cssi/SciSciGPT-SciSciNet huggingface dataset:
The Persistent Cultural Script of Judicial Dispassion

Insistence on judicial dispassion is a cultural script of unusual longevity and potency. But not only is the script wrong as a matter of human nature—emotion does not, in fact, invariably tend toward sloppiness, bias, and irrationality—it is also not quite so monolithic as it appears. Legal theorists, and judges themselves, sometimes have asserted that judicial emotion is inevitable and, perhaps, to be welcomed. But these dissents have neither eroded the script’s power nor blossomed into a robust theory of how emotion might coexist with, or even contribute to, judicial decision making. Close examination of this hidden intellectual history reveals why. Scholars and judges consistently have stumbled over foundational questions of emotion’s nature and value. Fortunately, the history reveals cures as well as causes. We can move forward by way of disciplined, sustained recourse to a newly vibrant emotional epistemology, a project that will create a distinct space for the story of judicial emotion.
RHRIs, NHRIs AND HUMAN RIGHTS NGOs

This paper reviews the characteristics of human rights NGOs and their evolving role within the existing international and regional human rights mechanisms, and further, in Asia, the way in which they have worked together for better human rights practices and the establishment of regional human rights institutions (RHRIs) in this region. It also examines the role of human rights NGOs in strengthening human rights protection systems at the national level, especially in cooperation with national human rights institutions (NHRIs). My broad argument is that not only have they contributed to strengthening the international human rights system, but have also been key partners of regional bodies to effectively implement international norms at the regional level. Human rights NGOs are increasingly becoming regional for a better protection and promotion of human rights issues of common concern in the region, and they actively cooperate with other NGOs across national borders. All three existing regional human rights systems in Europe, the Americas, and Africa also show how human rights NGOs can interact and effectively address their concerns and problems under their regional framework. In Asia, their initiatives, input, and efforts have become the strong foundation for the establishment of RHRIs in the region. Further, at the national level, especially in relation to the cooperation with NHRIs, role of human rights NGOs is vital for the effectiveness of NHRIs and ultimately can lead to strengthening the national system for the protection and promotion of human rights. In other words, NHRIs’ effectiveness for better human rights practices in each individual state depends mainly on how they can properly interact with the human rights NGOs and at the same time, on the level of interaction with civil society. In this paper, the case study of seven selected countries in Asia was provided to review the process of establishing NHRIs, both successful and unsuccessful ones, and more specifically, how human rights NGOs have worked together with NHRIs and their governments for the protection and promotion of human rights.
Trade Logistics in Developing Countries: The Case of the Middle East and North Africa

This paper presents an analysis of obstacles facing exporting firms in developing countries by diagnosing the efficiency of trade logistics in the Middle East and North Africa region (MNA). Using logistics chain analysis for six export commodities, it presents evidence that transport and non-transport logistics costs for export commodities from the MNA region are quite substantial, ranging from 7–25 per cent of landed product prices. Underlying these costs are key bottlenecks identified as: inefficient trucking and transport services, low export volume leading to long shipping times and the need for costly inventory accumulation, aggressive, obstructive customs authorities and procedures, low and inconsistent product quality, an underdeveloped transport intermediary sector, inefficient cross-border transit procedures and others. Recommended actions to address developing a national transport policy, overhauling the regulatory regime for the trucking sector, export promotion measures, increasing competition in port and air freight services, reorienting customs authorities towards trade facilitation and developing cross-border transit procedures similar to the TIR Carnets model.
The Path Between Sebastian's Hospitals: Fostering Reconciliation after a Tragedy

On October 8, 2007, Horst and Luisa Ferrero brought their healthy but short, three-year-old son Sebastian to a university hospital for a “routine” test to determine whether he lacked human growth hormone. Two days later, following a tragic string of errors, Sebastian was pronounced brain dead. Approximately two weeks later, the hospital offered a detailed public apology to the parents for Sebastian’s death. Several months after the apology, the parents began working collaboratively with the hospital to improve patient safety at the hospital and to advocate for a new children’s hospital in their community. This paper is a case study of these events, both the tragic events that led to Sebastian’s death and the parties’ responses to that tragedy. More specifically, I use this case to suggest three fundamental lessons related to client service. These are (i) the role of community in healing and legal decision making, (ii) the centrality of caring to the vocation of lawyering, and (iii) the importance of non-monetary interests, in particular meaning making, to many clients.
Direct Investment Liberalization, Commodity Trade and Income Distribution: What Can the WTO do?

This paper analyzes a model of two-way movement of physical capital, and examines the effects of direct investment liberalization on resource allocation, income distribution and commodity trade. If either country or both countries liberalizes investment under exogenously given commodity prices, some factor owners in a country will gain but some others will lose. If capital movement affects commodity prices, all factor owners in a country may be better off after multilateral investment liberalization. In these cases, it will be much easier for the home country to sign an agreement liberalizing investment flows.
Toward a Third-Wave Feminist Legal Theory: Young Women, Pornography and the Praxis of Pleasure

This article critically examines a growing body of non-legal writing by women who have proclaimed a "third-wave" of feminism and suggests the ways that legal theory might be enriched by this work. Scholars typically label the nineteenth-century woman suffrage movement as the "first wave" of feminism, and view the legal and social activism of the 1970s as the "second wave" of feminism. The third wave of feminism, with its intellectual origins in the response to the Clarence Thomas Senate confirmation hearings, is a reaction to the popular stereotype that feminists are humorless man-haters. Third-wave feminists proclaim their difference from second-wave feminists and celebrate "girl power," the joys of make-up and femininity, the complexity of human desire and the importance of fun. Using pornography as the central focus, this article explores the main themes and methods of third-wave feminism. Third-wave feminists view the making and consumption of pornography as a matter of personal preference and actively resist any role for law in the regulation of pornography. These thirdwave writings about pornography and other issues express: (1) dissatisfaction with earlier feminists; (2) the multiple nature of personal identity; (3) the joy of embracing a traditionally feminine appearance and feminine attributes; (4) the centrality of sexual pleasure and sexual self-awareness; (5) the obstacles to economic empowerment; and (6) the social and cultural impact of media and technology. The principal methods of third-wave feminism are personal storytelling, coalition-building and harnessing and interpreting the media. Substantively third-wave feminism is neo-legal in the sense that it does not imagine a full role for law in achieving equality between men and women. The article concludes by suggesting a possible third-wave feminist legal approach to internet regulation, domestic violence, prostitution, abortion, reproductive technology, child care, and sexual harassment.
The Structure of Constitutional Amendment Rules

The structure of formal constitutional amendment rules has received little scholarly attention. Constitutional designers therefore have few academic resources to guide them in designing the rules of formal amendment — perhaps the most important part of any constitution. In this Article, I fill that void by creating a new classification of formal amendment rules based on my analysis of formal amendment rules in constitutional democracies. I explain and illustrate that formal amendment rules are conceptually structured in three tiers: (1) foundations, which either entrench or are silent on the distinction between constitutional amendment and revision; (2) frameworks, consisting of one of six combinations of comprehensive, restricted or exceptional single-track or multi-track procedures; and (3) specifications, which supplement amendment foundations and frameworks with voting thresholds, quorum requirements, subject-matter restrictions, temporal requirements, electoral preconditions and defense mechanisms. I also show how constitutional designers may use this classification to manage federalism, express constitutional values, enhance or diminish the judicial role, and pursue democratic outcomes related to governance, constitutional endurance, and amendment difficulty. My larger purpose is to enliven the study of formal amendment rules by generating a research agenda into their structure and uses, and the options they present to constitutional designers.
Edit Distance in Near-Linear Time: it's a Constant Factor

We present an algorithm for approximating the edit distance between two strings of length $n$ in time $n^{1+\epsilon}$ , for any $\epsilon > 0$ , up to a constant factor. Our result completes a research direction set forth in the recent breakthrough paper [1], which showed the first constant-factor approximation algorithm with a (strongly) sub-quadratic running time. The recent results [2], [3] have shown near-linear complexity only under the restriction that the edit distance is close to maximal (equivalently, there is a near-linear additive approximation). In contrast, our algorithm obtains a constant-factor approximation in near-linear running time for any input strings.
Fair Mixup: Fairness via Interpolation

Training classifiers under fairness constraints such as group fairness, regularizes the disparities of predictions between the groups. Nevertheless, even though the constraints are satisfied during training, they might not generalize at evaluation time. To improve the generalizability of fair classifiers, we propose fair mixup, a new data augmentation strategy for imposing the fairness constraint. In particular, we show that fairness can be achieved by regularizing the models on paths of interpolated samples between the groups. We use mixup, a powerful data augmentation strategy to generate these interpolates. We analyze fair mixup and empirically show that it ensures a better generalization for both accuracy and fairness measurement in tabular, vision, and language benchmarks.
Efficient characterization of dynamic response variation using multi-fidelity data fusion through composite neural network

Abstract Uncertainties in a structure is inevitable, which generally lead to variation in dynamic response predictions. For a complex structure, brute force Monte Carlo simulation for response variation analysis is infeasible since one single run may already be computationally costly. Data driven meta-modeling approaches have thus been explored to facilitate efficient emulation and statistical inference. The performance of a meta-model hinges upon both the quality and quantity of training dataset. In actual practice, however, high-fidelity data acquired from high-dimensional finite element simulation or experiment are generally scarce, which poses significant challenge to meta-model establishment. In this research, we take advantage of the multi-level response prediction opportunity in structural dynamic analysis, i.e., acquiring rapidly a large amount of low-fidelity data from reduced-order modeling, and acquiring accurately a small amount of high-fidelity data from full-scale finite element analysis. Specifically, we formulate a composite neural network fusion approach that can fully utilize the multi-level, heterogeneous datasets obtained. It implicitly identifies the correlation of the low- and high-fidelity datasets, which yields improved accuracy when compared with the state-of-the-art. Comprehensive investigations using frequency response variation characterization as case example are carried out to demonstrate the performance.
ZeRO: Memory optimizations Toward Training Trillion Parameter Models

Large deep learning models offer significant accuracy gains, but training billions to trillions of parameters is challenging. Existing solutions such as data and model parallelisms exhibit fundamental limitations to fit these models into limited device memory, while obtaining computation, communication and development efficiency. We develop a novel solution, Zero Redundancy Optimizer (ZeRO), to optimize memory, vastly improving training speed while increasing the model size that can be efficiently trained. ZeRO eliminates memory redundancies in data- and model-parallel training while retaining low communication volume and high computational granularity, allowing us to scale the model size proportional to the number of devices with sustained high efficiency. Our analysis on memory requirements and communication volume demonstrates: ZeRO has the potential to scale beyond 1 Trillion parameters using today’s hardware. We implement and evaluate ZeRO: it trains large models of over 100B parameter with super-linear speedup on 400 GPUs, achieving throughput of 15 Petaflops. This represents an 8x increase in model size and 10x increase in achievable performance over state-of-the-art. In terms of usability, ZeRO can train large models of up to 13B parameters (e.g., larger than Megatron GPT 8. 3B and T5 11B) without requiring model parallelism which is harder for scientists to apply. Last but not the least, researchers have used the system breakthroughs of ZeRO to create Turing-NLG, the world’s largest language model at the time (17B parameters) with record breaking accuracy.
Migration choices of the boomerang generation: Does returning home dampen labor market adjustment?

Abstract This paper documents how the decisions of young adults to return to live with their parents (‘boomerang’) may contribute to low or declining levels of out-migration from weak labor markets. Using the Panel Study of Income Dynamics and a locational choice model, we find that the likelihood of a non-boomerang location being chosen by a young adult increases with local wages, while wages have a much smaller effect on selecting the boomerang option. In addition, the likelihood that a boomerang location is selected actually increases with the location's unemployment rate. The lower sensitivity of boomerang moves to positive labor market features is substantive in magnitude. We document two distinct ways in which boomerang moves dampen labor market adjustments. Among young adults living in the same labor market as their parent, simulated negative labor market shocks in the form of increases in unemployment accompanied by decreases in wages result in a greater increase in boomeranging than leaving the labor market, hence a residential rather than labor market adjustment. Among young adults living in a different labor market than their parent, boomerang moves are more likely to result in worse destination labor markets than are non-boomerang moves, though a large share of both types of moves are to weaker labor markets.
On symplectic fillings of virtually overtwisted torus bundles

We use Menke’s JSJ-type decomposition theorem for symplectic fillings to reduce the classification of strong and exact symplectic fillings of virtually overtwisted contact structures on torus bundles to the same problem for tight lens spaces. For virtually overtwisted structures on elliptic or parabolic torus bundles, this gives a complete classification. For virtually overtwisted structures on hyperbolic torus bundles, we show that every strong or exact filling arises from a filling of a tight lens space via round symplectic 1 –handle attachment, and we give a condition under which distinct tight lens space fillings yield the same torus bundle filling.
The pricing of global temperature shocks in the cost of equity capital

Abstract Using an APT model where global temperature shocks are a systematically priced factor, the risk premium is significant and positive. Evidence is provided that positive exposure to temperature shocks is related to increasing CO2 emissions by industry. The global impact on the cost of equity could be as high as 2.8% per year, implying a global GDP loss of $2.2 Trillion per year due to global temperature shocks.
Task Planning on Stochastic Aisle Graphs for Precision Agriculture

This work addresses task planning under uncertainty for precision agriculture applications whereby task costs are uncertain and the gain of completing a task is proportional to resource consumption (such as water consumption in precision irrigation). The goal is to complete all tasks while prioritizing those that are more urgent, and subject to diverse budget thresholds and stochastic costs for tasks. To describe agriculture-related environments that incorporate stochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph (SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-Action Planning (NBA-P), is proposed. NBA-P utilizes the underlying structure enabled by SAG, and tackles the task planning problem by simultaneously determining the optimal tasks to perform and an optimal time to exit (i.e. return to a base station), at run-time. The proposed approach is tested with both simulated data and real-world experimental datasets collected in a commercial vineyard, in both single- and multi-robot scenarios. In all cases, NBA-P outperforms other evaluated methods in terms of return per visited vertex, wasted resources resulting from aborted tasks (i.e. when a budget threshold is exceeded), and total visited vertices.
Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning

Developing data-driven subgrid-scale (SGS) models for large eddy simulations (LESs) has received substantial attention recently. Despite some success, particularly in a priori (offline) tests, challenges have been identified that include numerical instabilities in a posteriori (online) tests and generalization (i.e., extrapolation) of trained data-driven SGS models, for example, to higher Reynolds numbers. Here, using the stochastically forced Burgers turbulence as the test-bed, we show that deep neural networks trained using properly pre-conditioned (augmented) data yield stable and accurate a posteriori LES models. Furthermore, we show that transfer learning enables accurate/stable generalization to a flow with 10 × higher Reynolds number.
Novel approach for parallelizing pairwise comparison problems as applied to detecting segments identical by decent in whole-genome data

MOTIVATION Pairwise comparison problems arise in many areas of science. In genomics, datasets are already large and getting larger, and so operations that require pairwise comparisons-either on pairs of SNPs or pairs of individuals-are extremely computationally challenging. We propose a generic algorithm for addressing pairwise comparison problems that breaks a large problem (of order n2 comparisons) into multiple smaller ones (each of order n comparisons), allowing for massive parallelization. RESULTS We demonstrated that this approach is very efficient for calling identical by descent (IBD) segments between all pairs of individuals in the UK Biobank dataset, with a 250-fold savings in time and 750-fold savings in memory over the standard approach to detecting such segments across the full dataset. This efficiency should extend to other methods of IBD calling and, more generally, to other pairwise comparison tasks in genomics or other areas of science.
On Numerical Examination of Uniform Ensemble Controllability for Linear Ensemble Systems

In this letter, we propose a numerical approach to examine uniform ensemble controllability of linear ensemble systems. We show that the linear ensemble defined on the Banach space of compactly supported continuous functions is uniformly ensemble controllable if the differentiation set associated with the ensemble is dense, and only if the reachable set is dense, in the $L^{2}$ -space. We also demonstrate that under certain conditions, $L^{2}$ -denseness of the differentiation set is necessary for uniform ensemble controllability of a linear ensemble system. Then, we provide a tractable numerical method to test the denseness of an arbitrary set in Hilbert space with a quantifiable error bound, which informs uniform ensemble controllability. We conduct several numerical experiments to illustrate the efficacy and robustness of the proposed numerical approach.
Simultaneous Optimization for Concave Costs: Single Sink Aggregation or Single Source Buy-at-Bulk

We consider the problem of finding efficient trees to send information from k sources to a single sink in a network where information can be aggregated at intermediate nodes in the tree. Specifically, we assume that if information from j sources is traveling over a link, the total information that needs to be transmitted is f(j). One natural and important (though not necessarily comprehensive) class of functions is those which are concave, non-decreasing, and satisfy f(0) = 0. Our goal is to find a tree which is a good approximation simultaneously to the optimum trees for all such functions. This problem is motivated by aggregation in sensor networks, as well as by buy-at-bulk network design. We present a randomized tree construction algorithm that guarantees E[maxf Cf/C*(f)] ≤ 1 + log k, where Cf is a random variable denoting the cost of the tree for function f and C*(f) is the cost of the optimum tree for function f. To the best of our knowledge, this is the first result regarding simultaneous optimization for concave costs. We also show how to derandomize this result to obtain a deterministic algorithm that guarantees max_f Cf/C*(f) = O(log k). Both these results are much stronger than merely obtaining a guarantee on max_f E[Cf/C*(f)]. A guarantee on maxf E[Cf/C*(f)] can be obtained using existing techniques, but this does not capture simultaneous optimization since no one tree is guaranteed to be a good approximation for all f simultaneously. While our analysis is quite involved, the algorithm itself is very simple and may well find practical use. We also hope that our techniques will prove useful for other problems where one needs simultaneous optimization for concave costs.
Nerve Growth Factor

Publisher Summary This chapter discusses the structure of nerve growth factor (NGF). The amino acid sequence of mouse NGF exhibited significant structural relatedness with the family of insulins and proinsulins. The sequence of mouse NGF could be aligned with human proinsulin with only five deletions required to yield the maximum homology of 21% identical residue positions. This is indicative of distant, but significant evolutionary relatedness. The majority of the identical residues were seen to be clustered in the segments of NGF, which aligned with the insulin A and B chain regions of the proinsulin sequence, separated by exactly the 35 residues required to accommodate the C activation peptide of proinsulin. Thus, the order of similar regions of NGF is B chain-C peptide-A chain, the same order in which they occur in proinsulin. Because NGF is 118 residues and human proinsulin is 86 residues, 37 residues of the C terminal region of NGF extend beyond the proinsulin molecule. This region is, however, similar to insulin B chain, suggesting a repetitive structure arising from a gene duplication event. It is also noteworthy that three out of the six half cystinyl residues that occur in both proteins are in identical positions, and two of these remain paired in identical fashion in insulin and NGF.
Fast periodic Gaussian density fitting by range separation.

We present an efficient implementation of periodic Gaussian density fitting (GDF) using the Coulomb metric. The three-center integrals are divided into two parts by range-separating the Coulomb kernel, with the short-range part evaluated in real space and the long-range part in reciprocal space. With a few algorithmic optimizations, we show that this new method—which we call range-separated GDF (RSGDF)—scales sublinearly to linearly with the number of k-points for small to medium-sized k-point meshes that are commonly used in periodic calculations with electron correlation. Numerical results on a few three-dimensional solids show about ten-fold speedups over the previously developed GDF with little precision loss. The error introduced by RSGDF is about 10−5 Eh in the converged Hartree–Fock energy with default auxiliary basis sets and can be systematically reduced by increasing the size of the auxiliary basis with little extra work.
Track-Based Alignment of the Inner Detector of ATLAS

ATLAS is a multipurpose experiment at the LHC. The tracking system of ATLAS, embedded in a 2 T solenoidal field, is composed of different technologies: silicon planar sensors (pixel and microstrips) and drift-tubes. The procedure used to align the ATLAS tracker and the results of the alignment using data recorded during 2010 and 2011 using LHC proton-proton collision runs at 7 TeV are presented. Validation of the alignment is performed by measuring the alignment observables as well as many other physics observables, notably resonance invariant masses in a wide mass range (KS , J /Ψ and Z ). The E /p distributions for electrons from Z → ee and W → ev are also extensively used. The results indicate that, after the alignment with real data, the attained precision of the alignment constants is approximately 5 μm . The systematic errors due to the alignment that may affect physics results are under study.
Thermodynamically Governed Interior Models of Uranus and Neptune

Interior models of Uranus and Neptune often assume discrete layers, but sharp interfaces are expected only if major constituents are immiscible. Diffuse interfaces could arise if accretion favored a central concentration of the least volatile constituents (also incidentally the most dense); compositional gradients arising in such a structure would likely inhibit convection. Currently, two lines of evidence suggest possible hydrogen-water immiscibility in ice giant interiors. The first arises from crude extrapolation of the experimental hydrogen-water critical curve to $\sim 3$ GPa (Bali et al. 2013). The data are obtained for an impure system containing silicates, though Uranus and Neptune could also be "dirty." Current ab initio models disagree (Soubiran & Militzer 2015), though hydrogen and water are difficult to model from first-principles quantum mechanics with the necessary precision. The second argument for hydrogen-water immiscibility in ice giants, outlined herein, invokes reasoning about the gravitational and magnetic fields. While consensus remains lacking, here we examine the immiscible case. Applying the resulting thermodynamic constraints, we find that Neptune models with envelopes containing a substantial water mole fraction, as much as $\chi \gtrsim 0.1$ relative to hydrogen, can satisfy observations. In contrast, Uranus models appear to require $\chi \lesssim 0.01$, potentially suggestive of fully demixed hydrogen and water. Enough gravitational potential energy would be available from gradual hydrogen-water demixing, to supply Neptune's present-day heatflow for roughly ten solar system lifetimes. Hydrogen-water demixing could slow Neptune's cooling rate by an order of magnitude; different hydrogen-water demixing states could account for the different heatflows of Uranus and Neptune.
Machine Learning Approaches for Inferring Liver Diseases and Detecting Blood Donors from Medical Diagnosis

ABSTRACT For a medical diagnosis, health professionals use different kinds of pathological ways to make a decision for medical reports in terms of patients’ medical condition. In the modern era, because of the advantage of computers and technologies, one can collect data and visualize many hidden outcomes from them. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning data driven algorithms can be used to validate existing methods and help researchers to suggest potential new decisions. In this paper, multiple imputation by chained equations was applied to deal with missing data, and Principal Component Analysis to reduce the dimensionality. To reveal significant findings, data visualizations were implemented. We presented and compared many binary classifier machine learning algorithms (Artificial Neural Network, Random Forest, Support Vector Machine) which were used to classify blood donors and non-blood donors with hepatitis, fibrosis and cirrhosis diseases. From the data published in UCI-MLR [1], all mentioned techniques were applied to find one better method to classify blood donors and non-blood donors (hepatitis, fibrosis, and cirrhosis) that can help health professionals in a laboratory to make better decisions. Our proposed ML-method showed better accuracy score (e.g. 98.23% for SVM). Thus, it improved the quality of classification.
Isolation of SARS-CoV-2 from the air in a car driven by a COVID patient with mild illness.

OBJECTIVE: To determine if viable virus could be isolated from the air within a car driven by a patient infected with SARS-CoV-2, and to assess the size range of the infectious particles. METHODS: We used a Sioutas personal cascade impactor sampler (PCIS) to screen for SARS-CoV-2 in a car driven by a COVID-19 patient. The patient, who had only mild illness without fever or cough and was not wearing a mask, drove the car for 15 min with the air conditioning turned on and windows closed. The PCIS was clipped to the sun-visor above the front passenger seat and was retrieved from the car two hours after completion of the drive. RESULTS: SARS-CoV-2 was detectable at all PCIS stages by PCR and was cultured from the section of the sampler collecting particles in the 0.25-0.50 µm size range. CONCLUSIONS: Our data highlight the potential risk of SARS-CoV-2 transmission by minimally symptomatic persons in the closed space inside of a car and suggest that a substantial component of that risk is via aerosolized virus.
Measurements of acoustic radiation force of ultrahigh frequency ultrasonic transducers using model-based approach.

Even though ultrahigh frequency ultrasonic transducers over 60 MHz have been used for single-cell-level manipulation such as intracellular delivery, acoustic tweezers, and stimulation to investigate cell phenotype and cell mechanics, no techniques have been available to measure the actual acoustic radiation force (ARF) applied to target cells. Therefore, we have developed an approach to measure the ARF of ultrahigh frequency ultrasonic transducers using a theoretical model of the dynamics of a solid sphere in a gelatin phantom. To estimate ARF at the focus of a 130 MHz transducer, we matched measured maximum displacements of a solid sphere with theoretical calculations. We selected appropriate ranges of input voltages and pulse durations for single-cell applications, and the estimated ARF was in the range of tens of μN. To gauge the influence of pulse duration, an impulse of different pulse durations was estimated. Fluorescence resonance energy transfer live cell imaging was demonstrated to visualize calcium transport between cells after a target single cell was stimulated by the developed ultrasonic transducer.
Dnsmos: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors

Human subjective evaluation is the "gold standard" to evaluate speech quality optimized for human perception. Perceptual objective metrics serve as a proxy for subjective scores. The conventional and widely used metrics require a reference clean speech signal, which is unavailable in real recordings. Previous no-reference approaches correlate poorly with human ratings and are not widely adopted in the research community. One of the biggest use cases of these perceptual objective metrics is to evaluate noise suppression algorithms. This paper introduces a multi-stage self-teaching based perceptual objective metric that is designed to evaluate noise suppressors. The proposed method generalizes well in challenging test conditions with a high correlation to human ratings.
Understanding and improving artifact sharing in software engineering research

In recent years, many software engineering researchers have begun to include artifacts alongside their research papers. Ideally, artifacts, including tools, benchmarks, and data, support the dissemination of ideas, provide evidence for research claims, and serve as a starting point for future research. However, in practice, artifacts suffer from a variety of issues that prevent the realization of their full potential. To help the software engineering community realize the full potential of artifacts, we seek to understand the challenges involved in the creation, sharing, and use of artifacts. To that end, we perform a mixed-methods study including a survey of artifacts in software engineering publications, and an online survey of 153 software engineering researchers. By analyzing the perspectives of artifact creators, users, and reviewers, we identify several high-level challenges that affect the quality of artifacts including mismatched expectations between these groups, and a lack of sufficient reward for both creators and reviewers. Using Diffusion of Innovations (DoI) as an analytical framework, we examine how these challenges relate to one another, and build an understanding of the factors that affect the sharing and success of artifacts. Finally, we make recommendations to improve the quality of artifacts based on our results and existing best practices.
Flow modification associated with reduced genetic health of a river-breeding frog, Rana boylii

Abstract River regulation or flow modification—the hydrological alteration of flow by dams and diversions—has been implicated as a cause of fundamental change to downstream aquatic ecosystems. Flow modification changes the patterns and functionality of the natural flow regime, and has the potential to restrict population connectivity and gene flow in river-dependent organisms. Since population connectivity and the maintenance of genetic diversity are fundamental drivers of long-term persistence, understanding the extent flow modification impacts these critical attributes of genetic health is an important goal for long-term conservation. Foothill yellow-legged frogs (Rana boylii) were historically abundant throughout many western rivers but have declined since the onset of regulation. However, the extent to which R. boylii populations in rivers with altered flow regimes are maintaining connectivity and genetic diversity is unknown. Here we use genetic methods to investigate the impacts of flow alteration on R. boylii to explore their potential for long-term persistence under continued flow modification. We found R. boylii in rivers with flow modification showed striking patterns of isolation and trajectories of genetic diversity loss relative to unregulated rivers. For example, flow modification explained the greatest amount of variance in population genetic differentiation compared with other covariates including geographic distance. Importantly, patterns of connectivity and genetic diversity loss were observed regardless of flow alteration level but were most prominent in locations with the greatest flow modification intensity. Although our results do not bode well for long-term persistence of R. boylii populations under current flow regulation regimes, they do highlight the power of genetic monitoring for assessing population health in aquatic organisms.
The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution

The scarcity of accessible sites that are dynamic or cell type-specific in plants may be due in part to tissue heterogeneity in bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to Arabidopsis thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. We find that the entirety of a cell's regulatory landscape and its transcriptome independently capture cell type identity. We leverage this shared information on cell identity to integrate accessibility and transcriptome data to characterize developmental progression, endoreduplication and cell division. We further use the combined data to characterize cell type-specific motif enrichments of transcription factor families and link the expression of family members to changing accessibility at specific loci, resolving direct and indirect effects that shape expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development.
Do adverse health shocks induce myopic financial planning

Health and financial planning have both been found to be crucial to long-term financial stability. However, the impact of a health shock on financial planning horizon was not directly tested. This article traces the trajectories of the financial planning horizon before and after the occurrence of work-limiting health shocks, using longitudinal panel data from the Health and Retirement Study. Results show that, during the 10 years following a health shock, individuals are 20 to 39% more likely to focus on the near term (i.e., the next few months) than baseline levels. Moreover, people with lower socioeconomic status or poor general health are more prone to switching to myopic planning after a health shock.
Annotating and Modeling Fine-grained Factuality in Summarization

Recent pre-trained abstractive summarization systems have started to achieve credible performance, but a major barrier to their use in practice is their propensity to output summaries that are not faithful to the input and that contain factual errors. While a number of annotated datasets and statistical models for assessing factuality have been explored, there is no clear picture of what errors are most important to target or where current techniques are succeeding and failing. We explore both synthetic and human-labeled data sources for training models to identify factual errors in summarization, and study factuality at the word-, dependency-, and sentence-level. Our observations are threefold. First, exhibited factual errors differ significantly across datasets, and commonly-used training sets of simple synthetic errors do not reflect errors made on abstractive datasets like XSum. Second, human-labeled data with fine-grained annotations provides a more effective training signal than sentence-level annotations or synthetic data. Finally, we show that our best factuality detection model enables training of more factual XSum summarization models by allowing us to identify non-factual tokens in the training data.
ABEL-FRET: tether-free single-molecule FRET with hydrodynamic profiling.

Single-molecule Forster resonance energy transfer (smFRET) has become a versatile and widespread method to probe nanoscale conformation and dynamics. However, current experimental modalities often resort to molecule immobilization for long observation times and do not always approach the resolution limit of FRET-based nanoscale metrology. Here we present ABEL-FRET, an immobilization-free platform for smFRET measurements with ultrahigh resolving power in FRET efficiency. Importantly, single-molecule diffusivity is used to provide additional size and shape information for hydrodynamic profiling of individual molecules, which, together with the concurrently measured intramolecular conformation through FRET, enables a holistic and dynamic view of biomolecules and their complexes. ABEL-FRET combines anti-Brownian trapping for tether-free observation of biomolecules in solution with smFRET. ABEL-FRET offers ultrahigh resolution of FRET efficiency and enables simultaneous hydrodynamic profiling of molecular composition.
Notch Effects on Room Temperature Tensile and Bend Properties of Ni3Al and Ni3Al+B

The notched mechanical properties of Ni3AI and Ni3Al+B prepared by powder metallurgy techniques have been determined in both tension and bending at room temperature. Ten- sile tests performed using double notched specimens containing relatively blunt notches produced intergranular fracture in both Ni3Al and Ni3AI+B, with evidence of fracture initiating in an intergranular manner ahead of the blunt notch in both cases. Estimates of notched fracture toughness from bend tests and of local grain boundary fracture stress from the notched tensile tests suggest an increase in these values with boron addition.
UCPhrase: Unsupervised Context-aware Quality Phrase Tagging

Identifying and understanding quality phrases from context is a fundamental task in text mining. The most challenging part of this task arguably lies in uncommon, emerging, and domain-specific phrases. The infrequent nature of these phrases significantly hurts the performance of phrase mining methods that rely on sufficient phrase occurrences in the input corpus. Context-aware tagging models, though not restricted by frequency, heavily rely on domain experts for either massive sentence-level gold labels or handcrafted gazetteers. In this work, we propose UCPhrase, a novel unsupervised context-aware quality phrase tagger. Specifically, we induce high-quality phrase spans as silver labels from consistently co-occurring word sequences within each document. Compared with typical context-agnostic distant supervision based on existing knowledge bases (KBs), our silver labels root deeply in the input domain and context, thus having unique advantages in preserving contextual completeness and capturing emerging, out-of-KB phrases. Training a conventional neural tagger based on silver labels usually faces the risk of overfitting phrase surface names. Alternatively, we observe that the contextualized attention maps generated from a transformer-based neural language model effectively reveal the connections between words in a surface-agnostic way. Therefore, we pair such attention maps with the silver labels to train a lightweight span prediction model, which can be applied to new input to recognize (unseen) quality phrases regardless of their surface names or frequency. Thorough experiments on various tasks and datasets, including corpus-level phrase ranking, document-level keyphrase extraction, and sentence-level phrase tagging, demonstrate the superiority of our design over state-of-the-art pre-trained, unsupervised, and distantly supervised methods.
CTRAMER: An open-source software package for correlating interfacial charge transfer rate constants with donor/acceptor geometries in organic photovoltaic materials

In this paper, we present CTRAMER (Charge-Transfer RAtes from Molecular dynamics, Electronic structure, and Rate theory)—an open-source software package for calculating interfacial charge-transfer (CT) rate constants in organic photovoltaic (OPV) materials based on ab initio calculations and molecular dynamics simulations. The software is based on identifying representative donor/acceptor geometries within interfacial structures obtained from molecular dynamics simulation of donor/acceptor blends and calculating the corresponding Fermi's golden rule CT rate constants within the framework of the linearized-semiclassical approximation. While the methods used are well established, the integration of these state-of-the-art tools originating from different disciplines to study photoinduced CT processes with explicit treatment of the environment, in our opinion, makes this package unique and innovative. The software also provides tools for investigating other observables of interest. After outlining the features and implementation details, the usage and performance of the software are demonstrated with results from an example OPV system.
Fast linking numbers for topology verification of loopy structures

It is increasingly common to model, simulate, and process complex materials based on loopy structures, such as in yarn-level cloth garments, which possess topological constraints between inter-looping curves. While the input model may satisfy specific topological linkages between pairs of closed loops, subsequent processing may violate those topological conditions. In this paper, we explore a family of methods for efficiently computing and verifying linking numbers between closed curves, and apply these to applications in geometry processing, animation, and simulation, so as to verify that topological invariants are preserved during and after processing of the input models. Our method has three stages: (1) we identify potentially interacting loop-loop pairs, then (2) carefully discretize each loop's spline curves into line segments so as to enable (3) efficient linking number evaluation using accelerated kernels based on either counting projected segment-segment crossings, or by evaluating the Gauss linking integral using direct or fast summation methods (Barnes-Hut or fast multipole methods). We evaluate CPU and GPU implementations of these methods on a suite of test problems, including yarn-level cloth and chainmail, that involve significant processing: physics-based relaxation and animation, user-modeled deformations, curve compression and reparameterization. We show that topology errors can be efficiently identified to enable more robust processing of loopy structures.
Multiple morphogens and rapid elongation promote segmental patterning during development

The vertebrate hindbrain is segmented into rhombomeres (r) initially defined by distinct domains of gene expression. Previous studies have shown that noise-induced gene regulation and cell sorting are critical for the sharpening of rhombomere boundaries, which start out rough in the forming neural plate (NP) and sharpen over time. However, the mechanisms controlling simultaneous formation of multiple rhombomeres and accuracy in their sizes are unclear. We have developed a stochastic multiscale cell-based model that explicitly incorporates dynamic morphogenetic changes (i.e. convergent-extension of the NP), multiple morphogens, and gene regulatory networks to investigate the formation of rhombomeres and their corresponding boundaries in the zebrafish hindbrain. During pattern initiation, the short-range signal, fibroblast growth factor (FGF), works together with the longer-range morphogen, retinoic acid (RA), to specify all of these boundaries and maintain accurately sized segments with sharp boundaries. At later stages of patterning, we show a nonlinear change in the shape of rhombomeres with rapid left-right narrowing of the NP followed by slower dynamics. Rapid initial convergence improves boundary sharpness and segment size by regulating cell sorting and cell fate both independently and coordinately. Overall, multiple morphogens and tissue dynamics synergize to regulate the sizes and boundaries of multiple segments during development.