cssi/SciSciGPT-SciSciNet huggingface dataset:
"A" Business by Any Other Name: Firm Name Choice as a Signal of Firm Quality

This paper considers when a firm’s deliberately chosen name can signal meaningful information. The average plumbing firm whose name begins with A or a number receives five times more service complaints than other firms and also charges higher prices. Relatedly, plumbers with A names advertise more in the Yellow Pages and on Google, and doing so is positively correlated with receiving complaints. As the use of A names is more prevalent in larger markets, I reconcile these findings with a simple model in which firms have different qualities and consumers have heterogeneous search costs.
Taking Commercial Law Seriously: From Jurisprudence to Pedagogy

The Uniform Commercial Code (the "Code") is the singular expression of a jurisprudential vision. As the conventional wisdom suggests, American Legal Realism was both a reaction to Legal Formalism' as well as a striking vision of the role of law in modern society. The Code was, of course, fashioned in the image of one of its principal architects, Karl N. Llewellyn. No jurisprudence of the Code is possible without some account of Llewellyn's jurisprudence and the "realism" he fostered in his great legislative achievement.
Resource Allocation and Rate Gains in Practical Full-Duplex Systems

Full-duplex (FD) communication has the potential to substantially increase the throughput in wireless networks. However, the benefits of FD are still not well understood. In this paper, we characterize the FD rate gains in both single-channel and multi-channel use cases. For the single-channel case, we quantify the rate gain as a function of the remaining self-interference (SI) and signal-to-noise ratio values. We also provide a sufficient condition under which the sum of uplink and downlink rates on an FD channel is biconcave in the transmission power levels. Building on these results, we consider the multi-channel case. For that case, we introduce a new realistic model of a compact (e.g., smartphone) FD receiver and demonstrate its accuracy via measurements. We study the problem of jointly allocating power levels to different channels and selecting the frequency of maximum SI suppression, where the objective is to maximize the sum of the rates over uplink and downlink orthogonal frequency division multiplexing channels. We develop a polynomial time algorithm, which is nearly optimal, in practice, under very mild restrictions. To reduce the running time, we develop an efficient nearly optimal algorithm under the high SINR approximation. Finally, we demonstrate via numerical evaluations the capacity gains in different use cases and obtain insights into the impact of the remaining SI and wireless channel states on the performance.
Drawing the Legal Family Tree: An Empirical Comparative Study of 170 Dimensions of Property Law in 129 Jurisdictions

Traditional comparative private law scholars have a firm grasp of laws in several countries, but rarely of those in more than one hundred countries. Quantitative comparative private law scholars have placed dozens of countries into a legal family genealogy, but not based on a systematic understanding of legal substance around the world. Using a unique, hand-coded data set on 108 property doctrines (transformed into 170 binary variables) in 129 jurisdictions, we ran supervised and unsupervised machine-learning algorithms. Some of our findings confirm the conventional wisdom: French and German property laws are influential; mixed jurisdictions like South Africa and Scotland are one of a kind; common law jurisdictions form a group of their own; and a handful of formerly socialist countries, led by Russia, cluster together. Unlike the prior literature, however, we do not find that East Asian jurisdictions warrant a category of their own; but belong to distant groups. Spain and many Latin American countries form a separate group. Rather than finding a clear-cut common versus civil law division, we observe that the France-inspired group is one supercluster, separate from other jurisdictions.
Educational mobility across three generations of American women

Abstract We analyze the intergenerational transmission of education in a three-generation sample of women from the 20th century US. We find strong three-generation educational persistence, with the association between the education of grandmothers and their granddaughters approximately two times stronger than would be expected under the type of first-order autoregressive transmission structure that has been assumed in much of the existing two-generation mobility literature. These findings are robust to using alternative empirical specifications and sample constructions, and are successfully replicated in a second independently drawn data set. Analyses that include males in the youngest and oldest generations produce very similar estimates. A variety of potential mechanisms linking the educational outcomes of grandparents and grandchildren are discussed and where possible tested empirically.
Postmodern Censorship of Pacifist Content on Television and the Internet

This Essay explores the legal history of the censorship of pacifist and antiwar speech. It devotes particular attention to postmodern techniques for chilling the production of pacifist content, or reducing the total output of it. Pacifist speech is defined broadly, as speech advocating peaceful alternatives to war or militarism, articulating doctrines or principles which urge forswearing war or violence in international disputes, or expressing reasons to oppose specific military episodes or entire wars. A fundamental assumption of democratic governance is that the public keeps informed of important news and points of view
Deterministic Min-cut in Poly-logarithmic Max-flows

We give a deterministic (global) min-cut algorithm for weighted undirected graphs that runs in time $O(m^{1+\epsilon})$ plus polylog ( $n$ ) max-flow computations. Using the current best max-flow algorithms, this results in an overall running time of $\tilde{O}(m\cdot\min(\sqrt{m}, n^{2/3}))$ for weighted graphs, and $m^{4/3+o(1)}$ for unweighted (multi)-graphs. This is the first improvement in the running time of deterministic algorithms for the min-cut problem on general (weighted/multi) graphs since the early 1990s when a running time bound of $\tilde{O}(mn)$ was established for this problem.
Vertical dependence of horizontal variation of cloud microphysics: observations from the ACE-ENA field campaign and implications for warm-rain simulation in climate models

Abstract. In the current global climate models (GCMs), the nonlinearity effect of subgrid cloud variations on the parameterization of warm-rain process, e.g., the autoconversion rate, is often treated by multiplying the resolved-scale warm-rain process rates by a so-called enhancement factor (EF). In this study, we investigate the subgrid-scale horizontal variations and covariation of cloud water content ( qc ) and cloud droplet number concentration ( Nc ) in marine boundary layer (MBL) clouds based on the in situ measurements from a recent field campaign and study the implications for the autoconversion rate EF in GCMs. Based on a few carefully selected cases from the field campaign, we found that in contrast to the enhancing effect of qc and Nc variations that tends to make EF > 1, the strong positive correlation between qc and Nc results in a suppressing effect that tends to make EF 1. This effect is especially strong at cloud top, where the qc and Nc correlation can be as high as 0.95. We also found that the physically complete EF that accounts for the covariation of qc and Nc is significantly smaller than its counterpart that accounts only for the subgrid variation of qc , especially at cloud top. Although this study is based on limited cases, it suggests that the subgrid variations of Nc and its correlation with qc both need to be considered for an accurate simulation of the autoconversion process in GCMs.
Towards a theory of machine learning

We define a neural network as a septuple consisting of a state vector, an input projection, an output projection, a weight matrix, a bias vector, an activation map and a loss function. We argue that the loss function can be imposed either on the boundary (i.e. input and/or output neurons) or in the bulk (i.e. hidden neurons) for both supervised and unsupervised systems. We apply the principle of maximum entropy to derive a canonical ensemble of the state vectors subject to a constraint imposed on the bulk loss function by a Lagrange multiplier (or an inverse temperature parameter). We show that in an equilibrium the canonical partition function must be a product of two factors: a function of the temperature and a function of the bias vector and weight matrix. Consequently, the total Shannon entropy consists of two terms which represent respectively a thermodynamic entropy and a complexity of the neural network. We derive the first and second laws of learning: during learning the total entropy must decrease until the system reaches an equilibrium (i.e. the second law), and the increment in the loss function must be proportional to the increment in the thermodynamic entropy plus the increment in the complexity (i.e. the first law). We calculate the entropy destruction to show that the efficiency of learning is given by the Laplacian of the total free energy which is to be maximized in an optimal neural architecture, and explain why the optimization condition is better satisfied in a deep network with a large number of hidden layers. The key properties of the model are verified numerically by training a supervised feedforward neural network using the method of stochastic gradient descent. We also discuss a possibility that the entire universe on its most fundamental level is a neural network.
Optimal representative sample weighting

We consider the problem of assigning weights to a set of samples or data records, with the goal of achieving a representative weighting, which happens when certain sample averages of the data are close to prescribed values. We frame the problem of finding representative sample weights as an optimization problem, which in many cases is convex and can be efficiently solved. Our formulation includes as a special case the selection of a fixed number of the samples, with equal weights, i.e., the problem of selecting a smaller representative subset of the samples. While this problem is combinatorial and not convex, heuristic methods based on convex optimization seem to perform very well. We describe our open-source implementation rsw and apply it to a skewed sample of the CDC BRFSS dataset.
Detection of prenatal alcohol exposure using machine learning classification of resting-state functional network connectivity data.

Abstract Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is recognized as a significant public health concern. Advancements in the diagnosis of FASD have been hindered by a lack of consensus in diagnostic criteria and limited use of objective biomarkers. Previous research from our group utilized resting-state functional magnetic resonance imaging (fMRI) to measure functional network connectivity (FNC), which revealed several sex- and region-dependent alterations in FNC as a result of moderate PAE relative to controls. Considering that FNC is sensitive to moderate PAE, this study explored the use of FNC data and machine learning methods to detect PAE among a sample of rodents exposed to alcohol prenatally and controls. We utilized previously acquired resting state fMRI data collected from adult rats exposed to moderate levels of prenatal alcohol (PAE) or a saccharin control solution (SAC) to assess FNC of resting state networks extracted by spatial group independent component analysis (GICA). FNC data were subjected to binary classification using support vector machine (SVM) -based algorithms and leave-one-out-cross validation (LOOCV) in an aggregated sample of males and females (n = 48; 12 male PAE, 12 female PAE, 12 male SAC, 12 female SAC), a males-only sample (n = 24; 12 PAE, 12 SAC), and a females-only sample (n = 24; 12 PAE, 12 SAC). Results revealed that a quadratic SVM (QSVM) kernel was significantly effective for PAE detection in females. QSVM kernel-based classification resulted in accuracy rates of 62.5% for all animals, 58.3% for males, and 79.2% for females. Additionally, qualitative evaluation of QSVM weights implicates an overarching theme of several hippocampal and cortical networks in contributing to the formation of correct classification decisions by QSVM. Our results suggest that binary classification using QSVM and adult female FNC data is a potential candidate for the translational development of novel and non-invasive techniques for the identification of FASD.
On the K-theory of coordinate axes in affine space

Let k be a perfect field of characteristic p > 0 , let A d be the coordinate ring of the coordinate axes in affine d –space over k , and let I d be the ideal defining the origin. We evaluate the relative K –groups K q ( A d , I d ) in terms of p –typical Witt vectors of k . When d = 2 the result is due to Hesselholt, and for K 2 it is due to Dennis and Krusemeyer. We also compute the groups K q ( A d , I d ) in the case where k is an ind-smooth algebra over the rationals, the result being expressed in terms of algebraic de Rham forms. When k is a field of characteristic zero this calculation is due to Geller, Reid and Weibel.
Colonial Education, Political Elites, and Regional Political Inequality in Africa:

Political elites tend to favor their home region when distributing resources. But what explains how political power is distributed across a country’s regions to begin with? Explanations of cabinet ...
C-store: a column-oriented DBMS

This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of column-oriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures.We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging.
Character Randomized Benchmarking for Non-Multiplicity-Free Groups With Applications to Subspace, Leakage, and Matchgate Randomized Benchmarking

Experimentally relevant benchmarking options are proposed, expanding the available choices of benchmarking group while keeping the required resources comparable with previous methods.
Synthesis-for-testability using transformations

We address the problem of transforming a behavioral specification so that synthesis of a testable implementation from the new specification requires significantly less area and partial scan cost than synthesis from the original specification. A two-stage objective function, that estimates the area and testability of the final implementation, and also captures enabling effects of the transformations, is developed. Optimization is done using a new randomized branch and bound steepest descent algorithm. Application of the transformation algorithm on several examples demonstrates significant simultaneous improvement in both area and testability of the final implementations.
Differential neural plasticity of individual fingers revealed by fMRI neurofeedback.

It may be possible to remobilize fingers after neurological injury by altering neural activity patterns. Toward this end, we examined whether finger-related neural activity patterns could be modifi...
Dissolving partnerships under risk: An experimental investigation

Abstract We experimentally study a situation in which two players negotiate the dissolution of a risky partnership. In our experiments, subjects must simultaneously negotiate both a selling price and the identity of the buyer. Upon reaching an agreement, one party (the buyer) will be subject to ex post risk, while the other party (the seller) will not, and the negotiation determines which party will hold the risk. Our results show that buyers extract a premium which is increasing in the riskiness of the partnership value distribution. Moreover, during bargaining, the majority of subjects make offers in which they would be the buyer (and thus exposed to risk) if the offer is accepted. Allowing subjects to communicate has noticeable effects: agreements are significantly more frequent and sorting according to fairness ideas – such that the buyer is the player with the higher “fair price” – is more frequent. We also show that, when subjects are able to communicate, bargaining pairs who discuss risk exposure or fairness generally agree to a lower price, giving greater compensation to risk for the buyer.
Linearized two-layers neural networks in high dimension

We consider the problem of learning an unknown function f⋆ on the d-dimensional sphere with respect to the square loss, given i.i.d. samples {(yi,xi)}i≤n where xi is a feature vector uniformly distributed on the sphere and yi=f⋆(xi)+ei. We study two popular classes of models that can be regarded as linearizations of two-layers neural networks around a random initialization: the random features model of Rahimi–Recht (RF); the neural tangent model of Jacot–Gabriel–Hongler (NT). Both these models can also be regarded as randomized approximations of kernel ridge regression (with respect to different kernels), and enjoy universal approximation properties when the number of neurons N diverges, for a fixed dimension d. We consider two specific regimes: the infinite-sample finite-width regime, in which n=∞ while d and N are large but finite, and the infinite-width finite-sample regime in which N=∞ while d and n are large but finite. In the first regime, we prove that if dl+δ≤N≤dl+1−δ for small δ>0, then RF effectively fits a degree-l polynomial in the raw features, and NT fits a degree-(l+1) polynomial. In the second regime, both RF and NT reduce to kernel methods with rotationally invariant kernels. We prove that, if the sample size satisfies dl+δ≤n≤dl+1−δ, then kernel methods can fit at most a degree-l polynomial in the raw features. This lower bound is achieved by kernel ridge regression, and near-optimal prediction error is achieved for vanishing ridge regularization.
A game of thrones—Dynamics of internal CEO succession and outcome

We examine the dynamics of firms’ internal succession methods and find that horse race successions are common among the largest U.S. firms. Although heir and horse race CEO candidates are of similar quality, the consequences of these two succession methods differ significantly. We show that horse race successions induce conflict and are detrimental to the firm but not to the newly appointed CEOs. Our findings suggest firm’s succession methods influence the CEO labor market, CEO compensation, and firm performance. These findings highlight the importance of CEO succession planning in the form of grooming an heir.
An Optimal Deterministic Algorithm for Geodesic Farthest-Point Voronoi Diagrams in Simple Polygons

Given a set S of m point sites in a simple polygon P of n vertices, we consider the problem of computing the geodesic farthest-point Voronoi diagram for S in P. It is known that the problem has an Ω(n+mlog m) time lower bound. Previously, a randomized algorithm was proposed [Barba, SoCG 2019] that can solve the problem in O(n+mlog m) expected time. The previous best deterministic algorithms solve the problem in O(nlog log n+ mlog m) time [Oh, Barba, and Ahn, SoCG 2016] or in O(n+mlog m+mlog² n) time [Oh and Ahn, SoCG 2017]. In this paper, we present a deterministic algorithm of O(n+mlog m) time, which is optimal. This answers an open question posed by Mitchell in the Handbook of Computational Geometry two decades ago.
Fractalizing quantum codes

We introduce "fractalization", a procedure by which spin models are extended to higher-dimensional "fractal" spin models. This allows us to interpret type-II fracton phases, fractal symmetry-protected topological phases, and more, in terms of well understood lower-dimensional spin models. Fractalization is also useful for deriving new spin models and quantum codes from known ones. We construct higher dimensional generalizations of fracton models that host extended fractal excitations. Finally, by applying fractalization to a 2D subsystem code, we produce a family of locally generated 3D subsystem codes that are conjectured to saturate a quantum information storage tradeoff bound.
Where to find needles in a haystack

In many existing methods of multiple comparison, one starts with either Fisher’s p value or the local fdr. One commonly used p value, defined as the tail probability exceeding the observed test statistic under the null distribution, fails to use information from the distribution under the alternative hypothesis. The targeted region of signals could be wrong when the likelihood ratio is not monotone. The oracle local fdr based approaches could be optimal because they use the probability density functions of the test statistic under both the null and alternative hypotheses. However, the data-driven version could be problematic because of the difficulty and challenge of probability density function estimation. In this paper, we propose a new method, Cdf and Local fdr Assisted multiple Testing method (CLAT), which is optimal for cases when the p value based methods are optimal and for some other cases when p value based methods are not. Additionally, CLAT only relies on the empirical distribution function which quickly converges to the oracle one. Both the simulations and real data analysis demonstrate the superior performance of the CLAT method. Furthermore, the computation is instantaneous based on a novel algorithm and is scalable to large data sets.
Towards High Performance, Portability, and Productivity: Lightweight Augmented Neural Networks for Performance Prediction

Writing high-performance code requires significant expertise in the programming language, compiler optimizations, and hardware knowledge. This often leads to poor productivity and portability and is inconvenient for a non-programmer domain-specialist such as a Physicist. More desirable is a high-level language where the domain-specialist simply specifies the workload in terms of high-level operations (e.g., matrix-multiply(A, B)), and the compiler identifies the best implementation fully utilizing the heterogeneous platform. For creating a compiler that supports productivity, portability, and performance simultaneously, it is crucial to predict the performance of various available implementations (variants) of the dominant operations (kernels) contained in the workload on various hardware to decide (a) which variant should be chosen for each kernel in the workload, and (b) on which hardware resource the variant should run. To enable the performance prediction, we propose lightweight augmented neural networks for arbitrary combinations of kernel-variant-hardware. A key innovation is utilizing the mathematical complexity of the kernels as a feature to achieve higher accuracy. These models are compact to reduce training time and allow fast inference during compile-time and run-time. Using models with less than 75 parameters, and only 250 training data instances, we are able to obtain accurate performance predictions, significantly outperforming traditional feed-forward neural networks on 48 kernel-variant-hardware combinations. We further demonstrate that our variant-selection approach can be used in Halide implementations to obtain up to 1.7x speedup over Halide auto-scheduler.
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration

Decision makers often need to rely on imperfect probabilistic forecasts. While average performance metrics are typically available, it is difficult to assess the quality of individual forecasts and the corresponding utilities. To convey confidence about individual predictions to decision-makers, we propose a compensation mechanism ensuring that the forecasted utility matches the actually accrued utility. While a naive scheme to compensate decision-makers for prediction errors can be exploited and might not be sustainable in the long run, we propose a mechanism based on fair bets and online learning that provably cannot be exploited. We demonstrate an application showing how passengers could confidently optimize individual travel plans based on flight delay probabilities estimated by an airline.
Explicit Weil-pairing for Drinfeld modules

The goal of this paper is to define an analogue of the Weil-pairing for Drinfeld modules using explicit formulas and to deduce its main properties from these formulas. Our result generalizes the fo...
Implementing Admittance Relaying for Microgrid Protection

The rapid increase of distributed energy resources has led to the widespread deployment of microgrids. These flexible and efficient local energy grids are able to operate in both grid-connected mode and islanded mode; they are interfaced to the main power system by a fast semiconductor switch and commonly make use of inverter-interfaced generation. This paper focuses on inverter interfaced microgrids, which present a challenge for protection as they do not provide the high short-circuit current necessary for conventional time-overcurrent protection. The application of admittance relaying for the protection of inverter-interfaced microgrids is investigated as a potential solution. The comparison of analytical and simulated results of performed four experiments prove the suitability of admittance relaying for microgrids protection.
Is this product easy to control? Liabilities of using difficult-to-pronounce product names

Abstract This research studied difficult-to-pronounce product names which are prevalent in certain product categories. In study 1, consumers tried golf balls that varied in name pronounceability but were otherwise identical and, despite direct experience, concluded balls with difficult versus easy-to-pronounce names were less controllable and less preferable. In study 2, consumers were asked to look for a dog that was highly (less highly) controllable for an urban (rural) setting, and the dog with the difficult-to-pronounce name was viewed as less controllable because it seemed less familiar and was less preferred for the urban setting. Study 3 verified the effects of difficult-to-pronounce names on familiarity and controllability perceptions and found preference effects among those with high trait desire for control. Study 4 documented the prevalence of difficult-to-pronounce names on a popular ecommerce site for tires. Overall, our findings indicate managers should avoid using difficult-to-pronounce product names when consumers strongly desire product controllability.
Time-Resolved Focused Ion Beam Microscopy: Modeling, Estimation Methods, and Analyses

In a focused ion beam (FIB) microscope, source particles interact with a small volume of a sample to generate secondary electrons that are detected, pixel by pixel, to produce a micrograph. Randomness of the number of incident particles causes excess variation in the micrograph, beyond the variation in the underlying particle–sample interaction. We recently demonstrated that joint processing of multiple time-resolved measurements from a single pixel can mitigate this effect of source shot noise in helium ion microscopy. This paper is focused on establishing a rigorous framework for understanding the potential for this approach. It introduces idealized continuous- and discrete-time abstractions of FIB microscopy with direct electron detection and estimation-theoretic limits of imaging performance under these measurement models. Novel estimators for use with continuous-time measurements are introduced and analyzed, and estimators for use with discrete-time measurements are analyzed and shown to approach their continuous-time counterparts as time resolution is increased. Simulated FIB microscopy results are consistent with theoretical analyses and demonstrate that substantial improvements over conventional FIB microscopy image formation are made possible by time-resolved measurement.
Increased systemic HSP70B levels in spinal muscular atrophy infants

Despite newly available treatments for spinal muscular atrophy (SMA), novel circulating biomarkers are still critically necessary to track SMA progression and therapeutic response. To identify potential biomarkers, we performed whole-blood RNA sequencing analysis in SMA type 1 subjects under 1 year old and age-matched healthy controls. Our analysis revealed the Heat Shock Protein Family A Member 7 (HSPA7)/heat shock 70kDa protein 7 (HSP70B) as a novel candidate biomarker to track SMA progression early in life. Changes in circulating HSP70B protein levels were associated with changes in circulating neurofilament levels in SMA newborns and infants. Future studies will determine whether HSP70B levels respond to molecular therapies.
CDSeqR: fast complete deconvolution for gene expression data from bulk tissues

Biological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and an added new function to aid cell type annotation. The R package would be of interest for the broader R community. We developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating the CDSeq estimated cell types using single-cell RNA sequencing (scRNA-seq) data. This function allows users to readily interpret and visualize the CDSeq estimated cell types. In addition, this new function further allows the users to annotate CDSeq-estimated cell types using marker genes. We carried out additional validations of the CDSeqR software using synthetic, real cell mixtures, and real bulk RNA-seq data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. The existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell–cell interactions in the tissue microenvironment. Bulk level analyses neglect tissue heterogeneity, however, and hinder investigation of a cell-type-specific expression. The CDSeqR package may aid in silico dissection of bulk expression data, enabling researchers to recover cell-type-specific information.
Timing of Home Health Care Initiation and 30-Day Rehospitalizations among Medicare Beneficiaries with Diabetes by Race and Ethnicity.

Older adults with diabetes are at elevated risk of complications following hospitalization. Home health care services mitigate the risk of adverse events and facilitate a safe transition home. In the United States, when home health care services are prescribed, federal guidelines require they begin within two days of hospital discharge. This study examined the association between timing of home health care initiation and 30-day rehospitalization outcomes in a cohort of 786,734 Medicare beneficiaries following a diabetes-related index hospitalization admission during 2015. Of these patients, 26.6% were discharged to home health care. To evaluate the association between timing of home health care initiation and 30-day rehospitalizations, multivariate logistic regression models including patient demographics, clinical and geographic variables, and neighborhood socioeconomic variables were used. Inverse probability-weighted propensity scores were incorporated into the analysis to account for potential confounding between the timing of home health care initiation and the outcome in the cohort. Compared to the patients who received home health care within the recommended first two days, the patients who received delayed services (3–7 days after discharge) had higher odds of rehospitalization (OR, 1.28; 95% CI, 1.25–1.32). Among the patients who received late services (8–14 days after discharge), the odds of rehospitalization were four times greater than among the patients receiving services within two days (OR, 4.12; 95% CI, 3.97–4.28). Timely initiation of home health care following diabetes-related hospitalizations is one strategy to improve outcomes.
Vehicle Production and Sales by Segment in ASEAN.

Objective – a study has been conducted to scrutinize the vehicle production and sales by segment in ASEAN. Methodology/Technique – The study is prepared grounded on primary evidence and secondary material. The primary evidences have composed through oral interviews from stalwarts of the automotive industry. The secondary information has collected from the reports of international Insight of automotive industry and all existing literature has obtained from internet automotive websites, auto business magazines, and e-auto journals. Findings – Outcome of the research divulges that, in vehicle production and sales by segment i.e. Light Commercial Vehicle (LCV), Large Size and also Compact Vehicles in ASEAN expected to increase further in future years, so the Automotive Industries of ASEAN should uninterruptedly participate in Research and Development in order to keep up with their Global competitors in terms of technological advance.
Near field screech noise computation for an underexpanded supersonic jet by the CE/SE method

The space-time conservation element and solution element (CE/SE) method is employed to numerically study the near-field axisymmetric screech-tone noise of a typical underexpanded circular jet issuing from a sonic nozzle. For the computed cases, corresponding to fully expanded Mach numbers of 1.10, 1.15 and 1.19, the self-sustained feedback loop is automatically established. The computed shock-cell structure, acoustic wave length, screech tone frequencies, and sound pressure levels are in good agreement with experimental results.
Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling

Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models. One added benefit when using representations from multilingual models is that they facilitate zero-shot polylingual topic modeling. However, while it has been widely observed that pre-trained embeddings should be fine-tuned to a given task, it is not immediately clear what supervision should look like for an unsupervised task such as topic modeling. Thus, we propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual neural topic modeling. We consider fine-tuning on auxiliary tasks, constructing a new topic classification task, integrating the topic classification objective directly into topic model training, and continued pre-training. We find that fine-tuning encoder representations on topic classification and integrating the topic classification task directly into topic modeling improves topic quality, and that fine-tuning encoder representations on any task is the most important factor for facilitating cross-lingual transfer.
Frequency-doubled Nd:YAG MOPA laser system with programmable rectangular pulses up to 200 microseconds.

A compact frequency-doubled diode-pumped Nd:YAG master-oscillator power-amplifier laser system with programmable microsecond pulse length has been developed. Analog pulse shaping of the output from a single-frequency continuous-wave Nd:YAG oscillator, and subsequent amplification, allowed the generation of rectangular pulses with pulse lengths on the order of the Nd:YAG fluorescence lifetime. Temporally flat-top pulses of 1064 nm light with 520 mJ pulse energy, 2.6 kW peak power, and 200 μs duration, with linewidth below 10 kHz, were obtained at a repetition rate of 2 Hz. Second harmonic generation in a LBO crystal yielded pulses of 262 mJ and 1.3 kW peak power at 532 nm. The peak power can be maintained within 2.9% over the duration of the laser pulse, and long-term intensity stability of 1.1% was observed. The spatially flat-top beam at 1064 nm used in the amplifier is converted to a Gaussian beam at 532 nm with beam quality factor M2 = 1.41(14) during the second harmonic generation. This system has potential as a pump source for Ti:sapphire, dye, or optical parametric amplifiers to generate tunable high-power single-frequency radiation for applications in precision measurements and laser slowing.
Self-Supervised Self-Supervision by Combining Deep Learning and Probabilistic Logic.

Labeling training examples at scale is a perennial challenge in machine learning. Self-supervision methods compensate for the lack of direct supervision by leveraging prior knowledge to automatically generate noisy labeled examples. Deep probabilistic logic (DPL) is a unifying framework for self-supervised learning that represents unknown labels as latent variables and incorporates diverse self-supervision using probabilistic logic to train a deep neural network end-to-end using variational EM. While DPL is successful at combining pre-specified self-supervision, manually crafting self-supervision to attain high accuracy may still be tedious and challenging. In this paper, we propose Self-Supervised Self-Supervision (S4), which adds to DPL the capability to learn new self-supervision automatically. Starting from an initial "seed," S4 iteratively uses the deep neural network to propose new self-supervision. These are either added directly (a form of structured self-training) or verified by a human expert (as in feature-based active learning). Experiments show that S4 is able to automatically propose accurate self-supervision and can often nearly match the accuracy of supervised methods with a tiny fraction of the human effort.
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates

We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles. The disentangled meta-handles factorize all the plausible deformations of the shape, while each of them corresponds to an intuitive deformation. A new deformation can then be generated by sampling the co-efficients of the meta-handles in a specific range. We employ biharmonic coordinates as the deformation function, which can smoothly propagate the control points’ translations to the entire mesh. To avoid learning zero deformaion as meta-handles, we incorporate a target-fitting module which deforms the input mesh to match a random target. To enhance deformations’ plausibility, we employ a soft-rasterizer-based discriminator that projects the meshes to a 2D space. Our experiments demonstrate the superiority of the generated deformations as well as the interpretability and consistency of the learned meta-handles. The code is available at https://github.com/Colin97/DeepMetaHandles.
Tetrahedral-Mesh Simulation of Turbulent Flows with the Space-Time Conservative Schemes

Direct numerical simulations of turbulent flows are predominantly carried out using structured, hexahedral meshes despite decades of development in unstructured mesh methods. Tetrahedral meshes offer ease of mesh generation around complex geometries and the potential of an orientation free grid that would provide un-biased small-scale dissipation and more accurate intermediate scale solutions. However, due to the lack of consistent multi-dimensional numerical formulations in conventional schemes for triangular and tetrahedral meshes at the cell interfaces, numerical issues exist when flow discontinuities or stagnation regions are present. The space-time conservative conservation element solution element (CESE) method - due to its Riemann-solver-free shock capturing capabilities, non-dissipative baseline schemes, and flux conservation in time as well as space - has the potential to more accurately simulate turbulent flows using unstructured tetrahedral meshes. To pave the way towards accurate simulation of shock/turbulent boundary-layer interaction, a series of wave and shock interaction benchmark problems that increase in complexity, are computed in this paper with triangular/tetrahedral meshes. Preliminary computations for the normal shock/turbulence interactions are carried out with a relatively coarse mesh, by direct numerical simulations standards, in order to assess other effects such as boundary conditions and the necessity of a buffer domain. The results indicate that qualitative agreement with previous studies can be obtained for flows where, strong shocks co-exist along with unsteady waves that display a broad range of scales, with a relatively compact computational domain and less stringent requirements for grid clustering near the shock. With the space-time conservation properties, stable solutions without any spurious wave reflections can be obtained without a need for buffer domains near the outflow/farfield boundaries. Computational results for the isotropic turbulent flow decay, at a relatively high turbulent Mach number, show a nicely behaved spectral decay rate for medium to high wave numbers. The high-order CESE schemes offer very robust solutions even with the presence of strong shocks or widespread shocklets. The explicit formulation in conjunction with a close to unity theoretical upper Courant number bound has the potential to offer an efficient numerical framework for general compressible turbulent flow simulations with unstructured meshes.
Machine learning and evolutionary prediction of superhard B-C-N compounds

We build random forests models to predict elastic properties and mechanical hardness of a compound, using only its chemical formula as input. The model training uses over 10,000 target compounds and 60 features based on stoichiometric attributes, elemental properties, orbital occupations, and ionic bonding levels. Using the models, we construct triangular graphs for B-C-N compounds to map out their bulk and shear moduli, as well as hardness values. The graphs indicate that a 1:1 B-N ratio can lead to various superhard compositions. We also validate the machine learning results by evolutionary structure prediction and density functional theory. Our study shows that BC10N, B4C5N3, and B2C3N exhibit dynamically stable phases with hardness values >40 GPa, which are superhard materials that potentially could be synthesized by low-temperature plasma methods.
Experimental Implementation of an Adaptive Digital Autopilot

This paper develops an adaptive digital autopilot for quadcopters and presents experimental results. The adaptive digital autopilot is constructed by augmenting the PX4 autopilot control system architecture with adaptive digital control laws based on retrospective cost adaptive control (RCAC). In order to investigate the performance of the adaptive digital autopilot, the default gains of the fixed-gain autopilot are scaled by a small factor, which severely degrades its performance. This scenario thus provides a venue for determining the ability of the adaptive digital autopilot to compensate for the detuned fixed-gain autopilot. The adaptive digital autopilot is tested in simulation and physical flight tests, and the resulting performance improvements are examined.
SARS-CoV-2 screening testing in schools for children with intellectual and developmental disabilities.

Transmission of SARS-CoV-2 in schools primarily for typically developing children is rare. However, less is known about transmission in schools for children with intellectual and developmental disabilities (IDD), who are often unable to mask or maintain social distancing. The objectives of this study were to determine SARS-CoV-2 positivity and in-school transmission rates using weekly screening tests for school staff and students and describe the concurrent deployment of mitigation strategies in six schools for children with IDD. From November 23, 2020, to May, 28, 2021, weekly voluntary screening for SARS-CoV-2 with a high sensitivity molecular-based saliva test was offered to school staff and students. Weekly positivity rates were determined and compared to local healthcare system and undergraduate student screening data. School-based transmission was assessed among participants quarantined for in-school exposure. School administrators completed a standardized survey to assess school mitigation strategies. A total of 59 students and 416 staff participated. An average of 304 school staff and students were tested per week. Of 7289 tests performed, 21 (0.29%) new SARS-CoV-2 positive cases were identified. The highest weekly positivity rate was 1.2% (n = 4) across all schools, which was less than community positivity rates. Two cases of in-school transmission were identified, each among staff, representing 2% (2/103) of participants quarantined for in-school exposure. Mitigation strategies included higher than expected student mask compliance, reduced room capacity, and phased reopening. During 24 weeks that included the peak of the COVID-19 pandemic in winter 2020-21, we found lower rates of SARS-CoV-2 screening test positivity among staff and students of six schools for children with IDD compared to community rates. In-school transmission of SARS-CoV-2 was low among those quarantined for in-school exposure. However, the impact of the emerging SARS-CoV-2 Delta variant on the effectiveness of these proven mitigation strategies remains unknown. Prior to enrollment, this study was registered at ClinicalTrials.gov on September 25, 2020, identifier NCT04565509 , titled Supporting the Health and Well-being of Children with Intellectual and Developmental Disability During COVID-19 Pandemic.
Analysis of fully stalled compressor

An analysis yields a model for energy transfer in compressor stages operating in the closed-throttle condition. The derivation indicates that three geometry parameters (hub/tip ration, aspect ration, and rotor blade setting angle) influence the values of pressure coefficient when the compressor flow is close to zero.