows that these two features are related and together make the speed of adjustment of employment to shocks slower, especially when regulations are tightly enforced. Evidence suggests that strict labor market regulations also have an adverse effect on medium-term growth. While both regulations on prices (minimum wages) and quantities (employment protection) decrease the speed of adjustment to shocks, they appear to be binding in different phases of the cycle - the former affects mostly the (net) job creation margin and the latter the (net) job destruction margin. The results also highlight possible interactions between labor market regulations and the effectiveness of macro-stabilization tools - including exchange rate depreciation.
Estimating Regions of Attraction for Transitional Flows Using Quadratic Constraints

This letter describes a method for estimating regions of attraction and bounds on permissible perturbation amplitudes in nonlinear fluids systems. The proposed approach exploits quadratic constraints between the inputs and outputs of the nonlinearity on elliptical sets. This approach reduces conservatism and improves estimates for regions of attraction and bounds on permissible perturbation amplitudes over related methods that employ quadratic constraints on spherical sets. We present and investigate two algorithms for performing the analysis: an iterative method that refines the analysis by solving a sequence of semi-definite programs, and another based on solving a generalized eigenvalue problem with lower computational complexity, but at the cost of some precision in the final solution. The proposed algorithms are demonstrated on low-order mechanistic models of transitional flows. We further compare accuracy and computational complexity with analysis based on sum-of-squares optimization and direct-adjoint looping methods.
The Influence of Sport Nutrition Knowledge on Body Composition and Perceptions of Dietary Requirements in Collegiate Athletes

Sport nutrition knowledge has been shown to influence dietary habits of athletes. The purpose of the current study was to examine relationships between sport nutrition knowledge and body composition and examine potential predictors of body weight goals in collegiate athletes. Participants included National Collegiate Athletic Association Division III women (n = 42, height: 169.9 ± 6.9 cm; body mass: 67.1 ± 8.6 kg; fat-free mass: 51.3 ± 6.6 kg; body fat per cent: 24.2 ± 5.3%) and men (n = 25, height: 180.8 ± 7.2 cm; body mass: 89.2 ± 20.5 kg; fat-free mass: 75.9 ± 12.2 kg; body fat per cent: 13.5 ± 8.9%) athletes. Body composition was assessed via air displacement plethysmography. Athletes completed a validated questionnaire designed to assess sport nutrition knowledge and were asked questions about their perceived dietary energy and macronutrient requirements, as well as their body weight goal (i.e., lose, maintain, gain weight). Athletes answered 47.98 ± 11.29% of questions correctly on the nutrition questionnaire with no differences observed between sexes (men: 49.52 ± 11.76% vs. women: 47.03 ± 11.04%; p = 0.40). An inverse relationship between sport nutrition knowledge scores and body fat percentage (BF%) (r = -0.330; p = 0.008), and fat mass (r = -0.268; p = 0.032) was observed for all athletes. Fat mass (β = 0.224), BF% (β = 0.217), and body mass index (BMI) (β = 0.421) were all significant (p < 0.05) predictors of body weight goal in women. All athletes significantly (p < 0.001) underestimated daily energy (-1360 ± 610.2 kcal/day), carbohydrate (-301.6 ± 149.2 grams/day [g/day]), and fat (-41.4 ± 34.5 g/day) requirements. Division III collegiate athletes have a low level of sport nutrition knowledge, which was associated with a higher BF%. Women athletes with a higher body weight, BF% and BMI were more likely to select weight loss as a body weight goal. Athletes also significantly underestimated their energy and carbohydrate requirements based upon the demands of their sport, independent of sex.
MRI Image Recovery using Damped Denoising Vector AMP

Motivated by image recovery in magnetic resonance imaging (MRI), we propose a new approach to solving linear inverse problems based on iteratively calling a deep neural-network, sometimes referred to as plug-and-play recovery. Our approach is based on the vector approximate message passing (VAMP) algorithm, which is known for mean-squared error (MSE)-optimal recovery under certain conditions. The forward operator in MRI, however, does not satisfy these conditions, and thus we design new damping and initialization schemes to help VAMP. The resulting DD-VAMP++ algorithm is shown to outperform existing algorithms in convergence speed and accuracy when recovering images from the fastMRI database for the practical case of Cartesian sampling.
Pathogen and Endophyte Assemblages Co-vary With Beech Bark Disease Progression, Tree Decline, and Regional Climate

Plant-pathogen interactions are often considered in a pairwise manner with minimal consideration of the impacts of the broader endophytic community on disease progression and/or outcomes for disease agents and hosts. Community interactions may be especially relevant in the context of disease complexes (i.e, interacting or functionally redundant causal agents) and decline diseases (where saprobes and weak pathogens synergize the effects of primary infections and hasten host mortality). Here we describe the bark endophyte communities associated with a widespread decline disease of American beech, beech bark disease (BBD), caused by an invasive scale insect (Cryptococcus fagisuga) and two fungal pathogens, Neonectria faginata and N. ditissima. We show that the two primary fungal disease agents co-occur more broadly than previously understood (35.5% of infected trees), including within the same 1-cm diameter phloem samples. The two species appear to have contrasting associations with climate and stages of tree decline, wherein N. faginata was associated with warmer and N. ditissima with cooler temperatures. Neonectria ditissima showed a positive association with tree crown dieback – no such association was observed for N. faginata. Further, we identify fungal endophytes that may modulate disease progression as entomopathogens, mycoparasites, saprotrophs and/or additional pathogens, including Clonostachys rosea and Fusarium babinda. These fungi may alter the trajectory of disease via feedbacks with the primary disease agents or by altering symptom expression or rates of tree decline across the range of BBD.
Pairing by a Dynamical Interaction in a Metal

We consider pairing of itinerant fermions in a metal near a quantum-critical point (QCP) towards some form of particle-hole order (nematic, spin-density-wave, charge-density-wave, etc.). At a QCP, the dominant interaction between fermions comes from exchanging massless fluctuations of a critical order parameter. At low energies, this physics can be described by an effective model with the dynamical electron-electron interaction V(Ωm) ∝ 1/|Ωm|γ, up to some upper cutoff Λ. The case γ = 0 corresponds to BCS theory, and can be solved by summing up geometric series of Cooper logarithms. We show that for a finite γ, the pairing problem is still marginal (i.e., perturbation series are logarithmic), but one needs to go beyond logarithmic approximation to find the pairing instability. We discuss specifics of the pairing at γ > 0 in some detail and also analyze the marginal case γ = 0+, when V(Ωm) = λlog(Λ/|Ωm|). We show that in this case the summation of Cooper logarithms does yield the pairing instability at λlog2(Λ/Tc) = O(1), but the logarithmic series are not geometrical. We reformulate the pairing problem in terms of a renormalization group (RG) flow of the coupling, and show that the RG equation is different in the cases γ = 0, γ = 0+, and γ > 0.
Pressure dependence of Ce valence in CeRhIn5.

We have studied the Ce valence as a function of pressure in CeRhIn5 at 300 K and at 22 K using x-ray absorption spectroscopy in partial fluorescent yield mode. At room temperature, we found no detectable change in Ce valence greater than 0.01 up to a pressure of 5.5 GPa. At 22 K, the valence remains robust against pressure below 6 GPa, in contrast to the predicted valence crossover at P = 2.35 GPa. This work yields an upper limit for the change in Ce-valence and suggests that the critical valence fluctuation scenario, in its current form, is unlikely.
Duration and reliability of the silent period in individuals with spinal cord injury.

Design Prospective cohort study. Objectives We aim to better understand the silent period (SP), an inhibitory counterpart to the well-known motor evoked potential (MEP) elicited by transcranial magnetic stimulation (TMS), in individuals with spinal cord injury (SCI). Setting Veterans Affairs Hospital in New York. Methods EMG responses were measured in the target abductor pollicis brevis at rest (TMS at 120% of resting motor threshold (RMT)) and during maximal effort (TMS at 110% of RMT). Participants with chronic cervical SCI (n = 9) and AB participants (n = 12) underwent between 3 and 7 sessions of testing on separate days. The primary outcomes were the magnitude and reliability of SP duration, resting and active MEP amplitudes, and RMT. Results SCI participants showed significantly lower MEP amplitudes compared to AB participants. SCI SP duration was not significantly different from AB SP duration. SP duration demonstrated reduced intra-participant variability within and across sessions compared with MEP amplitudes. SCI participants also demonstrated a higher prevalence of SP 'interruptions' compared to AB participants. Conclusions In a small group of individuals with chronic cervical SCI, we confirmed the well-known findings that SCI individuals have lower TMS evoked potential amplitudes and a tendency toward higher TMS motor thresholds relative to able-bodied controls. We did not observe a significant difference in SP duration between individuals with versus without SCI. However, SP duration is a more reliable outcome within and across multiple sessions than MEP amplitude.
Coherence shifts in attribute evaluations.

In five experiments, people repeatedly judged individual options with respect to both overall value and attribute values. When required to choose between two snacks, each differing in two attributes (pleasure and nutrition), people9s assessments of value shifted from pre- to post-choice in the direction that spread the alternatives further apart so as to favor the winner, thereby increasing confidence in the choice. This shift was observed not only for ratings of overall value, but also for each of the two individual attributes. The magnitude of the coherence shift increased with choice difficulty as measured by the difference in initial ratings of overall value for the two options, as well as with a measure of attribute disparity (the degree to which individual attributes "disagree" with one another as to which option is superior). In Experiments 2-5, tasks other than explicit choice generated the same qualitative pattern of value changes, confidence, and response time. These findings support the hypothesis that active consideration of options, whether or not explicitly related to value, automatically refines the mental value representations for the options, which in turn allows them to be more precisely distinguished when later included in a value-based choice set.
What's my target? Individual analyst forecasts and last-chance earnings management

Abstract Kirk, Reppenhagen, and Tucker (2014) find that investors use individual analyst forecasts as additional earnings benchmarks. We investigate whether executives manage earnings to beat these individual benchmarks. Using year-end effective tax rate (ETR) manipulation as our setting, we find that firms decrease ETRs from 3rd to 4th quarter to meet or beat a greater percentage of individual forecasts. We also find some evidence that firms use incremental ETR changes to meet forecasts by key analysts. After controlling for the distance to the nearest forecast, our evidence shows that firms are more likely to beat an incremental forecast with a decrease in ETR compared to missing an incremental forecast with an increase in ETR. Our study highlights the strategic nature of earnings management by providing evidence that managers consider individual forecasts to calibrate earnings management decisions.
Search Planning of a UAV/UGV Team With Localization Uncertainty in a Subterranean Environment

A waypoint planning algorithm for an unmanned aerial vehicle (UAV) is presented that is teamed with an unmanned ground vehicle (UGV) for the task of search and rescue in a subterranean environment. The UAV and UGV are teamed such that the localization of the UAV is conducted on the UGV via the multisensor fusion of a fisheye camera, 3-D light detection and ranging, ranging radio, and a laser altimeter. Likewise, the trajectory planning of the UAV is conducted on the UGV, which is assumed to have a 3-D map of the environment (e.g., from simultaneous localization and mapping). The goal of the planning algorithm is to satisfy the mission's exploration criteria while reducing the localization error of the UAV by evaluating the belief space for potential exploration routes. The presented algorithm is evaluated in a relevant simulation environment where the planning algorithm is shown to be effective at reducing the localization errors of the UAV.
The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity

Humans are increasingly interacting with machines through language, sometimes in contexts where the user may not know they are talking to a machine (like over the phone or a text chatbot). We aim to understand how system designers and researchers might allow their systems to confirm its non-human identity. We collect over 2,500 phrasings related to the intent of “Are you a robot?”. This is paired with over 2,500 adversarially selected utterances where only confirming the system is non-human would be insufficient or disfluent. We compare classifiers to recognize the intent and discuss the precision/recall and model complexity tradeoffs. Such classifiers could be integrated into dialog systems to avoid undesired deception. We then explore how both a generative research model (Blender) as well as two deployed systems (Amazon Alexa, Google Assistant) handle this intent, finding that systems often fail to confirm their non-human identity. Finally, we try to understand what a good response to the intent would be, and conduct a user study to compare the important aspects when responding to this intent.
Breeding responses to environmental variation are age- and trait-dependent in female Nazca boobies

Age and environment are important determinants of reproductive parameters in long-lived organisms. These factors may interact to determine breeding responses to environmental change, yet few studies have examined the environmental dependence of aging patterns across the entire life span. We do so, using a 20-yr longitudinal data set of reproductive phenotypes in long-lived female Nazca boobies (Sula granti), a monogamous seabird breeding in the eastern tropical Pacific. Young and old females may suffer from inexperience and senescence, respectively, and/or practice reproductive restraint. Breeding performance (for breeding participation, breeding date, clutch size, egg volume, and offspring production) was expected to be lower in these age classes, particularly under environmental challenge, in comparison with middle-aged breeders. Sea surface temperature anomalies (SSTA) represented interannual variation in the El Nino-Southern Oscillation (ENSO) and were one proxy for environmental quality (a population count of clutch initiations was a second). Although only females lay eggs, both sexes care for eggs and nestlings, and the male partner's age, alone or in interaction with female age, was evaluated as a predictor of breeding performance. Middle-aged females performed better than young and old birds for all reproductive traits. Pairing with a young male delayed breeding (particularly for old females) and reduced clutch size, and pairing with an old male reduced offspring production. Challenging environments increased age effects on breeding probability and breeding date across young to middle ages and for offspring production across middle to old ages. However, important exceptions to the predicted patterns for clutch size and fledging success across young to middle ages suggested that trade-offs between fitness components may complicate patterns of trait expression across the life span. Relationships between breeding participation, environment, and individual quality and/or experience in young females may also contribute to unexpected patterns for clutch size and fledging success, traits expressed only in breeders. Finally, independent of age, breeding responses of female Nazca boobies to the ENSO did not follow expectations derived from oceanic forcing of primary productivity. During El Nino-like conditions, egg-laying traits (clutch size, breeding date) improved, but offspring production declined, whereas La Nina-like conditions were "poor" environments throughout the breeding cycle.
Thermogravimetry system designed for use in dispersion strengthening studies

Abstract A thermogravimetry system, designed to study the reduction of oxides in metal and alloy powders to be used in dispersion strengthened materials, is described. The apparatus was devised for use at high temperatures with controlled atmospheres. Experimental weight change and moisture evolution results for the thermal decomposition of calcium oxalate monohydrate in dry helium, and experimental weight change results for the reduction of nickel oxide in dry hydrogen and hydrogen containing 15,000 p.p.m. water vapor are presented. The system is currently being successfully applied to the evaluation of the reduction characteristics and the removal of impurities from metals and alloys to be used for dispersion strengthening.
Combinatorial anti-concentration inequalities, with applications

We prove several different anti-concentration inequalities for functions of independent Bernoulli-distributed random variables. First, motivated by a conjecture of Alon, Hefetz, Krivelevich and Tyomkyn, we prove some “Poisson-type” anti-concentration theorems that give bounds of the form 1/e + o(1) for the point probabilities of certain polynomials. Second, we prove an anti-concentration inequality for polynomials with nonnegative coefficients which extends the classical Erdős–Littlewood–Offord theorem and improves a theorem of Meka, Nguyen and Vu for polynomials of this type. As an application, we prove some new anti-concentration bounds for subgraph counts in random graphs.
Learning Invariant Representations using Inverse Contrastive Loss.

Learning invariant representations is a critical first step in a number of machine learning tasks. A common approach is given by the so-called information bottleneck principle in which an application dependent function of mutual information is carefully chosen and optimized. Unfortunately, in practice, these functions are not suitable for optimization purposes since these losses are agnostic of the metric structure of the parameters of the model. In our paper, we introduce a class of losses for learning representations that are invariant to some extraneous variable of interest by inverting the class of contrastive losses, i.e., inverse contrastive loss (ICL). We show that if the extraneous variable is binary, then optimizing ICL is equivalent to optimizing a regularized MMD divergence. More generally, we also show that if we are provided a metric on the sample space, our formulation of ICL can be decomposed into a sum of convex functions of the given distance metric. Our experimental results indicate that models obtained by optimizing ICL achieve significantly better invariance to the extraneous variable for a fixed desired level of accuracy. In a variety of experimental settings, we show applicability of ICL for learning invariant representations for both continuous and discrete protected/extraneous variables. The project page with code is available at https://github.com/adityakumarakash/ICL
Nonsense-mediated decay is highly stable across individuals and tissues

Precise interpretation of the effects of rare protein-truncating variants (PTVs) is important for accurate determination of variant impact. Current methods for assessing the ability of PTVs to induce nonsense-mediated decay (NMD) focus primarily on the position of the variant in the transcript. We used RNA sequencing of the Genotype Tissue Expression v.8 cohort to compute the efficiency of NMD using allelic imbalance for 2,320 rare (genome aggregation database minor allele frequency ≤ 1%) PTVs across 809 individuals in 49 tissues. We created an interpretable predictive model using penalized logistic regression in order to evaluate the comprehensive influence of variant annotation, tissue, and inter-individual variation on NMD. We found that variant position, allele frequency, the inclusion of ultra-rare and singleton variants, and conservation were predictive of allelic imbalance. Furthermore, we found that NMD effects were highly concordant across tissues and individuals. Due to this high consistency, we demonstrate in silico that utilizing peripheral tissues or cell lines provides accurate prediction of NMD for PTVs.
Imitation Learning as f-Divergence Minimization

We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any one of them. We show that the state-of-the-art methods such as GAIL and behavior cloning, due to their choice of loss function, often incorrectly interpolate between such modes. Our key insight is to minimize the right divergence between the learner and the expert state-action distributions, namely the reverse KL divergence or I-projection. We propose a general imitation learning framework for estimating and minimizing any f-Divergence. By plugging in different divergences, we are able to recover existing algorithms such as Behavior Cloning (Kullback-Leibler), GAIL (Jensen Shannon) and DAgger (Total Variation). Empirical results show that our approximate I-projection technique is able to imitate multi-modal behaviors more reliably than GAIL and behavior cloning.
Genomic heterogeneity affects the response to Daylight Saving Time.

Circadian clocks control the timing of many physiological events in the 24-h day. When individuals undergo an abrupt external shift (e.g., change in work schedule or travel across multiple time zones), circadian clocks become misaligned with the new time and may take several days to adjust. Chronic circadian misalignment, e.g., as a result of shift work, has been shown to lead to several physical and mental health problems. Despite the serious health implications of circadian misalignment, relatively little is known about how genetic variation affects an individual’s ability to entrain to abrupt external changes. Accordingly, we used the one-hour advance from the onset of daylight saving time (DST) as a natural experiment to comprehensively study how individual heterogeneity affects the shift of sleep/wake cycles in response to an abrupt external time change. We found that individuals genetically predisposed to a morning tendency adjusted to the advance in a few days, while genetically predisposed evening-inclined individuals had not shifted. Observing differential effects by genetic disposition after a one-hour advance underscores the importance of heterogeneity in adaptation to external schedule shifts. These genetic differences may affect how individuals adjust to jet lag or shift work as well.
On the derivation of the wave kinetic equation for NLS

A fundamental question in wave turbulence theory is to understand how the wave kinetic equation describes the long-time dynamics of its associated nonlinear dispersive equation. Formal derivations in the physics literature, dating back to the work of Peierls in 1928, suggest that such a kinetic description should hold (for well-prepared random data) at a large kinetic time scale and in a limiting regime where the size L of the domain goes to infinity and the strength of the nonlinearity goes to (weak nonlinearity). For the cubic nonlinear Schrodinger equation, and is related to the conserved mass of the solution via . In this paper, we study the rigorous justification of this monumental statement and show that the answer seems to depend on the particular scaling law in which the limit is taken, in a spirit similar to how the Boltzmann–Grad scaling law is imposed in the derivation of Boltzmann’s equation. In particular, there appear to be two favourable scaling laws: when approaches like or like (for arbitrary small ), we exhibit the wave kinetic equation up to time scales , by showing that the relevant Feynman-diagram expansions converge absolutely (as a sum over paired trees). For the other scaling laws, we justify the onset of the kinetic description at time scales and identify specific interactions that become very large for times beyond . In particular, the relevant tree expansion diverges absolutely there. In light of those interactions, extending the kinetic description beyond toward for such scaling laws seems to require new methods and ideas.
Smoothing of multiple structures on embedded Enriques manifolds

We show that given an embedding of an Enriques manifold of index d in a large enough projective space, there will exist embedded multiple structures with conormal bundle isomorphic to the trace zero module of the universal covering map, the universal cover being either a hyperkahler or a Calabi–Yau manifold. We then show that these multiple structures (also known as d-ropes) can be smoothed to smooth hyperkahler or Calabi–Yau manifolds respectively. Hence we obtain a flat family of hyperkahler (or Calabi–Yau) manifolds embedded in the same projective space which degenerates to an embedded d-rope structure on the given Enriques manifold of index d. The above shows that these d-rope structures on the embedded Enriques manifold are points of the Hilbert scheme containing the fibres of the above family. We show that they are smooth points of the Hilbert scheme when $$d=2$$ .
On the Intrinsic Differential Privacy of Bagging

Differentially private machine learning trains models while protecting privacy of the sensitive training data. The key to obtain differentially private models is to introduce noise/randomness to the training process. In particular, existing differentially private machine learning methods add noise to the training data, the gradients, the loss function, and/or the model itself. Bagging, a popular ensemble learning framework, randomly creates some subsamples of the training data, trains a base model for each subsample using a base learner, and takes majority vote among the base models when making predictions. Bagging has intrinsic randomness in the training process as it randomly creates subsamples. Our major theoretical results show that such intrinsic randomness already makes Bagging differentially private without the needs of additional noise. In particular, we prove that, for any base learner, Bagging with and without replacement respectively achieves $\left(N\cdot k \cdot \ln{\frac{n+1}{n}},1- (\frac{n-1}{n})^{N\cdot k}\right)$-differential privacy and $\left(\ln{\frac{n+1}{n+1-N\cdot k}}, \frac{N\cdot k}{n} \right)$-differential privacy, where $n$ is the training data size, $k$ is the subsample size, and $N$ is the number of base models. Moreover, we prove that if no assumptions about the base learner are made, our derived privacy guarantees are tight. We empirically evaluate Bagging on MNIST and CIFAR10. Our experimental results demonstrate that Bagging achieves significantly higher accuracies than state-of-the-art differentially private machine learning methods with the same privacy budgets.
Macroscopic Superposition States in Isolated Quantum Systems

For any choice of initial state and weak assumptions about the Hamiltonian, large isolated quantum systems undergoing Schrodinger evolution spend most of their time in macroscopic superposition states. The result follows from von Neumann’s 1929 Quantum Ergodic Theorem. As a specific example, we consider a box containing a solid ball and some gas molecules. Regardless of the initial state, the system will evolve into a quantum superposition of states with the ball in macroscopically different positions. Thus, despite their seeming fragility, macroscopic superposition states are ubiquitous consequences of quantum evolution. We discuss the connection to many worlds quantum mechanics.
Slow and Stale Gradients Can Win the Race

Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in runtime as it waits for the slowest workers (stragglers). Asynchronous methods can alleviate stragglers, but cause gradient staleness that can adversely affect the convergence error . In this work, we present a novel theoretical characterization of the speedup offered by asynchronous methods by analyzing the trade-off between the error in the trained model and the actual training runtime (wallclock time). The main novelty in our work is that our runtime analysis considers random straggling delays, which helps us design and compare distributed SGD algorithms that strike a balance between straggling and staleness. We also provide a new error convergence analysis of asynchronous SGD variants without bounded or exponential delay assumptions. Finally, based on our theoretical characterization of the error-runtime trade-off, we propose a method of gradually varying synchronicity in distributed SGD and demonstrate its performance on the CIFAR10 dataset.
Resource-Aware Session Types for Digital Contracts

Programming digital contracts comes with unique challenges, which include (i) expressing and enforcing protocols of interaction, (ii) controlling resource usage, and (iii) preventing the duplication or deletion of a contract’s assets. This article presents the design and type-theoretic foundation of Nomos, a programming language for digital contracts that addresses these challenges. To express and enforce protocols, Nomos is based on shared binary session types. To control resource usage, Nomos employs automatic amortized resource analysis. To prevent the duplication or deletion of assets, Nomos uses a linear type system. A monad integrates the effectful session-typed language with a general-purpose functional language. Nomos’ prototype implementation features linear-time type checking and efficient type reconstruction that includes automatic inference of resource bounds via off-the-shelf linear optimization. The effectiveness of the language is evaluated with case studies on implementing common smart contracts such as auctions, elections, and currencies. Nomos is completely formalized, including the type system, a cost semantics, and a transactional semantics to deploy Nomos contracts on a blockchain. The type soundness proof ensures that protocols are followed at run-time and that types establish sound upper bounds on the resource consumption, ruling out re-entrancy and out-of-gas vulnerabilities.
CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis.

Background The rise of spatial transcriptomics technologies is leading to new insights about how gene regulation happens in a spatial context. Determining which genes are expressed in similar spatial patterns can reveal gene regulatory relationships across cell types in a tissue. However, many current analysis methods do not take full advantage of the spatial organization of the data, instead treating pixels as independent features. Here, we present CoSTA: a novel approach to learn spatial similarities between gene expression matrices via convolutional neural network (ConvNet) clustering. Results By analyzing simulated and previously published spatial transcriptomics data, we demonstrate that CoSTA learns spatial relationships between genes in a way that emphasizes broader spatial patterns rather than pixel-level correlation. CoSTA provides a quantitative measure of expression pattern similarity between each pair of genes rather than only classifying genes into categories. We find that CoSTA identifies narrower, but biologically relevant, sets of significantly related genes as compared to other approaches. Conclusions The deep learning CoSTA approach provides a different angle to spatial transcriptomics analysis by focusing on the shape of expression patterns, using more information about the positions of neighboring pixels than would an overlap or pixel correlation approach. CoSTA can be applied to any spatial transcriptomics data represented in matrix form and may have future applications to datasets such as histology in which images of different genes are from similar but not identical biological sections.
Improved Upper Bounds for the Rigidity of Kronecker Products

The rigidity of a matrix A for target rank r is the minimum number of entries of A that need to be changed in order to obtain a matrix of rank at most r. At MFCS'77, Valiant introduced matrix rigidity as a tool to prove circuit lower bounds for linear functions and since then this notion received much attention and found applications in other areas of complexity theory. The problem of constructing an explicit family of matrices that are sufficiently rigid for Valiant’s reduction (Valiant-rigid) still remains open. Moreover, since 2017 most of the long-studied candidates have been shown not to be Valiant-rigid. Some of those former candidates for rigidity are Kronecker products of small matrices. In a recent paper (STOC'21), Alman gave a general non-rigidity result for such matrices: he showed that if an n× n matrix A (over any field) is a Kronecker product of d× d matrices M₁,… ,M_k (so n = d^k) (d ≥ 2) then changing only n^{1+e} entries of A one can reduce its rank to ≤ n^{1-γ}, where 1/γ is roughly 2^d/e². In this note we improve this result in two directions. First, we do not require the matrices M_i to have equal size. Second, we reduce 1/γ from exponential in d to roughly d^{3/2}/e² (where d is the maximum size of the matrices M_i), and to nearly linear (roughly d/e²) for matrices M_i of sizes within a constant factor of each other. As an application of our results we significantly expand the class of Hadamard matrices that are known not to be Valiant-rigid; these now include the Kronecker products of Paley-Hadamard matrices and Hadamard matrices of bounded size.
Do Online Communities Improve Job Performance in the Geographically Dispersed Organization

The past decade has witnessed the explosive implementation of corporate social tools and technologies such as corporate internal online communities. It is expected that such online communities can facilitate knowledge sharing among employees from different geographic locations. However, there is little empirical evidence on whether implementing an online community can lead to improved job performance at the individual level in the geographically dispersed organization. In this study, we fill this gap in the literature by using a quasi-natural experiment opportunity -- a large manufacturer introduced an internal online community for its geographically dispersed sales representatives. Specifically, the unique dataset collected from different departments of the company enables us to observe the representatives’ detailed community usage records, sales performances, and demographic information. Our difference-in-differences design and instrumental-variable analysis jointly demonstrate a positive and significant relationship between the representatives’ community usage and their sales performance. Furthermore, our results suggest that the online community has heterogeneous effects on different representatives, such that the online community is significantly more beneficial for representatives who have relatively fewer offline opportunities to observe and learn from their coworkers’ activities (that is, the representatives from the subunits with a smaller number of coworkers and lower average job performance). Our study has implications for managers regarding the benefits brought by corporate online communities as well as the critical role that content sharing and consumption play in enhancing the positive impact of such communities.
On the Design of Deep Priors for Unsupervised Audio Restoration

Unsupervised deep learning methods for solving audio restoration problems extensively rely on carefully tailored neural architectures that carry strong inductive biases for defining priors in the time or spectral domain. In this context, lot of recent success has been achieved with sophisticated convolutional network constructions that recover audio signals in the spectral domain. However, in practice, audio priors require careful engineering of the convolutional kernels to be effective at solving ill-posed restoration tasks, while also being easy to train. To this end, in this paper, we propose a new U-Net based prior that does not impact either the network complexity or convergence behavior of existing convolutional architectures, yet leads to significantly improved restoration. In particular, we advocate the use of carefully designed dilation schedules and dense connections in the U-Net architecture to obtain powerful audio priors. Using empirical studies on standard benchmarks and a variety of ill-posed restoration tasks, such as audio denoising, in-painting and source separation, we demonstrate that our proposed approach consistently outperforms widely adopted audio prior architectures.
Addiction-Associated Genetic Variants Implicate Brain Cell Type- and Region-Specific Cis-Regulatory Elements in Addiction Neurobiology.

Recent large genome-wide association studies have identified multiple confident risk loci linked to addiction-associated behavioral traits. Most genetic variants linked to addiction-associated traits lie in noncoding regions of the genome, likely disrupting cis-regulatory element (CRE) function. CREs tend to be highly cell type-specific and may contribute to the functional development of the neural circuits underlying addiction. Yet, a systematic approach for predicting the impact of risk variants on the CREs of specific cell populations is lacking. To dissect the cell types and brain regions underlying addiction-associated traits, we applied stratified linkage disequilibrium score regression to compare genome-wide association studies to genomic regions collected from human and mouse assays for open chromatin, which is associated with CRE activity. We found enrichment of addiction-associated variants in putative CREs marked by open chromatin in neuronal (NeuN+) nuclei collected from multiple prefrontal cortical areas and striatal regions known to play major roles in reward and addiction. To further dissect the cell type-specific basis of addiction-associated traits, we also identified enrichments in human orthologs of open chromatin regions of female and male mouse neuronal subtypes: cortical excitatory, D1, D2, and PV. Last, we developed machine learning models to predict mouse cell type-specific open chromatin, enabling us to further categorize human NeuN+ open chromatin regions into cortical excitatory or striatal D1 and D2 neurons and predict the functional impact of addiction-associated genetic variants. Our results suggest that different neuronal subtypes within the reward system play distinct roles in the variety of traits that contribute to addiction. SIGNIFICANCE STATEMENT We combine statistical genetic and machine learning techniques to find that the predisposition to for nicotine, alcohol, and cannabis use behaviors can be partially explained by genetic variants in conserved regulatory elements within specific brain regions and neuronal subtypes of the reward system. Our computational framework can flexibly integrate open chromatin data across species to screen for putative causal variants in a cell type- and tissue-specific manner for numerous complex traits.
Progressive Compressed Records: Taking a Byte Out of Deep Learning Data

Deep learning accelerators efficiently train over vast and growing amounts of data, placing a newfound burden on commodity networks and storage devices. A common approach to conserve bandwidth invo...
Effects of Uncertainty and Spatial Variability on Seepage Into Drifts in the Yucca Mountain Total System Performance Assessment Model

Seepage into the repository drifts is an important factor in total-system performance. Uncertainty and spatial variability are considered in the seepage calculations. The base-case results show 13.6% of the waste packages (WPs) have seepage. For 5th percentile uncertainty, 4.5% of the WPs have seepage and the seepage flow decreased by a factor of 2. For 95th percentile uncertainty, 21.5% of the WPs have seepage and the seepage flow increased by a factor of 2. Ignoring spatial variability resulted in seepage on 100% of the WPs, with a factor of 3 increase in the seepage flow.
Topology Optimization for Manufacturing with Accessible Support Structures

Abstract Metal additive manufacturing (AM) processes often fabricate a near-net shape that includes the as-designed part as well as the sacrificial support structures that need to be machined away by subtractive manufacturing (SM), for instance multi-axis machining. Thus, although AM is capable of generating highly complex parts, the limitations of SM due to possible collision between the milling tool and the workpiece can render an optimized part non-manufacturable. We present a systematic approach to topology optimization (TO) of parts for AM followed by SM to ensure removability of support structures, while optimizing the part’s performance. A central idea is to express the producibility of the part from the near-net shape in terms of accessibility of every support structure point using a given set of cutting tool assemblies and fixturing orientations. Our approach does not impose any artificial constraints on geometric complexity of the part, support structures, machining tools, and fixturing devices. We extend the notion of inaccessibility measure field (IMF) to support structures to identify the inaccessible points and capture their contributions to non-manufacturability by a continuous spatial field. IMF is then augmented to the sensitivity field to guide the TO towards a manufacturable design. The approach enables efficient and effective design space exploration by finding nontrivial complex designs whose near-net shape can be 3D printed and post-processed for support removal by machining with a custom set of tools and fixtures. We demonstrate the efficacy of our approach on nontrivial examples in 2D and 3D.
Confidence intervals for multiple isotonic regression and other monotone models

We consider the problem of constructing pointwise confidence intervals in the multiple isotonic regression model. Recently, Han and Zhang (2020) obtained a pointwise limit distribution theory for the so-called block max–min and min–max estimators (Fokianos, Leucht and Neumann (2020); Deng and Zhang (2020)) in this model, but inference remains a difficult problem due to the nuisance parameter in the limit distribution that involves multiple unknown partial derivatives of the true regression function. In this paper, we show that this difficult nuisance parameter can be effectively eliminated by taking advantage of information beyond point estimates in the block max–min and min–max estimators. Formally, let uˆ(x0) (resp. vˆ(x0)) be the maximizing lower-left (resp. minimizing upper-right) vertex in the block max–min (resp. min–max) estimator, and fˆn be the average of the block max–min and min–max estimators. If all (first-order) partial derivatives of f0 are nonvanishing at x0, then the following pivotal limit distribution theory holds: nuˆ,vˆ(x0)(fˆn(x0)−f0(x0))⇝σ·L1d. Here nuˆ,vˆ(x0) is the number of design points in the block [uˆ(x0),vˆ(x0)], σ is the standard deviation of the errors, and L1d is a universal limit distribution free of nuisance parameters. This immediately yields confidence intervals for f0(x0) with asymptotically exact confidence level and oracle length. Notably, the construction of the confidence intervals, even new in the univariate setting, requires no more efforts than performing an isotonic regression once using the block max–min and min–max estimators, and can be easily adapted to other common monotone models including, for example, (i) monotone density estimation, (ii) interval censoring model with current status data, (iii) counting process model with panel count data, and (iv) generalized linear models. Extensive simulations are carried out to support our theory.
Visual Perspective Taking for Opponent Behavior Modeling

In order to engage in complex social interaction, humans learn at a young age to infer what others see and cannot see from a different point-of-view, and learn to predict others’ plans and behaviors. These abilities have been mostly lacking in robots, sometimes making them appear awkward and socially inept. Here we propose an end-to-end long-term visual prediction framework for robots to begin to acquire both these critical cognitive skills, known as Visual Perspective Taking (VPT) and Theory of Behavior (TOB). We demonstrate our approach in the context of visual hide-and-seek – a game that represents a cognitive milestone in human development. Unlike traditional visual predictive model that generates new frames from immediate past frames, our agent can directly predict to multiple future timestamps (25 s), extrapolating by 175% beyond the training horizon. We suggest that visual behavior modeling and perspective taking skills will play a critical role in the ability of physical robots to fully integrate into real-world multi-agent activities.