cssi/SciSciGPT-SciSciNet huggingface dataset:
An Integrable Model for the Dynamics of Planetary Mean-motion Resonances

I consider the dynamics of mean motion resonances between pairs of co-planar planets and derive a new integrable Hamiltonian model for planets' resonant motion. The new model generalizes previously-derived integrable Hamiltonians for first-order resonances to treat higher-order resonances by exploiting a surprising near-symmetry of the full, non-integrable Hamiltonians of higher-order resonances. Whereas past works have frequently relied on truncated disturbing function expansions to derive integrable approximations to resonant motion, I show that no such expansion is necessary, thus enabling the new model to accurately capture the dynamics of both first- and higher-order resonances for eccentricities up to orbit-crossing. I demonstrate that predictions of the new integrable model agree well with numerical integrations of resonant planet pairs. Finally, I explore the secular evolution of resonant planets' eccentricities. I show that the secular dynamics are governed by conservation of an AMD-like quantity. I also demonstrate that secular frequencies depend on planets' resonant libration amplitude and this generally gives rise to a secular resonance inside the mean motion resonance at large libration amplitudes. Outside of the secular resonance the long-term dynamics are characterized small adiabatic modulations of the resonant motion while inside the secular resonance planets can experience large variations of the resonant trajectory over secular timescales. The integrable model derived in this work can serve as a framework for analyzing the dynamics of planetary MMRs in a wide variety of contexts.
One-Shot PIR: Refinement and Lifting

We study a class of private information retrieval (PIR) methods that we call one-shot schemes. The intuition behind one-shot schemes is the following. The user’s query is regarded as a dot product of a query vector and the message vector (database) stored at multiple servers. Privacy, in an information theoretic sense, is then achieved by encrypting the query vector using a secure linear code, such as secret sharing. Several PIR schemes in the literature, in addition to novel ones constructed here, fall into this class. One-shot schemes provide an insightful link between PIR and data security against eavesdropping. However, their download rate is not optimal, i.e., they do not achieve the PIR capacity. Our main contribution is two transformations of one-shot schemes, which we call refining and lifting. We show that refining and lifting one-shot schemes gives capacity-achieving schemes for the cases when the PIR capacity is known. In the other cases, when the PIR capacity is still unknown, refining and lifting one-shot schemes gives, for most parameters, the best download rate so far.
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control

We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system, such as when a robot must decide on the next action while still performing the previous action. Much like a person or an animal, the robot must think and move at the same time, deciding on its next action before the previous one has completed. In order to develop an algorithmic framework for such concurrent control problems, we start with a continuous-time formulation of the Bellman equations, and then discretize them in a way that is aware of system delays. We instantiate this new class of approximate dynamic programming methods via a simple architectural extension to existing value-based deep reinforcement learning algorithms. We evaluate our methods on simulated benchmark tasks and a large-scale robotic grasping task where the robot must "think while moving.''
In situ Raman spectroscopy studies of catalysts

In situ Raman spectroscopy is rapidly becoming a very popular catalyst characterization method because Raman cells are being designed that can combine in situ molecular characterization studies with simultaneous fundamental quantitative kinetic studies. The dynamic nature of catalyst surfaces requires that both sets of information be obtained for a complete fundamental understanding of catalytic phenomena under practical reaction conditions. Several examples are chosen to highlight the capabilities of in situ Raman spectroscopy to problems in heterogeneous catalysis: the structural determination of the number of terminal M=O bonds in surface metal oxide species that are present in supported metal oxide catalysts; structural transformations of the MoO3/SiO2 and MoO3/TiO2 supported metal oxide catalysts under various environmental conditions, which contrast the markedly different oxide–oxide interactions in these two catalytic systems; the location and relative reactivity of the different surface M–OCH3 intermediates present during CH3OH oxidation over V2O5/SiO2 catalysts; the different types of atomic oxygen species present in metallic silver catalysts and their role during CH3OH oxidation to H2CO and C2H4 epoxidation to C2H4O; and information about the oxidized and reduced surface metal oxide species, isolated as well as polymerized species, present in supported metal oxide catalysts during reaction conditions. In summary, in situ Raman spectroscopy is a very powerful catalyst characterization technique because it can provide fundamental molecular‐level information about catalyst surface structure and reactive surface intermediates under practical reaction conditions.
Designing radiation transport tests: Simulation-driven uncertainty-quantification of the COAX temperature diagnostic

Abstract One of the difficulties in developing accurate numerical models of radiation flow in a coupled radiation-hydrodynamics setting is accurately modeling the transmission across a boundary layer. The COAX experiment is a platform design to test this transmission including standard radiograph and flux diagnostics as well as a temperature diagnostic measuring the population of excitation levels and ionization states of a dopant embedded within the target material. Using a broad range of simulations, we study the experimental errors in this temperature diagnostic. We conclude with proposed physics experiments that show features that are much stronger than the experimental errors and provide the means to study transport models.
Investigating Crowdsourcing to Generate Distractors for Multiple-Choice Assessments

We present and analyze results from a pilot study that explores how crowdsourcing can be used in the process of generating distractors (incorrect answer choices) in multiple-choice concept inventories (conceptual tests of understanding). To our knowledge, we are the first to propose and study this approach. Using Amazon Mechanical Turk, we collected approximately 180 open-ended responses to several question stems from the Cybersecurity Concept Inventory of the Cybersecurity Assessment Tools Project and from the Digital Logic Concept Inventory. We generated preliminary distractors by filtering responses, grouping similar responses, selecting the four most frequent groups, and refining a representative distractor for each of these groups.
Inverting the Truck-Drone Network Problem to Find Best Case Configuration

Many industries are looking for ways to economically use truck/rail/ship fitted with drone technologies to augment the “last mile” delivery effort. While drone technologies abound, few, if any studies look at the proper configuration of the drone based on significant features of the problem: delivery density, operating area, drone range, and speed. Here, we first present the truck-drone problem and then invert the network routing problem such that the best case drone speed and range are fitted to the truck for a given scenario based on the network delivery density. By inverting the problem, a business can quickly determine the drone configuration (proper drone range and speed) necessary to optimize the delivery system. Additionally, we provide a more usable version of the truck-drone routing problem as a mixed integer program that can be easily adopted with standardized software used to solve linear programming. Furthermore, our computational metaheuristics and experiments conducted in support of this work are available for download. The metaheuristics used herein surpass current best-in-class algorithms found in literature.
Investigation of the Effect of Perforated Sheath on Thermal-Flow Characteristics Over a Gas Turbine Reverse-Flow Combustor—Part 2: Computational Analysis

The objective of Part 2 is to employ a computational scheme to investigate the difference in flow pattern, pressure drop, and heat transfer in a gas turbine’s dump diffuser and over the outer surface of the combustor with and without a sheath. Both experimental and computational studies are performed. In Part 1, the experiments are conducted under low pressure and temperature laboratory conditions to provide a database to validate the computation model, which is then used to simulate the thermal-flow field surrounding the combustor and transition piece under elevated gas turbine operating conditions.For laboratory conditions, the CFD results show that (a) the predicted local static pressure values are higher than the experimental data but the prediction of the global total pressure losses matches the experimental data very well; (b) the total pressure losses are 1.19% for the no-sheath case and 1.89% for the sheathed case, which are within 3% of the experimental values; and (c) the temperature difference between the sheathed and non-sheathed cases is in the range of 5∼10K or 16%–32% based on the temperature scale between the highest and lowest temperature in the computational domain.In summary, removing the sheath can harvest a significant pressure recovery of approximately 3% of the total pressure, but it will be subject to a wall temperature increase of about 500K (900°F or a 36% increase) on the outer radial part of the transition piece, where the flow is slow due to diffusion and recirculation in the large dump diffuser cavity near the turbine end. If modern advanced materials or coatings could sustain a wall temperature of about 200K higher than those currently available, the shield could be removed with the condition that a special cooling scheme (such as a water spray system) must be applied locally in this region. Otherwise, removal of the shield is not recommended.Copyright © 2013 by ASME
Dynamic Base Station Selection for Personal Communication Systems with Distributed Control Schemes

Distributed control schemes allow base stations in personal communication systems to be placed at locations corresponding to high expected traffic. This flexible base station placement creates significant overlapping coverage areas that can be utilized to improve system performance [1]. A new technique for dynamic base station selection in systems with overlapping cells is considered and its effect on traffic performance is characterized. The technique realizes robust performance for personal communication systems in fluctuating and heavily tapered traffic. A mathematical analysis based on a state transition model is used to evaluate performance of a system that employs the proposed technique. The results indicate that improved blocking probability and carried traffic performance are obtainable. Computer simulations were undertaken confirm the analytical results.
Structural Basis of Temperature Sensation by the TRP Channel TRPV3

We present structures of mouse TRPV3 in temperature-dependent open, closed and intermediate states that suggest two-step activation of TRPV3 by heat. During the strongly temperature-dependent first step, sensitization, the channel pore remains closed while S6 helices undergo α-to-π transitions. During the weakly temperature-dependent second step, channel opening, tight association of the S1–S4 and pore domains is stabilized by changes in the carboxy-terminal and linker domains. Cryo-EM structures of mouse TRPV3 in open, closed and intermediate states, obtained by incubation of the protein samples at different temperatures immediately prior to freezing, offer insight into conformational changes induced by heat in TRPV3.
Stability of Patterns in the Abelian Sandpile

We show that the patterns in the Abelian sandpile are stable. The proof combines the structure theory for the patterns with the regularity machinery for non-divergence form elliptic equations. The stability results allow one to improve weak-$$*$$ convergence of the Abelian sandpile to pattern convergence for certain classes of solutions.
SPICE: Self-Supervised Pitch Estimation

We propose a model to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation. We acknowledge the fact that obtaining ground truth annotations at the required temporal and frequency resolution is a particularly daunting task. Therefore, we propose to adopt a self-supervised learning technique, which is able to estimate pitch without any form of supervision. The key observation is that pitch shift maps to a simple translation when the audio signal is analysed through the lens of the constant-Q transform (CQT). We design a self-supervised task by feeding two shifted slices of the CQT to the same convolutional encoder, and require that the difference in the outputs is proportional to the corresponding difference in pitch. In addition, we introduce a small model head on top of the encoder, which is able to determine the confidence of the pitch estimate, so as to distinguish between voiced and unvoiced audio. Our results show that the proposed method is able to estimate pitch at a level of accuracy comparable to fully supervised models, both on clean and noisy audio samples, although it does not require access to large labeled datasets.
Higher Lawvere theories

Abstract We survey Lawvere theories at the level of ∞-categories, as an alternative framework for higher algebra (rather than ∞-operads). From a pedagogical perspective, they make many key definitions and constructions less technical. They also play a prominent role in equivariant homotopy theory and its relatives. Our main result establishes a universal property for the ∞-category of Lawvere theories, which completely characterizes the relationship between a Lawvere theory and its ∞-category of models. Many familiar properties of Lawvere theories follow directly. As a consequence, we establish a correspondence between enriched and module Lawvere theories, which implies that the Burnside category is a classifying object for additive categories. This completes a proof from our earlier paper on the commutative algebra of categories.
A Continuous Threshold Model of Cascade Dynamics

We present a continuous threshold model (CTM) of cascade dynamics for a network of agents with real-valued activity levels that change continuously in time. The model generalizes the linear threshold model (LTM) from the literature, where an agent becomes active (adopts an innovation) if the fraction of its neighbors that are active is above a threshold. With the CTM we study the influence on cascades of heterogeneity in thresholds for a network comprised of a chain of three clusters of agents, each distinguished by a different threshold. The system is most sensitive to change as the dynamics pass through a bifurcation point: if the bifurcation is supercritical the response will be contained, while if the bifurcation is subcritical the response will be a cascade. We show that there is a subcritical bifurcation, thus a cascade, in response to an innovation if there is a large enough disparity between the thresholds of sufficiently large clusters on either end of the chain; otherwise the response will be contained.
sBSNN: Stochastic-Bits Enabled Binary Spiking Neural Network With On-Chip Learning for Energy Efficient Neuromorphic Computing at the Edge

In this work, we propose stochastic Binary Spiking Neural Network (sBSNN) composed of stochastic spiking neurons and binary synapses (stochastic only during training) that computes probabilistically with one-bit precision for power-efficient and memory-compressed neuromorphic computing. We present an energy-efficient implementation of the proposed sBSNN using ‘stochastic bit’ as the core computational primitive to realize the stochastic neurons and synapses, which are fabricated in 90nm CMOS process, to achieve efficient on-chip training and inference for image recognition tasks. The measured data shows that the ‘stochastic bit’ can be programmed to mimic spiking neurons, and stochastic Spike Timing Dependent Plasticity (or sSTDP) rule for training the binary synaptic weights without expensive random number generators. Our results indicate that the proposed sBSNN realization offers possibility of up to $32\times $ neuronal and synaptic memory compression compared to full precision (32-bit) SNN and energy efficiency of 89.49 TOPS/Watt for two-layer fully-connected SNN.
Security and Privacy Challenges in Upcoming Intelligent Urban Micromobility Transportation Systems

Micromobility vehicles are gaining popularity due to their portable nature, and their ability to serve short distance urban commutes better than traditional modes of transportation. Most of these vehicles, offered by various micromobility service providers around the world, are shareable and can be rented (by-the-minute) by riders, thus eliminating the need of owning and maintaining a personal vehicle. However, the existing micromobility ecosystem comprising of vehicles, service providers, and their users, can be exploited as an attack surface by malicious entities - to compromise its security, safety, and privacy. In this short position paper, we outline potential privacy and security challenges related to a very popular urban micromobility platform, specifically, dockless battery-powered e-scooters.
Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision

Humans do not acquire perceptual abilities in the way we train machines. While machine learning algorithms typically operate on large collections of randomly-chosen, explicitly-labeled examples, human acquisition relies more heavily on multimodal unsupervised learning (as infants) and active learning (as children). With this motivation, we present a learning framework for sound representation and recognition that combines (i) a self-supervised objective based on a general notion of unimodal and cross-modal coincidence, (ii) a clustering objective that reflects our need to impose categorical structure on our experiences, and (iii) a cluster-based active learning procedure that solicits targeted weak supervision to consolidate categories into relevant semantic classes. By training a combined sound embedding/clustering/classification network according to these criteria, we achieve a new state-of-the-art unsupervised audio representation and demonstrate up to a 20-fold reduction in the number of labels required to reach a desired classification performance.
Weak mitoticity of bounded disjunctive and conjunctive truth-table autoreducible sets

Abstract Glaser et al. (SIAMJCOMP 2008 and TCS 2009) 2 proved existence of two sparse sets A and B in EXP, where A is 3-tt (truth-table) polynomial-time autoreducible but not weakly polynomial-time Turing mitotic and B is polynomial-time 2-tt autoreducible but not weakly polynomial-time 2-tt mitotic. We unify and strengthen both of those results by showing that there is a sparse set in EXP that is polynomial-time 2-tt autoreducible but not even weakly polynomial-time Turing mitotic. All these results indicate that polynomial-time autoreducibilities in general do not imply polynomial-time mitoticity at all with the only exceptions of the many-one and 1-tt reductions. On the other hand, however, we proved that every autoreducible set for the polynomial-time bounded disjunctive or conjunctive tt reductions is weakly mitotic for the polynomial-time tt reduction that makes logarithmically many queries only. This shows that autoreducible sets for reductions making more than one query could still be mitotic in some way if they possess certain special properties.
WEYL’S POLARIZATION THEOREM IN POSITIVE CHARACTERISTIC

Let V be an n-dimensional algebraic representation over an algebraically closed field K of a group G. For m > 0, we study the invariant rings K[Vm]G for the diagonal action of G on Vm. In characteristic zero, a theorem of Weyl tells us that we can obtain all the invariants in K[Vm]G by the process of polarization and restitution from K[Vn]G. In particular, this means that if K[Vn]G is generated in degree ≤ d, then so is K[Vm]G no matter how large m is. There are several explicit counterexamples to Weyl’s theorem in positive characteristic. However, when G is a (connected) reductive affine group scheme over ℤ and V*is a good G-module, we show that Weyl's theorem holds in sufficiently large characteristic. As a special case, we consider the ring of invariants R(n, m) for the left-right action of SLn × SLn on m-tuples of n × n matrices. In this case, we show that the invariants of degree ≤ n6 suffice to generate R(n, m) if the characteristic is larger than 2n6 + n2.
Diagnosis of the agricultural information, training and advice system in Bulgaria

Despite the great theoretical and practical significance, in Bulgaria there are no comprehensive analysis of the state and evolution of the system of agricultural information, training and advices in Bulgaria. The goal of this paper is to analyze the state and evolution of the system of agricultural information, training and advices in Bulgaria during the period after country’s EU accession, identify major trends in that area, make a comparison with other EU states, specify main problems, and suggest conclusions for improvement of policies during next programing period. The analysis has found out that in years after accession of the country to EU the number of the farm managers who undertook full agricultural training increases, but despite that almost 93% of them are still with practical experiences and without any agricultural training. The extent of participation of rural areas rests weak and constantly decreasing, and Bulgaria is among the last in EU in hours of formal and informal education and training. In years of EU membership the number of provided consultations is doubled and in recent years 17% of all registered agricultural producers and each tenth farmer in the country are consulted while the subjects of provided consultation widened. Also hundreds of events associated with knowledge and innovation transfer and sharing are organized as most of them are jointly organized by the National Advisory Service with the institutes of Agricultural Academy, agrarian and other universities, research and development organizations. The number of organized events, the overall number of participants, and the average number of participant per event tend to decrease.
Evolutionary Helium and CNO Anomalies in the Atmospheres and Winds of Massive Hot Stars

The ubiquitous evidence for processed material in the atmospheres, winds, and circumstellar ejecta of massive stars will be reviewed. A broad array of normal and peculiar evolutionary stages is considered, up to and including Type II supernova progenitors. The quantitative analysis of these spectra is difficult, and until recently for the most part only qualitative or approximate results have been available. However, several important current programs promise reliable abundance determinations, which will enable detailed comparisons with recent evolutionary calculations. A significant emerging result is that the morphologically normal majority of both hot and cool supergiants may already display an admixture of CNO-cycle products in their atmospheres. It may become possible in this way to identify blue supergiants returning from the red supergiant region, as appears to have been the case for the SN 1987A progenitor.
Navier-Stokes computations on swept-tapered wings, including flexibility

A procedure to couple the Navier-Stokes solutions with modal structural equations of motion is presented for computing aeroelastic responses of flexible fighter wings. The Navier-Stokes flow equations are solved by a finite-difference scheme with dynamic grids. The coupled aeroelastic equations of motion are solved using the linear-acceleration method. The configuration-adaptive dynamic grids are time-accurately generated using the aeroelastically deformed shape of the wing. The coupled calculations are compared with experiments when available. Effects of flexibility and pitch rate are demonstrated for flows with vortices. Turbulent flow computations are also compared with laminar flow computations.
Second Opinions and Price Competition: Inefficiency in the Market for Expert Advice

We consider a market in which an expert must exert costly but unobservable effort to identify the service that meets the consumer's need. In our model, experts offer competing contracts and the consumer may gather multiple opinions. We explore the incentives that a competitive sampling of prices and opinions provides for experts to exert effort and find that there is a tension between price competition and the equilibrium effort. In particular, the equilibrium fails to realize the second best welfare optimum. An intervention, that limits price competition via price control, increases welfare.
Betweenness, Metrics and Entropies in Lattices.

We investigate a class of metrics on lattices that are compatible with the partial order defined by the lattice using the ternary relation of betweenness that can be naturally defined on a metric space. The relationships between entropy-like functions and metrics defined on lattices are studied and we show the links that exists between various properties of entropies and properties of metrics. Applications to metrics defined on the lattice of partitions of finite sets are examined.
Compact atom interferometer using single laser

A typical atom interferometer requires vastly different laser frequencies at different stages of operation, e.g., near resonant light for laser cooling and far detuned light for atom optics, such that multiple lasers are typically employed. The number of laser units constrains the achievable minimum size and power in practical devices for resource critical environments such as space. We demonstrate a compact atom interferometer accelerometer operated by a single diode laser. This is achieved by dynamically changing the laser output frequency in GHz range while maintaining spectroscopic reference to an atomic transition via a sideband generated by phase modulation. At the same time, a beam path sharing configuration is also demonstrated for a compact sensor head design, in which atom interferometer beams share the same path as that of the cooling beam. This beam path sharing also significantly simplifies three-axis atomic accelerometry in microgravity using single sensor head.
Robust and efficient semi-supervised estimation of average treatment effects with application to electronic health records data.

We consider the problem of estimating the average treatment effect (ATE) in a semi-supervised learning setting, where a very small proportion of the entire set of observations are labeled with the true outcome but features predictive of the outcome are available among all observations. This problem arises, for example, when estimating treatment effects in electronic health records (EHR) data because gold-standard outcomes are often not directly observable from the records but are observed for a limited number of patients through small-scale manual chart review. We develop an imputation-based approach for estimating the ATE that is robust to misspecification of the imputation model. This effectively allows information from the predictive features to be safely leveraged to improve efficiency in estimating the ATE. The estimator is additionally doubly-robust in that it is consistent under correct specification of either an initial propensity score model or a baseline outcome model. It is also locally semiparametric efficient under an ideal semi-supervised model where the distribution of the unlabeled data is known. Simulations exhibit the efficiency and robustness of the proposed method compared to existing approaches in finite samples. We illustrate the method by comparing rates of treatment response to two biologic agents for treatment inflammatory bowel disease using EHR data from Partners' Healthcare.
Schumpeter in Vienna: A Study Abroad Course

This paper builds upon the work of Dalton and Logan (Rev Austrian Econ, 2020a) by describing the motivation for and mechanics of teaching a course dedicated to Schumpeter as a study abroad program in the city of Vienna. We argue that the qualities Vienna possesses, both historical and contemporary, make a good laboratory for exploring Schumpeter’s ideas and that the process of encountering a new culture through a study abroad course is the best way to internalize his theory of innovation. To do so, our paper first outlines the course content before describing the linkages between “techno-romantic” Vienna and Schumpeter’s intellectual development. We then describe specific examples for how instructors can use Vienna as a laboratory for teaching Schumpeter’s ideas. We close by sharing preparatory details for instructors and offering the perspective of a student who took this course in the summer of 2018.
The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization

Whole-genome expression data generated by microarray studies have shown promise for quantitative human health risk assessment. While numerous approaches have been developed to determine benchmark doses (BMDs) from probeset-level dose responses, sensitivity of the results to methods used for normalization of the data has not yet been systematically investigated. Normalization of microarray data converts raw hybridization signals to expression estimates that are expected to be proportional to the amounts of transcripts in the profiled specimens. Different approaches to normalization have been shown to greatly influence the results of some downstream analyses, including biological interpretation. In this study we evaluate the influence of microarray normalization methods on the transcriptomic BMDs. We demonstrate using in vivo data that the use of alternative pipelines for normalization of Affymetrix microarray data can have a considerable impact on the number of detected differentially expressed genes and pathways (processes) determined to be treatment responsive, which may lead to alternative interpretations of the data. In addition, we found that normalization can have a considerable effect (as much as ~30-fold in this study) on estimation of the minimum biological potency (transcriptomic point of departure). We argue for consideration of alternative normalization methods and their data-informed selection to most effectively interpret microarray data for use in human health risk assessment.
Test Agreement between Roche Cobas 6800 and Cepheid GeneXpert Xpress SARS-CoV-2 Assays at High Cycle Threshold Ranges.

The SARS-CoV-2 pandemic has changed the face of the globe and upended the daily lives of billions. In an effort to bring mass testing to as many as possible, multiple diagnostic tests, including molecular, antigen detection, and serological assays, have been rapidly developed. Under U.S. Food and
Implications of Error-Prone Long-Read Whole-Genome Shotgun Sequencing on Characterizing Reference Microbiomes.

Summary Long-read sequencing techniques, such as the Oxford Nanopore Technology, can generate reads that are tens of kilobases in length and are therefore particularly relevant for microbiome studies. However, owing to the higher per-base error rates than typical short-read sequencing, the application of long-read sequencing on microbiomes remains largely unexplored. Here we deeply sequenced two human microbiota mock community samples (HM-276D and HM-277D) from the Human Microbiome Project. We showed that assembly programs consistently achieved high accuracy (∼99%) and completeness (∼99%) for bacterial strains with adequate coverage. We also found that long-read sequencing provides accurate estimates of species-level abundance (R = 0.94 for 20 bacteria with abundance ranging from 0.005% to 64%). Our results not only demonstrate the feasibility of characterizing complete microbial genomes and populations from error-prone Nanopore sequencing data but also highlight necessary bioinformatics improvements for future metagenomics tool development.
Thermalization in the D1D5 CFT

It is generally agreed that black hole formation in gravity corresponds to thermalization in the dual CFT. It is sometimes argued that if the CFT evolution shows evidence of large redshift in gravity, then we have seen black hole formation in the CFT. We argue that this is not the case: a clock falling towards the horizon increases its redshift but remains intact as a clock; thus it is not ‘thermalized’. Instead, thermalization should correspond to a new phase after the phase of large redshift, where the infalling object turns into fuzzballs on reaching within planck distance of the horizon. We compute simple examples of the scattering vertex in the D1D5 CFT which, after many iterations, would lead to thermalization. An initial state made of two left-moving and two right-moving excitations corresponds, in gravity, to two gravitons heading towards each other. The thermalization vertex in the CFT breaks these excitations into multiple excitations on the left and right sides; we compute the amplitudes for several of these processes. We find secular terms that grow as t2 instead of oscillating with t; we conjecture that this may be a feature of processes leading to thermalization.
Adaptive designs for optimal observed Fisher information

Expected Fisher information can be found a priori and as a result its inverse is the primary variance approximation used in the design of experiments. This is in contrast with the common claim that the inverse of the observed Fisher information is a better approximation of the variance of the maximum likelihood estimator. Observed Fisher information cannot be known a priori; however, if an experiment is conducted sequentially, in a series of runs, the observed Fisher information from previous runs is known. In the current work, two adaptive designs are proposed that use the observed Fisher information from previous runs to inform the design of future runs.
Univariate likelihood projections and characterizations of the multivariate normal distribution

Abstract The problem of characterizing a multivariate distribution of a random vector using examination of univariate combinations of vector components is an essential issue of multivariate analysis. The likelihood principle plays a prominent role in developing powerful statistical inference tools. In this context, we raise the question: can the univariate likelihood function based on a random vector be used to provide the uniqueness in reconstructing the vector distribution? In multivariate normal (MN) frameworks, this question links to a reverse of Cochran’s theorem that concerns the distribution of quadratic forms in normal variables. We characterize the MN distribution through the univariate likelihood type projections. The proposed principle is employed to illustrate simple techniques for assessing multivariate normality via well-known tests that use univariate observations. The displayed testing strategy can exhibit high and stable power characteristics in comparison to the well-known procedures in various scenarios when observed vectors are non-MN distributed, whereas their components are normally distributed random variables. In such cases, classical multivariate normality tests, such as Shapiro–Wilk’s, Henze–Zirkler’s and Mardia’s tests, may break down completely.
On Bounds and Closed-Form Expressions for Capacities of Discrete Memoryless Channels With Invertible Positive Matrices

While capacities of discrete memoryless channels are well studied, it is still not possible to obtain a closed-form expression for the capacity of an arbitrary discrete memoryless channel (DMC). In this paper, we study a class of DMCs whose channel matrix is an invertible positive matrix. This class of channel matrices can be used to model many real-world settings. Next, an elementary technique based on Karush-Kuhn-Tucker (KKT) conditions is used to obtain (1) a good upper bound of a discrete memoryless channel having an invertible positive channel matrix and (2) a closed-form expression for the capacity if the channel matrix satisfies certain conditions related to its singular value and its Gershgorin's disk.
Bounding the dimension of points on a line

Abstract We use Kolmogorov complexity methods to give a lower bound on the effective Hausdorff dimension of the point ( x , a x + b ) , given real numbers a, b, and x. We apply our main theorem to a problem in fractal geometry, giving an improved lower bound on the (classical) Hausdorff dimension of generalized sets of Furstenberg type.
AI Trust in Business Processes: The Need for Process-Aware Explanations

Business processes underpin a large number of enterprise operations including processing loan applications, managing invoices, and insurance claims. The business process management (BPM) industry is expected to grow at approximately 16 Billion dollar by 2023. There is a large opportunity for infusing AI to reduce cost or provide better customer experience with a $15.7 trillion “potential contribution to the global economy by 2030”. To this end, the BPM literature is rich in machine learning solutions including unsupervised learning to gain insights on clusters of process traces, classification models to predict the outcomes, duration, or paths of partial process traces, extracting business process from documents, and models to recommend how to optimize a business process or navigate decision points. More recently, deep learning models including those from the NLP domain have been applied to process predictions.Unfortunately, very little of these innovations have been applied and adopted by enterprise companies. We assert that a large reason for the lack of adoption of AI models in BPM is that business users are risk-averse and do not implicitly trust AI models. There has, unfortunately, been little attention paid to explaining model predictions to business users with process context. We challenge the BPM community to build on the AI interpretability literature, and the AI Trust community to understand what it means to take advantage of business process artifacts in order to provide business level explanations.
Predicting Labor Market Competition: Leveraging Interfirm Network and Employee Skills

Human capital is a key component of the knowledge economy. Firms compete not only for consumers in the product market but also for human capital in the labor market. In this study, we perform an in...
High Offset Stems Are Protective of Dislocation in High-Risk Total Hip Arthroplasty.

Abstract Background Spinal stiffness has been shown to increase risk of dislocation due to impingement and instability. Increasing anteversion of the acetabular component has been suggested to prevent dislocation, but little has been discussed in terms of femoral or global offset restoration. The purpose of this study is to quantify dislocation rates after primary THA using standard versus high-offset femoral components and to determine how differences in offset affect impingement-free range of motion in a stiff spine cohort using a novel impingement model. Methods A total of 12,365 patients undergoing THA from 2016 to 2018 were retrospectively reviewed to determine dislocation rates and utilization of standard- versus high-offset stems. For 50 consecutive patients with spinal stiffness, a CT-based computer software impingement modeling system assessed bony or prosthetic impingement during simulated range of motion. The model was run 5 times for each patient with varying offsets. Range of motion was simulated in each scenario to determine the degree at which impingement occurred. Results There were 51 dislocations for a 0.41% dislocation rate. Total utilization of high-offset stems in the entire cohort was 49%. Of those patients who sustained a dislocation, 49 (96%) utilized a standard-offset stem. The impingement modeling demonstrated 5 degrees of added range of motion until impingement for every 1 mm offset increase. Conclusion In the impingement model, high-offset stems facilitated greater ROM before bony impingement and resulted in lower dislocation rates. In the setting of high-risk THA due to spinal stiffness, surgeons should consider the use of high-offset stems and pay attention to offset restoration.
Tick Size and Financial Reporting Quality in Small‐Cap Firms: Evidence from a Natural Experiment

Using a natural experiment (the SEC's 2016 Tick Size Pilot Program), we investigate the effects of an increase in tick size on financial reporting quality. The tick size pilot program reduces algorithmic trading (AT) and increases fundamental investors’ information acquisition and trading activities. This in turn increases the scrutiny of managers’ financial reporting choices and reduces their incentives to engage in misreporting. Using a difference‐in‐differences research design, we find a significant decrease in the magnitude of discretionary accruals, a significant reduction in the likelihood of just meeting or beating analysts’ forecasts, and a marginally significant decrease in restatements for the treated firms in the pilot program. Furthermore, we find that the change in financial reporting quality is concentrated in treated firms experiencing decreases in AT and increases in information acquisition activities. We also find that the mispricing of accruals is significantly lower for treated firms. Taken together, our results suggest that an increase in tick size has a causal effect on firms’ financial reporting quality.
Wigner–Smith Time-Delay Matrix for Electromagnetics: Theory and Phenomenology

The Wigner–Smith (WS) time-delay concepts have been used extensively in quantum mechanics to characterize delays experienced by particles interacting with a potential well. This article formally extends WS time-delay theory to Maxwell’s equations and explores its potential applications in electromagnetics. The WS time-delay matrix relates a lossless and reciprocal system’s scattering matrix to its frequency derivative and allows for the construction of modes that experience well-defined group delays when interacting with the system. The matrix’ entries for guiding, scattering, and radiating systems are energy-like overlap integrals of the electric and/or magnetic fields that arise upon excitation of the system via its ports. The WS time-delay matrix has numerous applications in electromagnetics, including the characterization of group delays in multiport systems, the description of electromagnetic fields in terms of elementary scattering processes, and the characterization of frequency sensitivities of fields and multiport antenna impedance matrices.
Conserved Luminal C-Terminal Domain Dynamically Controls Interdomain Communication in Sarcolipin

Sarcolipin (SLN) mediates Ca2+ transport and metabolism in muscle by regulating the activity of the Ca2+ pump SERCA. SLN has a conserved luminal C-terminal domain that contributes to its functional divergence among homologous SERCA regulators, but the precise mechanistic role of this domain remains poorly understood. We used all-atom molecular dynamics (MD) simulations of SLN totaling 77.5 μs to show that the N- (NT) and C-terminal (CT) domains function in concert. Analysis of the MD simulations showed that serial deletions of the SLN C-terminus do not affect the stability of the peptide nor induce dissociation of SLN from the membrane but promote a gradual decrease in both the tilt angle of the transmembrane helix and the local thickness of the lipid bilayer. Mutual information analysis showed that the NT and CT domains communicate with each other in SLN and that interdomain communication is partially or completely abolished upon deletion of the conserved segment Tyr29-Tyr31 as well as by serial deletions beyond this domain. Phosphorylation of SLN at residue Thr5 also induces changes in the communication between the CT and NT domains, which thus provides additional evidence for interdomain communication within SLN. We found that interdomain communication is independent of the force field used and lipid composition, which thus demonstrates that communication between the NT and CT domains is an intrinsic functional feature of SLN. We propose the novel hypothesis that the conserved C-terminus is an essential element required for dynamic control of SLN regulatory function.
The heat shock transcription factor HSF-1 protects Caenorhabditis elegans from peroxide stress

Abstract Cells induce conserved defense mechanisms that protect them from oxidative stress. How these defenses are regulated in multicellular organisms is incompletely understood. Here, we show that the heat shock transcription factor HSF-1 protects the nematode Caenorhabditis elegans from the oxidative stress caused by environmental peroxide. In response to a heat shock or a mild temperature increase, HSF-1 protects the nematodes from subsequent peroxide stress in a manner that depends on HSF-1’s transactivation domain. At constant temperature, HSF-1 protects the nematodes from peroxide stress independently of its transactivation domain, likely by inducing the expression of asp-4/cathepsin and dapk-1/dapk. Thus, two distinct HSF-1-dependent processes protect C. elegans from peroxide stress.
Organic Chemistry: A Call to Action for Diversity and Inclusion.

By now, most of us in the field of organic chemistry have become aware of the recent Perspective Article by Hudlicky published on the Angewandte Chemie, International Edition website(1) and then quickly removed as a result of rapid and strong denunciation on social media and in other forums. We have had complex emotional responses to the opinions expressed in this piece regarding the effects of diversity and inclusion efforts in chemistry: anger, that such regressive views were provided a platform in one of our leading chemistry journals; surprise, that the piece made it through the peer review process; and disappointment, that these views continue to persist, despite our hope that the climate for researchers in organic chemistry had improved since we were all trainees.
Time-Space Tradeoffs for Distinguishing Distributions and Applications to Security of Goldreich's PRG.

In this work, we establish lower-bounds against memory bounded algorithms for distinguishing between natural pairs of related distributions from samples that arrive in a streaming setting. In our first result, we show that any algorithm that distinguishes between uniform distribution on $\{0,1\}^n$ and uniform distribution on an $n/2$-dimensional linear subspace of $\{0,1\}^n$ with non-negligible advantage needs $2^{\Omega(n)}$ samples or $\Omega(n^2)$ memory. Our second result applies to distinguishing outputs of Goldreich's local pseudorandom generator from the uniform distribution on the output domain. Specifically, Goldreich's pseudorandom generator $G$ fixes a predicate $P:\{0,1\}^k \rightarrow \{0,1\}$ and a collection of subsets $S_1, S_2, \ldots, S_m \subseteq [n]$ of size $k$. For any seed $x \in \{0,1\}^n$, it outputs $P(x_{S_1}), P(x_{S_2}), \ldots, P(x_{S_m})$ where $x_{S_i}$ is the projection of $x$ to the coordinates in $S_i$. We prove that whenever $P$ is $t$-resilient (all non-zero Fourier coefficients of $(-1)^P$ are of degree $t$ or higher), then no algorithm, with $<n^\epsilon$ memory, can distinguish the output of $G$ from the uniform distribution on $\{0,1\}^m$ with a large inverse polynomial advantage, for stretch $m \le \left(\frac{n}{t}\right)^{\frac{(1-\epsilon)}{36}\cdot t}$ (barring some restrictions on $k$). The lower bound holds in the streaming model where at each time step $i$, $S_i\subseteq [n]$ is a randomly chosen (ordered) subset of size $k$ and the distinguisher sees either $P(x_{S_i})$ or a uniformly random bit along with $S_i$. Our proof builds on the recently developed machinery for proving time-space trade-offs (Raz 2016 and follow-ups) for search/learning problems.