cssi/SciSciGPT-SciSciNet huggingface dataset:
The misunderstood alliance between sports fans, players, and the antitrust laws

The baseball strike and the ongoing hostilities between the players' association and owners have evoked criticism and frustration among fans and others. Although the players successfully defeated the owners' most recent attempts to reduce major league competition, the threat of future imposition of competitive restraints by the owners remains. In this article Professor Stephen F. Ross argues that blanket restraints on the market for players affirmatively inhibit on-the-field competition and consequently offend the Sherman Act.The article begins with the proposition that monopsony - price-fixing behavior by buyers', rather than sellers' cartels - implicates the Sherman Act. Restraints on competition for players' services are thus not exempt from the antitrust laws. As the author notes, restraints of trade imposed by sports leagues are subject to antitrust scrutiny under a rule of reason: under the standard set forth in NCAA v. Board of Regents, such restraints are permissible where reasonably tailored to promote competitive balance.Professor Ross then applies the NCAA standard to blanket restraints imposed by sports leagues. He first demonstrates that the waiver rule employed by some leagues is tailored to promote league competition, because it allows inferior teams with large payrolls to rapidly improve in the standings by acquiring superior players. To counter the owners' arguments that restraints such as salary caps are necessary to maintain league competition, Professor Ross relies on empirical evidence of the effect of these restraints on competitive balance and demonstrates that, in addition to not promoting competition, blanket restraints affirmatively harm competitive balance. Salary caps, the author argues, not only transfer wealth from players to owners, but facilitate bland, uninteresting seasons in which the same teams have consistently good or bad win-loss records.The article includes a discussion of less restrictive means of promoting competitive balance, including revenue sharing and progressive salary caps, which might pass antitrust muster. Professor Ross concludes that free competition in the player market protects the interests of both fans and players, such that an alliance of consumers and players might be an effective way to combat the threat to baseball which blanket restraints may pose in the future.
Hurry Up and Wait: Negative Statutes of Limitation in the Government Tort Liability Setting

Negative statutes of limitation - which require a plaintiff to wait until a defendant considers the plaintiff's claims before plaintiff can sue in court - should be viewed as process rules, which do not bar a plaintiff from filing a court action while a defendant dithers.
Cash Flow Patterns as a Proxy for Firm Life Cycle

ABSTRACT: This study develops a firm life cycle proxy using cash flow patterns. The patterns provide a parsimonious indicator of life cycle stage that is free from distributional assumptions (i.e., uniformity). The proxy identifies differential behavior in the persistence and convergence patterns of profitability. For example, return on net operating assets (RNOA) does not mean-revert (spread of 7 percent after five years between mature and decline firms) when examined by life cycle stage, which has implications for growth rates and forecast horizons. Further, determinants of future profitability such as asset turnover and profit margin are differentially successful in generating increases in profitability conditional on life cycle stage. Finally, investors do not fully incorporate the information contained in cash flow patterns and, as a result, undervalue mature firms. The cash flow proxy is a robust tool that has applications in analysis, forecasting, valuation, and as a control variable for future re...
Volumes And Random Matrices

This article is an introduction to newly discovered relations between volumes of moduli spaces of Riemann surfaces or super Riemann surfaces, simple models of gravity or supergravity in two dimensions, and random matrix ensembles. (The article is based on a lecture at the conference on the Mathematics of Gauge Theory and String Theory, University of Auckland, January 2020. It has been submitted to a special issue of the Quarterly Journal of Mathematics in memory of Michael Atiyah.)
An airborne far-infrared spectrometer for astronomical observations

A Fabry-Perot spectrometer for airborne observations of astronomical spectral lines has been designed for use in the 50 to 200 μm region of the far-infrared. The spectrometer uses a fixed wavelength Fabry-Perot filter cooled to 4.2 K in conjunction with a high order scanning Fabry-Perot at room temperature. This arrangement provides high spectral resolution and also minimizes the thermal background radiation on the detector. The instrument has been successfully used to study all the presently detected fine structure lines between 50 and 120 μm.
The racial burden of voter list maintenance errors: Evidence from Wisconsin’s supplemental movers poll books

Administrative records are increasingly used to identify registered voters who may have moved, with potential movers then sent postcards asking them to confirm their address of registration. It is important to understand how often these registrants did not move, and how often such an error is not corrected by the postcard confirmation process, because uncorrected errors make it more difficult for a registrant to subsequently vote. While federal privacy protections generally prevent researchers from observing the data necessary to estimate these quantities, we are able to study this process in Wisconsin because special poll books, available via public records requests, listed those registrants who were identified as potential movers and did not respond to a subsequent postcard. At least 4% of these registrants cast a ballot at their address of registration, with minority registrants twice as likely as white registrants to do so.
Cooperative Control of Mobile Robots with Stackelberg Learning

Multi-robot cooperation requires agents to make decisions that are consistent with the shared goal without disregarding action-specific preferences that might arise from asymmetry in capabilities and individual objectives. To accomplish this goal, we propose a method named SLiCC: Stackelberg Learning in Cooperative Control. SLiCC models the problem as a partially observable stochastic game composed of Stackelberg bimatrix games, and uses deep reinforcement learning to obtain the payoff matrices associated with these games. Appropriate cooperative actions are then selected with the derived Stackelberg equilibria. Using a bi-robot cooperative object transportation problem, we validate the performance of SLiCC against centralized multi-agent Q-learning and demonstrate that SLiCC achieves better combined utility.
Entanglement formation in continuous-variable random quantum networks

Entanglement is not only important for understanding the fundamental properties of many-body systems, but also the crucial resource enabling quantum advantages in practical information processing tasks. Although previous works on quantum networks focus on discrete-variable systems, light—as the only traveling carrier of quantum information in a network—is bosonic and thus requires a continuous-variable description. We extend the study to continuous-variable quantum networks. By mapping the ensemble-averaged entanglement dynamics on an arbitrary network to a random-walk process on a graph, we are able to exactly solve the entanglement dynamics. We identify squeezing as the source of entanglement generation, which triggers a diffusive spread of entanglement with a "parabolic light cone”. A surprising linear superposition law in the entanglement growth is predicted by the theory and numerically verified, despite the nonlinear nature of the entanglement dynamics. The equilibrium entanglement distribution (Page curves) is exactly solved and has various shapes depending on the average squeezing density and strength.
Sets where Lip f is infinite and lip f is finite

Abstract We characterize the sets E ⊂ R such that there exists a continuous function f : R → R with lip f finite everywhere and Lip f infinite precisely on the set E.
Event-driven receding horizon control for distributed persistent monitoring in network systems

Abstract We address the multi-agent persistent monitoring problem defined on a set of nodes (targets) interconnected over a network topology. A measure of mean overall node state uncertainty evaluated over a finite period is to be minimized by controlling the motion of a cooperating team of agents. To address this problem, we propose an event-driven receding horizon control approach that is computationally efficient, distributed and on-line. The proposed controller differs from the existing on-line gradient-based parametric controllers and off-line greedy cycle search methods that often lead to either low-performing local optima or computationally intensive centralized solutions. A critical novel element in this controller is that it automatically optimizes its planning horizon length, thus making it parameter-free. We show that explicit globally optimal solutions can be obtained for every distributed optimization problem encountered at each event where the receding horizon controller is invoked. Numerical results are provided showing improvements compared to state of the art distributed on-line parametric control solutions.
Is the Word "Consumer" Biasing Trademark Law?

Our trademark law uses the term “consumer” constantly, reflexively, and unconsciously to label the subject of its purpose—the purchasing public. According to the U.S. Supreme Court, trademark law has “a specialized mission: to help consumers identify goods and services they wish to purchase, as well as those they want to avoid.” As one leading commentator puts it, “trademarks are a property of consumers’ minds,” and “the consumer, we are led to believe, is the measure of all things in trademark law.” Much criticism has been rightly levied against trademark law’s treatment of the consumer as passive, ignorant, and gullible. For instance, consumers are seen as requiring protection from any and all marketplace confusion and have no standing to sue under the Lanham Act. However, that a contributing factor to such treatment could be the linguistic bias stemming from the law’s label of the buying public as mere consumers—rather than, for instance, “citizens,”“ persons,” “individuals,” or “humans”—has not, until now, been directly addressed. This Article urges those involved in trademark and advertising law—e.g., judges, lawyers, lawmakers, and scholars—to rethink our ubiquitous use of the derogatory consumer label. To this end, the Article first explores consumer as a dehumanizing, anti-ecological, and nonsensical metaphor for “one that utilizes economic goods.” It then examines social psychology experiments finding that use of consumer has potentially deleterious effects for society given the negative stereotypes which it engenders as a social categorization. The Article claims, by extension, that the implicit linguistic bias inherent in consumer rhetoric might contribute to trademark law defining the public in a manner that is patronizing, biased, insulting, and indulgent of likelihood of confusion claims. The Article suggests that we either work to phase out the consumer label and replace it with more appropriate terminology (e.g., “citizen”), or at least pause to acknowledge the word’s potentially biasing effects.
When We First Met: Visual-Inertial Person Localization for Co-Robot Rendezvous

We aim to enable robots to visually localize a target person through the aid of an additional sensing modality – the target person’s 3D inertial measurements. The need for such technology may arise when a robot is to meet a person in a crowd for the first time or when an autonomous vehicle must rendezvous with a rider amongst a crowd without knowing the appearance of the person in advance. A person’s inertial information can be measured with a wearable device such as a smart-phone and can be shared selectively with an autonomous system during the rendezvous. We propose a method to learn a visual-inertial feature space in which the motion of a person in video can be easily matched to the motion measured by a wearable inertial measurement unit (IMU). The transformation of the two modalities into the joint feature space is learned through the use of a triplet loss which forces inertial motion features and video motion features generated by the same person to lie close in the joint feature space. To validate our approach, we compose a dataset of over 3,000 video segments of moving people along with wearable IMU data. We show that our method is able to localize a target person with 80.7% accuracy averaged over testing data with various number of candidates using only 5 seconds of IMU data and video.
Cell competition removes segmental aneuploid cells from Drosophila imaginal disc-derived tissues based on ribosomal protein gene dose

Aneuploid cells emerge when cellular division goes awry and a cell ends up with the wrong number of chromosomes, the tiny genetic structures carrying the instructions that control life’s processes. Aneuploidy can lead to fatal conditions during development, and to cancer in an adult organism. A safety mechanism may exist that helps the body to detect and remove these cells. Yet, exactly this happens is still poorly understood: in particular, it is unclear how cells manage to ‘count’ their chromosomes. One way they could do so is through the ribosomes, the molecular ‘factories’ that create the building blocks required for life. In a cell, every chromosome carries genes that code for the proteins (known as Rps) forming ribosomes. Aneuploidy will alter the number of Rp genes, and in turn the amount and type of Rps the cell produces, so that ribosomes and the genes for Rps could act as a ‘readout’ of aneuploidy. Ji et al set out to test this theory in fruit flies. The first experiment used a genetic manipulation technique called site-specific recombination to remove parts of chromosomes from cells in the developing eye and wing. Cells which retained all their Rp genes survived, while those that were missing some usually died – but only when the surrounding cells were normal. In this situation, healthy cells eliminated their damaged neighbours through a process known as cell competition. A second experiment, using radiation as an alternative method of damaging chromosomes, also gave similar results. The work by Ji et al. reveals how the body can detect and eliminate aneuploid cells, potentially before they can cause harm. If the same mechanism applies in humans, boosting cell competition may, one day, helps to combat diseases like cancer.
Asymmetric vortex sheet

We present a steady analytical solution of the incompressible Navier–Stokes equation for arbitrary viscosity in an arbitrary dimension d of space. It represents a d − 1 dimensional vortex “sheet” with an asymmetric profile of vorticity as a function of the normal coordinate z. This profile is related to the Hermite polynomials H μ ( z ), which are analytically continued to the negative fractional index μ = − d d − 1. In d = 2 dimensions, the solution degenerates to a constant vorticity flow. In d ≥ 3 dimensions, the vorticity is confined to the thin layer around the hyperplane with Gaussian decay on one side of the hyperplane and the power decay on another side. One can adjust the common scale of velocity so that the dissipation will stay finite at vanishing viscosity. In this limit, the width w of the viscous lawyer will shrink to zero as ν 3 5 for arbitrary dimension d >3. In d = 3 dimensions, this power law is also accompanied by powers of the logarithm.
Approximating annual mean incoming solar radiation

Abstract We derive the Legendre series expansion for the insolation distribution on rapidly rotating planets as a function of sine of the latitude and the planet's obliquity. We give an explicit formula for the coefficients of this series as it depends on the obliquity and approximate the convergence rate. We determine the optimal truncation of the series for use in climate models and compare this approximation to other approximations in the literature.
Doing More with Less: How Can Brazil Foster Development While Pursuing Fiscal Consolidation?

Following a benchmarking exercise, we estimate the spending required to reach satisfactory progress in the Sustainable Development Goals in the health, education, and infrastructure sectors in Brazil. We find that there is room for savings in education (up to 1.5 percentage point of GDP) and health (up to 2.5 percentage points of GDP) without compromising the quality of services but additional investments for over 3 percent of GDP per year are needed to close large infrastructure gaps in roads, water, and electricity by 2030. Brazil can do more with less, but increasing efficiency of public spending will require substantial reforms.
Single-Frequency Imaging and Material Characterization Using Reconfigurable Reflectarrays

Conventional security screening systems cannot operate in real time and often suffer from false alarms due to the profile-only imaging without further analysis on the object material properties. Reflectarrays were recently proposed as devices capable of performing real-time security screening using a single-frequency radar signal. This article presents first a physical optics (PO)-based simulation method to facilitate the design of a multireflectarray system. Then, an object material characterization method using geometrical optics (GO) is derived for such a system. The characterization process not only retrieves the complex relative permittivity of the object but also improves its profile reconstruction in terms of a more accurate thickness. Both simulations and experiments are carried out to verify the effectiveness and efficiency of the proposed methods. Primary results show great potentials for security screening, especially in scenarios where the inspection of human bodies for threat materials, such as narcotics and explosives, is required.
Triangulated Laman graphs, local stochastic matrices, and limits of their products

Abstract We derive conditions on the products of stochastic matrices guaranteeing the existence of a unique limit invariant distribution. Belying our approach is the hereby defined notion of restricted triangulated Laman graphs. The main idea is the following: to each triangle in the graph, we assign a stochastic matrix. Two matrices can be adjacent in a product only if their corresponding triangles share an edge in the graph. We provide an explicit formula for the limit invariant distribution of the product in terms of the individual stochastic matrices.
NMR assignment through linear programming

Nuclear Magnetic Resonance (NMR) Spectroscopy is the second most used technique (after X-ray crystallography) for structural determination of proteins. A computational challenge in this technique involves solving a discrete optimization problem that assigns the resonance frequency to each atom in the protein. This paper introduces LIAN (LInear programming Assignment for NMR), a novel linear programming formulation of the problem which yields state-of-the-art results in simulated and experimental datasets.
Electric dipole moments, new forces and dark matter

New sources of CP violation beyond the Standard Model are crucial to explain the baryon asymmetry in the Universe. We discuss the impact of new CP violating interactions in theories where a dark matter candidate is predicted by the cancellation of gauge anomalies. In these theories, the constraint on the dark matter relic density implies an upper bound on the new symmetry breaking scale from which all new states acquire their masses. We investigate in detail the predictions for electric dipole moments and show that if the relevant CP-violating phase is large, experiments such as the ACME collaboration will be able to fully probe the theory.
Socially intelligent task and motion planning for human-robot interaction.

As social beings, much human behavior is predicated on social context - the ambient social state that includes cultural norms, social signals, individual preferences, etc. In this paper, we propose a socially-aware task and motion planning algorithm that considers social context to generate appropriate and effective plans in human social environments (HSEs). The key strength of our proposed approach is that it explicitly models how potential actions not only affect objective cost, but also transform the social context in which it plans and acts. We investigate strategies to limit the complexity of our algorithm, so that our planner will remain tractable for mobile platforms in complex HSEs like hospitals and factories. The planner will also consider the relative importance and urgency of its tasks, which it uses to determine when it is and is not appropriate to violate social expectations to achieve its objective. This social awareness will allow robots to understand a fundamental rule of society: just because something makes your job easier, does not make it the right thing to do! To our knowledge, the proposed work is the first task and motion planning approach that supports socially intelligent robot policy for HSEs. Through this ongoing work, robots will be able to understand, respect, and leverage social context accomplish tasks both acceptably and effectively in HSEs.
HLA class I nucleotide sequences, 1992.

The HLA class I sequences included in this compilation are taken from publications listed in the papers: Nomenclature for factors of the HLA system, 1991 (Bodmer et al. 1992); Nomenclature for factors of the HLA system, 1990 (Bodmer et al. 1991); and Nomenclature for factors of the HLA system, 1989 (Bodmer et al. 1990). Due to the increased number of sequences we have only included sequences for exons 2, 3, and 4 in this compilation. Where discrepancies have arisen between reported sequences, the original authors have been contacted where possible, and necessary amendments to published sequences have been incorporated into this alignment. Future sequencing may identify errors in this list and we would welcome any evidence that helps to maintain the accuracy of this compilation. In the sequence alignments, identify between nucleotides is indicated by a hyphen (-). An unavailable sequence is indicated by a period (.). Gaps in the sequence are inserted to maintain the alignment between different alleles showing variation in amino acid number. *** DIRECT SUPPORT *** A4903038 00002
Post‐Cameralist Governance: Towards a Robust Political Economy of Bureaucracy

I develop a robust political economy of bureaucracy by highlighting the conditions necessary for hierarchical administrative bodies to govern protectively and productively, but not predatorily. These conditions are residual claimancy and jurisdictional competition. I make this argument by exploring a post-cameralist interpretation of governance. Cameralism arose as a governance philosophy in the fractured principalities of seventeenth-century Germany following the Thirty Years' War. Post-cameralism focuses not on particular cameralist governance strategies but on a paradigm which sees governance as an activity provided within a larger exchange order, rather than imposing itself on that order as in more conventional treatments of public economics. While a post-cameralist conception of governance comes with its own challenges, such as tensions with normative visions that promote self-governance, it nonetheless presents an intriguing synthesis of monocentric and polycentric insights.
Detecting Expressions with Multimodal Transformers

Developing machine learning algorithms to understand person-to-person engagement can result in natural user experiences for communal devices such as Amazon Alexa. Among other cues such as voice activity and gaze, a person’s audio-visual expression that includes tone of the voice and facial expression serves as an implicit signal of engagement between parties in a dialog. This study investigates deep-learning algorithms for audio-visual detection of user’s expression. We first implement an audio-visual baseline model with recurrent layers that shows competitive results compared to current state of the art. Next, we propose the transformer architecture with encoder layers that better integrate audio-visual features for expressions tracking. Performance on the Aff-Wild2 database shows that the proposed methods perform better than baseline architecture with recurrent layers with absolute gains approximately 2% for arousal and valence descriptors. Further, multimodal architectures show significant improvements over models trained on single modalities with gains of up to 3.6%. Ablation studies show the significance of the visual modality for the expression detection on the Aff-Wild2 database.
Global automorphic Sobolev theory and the automorphic heat kernel

Heat kernels arise in a variety of contexts including probability, geometry, and functional analysis; the automorphic heat kernel is particularly important in number theory and string theory. The typical construction of an automorphic heat kernel as a Poincare series presents analytic difficulties, which can be dealt with in special cases (e.g., hyperbolic spaces) but are often sidestepped in higher rank by restricting to the compact quotient case. In this paper, we present a new approach, using global automorphic Sobolev theory, a robust framework for solving automorphic PDEs that does not require any simplifying assumptions about the rank of the symmetric space or the compactness of the arithmetic quotient. We construct an automorphic heat kernel via its automorphic spectral expansion in terms of cusp forms, Eisenstein series, and residues of Eisenstein series. We then prove uniqueness of the automorphic heat kernel as an application of operator semigroup theory. Finally, we prove the smoothness of the automorphic heat kernel by proving that its automorphic spectral expansion converges in the C∞-topology.
TenFor: a tensor-based tool to extract interesting events from security forums

How can we get a security forum to "tell" us its activities and events of interest? We take a unique angle: we want to identify these activities without any a priori knowledge, which is a key difference compared to most of the previous problem formulations. Despite some recent efforts, mining security forums to extract useful information has received relatively little attention, while most of them are usually searching for specific information. We propose TenFor, an unsupervised tensor-based approach, to systematically identify important events in a three-dimensional space: (a) user, (b) thread, and (c) time. Our method consists of three high-level steps: (a) a tensor-based clustering across the three dimensions, (b) an extensive cluster profiling that uses both content and behavioral features, and (c) a deeper investigation, where we identify key users and threads within the events of interest. In addition, we implement our approach as a powerful and easy-to-use platform for practitioners. In our evaluation, we find that 83% of our clusters capture meaningful events and we find more meaningful clusters compared to previous approaches. Our approach and our platform constitute an important step towards detecting activities of interest from a forum in an unsupervised learning fashion in practice.
Unification modulo a partial theory of exponentiation

Modular exponentiation is a common mathematical operation in modern cryptography. This, along with modular multiplication at the base and exponent levels (to different moduli) plays an important role in a large number of key agreement protocols. In our earlier work, we gave many decidability as well as undecidability results for multiple equational theories, involving various properties of modular exponentiation. Here, we consider a partial subtheory focussing only on exponentiation and multiplication operators. Two main results are proved. The first result is positive, namely, that the unification problem for the above theory (in which no additional property is assumed of the multiplication operators) is decidable. The second result is negative: if we assume that the two multiplication operators belong to two different abelian groups, then the unification problem becomes undecidable.
Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning.

An increasing number of density maps of macromolecular structures, including proteins and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there are still substantial fractions of maps determined at intermediate or low resolutions, where extracting structure information is not trivial. Here, we report a new computational method, Emap2sec+, which identifies DNA or RNA as well as the secondary structures of proteins in cryo-EM maps of 5 to 10 A resolution. Emap2sec+ employs the deep Residual convolutional neural network. Emap2sec+ assigns structural labels with associated probabilities at each voxel in a cryo-EM map, which will help structure modeling in an EM map. Emap2sec+ showed stable and high assignment accuracy for nucleotides in low resolution maps and improved performance for protein secondary structure assignments than its earlier version when tested on simulated and experimental maps. It is challenging to extract structural information from EM density maps at intermediate or low resolutions. Here, the authors present Emap2sec+, a program for detecting nucleotides and protein secondary structures in EM density maps at 5 to 10 A resolution.
Engineering Phonon Leakage in Nanomechanical Resonators

A cavity optomechanical system enables optical readout and control of the phonons trapped in a nanomechanical resonator. For a network that uses mechanical waves to distribute quantum information, we must find ways to move phonons in and out of cavities, without altering the other properties of the system. This study solves the problem by judiciously breaking specific symmetries of the structure, to generate controllable phonon leakage out of a resonator on a chip without modifying the optical properties. Interestingly, disorder is observed to cause fluctuations in the leakage rate, which counterintuitively could $r\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}e$ losses.
Advanced Biophysical Model to Capture Channel Variability for EQS Capacitive HBC

Human Body Communication (HBC) has come up as a promising alternative to traditional radio frequency (RF) Wireless Body Area Network (WBAN) technologies. This is essentially due to HBC providing a broadband communication channel with enhanced signal security in the physical layer due to lower radiation from the human body as compared to its RF counterparts. An in-depth understanding of the mechanism for the channel loss variability and associated biophysical model needs to be developed before electro-quasistatic (EQS) HBC can be used more frequently in WBAN consumer and medical applications. Recent developments have shown biophysical models that capture the channel response for fixed transmitter and receiver positions on the human body which do not capture the variability in the HBC channel for varying positions of the devices with respect to the body. In this study, we provide a detailed analysis of the change in path loss in a capacitive-HBC channel in the EQS domain. Causes of channel loss variability namely: inter-device coupling and effects of fringe fields due to body's shadowing effects are investigated. FEM based simulation results are used to analyze the channel response of human body for different positions and sizes of the device which are further verified using measurement results to validate the developed biophysical model. Using the biophysical model, we develop a closed form equation for the path loss in a capacitive HBC channel which is then analyzed as a function of the geometric properties of the device and the position with respect to the human body which will help pave the path towards future EQS-HBC WBAN design.
Reduction and coherent states

We apply a quantum version of dimensional reduction to Gaussian coherent states in Bargmann space to obtain squeezed states on complex projective spaces. This leads to a definition of a family of squeezed spin states (Definition 1.13) with excellent semiclassical properties, governed by a symbol calculus. We prove semiclassical norm estimates and a propagation result.
Relativistic thermal plasmas - Effects of magnetic fields

Processes and equilibria in finite, relativistic, thermal plasmas are investigated, taking into account electron-positron creation and annihilation, photon production by internal processes, and photon production by a magnetic field. Inclusion of the latter extends previous work on such plasmas. The basic relations for thermal, Comptonized synchrotron emission are analyzed, including emission and absorption without Comptonization, Comptonized thermal synchrotron emission, and the Comptonized synchrotron and bremsstrahlung luminosities. Pair equilibria are calculated, including approximations and dimensionless parameters, the pair balance equation, maximum temperatures and field strengths, and individual models and cooling curves.
Altered neural oscillations and behavior in a genetic mouse model of NMDA receptor hypofunction

Abnormalities in electroencephalographic (EEG) biomarkers occur in patients with schizophrenia and those clinically at high risk for transition to psychosis and are associated with cognitive impairment. Converging evidence suggests N-methyl-D-aspartate receptor (NMDAR) hypofunction plays a central role in the pathophysiology of schizophrenia and likely contributes to biomarker impairments. Thus, characterizing these biomarkers is of significant interest for early diagnosis of schizophrenia and development of novel treatments. We utilized in vivo EEG recordings and behavioral analyses to perform a battery of electrophysiological biomarkers in an established model of chronic NMDAR hypofunction, serine racemase knockout (SRKO) mice, and their wild-type littermates. SRKO mice displayed impairments in investigation-elicited gamma power that corresponded with reduced short-term social recognition and enhanced background (pre-investigation) gamma activity. Additionally, SRKO mice exhibited sensory gating impairments in both evoked-gamma power and event-related potential amplitude. However, other biomarkers including the auditory steady-state response, sleep spindles, and state-specific power spectral density were generally neurotypical. In conclusion, SRKO mice demonstrate how chronic NMDAR hypofunction contributes to deficits in certain translationally-relevant EEG biomarkers altered in schizophrenia. Importantly, our gamma band findings suggest an aberrant signal-to-noise ratio impairing cognition that occurs with NMDAR hypofunction, potentially tied to impaired task-dependent alteration in functional connectivity.
Invariant Gibbs measures for the three-dimensional wave equation with a Hartree nonlinearity I: measures

In this two-paper series, we prove the invariance of the Gibbs measure for a three-dimensional wave equation with a Hartree nonlinearity. The main novelty is the singularity of the Gibbs measure with respect to the Gaussian free field. The singularity has several consequences in both measure-theoretic and dynamical aspects of our argument. In this paper, we construct and study the Gibbs measure. Our approach is based on earlier work of Barashkov and Gubinelli for the $$\Phi ^4_3$$ -model. Most importantly, our truncated Gibbs measures are tailored towards the dynamical aspects in the second part of the series. In addition, we develop new tools dealing with the non-locality of the Hartree interaction. We also determine the exact threshold between singularity and absolute continuity of the Gibbs measure depending on the regularity of the interaction potential.
Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens

Major depressive disorder is a debilitating disease affecting 264 million people worldwide. While many antidepressant medications are available, few clinical guidelines support choosing among them. Decision support tools (DSTs) embodying machine learning models may help improve the treatment selection process, but often fail in clinical practice due to poor system integration. We use an iterative, co-design process to investigate clinicians’ perceptions of using DSTs in antidepressant treatment decisions. We identify ways in which DSTs need to engage with the healthcare sociotechnical system, including clinical processes, patient preferences, resource constraints, and domain knowledge. Our results suggest that clinical DSTs should be designed as multi-user systems that support patient-provider collaboration and offer on-demand explanations that address discrepancies between predictions and current standards of care. Through this work, we demonstrate how current trends in explainable AI may be inappropriate for clinical environments and consider paths towards designing these tools for real-world medical systems.
Reverberation mapping of active galactic nuclei: from X-ray corona to dusty torus.

Summary The central engines of active galactic nuclei (AGNs) are powered by accreting supermassive black holes, and while AGNs are known to play an important role in galaxy evolution, the key physical processes occur on scales that are too small to be resolved spatially (aside from a few exceptional cases). Reverberation mapping is a powerful technique that overcomes this limitation by using echoes of light to determine the geometry and kinematics of the central regions. Variable ionizing radiation from close to the black hole drives correlated variability in surrounding gas/dust but with a time delay due to the light travel time between the regions, allowing reverberation mapping to effectively replace spatial resolution with time resolution. Reverberation mapping is used to measure black hole masses and to probe the innermost X-ray emitting region, the UV/optical accretion disk, the broad emission line region, and the dusty torus. In this article, we provide an overview of the technique and its varied applications.
Exploitative abuse of a dominant position: a bad idea that now should be abandoned

Exploitative abuse of a dominant position is a long-recognized category of infringements of what is now Article 102 TFEU. Article 102’s prohibition originated in the EEC Treaty, which broke down ba...
Application of a novel haplotype-based scan for local adaptation to study high-altitude adaptation in rhesus macaques.

When natural populations split and migrate to different environments, they may experience different selection pressures that can lead to local adaptation. To capture the genomic patterns of a local selective sweep, we develop XP-nSL, a genomic scan for local adaptation that compares haplotype patterns between two populations. We show that XP-nSL has power to detect ongoing and recently completed hard and soft sweeps, and we then apply this statistic to search for evidence of adaptation to high altitude in rhesus macaques. We analyze the whole genomes of 23 wild rhesus macaques captured at high altitude (mean altitude > 4000 m above sea level) to 22 wild rhesus macaques captured at low altitude (mean altitude < 500 m above sea level) and find evidence of local adaptation in the high-altitude population at or near 303 known genes and several unannotated regions. We find the strongest signal for adaptation at EGLN1, a classic target for convergent evolution in several species living in low oxygen environments. Furthermore, many of the 303 genes are involved in processes related to hypoxia, regulation of ROS, DNA damage repair, synaptic signaling, and metabolism. These results suggest that, beyond adapting via a beneficial mutation in one single gene, adaptation to high altitude in rhesus macaques is polygenic and spread across numerous important biological systems.
Correction to: Entropy in a Closed Manifold and Partial Regularity of Mean Curvature Flow Limit of Surfaces

We correct some mistakes in “Entropy in A Closed Manifold and Partial Regularity of Mean Curvature Flow Limit of Surfaces”.
The next generation of crystal detectors

Crystal detectors have been used widely in high energy and nuclear physics experiments, medical instruments and homeland security applications. Novel crystal detectors are continuously being discovered and developed in academia and in industry. In high energy and nuclear physics experiments, total absorption electromagnetic calorimeters (ECAL) made of inorganic crystals are known for their superb energy resolution and detection efficiency for photon and electron measurements. A crystal ECAL is thus the choice for those experiments where precision measurements of photons and electrons are crucial for their physics missions. For future HEP experiments at the energy and intensity frontiers, however, the crystal detectors used in the above mentioned ECALs are either not bright and fast enough, or not radiation hard enough. Crystal detectors have also been proposed to build a Homogeneous Hadron Calorimeter (HHCAL) to achieve unprecedented jet mass resolution by duel readout of both Cherenkov and scintillation light, where development of cost-effective crystal detectors is a crucial issue because of the huge crystal volume required. This paper discusses several R&D directions for the next generation of crystal detectors for future HEP experiments.
PHEW : Constructing Sparse Networks that Learn Fast and Generalize Well without Training Data

Methods that sparsify a network at initialization are important in practice because they greatly improve the efficiency of both learning and inference. Our work is based on a recently proposed decomposition of the Neural Tangent Kernel (NTK) that has decoupled the dynamics of the training process into a data-dependent component and an architecture-dependent kernel - the latter referred to as Path Kernel. That work has shown how to design sparse neural networks for faster convergence, without any training data, using the Synflow-L2 algorithm. We first show that even though Synflow-L2 is optimal in terms of convergence, for a given network density, it results in sub-networks with "bottleneck" (narrow) layers - leading to poor performance as compared to other data-agnostic methods that use the same number of parameters. Then we propose a new method to construct sparse networks, without any training data, referred to as Paths with Higher-Edge Weights (PHEW). PHEW is a probabilistic network formation method based on biased random walks that only depends on the initial weights. It has similar path kernel properties as Synflow-L2 but it generates much wider layers, resulting in better generalization and performance. PHEW achieves significant improvements over the data-independent SynFlow and SynFlow-L2 methods at a wide range of network densities.
Islamic Legal Canons as Memes

In her essay concluding the Roundtable, Intisar Rabb invites us to conduct a thought experiment— to think of legal canons as memes, that is, as cultural elements in circulation that, like genes, self-replicate and accrue to the benefit of human society. Just as memes spread, so do legal canons—principles that guide legal interpretation—from one scholar to another, from one written record to the other. Describing at length multiple angles from which legal canons can be categorized, Rabb shows that the many and varied types of canons illustrate how deeply embedded canons are in the social, cultural, and also legal culture that produces them. That, in turn, invites close collaboration between legal historians and data scientists to enable a mapping of a “meme pool” for legal canons, which she pursues through developing the Courts & Canons project at Harvard Law School: through digital tools, we will be able to trace the curious textual travels of legal canons (as memes), and through that, the transmission of cultures, practices, and ideas in through all manner of texts (their meme pool) recording the history and practice of law and society in the Muslim world.
Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework

Advanced tensor decomposition, such as tensor train (TT) and tensor ring (TR), has been widely studied for deep neural network (DNN) model compression, especially for recurrent neural networks (RNNs). However, compressing convolutional neural networks (CNNs) using TT/TR always suffers significant accuracy loss. In this paper, we propose a systematic framework for tensor decomposition-based model compression using Alternating Direction Method of Multipliers (ADMM). By formulating TT decomposition-based model compression to an optimization problem with constraints on tensor ranks, we leverage ADMM technique to systemically solve this optimization problem in an iterative way. During this procedure, the entire DNN model is trained in the original structure instead of TT format, but gradually enjoys the desired low tensor rank characteristics. We then decompose this uncompressed model to TT format, and fine-tune it to finally obtain a high-accuracy TT-format DNN model. Our framework is very general, and it works for both CNNs and RNNs, and can be easily modified to fit other tensor decomposition approaches. We evaluate our proposed framework on different DNN models for image classification and video recognition tasks. Experimental results show that our ADMM-based TT-format models demonstrate very high compression performance with high accuracy. Notably, on CIFAR-100, with 2.3× and 2.4× compression ratios, our models have 1.96% and 2.21% higher top-1 accuracy than the original ResNet-20 and ResNet-32, respectively. For compressing ResNet-18 on ImageNet, our model achieves 2.47× FLOPs reduction without accuracy loss.
Liquid Motion Experiment flight test results

The Liquid Motion Experiment (LME), designed to study the effects of liquid motion in rotating tanks, was flown on STS 84. LME was essentially a spin table that created a realistic nutation motion of scale-model tanks containing liquid. TWo spherical and two cylindrical transparent tanks were tested simultaneously, and three sets of such tanks were employed to vary liquid viscosity, fill level, and propellant management device (PMD) design. All the tanks were approximately 4.5 inches diameter. The primary test measurements were the radial and tangential torques exerted on the tanks by the liquid. Resonant frequencies and damping of the liquid oscillations were determined by sine sweep tests. For a given tank shape, the resonant frequency depended on fill level. For the cylindrical tanks, the resonances had somewhat different frequencies for the tangential axis (0.55 to 0.75 times spin rate) and the radial axis (0.73 to 0.78 times spin rate), and the tangential axis resonance agreed more closely with available analytical models. For the spherical tanks, the resonant frequencies were between 0.74 to 0.77 times the spin rate and were the same for the tangential and radial axes. The damping coefficients varied from about I% to 3% of critical, depending on tank shape, fill level, and liquid viscosity. 'Me viscous energy dissipation rates of the liquid oscillations were determined from sine dwell tests. The LME energy dissipation rates varied from 0.3 to 0.5 times the estimates obtained from scaling previous ground tests and spacecraft flight data. The PNDs sometimes enhanced the resonances and energy dissipation rates and sometimes decreased them, which points out the need to understand better the effects of PMD on liquid motion as a function of PMD and tank design.