cssi/SciSciGPT-SciSciNet huggingface dataset:
A Large-Scale Observational Study on the Temporal Trends and Risk Factors of Opioid Overdose: Real-World Evidence for Better Opioids.

Background The USA is in the midst of an opioid overdose epidemic. To address the epidemic, we conducted a large-scale population study on opioid overdose. Objectives The primary objective of this study was to evaluate the temporal trends and risk factors of inpatient opioid overdose. Based on its patterns, the secondary objective was to examine the innate properties of opioid analgesics underlying reduced overdose effects. Methods A retrospective cross-sectional study was conducted based on a large-scale inpatient electronic health records database, Cerner Health Facts®, with (1) inclusion criteria for participants as patients admitted between 1 January, 2009 and 31 December, 2017 and (2) measurements as opioid overdose prevalence by year, demographics, and prescription opioid exposures. Results A total of 4,720,041 patients with 7,339,480 inpatient encounters were retrieved from Cerner Health Facts®. Among them, 30.2% patients were aged 65+ years, 57.0% female, 70.1% Caucasian, 42.3% single, 32.0% from the South, and 80.8% in an urban area. From 2009 to 2017, annual opioid overdose prevalence per 1000 patients significantly increased from 3.7 to 11.9 with an adjusted odds ratio (aOR): 1.16, 95% confidence interval (CI) 1.15-1.16. Compared to the major demographic counterparts, being in (1) age group: 41-50 years (overall aOR 1.36, 95% CI 1.31-1.40) or 51-64 years (overall aOR 1.35, 95% CI 1.32-1.39), (2) marital status: divorced (overall aOR 1.19, 95% CI 1.15-1.23), and (3) census region: West (overall aOR 1.32, 95% CI 1.28-1.36) were significantly associated with a higher odds of opioid overdose. Prescription opioid exposures were also associated with an increased odds of opioid overdose, such as meperidine (overall aOR 1.09, 95% CI 1.06-1.13) and tramadol (overall aOR 2.20, 95% CI 2.14-2.27). Examination on the relationships between opioid analgesic properties and their association strengths, aORs, and opioid overdose showed that lower aOR values were significantly associated with (1) high molecular weight, (2) non-interaction with multi-drug resistance protein 1 or interaction with cytochrome P450 3A4, and (3) non-interaction with the delta opioid receptor or kappa opioid receptor. Conclusions The significant increasing trends of opioid overdose at the inpatient care setting from 2009 to 2017 suggested an ongoing need for efforts to combat the opioid overdose epidemic in the USA. Risk factors associated with opioid overdose included patient demographics and prescription opioid exposures. Moreover, there are physicochemical, pharmacokinetic, and pharmacodynamic properties underlying reduced overdose effects, which can be utilized to develop better opioids.
Amorphous complexions alter the tensile failure of nanocrystalline Cu-Zr alloys

Abstract Grain boundary-based mechanisms are known to control the plastic deformation and failure of nanocrystalline metals, with manipulation of the boundary structure a promising path for tuning this response. In this study, the role of interfacial structural disorder on plasticity and failure of nanocrystalline Cu-Zr alloys is investigated with in situ scanning electron microscopy tensile deformation experiments. Two model materials are created, one with only the typical ordered grain boundaries and another with amorphous intergranular films interspersed into the boundary network, while the microstructures are otherwise identical. Hence, the importance of complexion type on plasticity and failure is isolated by only varying complexion structure. The tensile experiments show that failure of the samples containing amorphous films is significantly retarded, as evidenced by an increase in the cross-sectional area reduction, a decrease in the occurrence of shear-dominated failure, a decrease in strain localization, and fracture surfaces with more elongated dimple features. As a whole, this study provides direct evidence that structural disorder at the grain boundaries can be beneficial for improving the ductility of nanocrystalline metals.
Few-shot Intent Classification and Slot Filling with Retrieved Examples.

Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain. In this paper, we explore retrieval-based methods for intent classification and slot filling tasks in few-shot settings. Retrieval-based methods make predictions based on labeled examples in the retrieval index that are similar to the input, and thus can adapt to new domains simply by changing the index without having to retrain the model. However, it is non-trivial to apply such methods on tasks with a complex label space like slot filling. To this end, we propose a span-level retrieval method that learns similar contextualized representations for spans with the same label via a novel batch-softmax objective. At inference time, we use the labels of the retrieved spans to construct the final structure with the highest aggregated score. Our method outperforms previous systems in various few-shot settings on the CLINC and SNIPS benchmarks.
Clan embeddings into trees, and low treewidth graphs

In low distortion metric embeddings, the goal is to embed a host “hard” metric space into a “simpler” target space while approximately preserving pairwise distances. A highly desirable target space is that of a tree metric. Unfortunately, such embedding will result in a huge distortion. A celebrated bypass to this problem is stochastic embedding with logarithmic expected distortion. Another bypass is Ramsey-type embedding, where the distortion guarantee applies only to a subset of the points. However, both these solutions fail to provide an embedding into a single tree with a worst-case distortion guarantee on all pairs. In this paper, we propose a novel third bypass called clan embedding. Here each point x is mapped to a subset of points f(x), called a clan, with a special chief point χ(x)∈ f(x). The clan embedding has multiplicative distortion t if for every pair (x,y) some copy y′∈ f(y) in the clan of y is close to the chief of x: miny′∈ f(y)d(y′,χ(x))≤ t· d(x,y). Our first result is a clan embedding into a tree with multiplicative distortion O(logn/є) such that each point has 1+є copies (in expectation). In addition, we provide a “spanning” version of this theorem for graphs and use it to devise the first compact routing scheme with constant size routing tables. We then focus on minor-free graphs of diameter prameterized by D, which were known to be stochastically embeddable into bounded treewidth graphs with expected additive distortion є D. We devise Ramsey-type embedding and clan embedding analogs of the stochastic embedding. We use these embeddings to construct the first (bicriteria quasi-polynomial time) approximation scheme for the metric ρ-dominating set and metric ρ-independent set problems in minor-free graphs.
Boundary expansion for the Loewner-Nirenberg problem in domains with conic singularities

Abstract We study asymptotic behaviors of solutions to the Loewner-Nirenberg problem in domains with conic singularities and establish asymptotic expansions with respect to two normal directions simultaneously. An elliptic operator on spherical domains with coefficients singular on boundary plays an important role. Key step is the study of the eigenvalue and eigenfunction estimates of such operators.
DoT: An efficient Double Transformer for NLP tasks with tables

Transformer-based approaches have been successfully used to obtain state-of-the-art accuracy on natural language processing (NLP) tasks with semi-structured tables. These model architectures are typically deep, resulting in slow training and inference, especially for long inputs. To improve efficiency while maintaining a high accuracy, we propose a new architecture, DoT, a double transformer model, that decomposes the problem into two sub-tasks: A shallow pruning transformer that selects the top-K tokens, followed by a deep task-specific transformer that takes as input those K tokens. Additionally, we modify the task-specific attention to incorporate the pruning scores. The two transformers are jointly trained by optimizing the task-specific loss. We run experiments on three benchmarks, including entailment and question-answering. We show that for a small drop of accuracy, DoT improves training and inference time by at least 50%. We also show that the pruning transformer effectively selects relevant tokens enabling the end-to-end model to maintain similar accuracy as slower baseline models. Finally, we analyse the pruning and give some insight into its impact on the task model.
Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints

We consider the problem of estimating a $d$-dimensional $s$-sparse discrete distribution from its samples observed under a $b$-bit communication constraint. The best-known previous result on $\ell_2$ estimation error for this problem is $O\lp \frac{s\log\lp {d}/{s}\rp}{n2^b}\rp$. Surprisingly, we show that when sample size $n$ exceeds a minimum threshold $n^*(s, d, b)$, we can achieve an $\ell_2$ estimation error of $O\lp \frac{s}{n2^b}\rp$. This implies that when $n>n^*(s, d, b)$ the convergence rate does not depend on the ambient dimension $d$ and is the same as knowing the support of the distribution beforehand. We next ask the question: ``what is the minimum $n^*(s, d, b)$ that allows dimension-free convergence?''. To upper bound $n^*(s, d, b)$, we develop novel localization schemes to accurately and efficiently localize the unknown support. For the non-interactive setting, we show that $n^*(s, d, b) = O\lp \min \lp {d^2\log^2 d}/{2^b}, {s^4\log^2 d}/{2^b}\rp \rp$. Moreover, we connect the problem with non-adaptive group testing and obtain a polynomial-time estimation scheme when $n = \tilde{\Omega}\lp{s^4\log^4 d}/{2^b}\rp$. This group testing based scheme is adaptive to the sparsity parameter $s$, and hence can be applied without knowing it. For the interactive setting, we propose a novel tree-based estimation scheme and show that the minimum sample-size needed to achieve dimension-free convergence can be further reduced to $n^*(s, d, b) = \tilde{O}\lp {s^2\log^2 d}/{2^b} \rp$.
Creation of quantum entangled states of Rydberg atoms via chirped adiabatic passage.

Entangled states are crucial for modern quantum enabled technology which makes their creation key for future developments. In this paper, a robust quantum control methodology is presented to create entangled states of two typical classes, the W and the Greenberger–Horne–Zeilinger (GHZ). It was developed from the analysis of a chain of alkali atoms $$^{87}Rb$$ interaction with laser pulses, which leads to the two-photon transitions from the ground to the Rydberg states with a predetermined magnetic quantum number. The methodology is based on the mechanism of the two-photon excitation, adiabatic for the GHZ and non-adiabatic for the W state, induced by the overlapping chirped pulses and governed by the Rabi frequency, the one-photon detuning, and the strength of the Rydberg–Rydberg interactions.
A Mathematical Approach to Improve Energy-Water Nexus Reliability Using a Novel Multi-stage Adjustable Fuzzy Robust Approach

A system of a systems approach that analyzes energy and water systems simultaneously is called energy-water nexus. Neglecting the interrelationship between energy and water drives vulnerabilities whereby limits on one resource can cause constraints on the other resource. Power plant energy production directly depends on water availability, and an outage of the power systems will affect the wastewater treatment facility processes. Therefore, it is essential to integrate energy and water planning models. As mathematical energy-water nexus problems are complex, involve many uncertain parameters, and are large-scale, we proposed a novel multi-stage adjustable Fuzzy robust approach that balances the solutions’ robustness against the budget-constraints. Scenario-based analysis indicates that the proposed approach generates flexible and robust decisions that avoid excessive costs compared to conservative methods.
Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

Today’s high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today’s HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multithreaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0× and 6.8× on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3× over the multithread encoder on two 28-core Xeon Platinum 8280 CPUs.
PPT-Multicore: performance prediction of OpenMP applications using reuse profiles and analytical modeling

We present PPT-Multicore, an analytical model embedded in the Performance Prediction Toolkit (PPT) to predict parallel applications’ performance running on a multicore processor. PPT-Multicore builds upon our previous work towards a multicore cache model. We extract LLVM basic block labeled memory trace using an architecture-independent LLVM-based instrumentation tool only once in an application’s lifetime. The model uses the memory trace and other parameters from an instrumented sequentially executed binary. We use probabilistic and computationally efficient reuse profiles to predict the cache hit rates and runtimes of OpenMP programs’ parallel sections. We model Intel’s Broadwell, Haswell, and AMD’s Zen2 architectures and validate our framework using different applications from PolyBench and PARSEC benchmark suites. The results show that PPT-Multicore can predict cache hit rates with an overall average error rate of 1.23% while predicting the runtime with an error rate of 9.08%.
Inference on estimators defined by mathematical programming

Abstract We propose an inference procedure for a class of estimators defined as the solutions to linear and convex quadratic programming problems in which the coefficients in both the objective function and the constraints of the problem are estimated from data and hence involve sampling error. We argue that the Karush–Kuhn–Tucker conditions that characterize the solutions to these programming problems can be treated as moment conditions; by doing so, we transform the problem of inference on the solution to a constrained optimization problem (which is non-standard) into one involving inference on inequalities with pre-estimated coefficients, which is better understood. Our approach is valid regardless of whether the problem has a unique solution or multiple solutions. We apply our method to various portfolio selection models, in which the confidence sets can be non-convex, lower-dimensional manifolds.
Reproducibility of protein x-ray diffuse scattering and potential utility for modeling atomic displacement parameters

Protein structure and dynamics can be probed using x-ray crystallography. Whereas the Bragg peaks are only sensitive to the average unit-cell electron density, the signal between the Bragg peaks-diffuse scattering-is sensitive to spatial correlations in electron-density variations. Although diffuse scattering contains valuable information about protein dynamics, the diffuse signal is more difficult to isolate from the background compared to the Bragg signal, and the reproducibility of diffuse signal is not yet well understood. We present a systematic study of the reproducibility of diffuse scattering from isocyanide hydratase in three different protein forms. Both replicate diffuse datasets and datasets obtained from different mutants were similar in pairwise comparisons (Pearson correlation coefficient ≥0.8). The data were processed in a manner inspired by previously published methods using custom software with modular design, enabling us to perform an analysis of various data processing choices to determine how to obtain the highest quality data as assessed using unbiased measures of symmetry and reproducibility. The diffuse data were then used to characterize atomic mobility using a liquid-like motions (LLM) model. This characterization was able to discriminate between distinct anisotropic atomic displacement parameter (ADP) models arising from different anisotropic scaling choices that agreed comparably with the Bragg data. Our results emphasize the importance of data reproducibility as a model-free measure of diffuse data quality, illustrate the ability of LLM analysis of diffuse scattering to select among alternative ADP models, and offer insights into the design of successful diffuse scattering experiments.
Multiple neuronal networks coordinate Hydra mechanosensory behavior

Hydra vulgaris is an emerging model organism for neuroscience due to its small size, transparency, genetic tractability, and regenerative nervous system; however, fundamental properties of its sensorimotor behaviors remain unknown. Here, we use microfluidic devices combined with fluorescent calcium imaging and surgical resectioning to study how the diffuse nervous system coordinates Hydra's mechanosensory response. Mechanical stimuli cause animals to contract, and we find this response relies on at least two distinct networks of neurons in the oral and aboral regions of the animal. Different activity patterns arise in these networks depending on whether the animal is contracting spontaneously or contracting in response to mechanical stimulation. Together, these findings improve our understanding of how Hydra's diffuse nervous system coordinates sensorimotor behaviors. These insights help reveal how sensory information is processed in an animal with a diffuse, radially symmetric neural architecture unlike the dense, bilaterally symmetric nervous systems found in most model organisms.
Relational Algorithms for k-Means Clustering

This paper gives a k-means approximation algorithm that is efficient in the relational algorithms model. This is an algorithm that operates directly on a relational database without performing a join to convert it to a matrix whose rows represent the data points. The running time is potentially exponentially smaller than N, the number of data points to be clustered that the relational database represents. Few relational algorithms are known and this paper offers techniques for designing relational algorithms as well as characterizing their limitations. We show that given two data points as cluster centers, if we cluster points according to their closest centers, it is NP-Hard to approximate the number of points in the clusters on a general relational input. This is trivial for conventional data inputs and this result exemplifies that standard algorithmic techniques may not be directly applied when designing an efficient relational algorithm. This paper then introduces a new method that leverages rejection sampling and the k-means++ algorithm to construct a O(1)-approximate k-means solution.
Overconfidence and welfare in a differentiated duopoly

We examine whether owners' decisions to delegate responsibilities to overconfident managers improve welfare. We develop a duopoly model with product differentiation, where firms compete in research and development (R&D) and output . Before firms compete, each owner makes a strategic decision whether to hire an overconfident manager. The results reveal that under not too much productive R&D technology, it is optimal for owners to hire overconfident managers. Owners will hire overconfident managers who over-invest in R&D, and this decision improves welfare if either R&D productivity is low, or spillovers are large, or products are less substitutes.
Uncertainty Spillovers for Markets and Policy

We live in a world filled with uncertainty. In this essay, I show that featuring this phenomenon more in economic analyses adds to our understanding of how financial markets work and how best to de...
WWPD elements of big mapping class groups

We study mapping class groups of infinite type surfaces with isolated punctures and their actions on the loop graphs introduced by Bavard-Walker. We classify all of the mapping classes in these actions which are loxodromic with a WWPD action on the corresponding loop graph. The WWPD property is a weakening of Bestvina-Fujiwara's weak proper discontinuity and is useful for constructing non-trivial quasimorphisms. We use this classification to give a sufficient criterion for subgroups of big mapping class groups to have infinite-dimensional second bounded cohomology and use this criterion to give simple proofs that certain natural subgroups of big mapping class groups have infinite-dimensional second bounded cohomology.
Outdoor sub-THz Position Location and Tracking using Field Measurements at 142 GHz

Future sub-THz cellular deployments may be utilized to complement the coverage of the global positioning system (GPS) and provide centimeter-level accuracy. In this work, we use measurement data at 142 GHz to test a map-based position location algorithm in an outdoor urban microcell (UMi) environment. We utilize an extended Kalman filter (EKF) to track the position of the user equipment (UE) along a rectangular track, with the transmitter-receiver separation distances varying from 24.3 m to 52.8 m. The position and velocity of the UE are tracked by the EKF, with measurements of the angle of arrival and time of flight information obtained along an outdoor track, to provide a mean accuracy of 24.8 cm at 142 GHz, over 34 UE locations, using a single base station in line-of-sight and non-line-of-sight.
SARS-CoV-2 Spike Pseudoviruses: A Useful Tool to Study Virus Entry and Address Emerging Neutralization Escape Phenotypes

SARS-CoV-2 genetic variants are emerging around the globe. Unfortunately, several SARS-CoV-2 variants, especially variants of concern (VOCs), are less susceptible to neutralization by the convalescent and post-vaccination sera, raising concerns of increased disease transmissibility and severity. Recent data suggests that SARS-CoV-2 neutralizing antibody levels are a reliable correlate of vaccine-mediated protection. However, currently used BSL3-based virus micro-neutralization (MN) assays are more laborious, time-consuming, and expensive, underscoring the need for BSL2-based, cost-effective neutralization assays against SARS-CoV-2 variants. In light of this unmet need, we have developed a BSL-2 pseudovirus-based neutralization assay (PBNA) in cells expressing the human angiotensin-converting enzyme-2 (hACE2) receptor for SARS-CoV-2. The assay is reproducible (R2 = 0.96), demonstrates a good dynamic range and high sensitivity. Our data suggest that the biological Anti-SARS-CoV-2 research reagents such as NIBSC 20/130 show lower neutralization against B.1.351 SA (South Africa) and B.1.1.7 UK (United Kingdom) VOC, whereas a commercially available monoclonal antibody MM43 retains activity against both these variants. SARS-CoV-2 spike PBNAs for VOCs would be useful tools to measure the neutralization ability of candidate vaccines in both preclinical models and clinical trials and would further help develop effective prophylactic countermeasures against emerging neutralization escape phenotypes.
Sparse domination results for compactness on weighted spaces

By means of appropriate sparse bounds, we deduce compactness on weighted $$L^p(w)$$ spaces, $$1<p<\infty$$ , for all Calderon–Zygmund operators having compact extensions on $$L^2({\mathbb {R}}^n)$$ . Similar methods lead to new results on boundedness and compactness of Haar multipliers on weighted spaces. In particular, we prove weighted bounds for weights in a class strictly larger than the typical $$A_p$$ class.
Emulator embedded neural networks for multi-fidelity conceptual design exploration of hypersonic vehicles

In this work, an emulator embedded neural network is proposed for efficient modeling of high-dimensional predictors with multi-fidelity data sources. The emulators represent prior knowledge or expertise implemented intrusively into a neural network in forms of legacy models, physics-based models, surrogate models, etc. In this research, multi-fidelity models are embedded into selected neurons of the hidden layers to accelerate neural network training by leveraging low-fidelity models and reducing the dependency on high-fidelity data. Demonstrated with several numerical examples, the proposed method shows its promising performance including significant reduction of training data needed to achieve the desired accuracy, increase in accuracy up to one order magnitude with the same number of training data samples, reduced risk of overfitting, and robust fitting with randomly initialized weights. The proposed method is demonstrated with multiple analytical examples, as well as a representative hypersonic vehicle structural design problem.
Network Determinants of Cross-Border Merger and Acquisition Decisions

This paper assesses whether cross-border M&A decisions exhibit network effects. We estimate exponential random graph models (ERGM) and temporal exponential random graph models (TERGM) to evaluate the determinants of cross-country M&A investments at the sectoral level. The results show that transitivity matters: a country is more likely to invest in a new destination if one of its existing partners has already made some investments there. In line with the literature on export platforms and informational barriers, we find a sizable impact of third country effects on the creation of new investments. This effect is sizable and larger than some of the more traditional M&A determinants, such as trade openness.
Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model

Internet of Things (IoT) allowed smart homes to improve the quality and the comfort of our daily lives. However, these conveniences introduced several security concerns that increase rapidly. IoT devices, smart home hubs, and gateway raise various security risks. The smart home gateways act as a centralized point of communication between the IoT devices, which can create a backdoor into network data for hackers. One of the common and effective ways to detect such attacks is intrusion detection in the network traffic. In this paper, we proposed an intrusion detection system (IDS) to detect anomalies in a smart home network using a bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) hybrid model. The BiLSTM recurrent behavior provides the intrusion detection model to preserve the learned information through time, and the CNN extracts perfectly the data features. The proposed model can be applied to any smart home network gateway.
Broadband circularly polarized thermal radiation from magnetic Weyl semimetals

We numerically demonstrate that a planar slab made of magnetic Weyl semimetal (a class of topological materials) can emit high-purity circularly polarized (CP) thermal radiation over a broad mid- and long-wave infrared wavelength range for a significant portion of its emission solid angle. This effect fundamentally arises from the strong infrared gyrotropy or nonreciprocity of these materials, which primarily depends on the momentum separation between Weyl nodes in the band structure. We clarify the dependence of this effect on the underlying physical parameters and highlight that the spectral bandwidth of CP thermal emission increases with increasing momentum separation between the Weyl nodes. We also demonstrate, using the recently developed thermal discrete dipole approximation (TDDA) computational method, that finite-size bodies of magnetic Weyl semimetals can emit spectrally broadband CP thermal light, albeit over smaller portion of the emission solid angle compared to the planar slabs. Our work identifies unique fundamental and technological prospects of magnetic Weyl semimetals for engineering thermal radiation and designing efficient CP light sources.
Exterior Dissipation, Proportional Decay, and Integrals of Motion.

Given a dynamical system with $m$ independent conserved quantities, we construct a multiparameter family of new systems in which these quantities evolve monotonically and proportionally, and are replaced by $m\ensuremath{-}1$ conserved linear combinations of themselves, with any of the original quantities as limiting cases. The modification of the dynamics employs an exterior product of gradients of the original quantities, and often evolves the system toward asymptotic linear dependence of these gradients in a nontrivial state. The process both generalizes and provides additional structure to existing techniques for selective dissipation in the literature on fluids and plasmas, nonequilibrium thermodynamics, and nonlinear controls. It may be iterated or adapted to obtain any reduction in the degree of integrability. It may enable discovery of extremal states, limit cycles, or solitons, and the construction of new integrable systems from superintegrable systems. We briefly illustrate the approach by its application to the cyclic three-body Toda lattice, driven from an aperiodic orbit toward a limit cycle.
The Human STAT2 Coiled-Coil Domain Contains a Degron for Zika Virus Interferon Evasion

The ability of viruses to evade the host antiviral immune system determines their level of replication fitness, species specificity, and pathogenic potential. Flaviviruses rely on the subversion of innate immune barriers including the type I and type III IFN antiviral systems. Zika virus infection induces the degradation of STAT2, an essential component of the IFN stimulated gene transcription factor, ISGF3. The mechanisms that lead to STAT2 degradation by Zika virus are poorly understood, but it is known to be mediated by the viral NS5 protein that binds to STAT2 and targets it for proteasome-mediated destruction. To better understand how NS5 engages and degrades STAT2, functional analysis of the protein interactions that lead to Zika virus and NS5-dependent STAT2 proteolysis were investigated. Data implicate the STAT2 coiled-coil domain as necessary and sufficient for NS5 interaction and proteasome degradation after Zika virus infection. Molecular dissection reveals that the first two α-helices of the STAT2 coiled-coil contain a specific targeting region for IFN antagonism. These functional interactions provide a more complete understanding of the essential protein-protein interactions needed for Zika virus evasion of the host antiviral response, and identifies new targets for antiviral therapeutic approaches. Importance Zika virus infection can cause mild fever, rash, and muscle pain, and in rare cases lead to brain or nervous system diseases including Guillain-Barre syndrome. Infections in pregnant women can increase the risk of miscarriage or serious birth defects including brain anomalies and microcephaly. There are no drugs or vaccines for Zika disease. Zika virus is known to break down the host antiviral immune response, and this research project reveals how the virus suppresses interferon signaling, and may reveal therapeutic vulnerabilities.
Does Preprocessing Help Training Over-parameterized Neural Networks?

Deep neural networks have achieved impressive performance in many areas. Designing a fast and provable method for training neural networks is a fundamental question in machine learning. The classical training method requires paying $\Omega(mnd)$ cost for both forward computation and backward computation, where $m$ is the width of the neural network, and we are given $n$ training points in $d$-dimensional space. In this paper, we propose two novel preprocessing ideas to bypass this $\Omega(mnd)$ barrier: $\bullet$ First, by preprocessing the initial weights of the neural networks, we can train the neural network in $\widetilde{O}(m^{1-\Theta(1/d)} n d)$ cost per iteration. $\bullet$ Second, by preprocessing the input data points, we can train the neural network in $\widetilde{O} (m^{4/5} nd )$ cost per iteration. From the technical perspective, our result is a sophisticated combination of tools in different fields, greedy-type convergence analysis in optimization, sparsity observation in practical work, high-dimensional geometric search in data structure, concentration and anti-concentration in probability. Our results also provide theoretical insights for a large number of previously established fast training methods. In addition, our classical algorithm can be generalized to the Quantum computation model. Interestingly, we can get a similar sublinear cost per iteration but avoid preprocessing initial weights or input data points.
Real-Time, Deep Synthetic Aperture Sonar (SAS) Autofocus

Synthetic aperture sonar (SAS) requires precise time-of-flight measurements of the transmitted/received waveform to produce well-focused imagery. It is not uncommon for errors in these measurements to be present resulting in image defocusing. To overcome this, an autofocus algorithm is employed as a post-processing step after image reconstruction to improve image focus. A particular class of these algorithms can be framed as a sharpness/contrast metric-based optimization. To improve convergence, a hand-crafted weighting function to remove “bad” areas of the image is sometimes applied to the image-under-test before the optimization procedure. Additionally, dozens of iterations are necessary for convergence which is a large compute burden for low size, weight, and power (SWaP) systems. We propose a deep learning technique to overcome these limitations and implicitly learn the weighting function in a data-driven manner. Our proposed method, which we call Deep Autofocus, uses features from the single-look-complex (SLC) to estimate the phase correction which is applied in k-space. Furthermore, we train our algorithm on batches of training imagery so that during deployment, only a single iteration of our method is sufficient to autofocus. We show results demonstrating the robustness of our technique by comparing our results to four commonly used image sharpness metrics. Our results demonstrate Deep Autofocus can produce imagery perceptually better than common iterative techniques but at a lower computational cost. We conclude that Deep Autofocus can provide a more favorable cost-quality tradeoff than alternatives with significant potential of future research.
THE FRIEDMAN RULE: EXPERIMENTAL EVIDENCE

We explore the celebrated Friedman rule for optimal monetary policy in the context of a laboratory economy based on the Lagos-Wright model. The rule that Friedman proposed can be shown to be optimal in a wide variety of different monetary models, including the Lagos-Wright model. However, we are not aware of any prior empirical evidence evaluating the welfare consequences of the Friedman rule. We explore two implementations of the Friedman rule in the laboratory. The first is based on a deflationary monetary policy where the money supply contracts to offset time discounting. The second implementation pays interest on money removing the private marginal cost from holding money. We explore the welfare consequences of these two theoretically equivalent implementations of the Friedman Rule and compare results with two other policy regimes, a constant money supply regime and another regime advocated by Friedman, where the supply of money grows at a constant k-percent rate. We find that, counter to theory, the Friedman rule is not welfare improving, performing no better than a constant money regime. By one welfare measure, we find that the k-percent money growth rate regime performs best.
Comparing Suicide Risk Insights derived from Clinical and Social Media data

Suicide is the 10th leading cause of death in the US and the 2nd leading cause of death among teenagers. Clinical and psychosocial factors contribute to suicide risk (SRFs), although documentation and self-expression of such factors in EHRs and social networks vary. This study investigates the degree of variance across EHRs and social networks. We performed subjective analysis of SRFs, such as self-harm, bullying, impulsivity, family violence/discord, using >13.8 Million clinical notes on 123,703 patients with mental health conditions. We clustered clinical notes using semantic embeddings under a set of SRFs. Likewise, we clustered 2180 suicidal users on r/SuicideWatch (~30,000 posts) and performed comparative analysis. Top-3 SRFs documented in EHRs were depressive feelings (24.3%), psychological disorders (21.1%), drug abuse (18.2%). In r/SuicideWatch, gun-ownership (17.3%), self-harm (14.6%), bullying (13.2%) were Top-3 SRFs. Mentions of Family violence, racial discrimination, and other important SRFs contributing to suicide risk were missing from both platforms.
Time-dependent phase quantification and local structure analysis of hydroxide-activated slag via X-ray total scattering and molecular modeling

Abstract Here, an approach to quantify the amorphous-to-disordered/crystalline transformation occurring in NaOH-activated ground granulated blast-furnace slag (GGBS) is outlined that combines atomistic modeling with in situ pair distribution function (PDF) analysis. Firstly, by using force-field molecular dynamics (MD) simulations, a detailed structural representation is generated for the amorphous GGBS that is in agreement with experimental X-ray scattering data. Use of this structural representation along with literature-derived structures for the reaction products allows for real space X-ray PDF refinement of the alkaline activation of GGBS, resulting in the quantification of all phases and the degree of reaction (DOR) as a function of reaction time. All phases and the DOR are seen to approximately follow a logarithmic-type time-dependent behavior up to 5 months, while at the early age (up to 11 h), the DOR is accurately captured by a modified pseudo-single step first-order reaction model. Lastly, the evolution of DOR is found to agree with several other complementary in situ data containing quantitative reaction information, including isothermal conduction calorimetry, Fourier transform infrared spectroscopy, and quasi-elastic neutron scattering.
DPGen: Automated Program Synthesis for Differential Privacy

Differential privacy has become a de facto standard for releasing data in a privacy-preserving way. Creating a differentially private algorithm is a process that often starts with a noise-free (non-private) algorithm. The designer then decides where to add noise, and how much of it to add. This can be a non-trivial process -- if not done carefully, the algorithm might either violate differential privacy or have low utility. In this paper, we present DPGen, a program synthesizer that takes in non-private code (without any noise) and automatically synthesizes its differentially private version (with carefully calibrated noise). Under the hood, DPGen uses novel algorithms to automatically generate a sketch program with candidate locations for noise, and then optimize privacy proof and noise scales simultaneously on the sketch program. Moreover, DPGen can synthesize sophisticated mechanisms that adaptively process queries until a specified privacy budget is exhausted. When evaluated on standard benchmarks, DPGen is able to generate differentially private mechanisms that optimize simple utility functions within 120 seconds. It is also powerful enough to synthesize adaptive privacy mechanisms.
How IT allows E-Participation in Policy-Making Process

With the art and practice of government policy-making, public work, and citizen participation, many governments adopt information and communication technologies (ICT) as a vehicle to facilitate their relationship with citizens. This participation process is widely known as E- Participation or “Electronic Participation”. This article focuses on different performance indicators and the relevant tools for each level. Despite the growing scientific and pragmatic significance of e-participation, that area still was not able to grow as it was expected. Our diverse set of knowledge and e-participation policies and its implementation is very limited. This is the key reason why e-participation initiatives in practice often fall short of expectations. This study collects the existing perceptions from the various interdisciplinary scientific literature to determine a unifying definition and demonstrates the strong abilities of e-participation and other related components which have great potential in the coming years.
Corticothalamic projections deliver enhanced-responses to medial geniculate body as a function of the temporal reliability of the stimulus.

KEY POINTS In response to a less temporally distinct repeating sound with low modulation depth, medial geniculate body (MGB) single-units show a switch from adaptation toward repetition-enhancement. Repetition enhancement was reversed by blockade of MGB inputs from the auditory cortex. Collectively, these data argue that diminished acoustic temporal cues such as weak modulation engage cortical processes to enhance coding of those cues in auditory thalamus. ABSTRACT Aging and challenging signal-in-noise conditions are known to engage the use of cortical resources to help maintain speech understanding. Extensive corticothalamic projections are thought to provide attentional, mnemonic and cognitive-related inputs in support of sensory inferior colliculus (IC) inputs to the medial geniculate body (MGB). Here we show that a decrease in modulation depth, a temporally less distinct periodic acoustic signal, leads to a jittered ascending temporal code, changing MGB unit responses from adapting responses to responses showing repetition-enhancement, posited to aid identification of important communication and environmental sounds. Young-adult male Fischer Brown Norway rats, injected with the inhibitory opsin archaerhodopsin T (ArchT) into the primary auditory cortex (A1), were subsequently studied using optetrodes to record single-units in MGB. Decreasing the modulation depth of acoustic stimuli significantly increased repetition-enhancement. Repetition-enhancement was blocked by optical inactivation of corticothalamic terminals in MGB. These data support a role for corticothalamic projections in repetition-enhancement, implying that predictive anticipation could be used to improve neural representation of weakly modulated sounds. A: The ascending sensory code from inferior colliculus (IC) shows adaptive responses to distinct repeating modulated stimuli which are reflected in unit responses at the level of medial geniculate body (MGB). B: MGB unit responses to weakly modulated/less distinct stimuli engage additional cortical resources as predictions changing counterproductive adaptive responses toward repetition-enhancement. C: Corticothalamic (CT)/top-down blockade during weakly modulated/less distinct stimuli blocks delivery of predictive information to MGB reversing repetition-enhancement back toward adaptive responses. This article is protected by copyright. All rights reserved.
Do Long-Range Language Models Actually Use Long-Range Context?

Language models are generally trained on short, truncated input sequences, which limits their ability to use discourse-level information present in long-range context to improve their predictions. Recent efforts to improve the efficiency of self-attention have led to a proliferation of long-range Transformer language models, which can process much longer sequences than models of the past. However, the ways in which such models take advantage of the long-range context remain unclear. In this paper, we perform a fine-grained analysis of two long-range Transformer language models (including the Routing Transformer, which achieves state-of-the-art perplexity on the PG-19 long-sequence LM benchmark dataset) that accept input sequences of up to 8K tokens. Our results reveal that providing long-range context (i.e., beyond the previous 2K tokens) to these models only improves their predictions on a small set of tokens (e.g., those that can be copied from the distant context) and does not help at all for sentence-level prediction tasks. Finally, we discover that PG-19 contains a variety of different document types and domains, and that long-range context helps most for literary novels (as opposed to textbooks or magazines).
Efficient Bayesian network structure learning via local Markov boundary search

We analyze the complexity of learning directed acyclic graphical models from observational data in general settings without specific distributional assumptions. Our approach is information-theoretic and uses a local Markov boundary search procedure in order to recursively construct ancestral sets in the underlying graphical model. Perhaps surprisingly, we show that for certain graph ensembles, a simple forward greedy search algorithm (i.e. without a backward pruning phase) suffices to learn the Markov boundary of each node. This substantially improves the sample complexity, which we show is at most polynomial in the number of nodes. This is then applied to learn the entire graph under a novel identifiability condition that generalizes existing conditions from the literature. As a matter of independent interest, we establish finite-sample guarantees for the problem of recovering Markov boundaries from data. Moreover, we apply our results to the special case of polytrees, for which the assumptions simplify, and provide explicit conditions under which polytrees are identifiable and learnable in polynomial time. We further illustrate the performance of the algorithm, which is easy to implement, in a simulation study. Our approach is general, works for discrete or continuous distributions without distributional assumptions, and as such sheds light on the minimal assumptions required to efficiently learn the structure of directed graphical models from data.