cssi/SciSciGPT-SciSciNet huggingface dataset:
Culture, democracy, and market reforms: Evidence from transition countries

Abstract Transition countries differed widely in the extent to which they implemented market reforms. This paper empirically investigates the role of culture as a potential determinant of market reform adoption among the post-socialist economies of Eastern Europe and Central Asia. I examine an unbalanced panel of up to 21 transition countries over the period 1989–2001 and estimate the relationship between different cultural typologies and the adoption of liberalizing reforms. I report that a sense of individualism and intolerance for unequal dispersion of power within societies are robustly associated with greater market reform efforts. Only limited evidence exists for the influence of other cultural attitudes.
Is United States Corporate Tax Policy Outsourcing America - A Critical Analysis of the Proposed Tax Holiday for Trapped CFC Earnings

U.S.-based multinational corporations are calling for a tax holiday that would allow them to repatriate their foreign earnings at little tax cost. With over $1 trillion in earnings trapped offshore and untaxed by the U.S., these corporations characterize the proposed repatriation holiday as a stimulus of sorts for the flagging U.S. economy. But the last time the U.S. opened this loophole, it did little to stimulate domestic reinvestment or jobs creation. U.S. firms repatriated hundreds of billions of dollars under the 2005 program but eschewed domestic reinvestment and jobs creation in favor of maneuvers, like stock buybacks and dividends, that funneled the cash to shareholders. This paper examines the perils of an open ended tax holiday and proposes a more targeted solution for putting those offshore deferred U.S. MNC earnings to work creating U.S. jobs. Our proposal would force corporate America justify their participation in the subsidy and answer the question of how they will use their repatriated offshore earnings — to enrich Wall Street shareholders or to make their U.S. work force more competitive in the world economy.
Misbehaving Lawyers: Cross-Country Comparisons

Lawyer misbehaviour occurs in every country and regulators often struggle to address it effectively. This article looks at six case studies of disciplined lawyers in Australia, Canada, the Netherlands, New Zealand and the United Kingdom. It notes the similarities in the cases and to disciplined lawyers previously described in case studies in the United States. In particular, these case studies involved male lawyers predominantly working in solo or small firms who were insufficiently exposed to positive professional values early in practice. They were willing to lie to achieve their goals and were motivated, at least in part, by money. The article considers how the love of money, the behavioural disposition known as Machiavellianism, and the need to maintain self-esteem may have affected the lawyers’ conduct. It also discusses the need to deal more effectively with recidivists, like the lawyers in these case studies, and identifies some possible regulatory responses to recidivism.
Induction, Space and Positive Ethics

One may purport that ones’ awareness of space for scientific purposes comes about from a potential awareness of its’ absence that is derived from times when ones attention is not focused on it. Yet simply one might extract the notion that space and entailed properties of it are elemental —i.e. conceptually non reducible and that from which all emanates. The words non-ethical induction, entailing the existence of ethical induction, if compared in a corresponding manner (to indivisible space and the attentive awareness of it), also entail that the ethics of induction in science are dependant on attentive focus. In the following description, I will attempt to draw some logical conclusions employing this analogy regardless of its potential validity or invalidity and then relate these conclusions to actual circumstances in order to lend them substance. Entailed with the terminology non ethical induction is the existence of ethical induction. The intended parallel hopes to extract the following: 1) Inductively arising applications are innately unethical. 2) Inductive tendencies, the inclination to wish to induce, are the product of attention dependant conceptual development. 3) Space is considered to be indivisible/non reducible, the elemental construction unit of the world; in the same parallel an ethic is deduced to be elemental and universal to the world also. As an example to build on: as publication space is limited here (excuse the pun) I must approach the topic more aggressively than usual in order to create space for my ideas, and from this notion will conclude at the same time that all processes whether scientific/molecular in origin, psychological, psychical, of mind or matter emanate in a parallel fashion from the properties of physical space. An elemental force is also postulated to exist that enables uniqueness as explanation for the means of physical separation of surfaces. In the case of my text, the surface area that I am constrained to in order to contain my thoughts involve the energy—energies and metabolic process of the chemicals that compose my conscious awareness in the expression of my ideas entail physiological considerations and chemical properties arrived at from the interactions of surfaces—as well as relations of memory, behavior that may be represented as contained up to the present with all the possible paths of nature that have a limit if considered up to the present moments, and are also derived from the same unit principle of space/force [time = space = force] and the arrangements of surfaces, emergence of structure(s):
Representation of Ecological Systems within the Protected Areas Network of the Continental United States

If conservation of biodiversity is the goal, then the protected areas network of the continental US may be one of our best conservation tools for safeguarding ecological systems (i.e., vegetation communities). We evaluated representation of ecological systems in the current protected areas network and found insufficient representation at three vegetation community levels within lower elevations and moderate to high productivity soils. We used national-level data for ecological systems and a protected areas database to explore alternative ways we might be able to increase representation of ecological systems within the continental US. By following one or more of these alternatives it may be possible to increase the representation of ecological systems in the protected areas network both quantitatively (from 10% up to 39%) and geographically and come closer to meeting the suggested Convention on Biological Diversity target of 17% for terrestrial areas. We used the Landscape Conservation Cooperative framework for regional analysis and found that increased conservation on some private and public lands may be important to the conservation of ecological systems in Western US, while increased public-private partnerships may be important in the conservation of ecological systems in Eastern US. We have not assessed the pros and cons of following the national or regional alternatives, but rather present them as possibilities that may be considered and evaluated as decisions are made to increase the representation of ecological systems in the protected areas network across their range of ecological, geographical, and geophysical occurrence in the continental US into the future.
Innovations in Providing Access to Research and Knowledge: Directions for Systems Improvement in the Law Library

ABSTRACT In this article, the authors explore the future of legal reference services, continuing the discussion raised in their previous article, at 26 LRSQ 57 (No. 1/2, 2007). Again considering examples of popular and effective services from the non-library (commercial) world, they examine underlying trends and suggest points for improvement in library systems. They concentrate their focus on the academic law library, and on the changing role of the reference librarian.
Do Individual Investors Cause Post-Earnings Announcement Drift? Direct Evidence from Personal Trades

ABSTRACT: This study tests whether nai¨ve trading by individual investors, or some class of individual investors, causes post-earnings announcement drift (PEAD). Inconsistent with the individual trading hypothesis, individual investor trading fails to subsume any of the power of extreme earnings surprises to predict future abnormal returns. Moreover, individuals are significant net buyers after both negative and positive extreme earnings surprises, consistent with an attention effect, but not with their trades causing PEAD. Finally, we find no indication that trading by individuals explains the concentration of drift at subsequent earnings announcement dates.
Are Human Genes Patentable

IIC (2013) 44:747–749 DOI 10.1007/s40319-013-0102-x EDITORIAL Are Human Genes Patentable? Dan L. Burk Published online: 20 September 2013 O Max Planck Institute for Intellectual Property and Competition Law, Munich 2013 On June 13, 2013 the United States Supreme Court delivered its long-anticipated opinion in Association for Molecular Pathology v. Myriad, 1 a decision addressing the question posed by the Court to the parties, ‘‘Are human genes patentable?’’ 2 As has already been widely reported in the popular press, in a relatively short and essentially unanimous opinion authored by Justice Thomas, the Court held DNA sequences extracted from human cells to constitute products of nature, outside patentable subject matter. But what has been less widely reported is the simultaneous holding that other human gene sequences, entailing equivalent information, remain within patentable subject matter. The reasoning behind this distinction is puzzling and contradictory, and leaves the law in this area far more confused and uncertain than before the Court undertook its answer to the gene patenting question. Two types of claimed nucleotide sequences were at issue in the case: genomic or gDNA that was extracted and isolated from human cells, and complementary or cDNA that is produced in a laboratory procedure called reverse transcription, using messenger RNA as a template. A three-judge panel of the intermediate Federal Circuit court of appeal had unanimously found the cDNA to be the patent eligible product of human intervention, but the Federal Circuit judges split over the patentability of the isolated gDNA molecules. 3 The principal opinion, written by Judge Lourie, found the gDNA molecules to be patentable subject matter due to their chemical separation from the chromosome in which they were naturally 569 U.S. ___ (2013). 133 S.Ct. 694 (2012) (granting certiorari). Ass’n for Molecular Pathology v. U.S. Patent & Trademark Office, 689 F.3d 1303, 1329 (Fed. Cir. D. L. Burk (&) Professor of Law University of California, Irvine, 401 East Peltason Dr., Law 4800-N, Irvine, CA 92697-8000, USA e-mail: dburk@law.uci.edu
Global Venue Controls Are Coming: A Reply to Professor LoPucki

This Article details my disagreements with Professor Lynn LoPucki's article "Global and out of Control" (79 Am. Bankr. L.J. 79). Part I discusses universalism and territorialism, especially the modified version of universalism that I support. Part II examines the international venue provisions of the Model Law and the EU Regulation. Part III introduces the relevant venue shopping cases. Only two groups of cases are relevant for the purpose of this paper: the French and German subsidiaries of Daisytek and Eurofood (a subsidiary of Parmalat SpA, the Italian conglomerate). None of the other cases that Professor LoPucki discusses was subject to the venue provisions of either the EU Regulation or the Model Law. Part IV explains the amendments that I believe are needed for the EU Regulation and the Model Law to deal more effectively with venue decisions and discusses how these changes would affect the venue decisions in Eurofood and the continental Daisytek subsidiaries. Finally, Part V contains concluding remarks.
The affective and interpersonal consequences of obesity

The incidence of obesity in the United States has tripled over the past fifty years, posing significant challenges for organizations. We build on stereotype content research and offer an overarching framework to understand individuals’ affective, cognitive, and behavioral responses to obesity. Across five studies, we demonstrate that individuals associate obesity with perceptions of low competence. Perceptions of low competence predict affective (disgust, sympathy) and behavioral (low help, high harm) responses to obesity. Consistent with the BIAS Map (Cuddy, Fiske, & Glick, 2007), these discriminatory responses are moderated by perceptions of warmth. We demonstrate that, in some cases, shifting perceptions of warmth is just as effective as losing weight for curtailing discrimination towards the obese. Our findings demonstrate that social categorization is labile and we offer prescriptive advice for individuals seeking to change the way others perceive them.
Regulating Sexual Harm: Strangers, Intimates, and Social Institutional Reform

The criminal regulation of sexual harm in the United States is afflicted by deep pathology. Although sexual harm appears before the law in a variety of forms—from violent rape, to indecent exposure, to the sexual touching by an older child of a younger child—the prevailing U.S. criminal regulatory framework responds to this wide range of conduct with remarkable uniformity. All persons so convicted are labeled “sex offenders,” and most are subjected to registration, community notification, and residential restrictions, among other sanctions. These measures purport to prevent the perpetration of further criminal sexual harm by publicizing the identities and restricting the residential opportunities of persons presumed to be strangers to their victims. But even as these measures render many subject to them homeless and unemployable, sexual abuse remains pervasive and significantly underreported in our schools, prisons, military, and between intimates in families. Thus, at once, the U.S. criminal regulatory regime constructs a peculiarly overbroad category of feared persons, compels a misguided approach to this population, and neglects the most prevalent forms of vulnerability to sexual predation and assault. This Essay argues that an alternative social institutional reform framework could address
Participation and investment decisions in a retirement plan: the influence of colleagues' choices

Abstract This paper investigates whether peer effects play an important role in retirement savings decisions. We use individual data from employees of a large university to study whether individual decisions to enroll in a Tax Deferred Account plan sponsored by the university, and the choice of the mutual fund vendor for people who choose to enroll, are affected by the decisions of other employees in the same department. To overcome the identification problems, we divide the departments into sub-groups (along gender, status, age, and tenure lines) and we instrument the average participation of each peer group by the salary or tenure structure in this group. Our results suggest that peer effects may be an important determinant of savings decisions.
Do banks actively manage their liquidity

We test whether and how U.S. commercial banks actively managed their liquidity positions between 1992 and 2012, prior to the implementation of the Basel III liquidity rules. On average, the data are consistent with a liquidity management regime in which banks targeted the traditional loans-to-core deposits (LTCD) ratio. Perhaps surprisingly, the data are also consistent on average with the net stable funding ratio (NSFR), a regulatory liquidity ratio that was not formally introduced by the Bank for International Settlements until 2010. We find evidence of LTCD and (implicit) NSFR targeting at banks of all sizes, but concordance is strongest for small banks and weakest for so-called SIFI banks. As banks increase in size, they set lower liquidity targets—often in violation of the coming Basel III standards—but manage those targets more efficiently.
Partisan Gerrymandering and Disaggregated Redistricting

Courts and scholars have long struggled over whether and how to police partisan gerrymandering. For all the disagreement about how to resolve these questions, however, there has been one consistent point of concurrence - that courts should treat congressional gerrymanders and state legislative gerrymanders identically. Constitutional jurisprudence and legal scholarship have always assumed that these two types of gerrymanders pose the same problems and are subject to the same solutions. Last Term in Vieth v. Jubelirer, the Court entrenched this assumption in constitutional doctrine. This Article demonstrates that, contrary to the conventional view, congressional and state legislative gerrymanders raise distinct conceptual, normative, and constitutional questions. State legislative gerrymanders differ from congressional gerrymanders in a crucial way: a state legislative gerrymander affects the composition of the entire legislative assembly, while a congressional gerrymander affects the composition of only a small part of the legislature. This difference is significant because the standard contemporary accounts about why partisan gerrymanders are harmful describe harms that turn on the structure of representation in the legislative assembly as a whole - not on the consequences of redistricting for a small subset of the legislature. Such legislature-wide harms can be identified by a court reviewing a state legislative gerrymander, because the court assesses the redistricting plan that affects the composition of the entire legislature. But a court reviewing a single state's federal congressional gerrymander cannot identify such harms. The Article argues that the Justices' efforts in Vieth to directly identify the existence of harms caused by Pennsylvania's alleged congressional gerrymander were therefore misguided. If courts are to police congressional partisan gerrymanders, they instead need to develop strategies for state-level intervention that reduce the risk of congress-wide injuries.
Polarization and the Decline of Economic Voting in American National Elections

Objective There is substantial evidence that American voters blame or credit the president for the state of the economy when making electoral decisions. However, a variety of findings on economic voting, cognitive biases in information processing, and party polarization indicate that both objective and subjective economic information should become less important to voters as partisan polarization increases. We evaluate whether partisan polarization attenuates the link between economic performance and citizens’ votes. Methods We estimate statistical models of the incumbent party vote shares in U.S. presidential elections from 1952 to 2016 including as predictive terms national partisan polarization (DW‐NOMINATE) and the interaction between polarization and economic growth (annualized second quarter GDP change in election years). Results We find support for our expectation that greater partisan polarization mitigates the association between economic performance and American election returns. Conclusion Economic performance exerts less influence on vote choices when parties are highly polarized than when they are not. Also, currently high levels of partisan polarization in the United States indicate elections will remain competitive, even if economic conditions otherwise favor or undermine an incumbent candidate's chances of winning.
How corruptible are you? Bribery under uncertainty☆

Abstract We model corruption in a society as a result of bargaining for bribes between private citizens and public officials. We investigate the role that incomplete information with respect to the intrinsic moral cost of one's potential corruption partner plays out in his or her propensity to engage in bribery, and, consequently, the equilibrium level of corruption in the society. We assume that the cost of engaging in corruption is subject to strategic complementarities, which may lead to multiple corruption equilibria. We find that corruption is lowest when potential bribers and potential bribees are uncertain regarding each other's “corruptibility” and have asymmetric bargaining powers. Our uncertainty result provides theoretical support in favor of anti-corruption strategies, such as staff rotation in public offices, aimed at decreasing the social closeness of bribers and bribees. Our bargaining power result suggests that, under uncertainty, monopolistic public good provision has the same corruption-reducing effect as competitive public good provision.
A Matter of Context: Social Framework Evidence in Employment Discrimination Class Actions

In litigation disputes over the certification of employment discrimination class actions, social scientists have come to play a central, yet controversial, role. Organizational behavioralists and social psychologists regularly testify for the plaintiffs, offering what is commonly referred to as social framework testimony. These experts explain the general social science research on the operation of stereotyping and bias in decision making and examine the challenged workplace to identify those policies and practices that research has shown will tend to increase and those that will tend to limit the likely impact of these factors. Defendants fight hard against the admission of social framework experts, and some courts have agreed that the testimony should not be allowed. Because of the importance of this testimony to ferreting out large-scale discrimination in the workplace, the stakes in the debate over its admissibility are considerable. This Essay puts the debate over social framework expert testimony in context, explaining what the testimony is and the role it has played in employment discrimination litigation, with a particular focus on the way the testimony has been offered in class action suits like Dukes v. Wal-Mart Stores, Inc. It explains how the normal rules of evidence law should apply to social framework expert testimony and demonstrates that under the
Approval Voting and Incentives in Crowdsourcing

The growing need for labeled training data has made crowdsourcing an important part of machine learning. The quality of crowdsourced labels is, however, adversely affected by three factors: (1) the workers are not experts; (2) the incentives of the workers are not aligned with those of the requesters; and (3) the interface does not allow workers to convey their knowledge accurately, by forcing them to make a single choice among a set of options. In this paper, we address these issues by introducing approval voting to utilize the expertise of workers who have partial knowledge of the true answer, and coupling it with a ("strictly proper") incentive-compatible compensation mechanism. We show rigorous theoretical guarantees of optimality of our mechanism together with a simple axiomatic characterization. We also conduct preliminary empirical studies on Amazon Mechanical Turk which validate our approach.
MB-GAN: Microbiome Simulation via Generative Adversarial Network

Background Trillions of microbes inhabit the human body and have a profound effect on human health. The recent development of metagenome-wide association studies and other quantitative analysis methods accelerate the discovery of the associations between human microbiome and diseases. To assess the strengths and limitations of these analytical tools, simulating realistic microbiome datasets is critically important. However, simulating the real microbiome data is challenging because it is difficult to model their correlation structure using explicit statistical models. Results To address the challenge of simulating realistic microbiome data, we designed a novel simulation framework termed MB-GAN, by using a generative adversarial network (GAN) and utilizing methodology advancements from the deep learning community. MB-GAN can automatically learn from given microbial abundances and compute simulated abundances that are indistinguishable from them. In practice, MB-GAN showed the following advantages. First, MB-GAN avoids explicit statistical modeling assumptions, and it only requires real datasets as inputs. Second, unlike the traditional GANs, MB-GAN is easily applicable and can converge efficiently. Conclusions By applying MB-GAN to a case-control gut microbiome study of 396 samples, we demonstrated that the simulated data and the original data had similar first-order and second-order properties, including sparsity, diversities, and taxa-taxa correlations. These advantages are suitable for further microbiome methodology development where high-fidelity microbiome data are needed.
A chemically consistent graph architecture for massive reaction networks applied to solid-electrolyte interphase formation

Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction networks have been limited in size by chemically inconsistent graph representations of multi-reactant reactions (e.g. A + B → C) that cannot enforce stoichiometric constraints, precluding the use of optimized shortest-path algorithms. Here, we report a chemically consistent graph architecture that overcomes these limitations using a novel multi-reactant representation and iterative cost-solving procedure. Our approach enables the identification of all low-cost pathways to desired products in massive reaction networks containing reactions of any stoichiometry, allowing for the investigation of vastly more complex systems than previously possible. Leveraging our architecture, we construct the first ever electrochemical reaction network from first-principles thermodynamic calculations to describe the formation of the Li-ion solid electrolyte interphase (SEI), which is critical for passivation of the negative electrode. Using this network comprised of nearly 6000 species and 4.5 million reactions, we interrogate the formation of a key SEI component, lithium ethylene dicarbonate. We automatically identify previously proposed mechanisms as well as multiple novel pathways containing counter-intuitive reactions that have not, to our knowledge, been reported in the literature. We envision that our framework and data-driven methodology will facilitate efforts to engineer the composition-related properties of the SEI - or of any complex chemical process - through selective control of reactivity.
Mean Field Equilibrium: Uniqueness, Existence, and Comparative Statics

The standard solution concept for stochastic games is Markov perfect equilibrium (MPE); however, its computation becomes intractable as the number of players increases. Instead, we consider mean field equilibrium (MFE) that has been popularized in the recent literature. MFE takes advantage of averaging effects in models with a large number of players. We make three main contributions. First, our main result provides conditions that ensure the uniqueness of an MFE. We believe this uniqueness result is the first of its nature in the class of models we study. Second, we generalize previous MFE existence results. Third, we provide general comparative statics results. We apply our results to dynamic oligopoly models and to heterogeneous agent macroeconomic models commonly used in previous work in economics and operations.
Sklyanin algebras revisited

We construct a functor from the category of elliptic curves to a category of noncommutative tori. Our proof is based on an isomorphism between the Sklyanin algebras and dense sub-algebras of the noncommutative tori.
Online Photometric Calibration of Automatic Gain Thermal Infrared Cameras

Thermal infrared cameras are increasingly being used in various applications such as robot vision, industrial inspection and medical imaging, thanks to their improved resolution and portability. However, the performance of traditional computer vision techniques developed for electro-optical imagery does not directly translate to the thermal domain due to two major reasons: these algorithms require photometric assumptions to hold, and methods for photometric calibration of RGB cameras cannot be applied to thermal-infrared cameras due to difference in data acquisition and sensor phenomenology. In this letter, we take a step in this direction, and introduce a novel algorithm for online photometric calibration of thermal-infrared cameras. Our proposed method does not require any specific driver/hardware support and hence can be applied to any commercial off-the-shelf thermal IR camera. We present this in the context of visual odometry and SLAM algorithms, and demonstrate the efficacy of our proposed system through extensive experiments for both standard benchmark datasets, and real-world field tests with a thermal-infrared camera in natural outdoor environments.
Malware Classification using Long Short-term Memory Models.

Signature and anomaly based techniques are the quintessential approaches to malware detection. However, these techniques have become increasingly ineffective as malware has become more sophisticated and complex. Researchers have therefore turned to deep learning to construct better performing model. In this paper, we create four different long-short term memory (LSTM) based models and train each to classify malware samples from 20 families. Our features consist of opcodes extracted from malware executables. We employ techniques used in natural language processing (NLP), including word embedding and bidirection LSTMs (biLSTM), and we also use convolutional neural networks (CNN). We find that a model consisting of word embedding, biLSTMs, and CNN layers performs best in our malware classification experiments.
Effect of Acceleration and Escape of Energetic Particles on Spectral Steepening at Shocks

Energetic particles spectra at interplanetary shocks often exhibit a power law within a narrow momentum range softening at higher energy. We introduce a transport equation accounting for particle acceleration and escape with diffusion contributed by self-generated turbulence close to the shock and by pre-existing turbulence far upstream. The upstream particle intensity steepens within one diffusion length from the shock as compared with diffusive shock acceleration rollover. The momentum spectrum, controlled by macroscopic parameters such as shock compression, speed, far upstream diffusion coefficient and escape time at the shock, can be reduced to a log-parabola and also to a broken power law. In the case of upstream uniform diffusion coefficient, the largely used power law/exponential cut off solution is retrieved.
Leave-one-out Unfairness

We introduce leave-one-out unfairness, which characterizes how likely a model's prediction for an individual will change due to the inclusion or removal of a single other person in the model's training data. Leave-one-out unfairness appeals to the idea that fair decisions are not arbitrary: they should not be based on the chance event of any one person's inclusion in the training data. Leave-one-out unfairness is closely related to algorithmic stability, but it focuses on the consistency of an individual point's prediction outcome over unit changes to the training data, rather than the error of the model in aggregate. Beyond formalizing leave-one-out unfairness, we characterize the extent to which deep models behave leave-one-out unfairly on real data, including in cases where the generalization error is small. Further, we demonstrate that adversarial training and randomized smoothing techniques have opposite effects on leave-one-out fairness, which sheds light on the relationships between robustness, memorization, individual fairness, and leave-one-out fairness in deep models. Finally, we discuss salient practical applications that may be negatively affected by leave-one-out unfairness.
Error Correcting Codes for Uncompressed Messages.

Most types of messages we transmit (e.g., video, audio, images, text) are not fully compressed, since they do not have known efficient and information theoretically optimal compression algorithms. When transmitting such messages, standard error correcting codes fail to take advantage of the fact that messages are not fully compressed. We show that in this setting, it is sub-optimal to use standard error correction. We consider a model where there is a set of "valid messages" which the sender may send that may not be efficiently compressible, but where it is possible for the receiver to recognize valid messages. In this model, we construct a (probabilistic) encoding procedure that achieves better tradeoffs between data rates and error-resilience (compared to just applying a standard error correcting code). Additionally, our techniques yield improved efficiently decodable (probabilistic) codes for fully compressed messages (the standard setting where the set of valid messages is all binary strings) in the high-rate regime.
Collision in the interior of wormhole

The Schwarzschild wormhole has been interpreted as an entangled state. If Alice and Bob fall into each of the black hole, they can meet in the interior. We interpret this meeting in terms of the quantum circuit that prepares the entangled state. Alice and Bob create growing perturbations in the circuit, and we argue that the overlap of these perturbations represents their meeting. We compare the gravity picture with circuit analysis, and identify the post-collision region as the region storing the gates that are not affected by any of the perturbations.
TALE: Transformer-based protein function Annotation with joint sequence–Label Embedding

MOTIVATION Facing the increasing gap between high-throughput sequence data and limited functional insights, computational protein function annotation provides a high-throughput alternative to experimental approaches. However, current methods can have limited applicability while relying on protein data besides sequences, or lack generalizability to novel sequences, species and functions. RESULTS To overcome aforementioned barriers in applicability and generalizability, we propose a novel deep learning model using only sequence information for proteins, named Transformer-based protein function Annotation through joint sequence-Label Embedding (TALE). For generalizability to novel sequences we use self attention-based transformers to capture global patterns in sequences. For generalizability to unseen or rarely seen functions (tail labels), we embed protein function labels (hierarchical GO terms on directed graphs) together with inputs/features (1D sequences) in a joint latent space. Combining TALE and a sequence similarity-based method, TALE+ outperformed competing methods when only sequence input is available. It even outperformed a state-of-the-art method using network information besides sequence, in two of the three gene ontologies. Furthermore, TALE and TALE+ showed superior generalizability to proteins of low similarity, new species, or rarely annotated functions compared to training data, revealing deep insights into the protein sequence-function relationship. Ablation studies elucidated contributions of algorithmic components toward the accuracy and the generalizability. AVAILABILITY The data, source codes and models are available at https://github.com/Shen-Lab/TALE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
The advanced light source ? a new tool for research in atomic physics

The Advanced Light Source, a third-generation national synchrotron-radiation facility now under construction at the Lawrence Berkeley Laboratory in Berkeley, California, is scheduled to begin serving qualified users across a broad spectrum of research areas in the spring of 1993. Undulators will generate high-brightness, partially coherent, plane polarized, soft x-ray and ultraviolet (XUV) radiation from below 10 eV to above 2 keV. Wigglers and bend magnets will generate high fluxes of x-rays to photon energies above 10 keV. The ALS will have an extensive research program in which XUV radiation is used to study matter in all its varied gaseous, liquid, and solid forms.
An algebraic manipulator using SLIP

The notation for representing algebraic expressions to be manipulated by a SLIP type list processor is described in detail. This notation in general allows algebraic expressions to be represented in the form of list structures used for algebraic manipulation.
Compressed Sensing-Based Super-Resolution Ultrasound Imaging for Faster Acquisition and High Quality Images

Goal: Typical SRUS images are reconstructed by localizing ultrasound microbubbles (MBs) injected in a vessel using normalized 2-dimensional cross-correlation (2DCC) between MBs signals and the point spread function of the system. However, current techniques require isolated MBs in a confined area due to inaccurate localization of densely populated MBs. To overcome this limitation, we developed the l1-homotopy based compressed sensing (L1H-CS) based SRUS imaging technique which localizes densely populated MBs to visualize microvasculature in vivo. Methods: To evaluate the performance of L1H-CS, we compared the performance of 2DCC, interior-point method based compressed sensing (CVX-CS), and L1H-CS algorithms. Localization efficiency was compared using axially and laterally aligned point targets (PTs) with known distances and randomly distributed PTs generated by simulation. We developed post-processing techniques including clutter reduction, noise equalization, motion compensation, and spatiotemporal noise filtering for in vivo imaging. We then validated the capabilities of L1H-CS based SRUS imaging technique with high-density MBs in a mouse tumor model, kidney, and zebrafish dorsal trunk, and brain. Results: Compared to 2DCC and CVX-CS algorithms, L1H-CS achieved faster data acquisition time and considerable improvement in SRUS image quality. Conclusions and Significance: These results demonstrate that the L1H-CS based SRUS imaging technique has the potential to examine microvasculature with reduced acquisition and reconstruction time to acquire enhanced SRUS image quality, which may be necessary to translate it into clinics.
CONSTAX2: Improved taxonomic classification of environmental DNA markers.

SUMMARY CONSTAX - the CONSensus TAXonomy classifier - was developed for accurate and reproducible taxonomic annotation of fungal rDNA amplicon sequences and is based upon a consensus approach of RDP, SINTAX, and UTAX algorithms. CONSTAX2 extends these features to classify prokaryotes as well as eukaryotes and incorporates BLAST-based classifiers to reduce classification errors. Additionally, CONSTAX2 implements a conda-installable command line tool with improved classification metrics, faster training, multithreading support, capacity to incorporate external taxonomic databases, and new isolate matching and high-level taxonomy tools, replete with documentation and example tutorials. AVAILABILITY CONSTAX2 is available at https://github.com/liberjul/CONSTAXv2, and is packaged for Linux and MacOS from Bioconda with use under the MIT License. A tutorial and documentation are available at https://constax.readthedocs.io/en/latest/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
FREE SPEECH, PRIVACY, AND AUTONOMY

While autonomy arguments provide a compelling foundation for free speech, they also support individual privacy rights. Considering how speech and privacy may be justified, I will argue that the speech necessary for self-government does not need to include details that would violate privacy rights. Additionally, I will argue that if viewed as a kind of intangible property right, informational privacy should limit speech and expression in a range of cases. In a world where we have an overabundance of content to consume, much of which could be called “information pollution,” and where there are numerous platforms to broadcast one’s expressions, it is increasingly difficult to maintain that speech should trump privacy.
Dynamic modeling and real-time management of a system of EV fast-charging stations

Abstract Demand for electric vehicles (EVs), and thus EV charging, has steadily increased over the last decade. Studies suggest that fast-charging facilities are crucial for the EV market. However, there is limited fast-charging infrastructure in most parts of the world to support EV travel, especially long-distance trips. The goal of this study is to develop a stochastic dynamic simulation modeling framework of a regional system of EV fast-charging stations for real-time management and strategic planning (i.e., capacity allocation) purposes. To model EV user behavior, specifically fast-charging station choices, the framework incorporates a multinomial logit station choice model that considers station charging prices, expected wait times, and detour distances. To capture the dynamics of supply and demand at each EV fast-charging station, the framework incorporates a multi-server queueing model in the simulation. The study assumes that multiple fast-charging stations are managed by a single entity (public or private) and that the demand for these stations are interrelated (through the station choice model). To manage the system of stations, this study proposes and tests dynamic demand-responsive price adjustment (DDRPA) schemes based on station queue lengths. The study applies the modeling framework to a system of EV fast-charging stations in Southern California. The computational results indicate that DDRPA strategies are an effective mechanism to balance charging demand across fast-charging stations. Specifically, compared to the no DDRPA scheme case, the quadratic DDRPA scheme reduces average wait time by 26%, increases charging station revenue (and user costs) by 5.8%, while, most importantly, increasing social welfare by 2.7% in the base scenario. Moreover, the study also illustrates that the modeling framework can evaluate the allocation of EV fast-charging station capacity, to identify stations that require additional chargers and areas that would benefit from additional fast-charging stations.
Coupled Charge and Radiation Transport Processes in Thermophotovoltaic and Thermoradiative Cells

Energy converters that recover thermal radiation have the potential for high-efficiency solid-state electricity generation. Modeling of these systems to date has made limiting assumptions that do not fully capture the nuances of charge and radiation interactions. To remedy this, the authors propose a fully coupled iterative model to properly account for photon and charge-carrier dynamics in both near-field and far-field thermophotovoltaic and thermoradiative systems. This approach can be used to better understand and design devices and materials that utilize thermal radiation.
A local limit theorem for a family of non-reversible Markov chains

By proving a local limit theorem for higher-order transitions, we determine the time required for necklace chains to be close to stationarity. Because necklace chains, built by arranging identical smaller Markov chains around a directed cycle, are not reversible, have little symmetry, do not have uniform stationary distributions, and can be nearly periodic, prior general bounds on rates of convergence of Markov chains either do not apply or give poor bounds. Necklace chains can serve as test cases for future techniques for bounding rates of convergence.
Revisiting the Neural Architecture of Adolescent Decision-Making: Univariate and Multivariate Evidence for System-Based Models.

Understanding adolescent decision-making is significant for informing basic models of neurodevelopment as well as for the domains of public health and criminal justice. System-based theories posit that adolescent decision-making is guided by activity amongst reward and control processes. While successful at explaining behavior, system-based theories have received inconsistent support at the neural level, perhaps because of methodological limitations. Here, we used two complementary approaches to overcome said limitations and rigorously evaluate system-based models. Using decision-level modeling of fMRI data from a risk-taking task in a sample of 2000+ decisions across 51 human adolescents (25 females, mean age = 15.00 years), we find support for system-based theories of decision-making. Neural activity in lateral prefrontal cortex and a multivariate pattern of cognitive control both predicted a reduced likelihood of risk-taking, whereas increased activity in the nucleus accumbens predicted a greater likelihood of risk-taking. Interactions between decision-level brain activity and age were not observed. These results garner support for system-based accounts of adolescent decision-making behavior. SIGNIFICANCE STATEMENT: Adolescent decision-making behavior is of great import for basic science, and carries equally consequential implications for public health and criminal justice. While dominant psychological theories seeking to explain adolescent decision-making have found empirical support, their neuroscientific implementations have received inconsistent support. This may be partly due to statistical approaches employed by prior neuroimaging studies of system-based theories. We used brain modeling—an approach that predicts behavior from brain activity—of univariate and multivariate neural activity metrics to better understand how neural components of psychological systems guide decision behavior in adolescents. We found broad support for system-based theories such that neural systems involved in cognitive control predicted a reduced likelihood to make risky decisions, whereas value-based systems predicted greater risk-taking propensity.
Central bank tone and currency risk premia

Abstract I analyze how the tone of central bank press conferences impacts risk premia in the currency market. Tone is measured as the difference between the number of hawkish and dovish phrases mafide during a press conference, as a fraction of total phrases. A one standard deviation increase in the hawkishness of a press conference is associated with a 1.5% decline in the variance risk premium, a 4.9% decline in the subsequent variance swap return, and a 0.2% increase in option implied risk aversion. The impact of tone comes primarily from the questions & answers, or the unscripted portion of the press conference.
Cyberphysical Security Through Resiliency: A Systems-Centric Approach

Cyberphysical systems require resiliency techniques for defense, and multicriteria resiliency problems need an approach that evaluates systems for current threats and potential design solutions. A systems-oriented view of cyberphysical security, termed Mission Aware, is proposed based on a holistic understanding of mission goals, system dynamics, and risk.