cssi/SciSciGPT-SciSciNet huggingface dataset:
Strategic inefficiencies and federal redistribution during uncoordinated response to pandemic waves

Abstract Optimal policy during an epidemic calls for depressed economic activity to slow down the outbreak. Sometimes, these decisions are left to local authorities (e.g. states). This creates an externality, as the outbreak does not respect states’ boundaries. The externality directly exacerbates the outbreak. Indirectly, it creates a free-rider problem, because local policymakers pass the cost of fighting the outbreak on to other states. A standard system of distortionary taxes and lump-sum transfers can implement the optimal allocation, with higher tax rates required if states behave strategically. A strategic system of taxes and transfers, rewarding states which depress their economies more than average, improves the outcomes by creating a race-to-the-bottom type of response. In a symmetric equilibrium, the optimal tax rate is lower if states behave strategically.
Parallel Rap1>RalGEF>Ral and Ras signals sculpt the C. elegans nervous system.

Ras is the most commonly mutated oncogene in humans and uses three oncogenic effectors: Raf, PI3K, and RalGEF activation of Ral. Understanding the importance of RalGEF>Ral signaling in cancer is hampered by the paucity of knowledge about their function in animal development, particularly in cell movements. We found that mutations that disrupt function of RalGEF or Ral enhance migration phenotypes of mutants for genes with established roles in cell migration. We used as a model the migration of the canal associated neurons (CANs), and validated our results in HSN cell migration, neurite guidance, and general animal locomotion. These functions of RalGEF and Ral are specific to their control of Ral signaling output rather than other published functions of these proteins. In this capacity Ral functions cell autonomously as a permissive developmental signal. In contrast, we observed Ras, the canonical activator of RalGEF>Ral signaling in cancer, to function as an instructive signal. Furthermore, we unexpectedly identified a function for the close Ras relative, Rap1, consistent with activation of RalGEF>Ral. These studies define functions of RalGEF>Ral, Rap1 and Ras signaling in morphogenetic processes that fashion the nervous system. We have also defined a model for studying how small GTPases partner with downstream effectors. Taken together, this analysis defines novel molecules and relationships in signaling networks that control cell movements during development of the nervous system.
A Survey of Singular Value Decomposition Methods for Distributed Tall/Skinny Data

The Singular Value Decomposition (SVD) is one of the most important matrix factorizations, enjoying a wide variety of applications across numerous application domains. In statistics and data analysis, the common applications of SVD inclue Principal Components Analysis (PCA) and regression. Usually these applications arise on data that has far more rows than columns, so-called "tall/skinny" matrices. In the big data analytics context, this may take the form of hundreds of millions to billions of rows with only a few hundred columns. There is a need, therefore, for fast, accurate, and scalable tall/skinny SVD implementations which can fully utilize modern computing resources. To that end, we present a survey of three different algorithms for computing the SVD for these kinds of tall/skinny data layouts using MPI for communication. We contextualize these with common big data analytics techniques. Finally, we present both CPU and GPU timing results from the Summit supercomputer, and discuss possible alternative approaches.
Designing Environments Conducive to Interpretable Robot Behavior

Designing robots capable of generating interpretable behavior is essential for effective human-robot collaboration. This requires robots to be able to generate behavior that aligns with human expectations but exhibiting such behavior in arbitrary environments could be quite expensive for robots, and in some cases, the robot may not even be able to exhibit expected behavior. However, in structured environments (like warehouses, restaurants, etc.), it may be possible to design the environment so as to boost the interpretability of a robot’s behavior or to shape the human’s expectations of the robot’s behavior. In this paper, we investigate the opportunities and limitations of environment design as a tool to promote a particular type of interpretable behavior - known in the literature as explicable behavior. We formulate a novel environment design framework that considers design over multiple tasks and over a time horizon. In addition, we explore the longitudinal effect of explicable behavior and the trade-off that arises between the cost of design and the cost of generating explicable behavior over an extended time horizon.
Corporate governance and corporate agility

Abstract This paper suggests the topic of corporate agility as a fruitful area for corporate governance research. Corporate agility, which refers to a firms ability to adapt to changes in its environment, is likely to be a critical, yet understudied, determinant of firm performance and survival. This paper proposes an inverse relation between the centralization of decision-making in firms and agility. It also proposes that certain governance structures often viewed as increasing agency costs, such as insider-controlled boards and dual class stock, may actually increase agility and thereby improve firm performance and the chances of survival. The paper concludes with a discussion of specific ways in which agility might be incorporated into the corporate governance literature.
Bank Network Analysis in the ECCU

This paper applies network analysis to assess the extent of systemic vulnerabilities in the ECCU banking system. It includes two sets of illustrative stress tests. First, solvency and liquidity shocks to each individual bank and the impact on other banks in the network through their biltareal net asset exposures. Second, country and region-wide tail shocks to GDP affecting capital and liquidity of all banks in the shocked jurisdictions, followed by the rippling effects through the regional network. The results identify systemic institutions that merit hightened attention by the regulator, as determined by the degree of connectivity with the rest of the system, and the extent to which they are vulnerable to the failure of other banks.
Positive reinforcement is just the beginning: Associative learning principles for energy efficiency and climate sustainability

Abstract A major cause of global climate change, human behavior has long been recognized as an essential part of the solution as well. Behavior change methods in turn rely in part on associative learning principles. Some learning principles, such as positive reinforcement and delay discounting, are already integrated into energy research and interventions. However, others remain underutilized. In this paper, we review selected learning principles, suggesting how they can enhance both our understanding of the behavioral challenges and our effectiveness in addressing them. We seek to interest and involve researchers and practitioners in a variety of energy and sustainability specializations.
Children and young adults hospitalized for severe COVID-19 exhibit thrombotic coagulopathy.

We report the clinical and laboratory coagulation characteristics of 27 pediatric and young adult patients (2 months to 21 years) treated for symptomatic COVID-19 at a children's hospital in the Bronx, New York, between March 1 and May 31, 2020. D-Dimer was > 0.5 µg/mL (upper limit of normal) in 25 (93%) patients at admission; 11 (41%) developed peak D-dimer > 5 µg/mL during admission. Seven (26%) patients developed venous thromboembolism: three with deep vein thrombosis and four with pulmonary embolism. Requirement of increased ventilatory support was a risk factor for thrombosis (P = 0.006). Three of eight (38%) patients on prophylactic anticoagulation developed thrombosis; however, no patients developed VTE on low-molecular-weight heparin prophylaxis titrated to anti-Xa level. Manifestation of COVID-19 disease was severe or critical in 16 (59%) patients. Four (15%) patients died of COVID-19 complications: all had comorbidities. Elevated D-dimer and increased VTE rate were observed in this young cohort, particularly in those with severe respiratory complications, suggesting thrombotic coagulopathy. More data are needed to guide thromboprophylaxis in this age group.
An activity-based fluorescent sensor for the detection of the phenol sulfotransferase SULT1A1 in living cells

Human phenol sulfotransferases mediate the transfer of a sulfuryl moiety from the activated sulfate donor PAPS to hydroxy-containing substrates, altering substrate solubility and charge to affect phase II metabolism and cell signaling. Here, we present the development, computational modeling, in vitro enzymology, and biological application of STS-3, an activity-based fluorescent sensor for the SULT1A1 isoform.
A survey of inverse reinforcement learning: Challenges, methods and progress

Abstract Inverse reinforcement learning ( IRL ) is the problem of inferring the reward function of an agent, given its policy or observed behavior. Analogous to RL , IRL is perceived both as a problem and as a class of methods. By categorically surveying the extant literature in IRL , this article serves as a comprehensive reference for researchers and practitioners of machine learning as well as those new to it to understand the challenges of IRL and select the approaches best suited for the problem on hand. The survey formally introduces the IRL problem along with its central challenges such as the difficulty in performing accurate inference and its generalizability, its sensitivity to prior knowledge, and the disproportionate growth in solution complexity with problem size. The article surveys a vast collection of foundational methods grouped together by the commonality of their objectives, and elaborates how these methods mitigate the challenges. We further discuss extensions to the traditional IRL methods for handling imperfect perception, an incomplete model, learning multiple reward functions and nonlinear reward functions. The article concludes the survey with a discussion of some broad advances in the research area and currently open research questions.
A physically-based small-signal circuit model for heterostructure acoustic charge transport devices

The small-signal operation of heterostructure acoustic charge transport (HACT) devices is related to the physical geometries, material parameters, and operating conditions of the device. A model of charge injection based on subthreshold current in GaAs FETs is developed. Good agreement is obtained between measured and predicted current-voltage curves. The model is then applied to a linear model developed previously by the authors to study gain and noise figure (NF), implemented with the Libra microwave analysis program. The predicted gain is for a 160-tap HACT device less than 1 dB from the measured data (above -30 dB insertion loss). The predicted midband noise figure (NF) is within 0.5 dB of the measured NF. >
An Analytics-Driven Approach for Optimal Individualized Diabetes Screening

Type 2 diabetes is a chronic disease that affects millions of Americans and puts a significant burden on the healthcare system. The medical community sees screening patients to identify and treat prediabetes and diabetes early as an important goal; however, universal population screening is operationally not feasible, and screening policies need to take characteristics of the patient population into account. For instance, the screening policy for a population in an affluent neighborhood may differ from that of a safety‐net hospital. The problem of optimal diabetes screening—whom to screen and when to screen—is clearly important, and small improvements could have an enormous impact. However, the problem is typically only discussed from a practical viewpoint in the medical literature; a thorough theoretical framework from an operational viewpoint is largely missing. In this study, we propose an approach that builds on multiple methods—partially observable Markov decision process (POMDP), hidden Markov model (HMM), and predictive risk modeling (PRM). It uses available clinical information, in the form of electronic health records (EHRs), on specific patient populations to derive an optimal policy, which is used to generate screening decisions, individualized for each patient. The POMDP model, used for determining optimal decisions, lies at the core of our approach. We use HMM to estimate the cohort‐specific progression of diabetes (i.e., transition probability matrix) and the emission matrix. We use PRM to generate observations—in the form of individualized risk scores—for the POMDP. Both HMM and PRM are learned from EHR data. Our approach is unique because (i) it introduces a novel way of incorporating predictive modeling into a formal decision framework to derive an optimal screening policy; and (ii) it is based on real clinical data. We fit our model using data on a cohort of more than 60,000 patients over 5 years from a large safety‐net health system and then demonstrate the model's utility by conducting a simulation study. The results indicate that our proposed screening policy outperforms existing guidelines widely used in clinical practice. Our estimates suggest that implementing our policy for the studied cohort would add one quality‐adjusted life year for every patient, and at a cost that is 35% lower, compared with existing guidelines. Our proposed framework is generalizable to other chronic diseases, such as cancer and HIV.
Distributed resource allocation with binary decisions via Newton-like Neural Network dynamics

Abstract This paper aims to solve a distributed resource allocation problem with binary local constraints. The problem is formulated as a binary program with a cost function defined by the summation of agent costs plus a global mismatch/penalty term. We propose a modification of the Hopfield Neural Network (HNN) dynamics in order to solve this problem while incorporating a novel Newton-like weighting factor. This addition lends itself to fast avoidance of saddle points, which the gradient-like HNN is susceptible to. Turning to a multi-agent setting, we reformulate the problem and develop a distributed implementation of the Newton-like dynamics. We show that if a local solution to the distributed reformulation is obtained, it is also a local solution to the centralized problem. A main contribution of this work is to show that the probability of converging to a saddle point of an appropriately defined energy function in both the centralized and distributed settings is zero under light assumptions. Finally, we enlarge our algorithm with an annealing technique which gradually learns a feasible binary solution. Simulation results demonstrate that the proposed methods are competitive with centralized greedy and SDP relaxation approaches in terms of solution quality, while the main advantage of our approach is a significant improvement in runtime over the SDP relaxation method and the distributed quality of implementation.
Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19.

Standard transcriptomic analyses alone have limited power in capturing the molecular mechanisms driving disease pathophysiology and outcomes. To overcome this, unsupervised network analyses are used to identify clusters of genes that can be associated with distinct molecular mechanisms and outcomes for a disease. In this study, we developed an integrated network analysis framework that integrates transcriptional signatures from multiple model systems with protein-protein interaction data to find gene modules. Through a meta-analysis of different enriched features from these gene modules, we extract communities of highly interconnected features. These clusters of higher-order features, working as a multifeatured machine, enable collective assessment of their contribution for disease or phenotype characterization. We show the utility of this workflow using transcriptomics data from three different models of SARS-CoV-2 infection and identify several pathways and biological processes that could enable in understanding or hypothesizing molecular signatures inducing pathophysiological changes, risks, or sequelae of COVID-19.
Convergence of stochastic-extended Lagrangian molecular dynamics method for polarizable force field simulation

Abstract Extended Lagrangian molecular dynamics (XLMD) is a general method for performing molecular dynamics simulations using quantum and classical many-body potentials. Recently several new XLMD schemes have been proposed and tested on several classes of many-body polarization models such as induced dipoles or Drude charges, by creating an auxiliary set of these same degrees of freedom that are reversibly integrated through time. This gives rise to a singularly perturbed Hamiltonian system that provides a good approximation to the time evolution of the real mutual polarization field. To further improve upon the accuracy of the XLMD dynamics in the context of classical polarizable force field simulation, and to potentially extend it to other many-body potentials, we introduce a stochastic modification which leads to a set of singularly perturbed Langevin equations with degenerate noise. We prove that the resulting Stochastic-XLMD converges to the accurate dynamics, and the convergence rate is both sharp and is independent of the accuracy of the initial polarization field. We carefully study the scaling of the damping factor and numerical noise for efficient numerical simulation for Stochastic-XLMD, and we demonstrate the effectiveness of the method for water molecules described by a polarizable force field.
Discovery of electrochemically induced grain boundary transitions.

Electric fields and currents, which are used in innovative materials processing and electrochemical energy conversion, can often alter microstructures in unexpected ways. However, little is known about the underlying mechanisms. Using ZnO-Bi2O3 as a model system, this study uncovers how an applied electric current can change the microstructural evolution through an electrochemically induced grain boundary transition. By combining aberration-corrected electron microscopy, photoluminescence spectroscopy, first-principles calculations, a generalizable thermodynamic model, and ab initio molecular dynamics, this study reveals that electrochemical reduction can cause a grain boundary disorder-to-order transition to markedly increase grain boundary diffusivities and mobilities. Consequently, abruptly enhanced or abnormal grain growth takes place. These findings advance our fundamental knowledge of grain boundary complexion (phase-like) transitions and electric field effects on microstructural stability and evolution, with broad scientific and technological impacts. A new method to tailor the grain boundary structures and properties, as well as the microstructures, electrochemically can also be envisioned. Electric fields and currents can alter microstructures of materials in unexpected ways. Here the authors report how electrochemical reduction can cause a grain boundary disorder-to-order transition and show the electric field effects on microstructural stability and evolution.
Simultaneous cubic and quadratic diagonal equations in 12 prime variables

The system of equations $$\begin{aligned}&u_1p_1^2 + \cdots + u_sp_s^2 = 0,\\&v_1p_1^3 + \cdots + v_sp_s^3 = 0 \end{aligned}$$ has prime solutions $$(p_1, \ldots , p_s)$$ for $$s \ge 12$$ , assuming that the system has solutions modulo each prime p. This is proved via the Hardy–Littlewood circle method, building on Wooley’s work on the corresponding system over the integers and recent results on Vinogradov’s mean value theorem. Additionally, a set of sufficient conditions for local solvability is given: If both equations are solvable modulo 2, the quadratic equation is solvable modulo 3, and for each prime p at least 7 of each of the $$u_i$$ , $$v_i$$ are not zero modulo p, then the system has solutions modulo each prime p.
Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis

In the analysis of large-scale network data, a fundamental operation is the comparison of observed phenomena to the predictions provided by null models: when we find an interesting structure in a family of real networks, it is important to ask whether this structure is also likely to arise in random networks with similar characteristics to the real ones. A long-standing challenge in network analysis has been the relative scarcity of reasonable null models for networks; arguably the most common such model has been the configuration model, which starts with a graph G and produces a random graph with the same node degrees as G. This leads to a very weak form of null model, since fixing the node degrees does not preserve many of the crucial properties of the network, including the structure of its subgraphs. Guided by this challenge, we establish a new family of network null models that operate on the k-core decomposition. For a graph G, the k-core is its maximal subgraph of minimum degree k; and the core number of a node v in G is the largest k such that v belongs to the k-core of G. We provide the first efficient sampling algorithm to solve the following basic combinatorial problem: given a graph G, produce a random graph sampled nearly uniformly from among all graphs with the same sequence of core numbers as G. This opens the opportunity to compare observed networks G with random graphs that exhibit the same core numbers, a comparison that preserves aspects of the structure of G that are not captured by more local measures like the degree sequence. We illustrate the power of this core-based null model on some fundamental tasks in network analysis, including the enumeration of networks motifs.
More Rational or More Emotional Than Others? Lay Beliefs About Decision-Making Strategies

Research demonstrates that people utilize both reasoning and feeling in decision making and that both strategies can be advantageous. However, little is known about how people perceive their decision making relative to others. Despite research findings and popular appeals supporting the use of affective decision processes, across a series of studies, we find that individuals believe they rely more on reasoning, and less on feelings, than others. These effects are driven by the motivation to self-enhance where, in most contexts, individuals believe the use of reasoning is superior, and self-enhancing, compared to the use of feelings. Consistent with this mechanism, beliefs that one’s decisions are more rational than others’ are: (a) stronger for those who exhibit greater beliefs in the superiority of reasoning (vs. feeling), (b) attenuated when the decision context precludes motivational thinking about the self or the self is affirmed, and (c) reversed when the use of feelings is perceived as more self-enhancing. We demonstrate downstream consequences (e.g., decision delegation), rule out alternative explanations, and discuss practical implications of these lay beliefs.
Coherence and decoherence in the Harper-Hofstadter model

This paper presents a tube-geometry Harper-Hofstadter model, where coherence is stabilized by quasi-disorder in interferometric experiments akin to Aharonov-Bohm interferometers.
A Survey of Optimization by Building and Using Probabilistic Models

This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the exploration of the search space. It settles the algorithms in the field of genetic and evolutionary computation where they have been originated, and classifies them into a few classes according to the complexity of models they use. Algorithms within each class are briefly described and their strengths and weaknesses are discussed.
Arithmetic of weighted Catalan numbers

Abstract In this paper, we study arithmetic properties of weighted Catalan numbers. Previously, Postnikov and Sagan found conditions under which the 2-adic valuations of the weighted Catalan numbers are equal to the 2-adic valuations of the Catalan numbers. We obtain the same result under weaker conditions by considering a map from a class of functions to 2-adic integers. These methods are also extended to q-weighted Catalan numbers, strengthening a previous result by Konvalinka. Finally, we prove some results on the periodicity of weighted Catalan numbers modulo an integer and apply them to the specific case of the number of combinatorial types of Morse links. Many open questions are mentioned.
Characterizing People's Daily Activity Patterns in the Urban Environment: A Mobility Network Approach with Geographic Context-Aware Twitter Data

People’s daily activities in the urban environment are complex and vary by individual. Existing studies using mobile phone data revealed distinct and recurrent transitional activity patterns, known...
Existence of embedded minimal disks

We give a generalization of Meeks–Yau’s celebrated embeddedness result for the solutions of the Plateau problem for extreme curves.
Model-based reinforcement learning for approximate optimal control with temporal logic specifications

In this paper we study the problem of synthesizing optimal control policies for uncertain continuous-time nonlinear systems from syntactically co-safe linear temporal logic (scLTL) formulas. We formulate this problem as a sequence of reach-avoid optimal control sub-problems. We show that the resulting hybrid optimal control policy guarantees the satisfaction of a given scLTL formula by constructing a barrier certificate. Since solving each optimal control problem may be computationally intractable, we take a learning-based approach to approximately solve this sequence of optimal control problems online without requiring full knowledge of the system dynamics. Using Lyapunov-based tools, we develop sufficient conditions under which our approximate solution maintains correctness. Finally, we demonstrate the efficacy of the developed method with a numerical example.
Areas of totally geodesic surfaces of hyperbolic $3$-orbifolds

The geodesic length spectrum of a complete, finite volume, hyperbolic 3-orbifold M is a fundamental invariant of the topology of M via Mostow-Prasad Rigidity. Motivated by this, the second author and Reid defined a two-dimensional analogue of the geodesic length spectrum given by the multiset of isometry types of totally geodesic, immersed, finite-area surfaces of M called the geometric genus spectrum. They showed that if $M$ is arithmetic and contains a totally geodesic surface, then the geometric genus spectrum of M determines its commensurability class. In this paper we define a coarser invariant called the totally geodesic area set given by the set of areas of surfaces in the geometric genus spectrum. We prove a number of results quantifying the extent to which non-commensurable arithmetic hyperbolic 3-orbifolds can have arbitrarily large overlaps in their totally geodesic area sets.
Compensation, Commodification, and Disablement: How Law Has Dehumanized Laboring Bodies and Excluded Nonlaboring Humans

This essay reviews Nate Holdren's "Injury Impoverished: Workplace Accidents, Capitalism, and Law in the Progressive Era" (Cambridge University Press, 2020), which explores the changes in legal imagination that accompanied the rise of workers' compensation programs. The essay foregrounds Holdren’s insights about disability. "Injury Impoverished" illustrates the meaning and material consequences that the law has given to work-related impairments over time and documents the naturalization of disability-based exclusion from the formal labor market. In the present day, with so many social benefits tied to employment, this exclusion is particularly troubling.
Monotonicity preservation properties of kernel regression estimators

Abstract Three common classes of kernel regression estimators are considered: the Nadaraya–Watson (NW) estimator, the Priestley–Chao (PC) estimator, and the Gasser–Muller (GM) estimator. It is shown that (i) the GM estimator has a certain monotonicity preservation property for any kernel K , (ii) the NW estimator has this property if and only the kernel K is log concave, and (iii) the PC estimator does not have this property for any kernel K . Other related properties of these regression estimators are discussed.
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval

Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow inference speed due to enormous computational cost mainly from cross-modal attention in Transformer architecture. When applied to real-life applications, such latency and computation demand severely deter the practical use of pre-trained models. In this paper, we study Image-text retrieval (ITR), the most mature scenario of V+L application, which has been widely studied even prior to the emergence of recent pre-trained models. We propose a simple yet highly effective approach, LightningDOT that accelerates the inference time of ITR by thousands of times, without sacrificing accuracy. LightningDOT removes the time-consuming cross-modal attention by extracting pre-cached feature indexes offline, and employing instant dot-product matching online, which significantly speeds up retrieval process. In fact, our LightningDOT achieves superior performance across mainstream ITR benchmarks such as Flickr30k and COCO datasets, outperforming existing pre-trained models that consume 1000 times magnitude of computational hours using the same features.
Wrapped Fivebranes Redux

The near-horizon region of Neveu-Schwarz fivebranes provides interesting examples of gauge/gravity duality. We revisit the structure of wrapped and/or intersecting fivebranes using the tools of null-gauged WZW models in worldsheet string theory, revealing the effective geometry of the fivebrane throat in a variety of examples. Variant gaugings yield linear dilaton fivebrane throats with AdS3 caps, providing a wealth of information about the near-BPS structure of the corresponding spacetime CFT duals.
SPARRING: A Deliberate Practice Pedagogy for Business Education

Many higher education institutions are calling for more experiential learning to improve learning outcomes and better prepare students for the workplace. The need for enhanced experiential education in business schools, and in the decision sciences, is especially important. However, there is a lack of common practical understanding of how to implement this type of learning in business education. We address this gap using deliberate practice as a conceptual framework. Deliberate practice is a proven training technique for developing high levels of expertise. In spite of growing evidence of its relevance to higher education, two obstacles inhibit its widespread adoption by business educators. First, due to its origins as a one-on-one coaching technique, deliberate practice is difficult to apply in traditional university classrooms. Second, because the curriculum must be designed from the start to implement deliberate practice principles, instructional design guidelines are needed to direct the efforts of faculty. To overcome these obstacles, we develop an eight-step instructional design model called SPARRING that incorporates all deliberate practice principles into one framework. We then discuss 13 pedagogical techniques that instructors can use in and outside of class to implement deliberate practice in business education. Using the SPARRING model, we show that these techniques can deliver an authentic and effective deliberate practice experience. We also discuss the application of the SPARRING model at the program and school levels. At a time of major disruptions in higher education, the SPARRING model offers a new pathway for traditional residential universities interested in teaching real-world skills valued by employers in a manner not easily replicated by artificial intelligence (AI).
FemtoSats for Exploring Permanently Shadowed Regions on the Moon

Rapid advancement in space exploration has been possible thanks to the accelerated miniaturization of electronics components on a spacecraft that reduces satellites' mass, volume, and cost. Yet, access to space remains a distant dream as there is growing complexity in what is required of satellites and increasing space traffic. Interplanetary exploration is even harder and has limited possibilities for low-cost missions. All of these factors make even CubeSats, the entry-level standard, too expensive for most, and therefore, a better way needs to be found. In this paper, we propose using FemtoSats, a low-mass, low-cost, disposable solution that exploits the latest advances in electronics and is relatively easy to integrate. FemtoSats are sub-100-gram spacecraft. The FemtoSat concept is based on launching a swarm where the main tasks are divided between the members of the swarm. This means that if one fails, the swarm can take its place and therefore substitute it without risking the whole mission. In this paper, we explore the utility of FemtoSats to explore and map a Lunar PSR. This concept was recognized as a finalist for the NASA BIG Competition in 2020. This is an example of a high-risk, high-reward mission where losing one FemtoSat does not mean the mission is in danger as it happens with regular satellite missions. The work developed here consists of making a generic conceptual design for FemtoSats and apply it to the exploration of the PSR's. The work results in the development of laboratory prototypes and simulations. We are exploring an innovative approach by implementing LASER power beaming to power and keep-alive the FemtoSats in a cold-dark region such as the PSRs. Within the PSR, we want to prove that batteries can be charged through a laser beam. Currently, the FemtoSat design for the PSR is being tested, and the first results look promising. Advances made on this project will take FemtoSat technology one major step closer towards flight readiness and operations and eventually towards applications on ambitious next-generation science exploration missions.
The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries

This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.
RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems

For task-oriented dialog systems to be maximally useful, it must be able to process conversations in a way that is (1) generalizable with a small number of training examples for new task domains, and (2) robust to user input in various styles, modalities, or domains. In pursuit of these goals, we introduce the RADDLE benchmark, a collection of corpora and tools for evaluating the performance of models across a diverse set of domains. By including tasks with limited training data, RADDLE is designed to favor and encourage models with a strong generalization ability. RADDLE also includes a diagnostic checklist that facilitates detailed robustness analysis in aspects such as language variations, speech errors, unseen entities, and out-of-domain utterances. We evaluate recent state-of-the-art systems based on pre-training and fine-tuning, and find that grounded pre-training on heterogeneous dialog corpora performs better than training a separate model per domain. Adversarial training is also proposed to improve model robustness against noisy inputs. Overall, existing models are less than satisfactory in robustness evaluation, which suggests opportunities for future improvement.
Using ethnographic methods to classify the human experience in medicine: a case study of the presence ontology.

Objective Although social and environmental factors are central to provider-patient interactions, the data that reflect these factors can be incomplete, vague, and subjective. We sought to create a conceptual framework to describe and classify data about presence, the domain of interpersonal connection in medicine. Methods Our top-down approach for ontology development based on the concept of "relationality" included the following: 1) a broad survey of the social sciences literature and a systematic literature review of >20 000 articles around interpersonal connection in medicine, 2) relational ethnography of clinical encounters (n = 5 pilot, 27 full), and 3) interviews about relational work with 40 medical and nonmedical professionals. We formalized the model using the Web Ontology Language in the Protege ontology editor. We iteratively evaluated and refined the Presence Ontology through manual expert review and automated annotation of literature. Results and discussion The Presence Ontology facilitates the naming and classification of concepts that would otherwise be vague. Our model categorizes contributors to healthcare encounters and factors such as communication, emotions, tools, and environment. Ontology evaluation indicated that cognitive models (both patients' explanatory models and providers' caregiving approaches) influenced encounters and were subsequently incorporated. We show how ethnographic methods based in relationality can aid the representation of experiential concepts (eg, empathy, trust). Our ontology could support investigative methods to improve healthcare processes for both patients and healthcare providers, including annotation of videotaped encounters, development of clinical instruments to measure presence, or implementation of electronic health record-based reminders for providers. Conclusion The Presence Ontology provides a model for using ethnographic approaches to classify interpersonal data.
Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM

Epigenetic changes, such as aberrant DNA methylation, contribute to cancer clonal expansion and disease progression. However, identifying subpopulation-level changes in a heterogeneous sample remains challenging. Thus, we have developed a computational approach, DXM, to deconvolve the methylation profiles of major allelic subpopulations from the bisulfite sequencing data of a heterogeneous sample. DXM does not require prior knowledge of the number of subpopulations or types of cells to expect. We benchmark DXM's performance and demonstrate improvement over existing methods. We further experimentally validate DXM predicted allelic subpopulation-methylation profiles in four Diffuse Large B-Cell Lymphomas (DLBCLs). Lastly, as proof-of-concept, we apply DXM to a cohort of 31 DLBCLs and relate allelic subpopulation methylation profiles to relapse. We thus demonstrate that DXM can robustly find allelic subpopulation methylation profiles that may contribute to disease progression using bisulfite sequencing data of any heterogeneous sample.
Cumulative energy effect in the shock-discontinuity interaction under real-gas conditions

Abstract A recently proposed technique for improving ignition timing is investigated for real-gas effects. A correction to the model describing the cumulative energy effect in the presence of the shock-discontinuity interaction was done by re-considering the Rankine–Hugoniot relations and the shock refraction equations. Non-dissociating gas in thermal equilibrium state, with excited translational, rotational, vibrational, and electronic degrees of freedom is considered. When applied to N2 gas at T = 3000 K, 11% density increase and 17% temperature and 7% pressure jump decrease were found, compared to that in ideal gas. The derived relations are largely built on experimental data thus avoiding the complexity of careful accounting for all real gas effects and still offering satisfactory precision levels. The main result is that in real gases the effect in the interaction can be stronger than that in an ideal gas. The findings can be of interest in design of efficient combustors for hypersonic systems, detonation, space and craft design, shock-flame interaction, in astrophysics, and fusion research.
Commensal microbiota regulates skin barrier function and repair via signaling through the aryl hydrocarbon receptor

The epidermis forms a barrier that defends the body from desiccation and entry of harmful substances, while also sensing and integrating environmental signals. The tightly orchestrated cellular changes needed for the formation and maintenance of this epidermal barrier occur in the context of the skin microbiome. Using germ-free mice, we demonstrate the microbiota is necessary for proper differentiation and repair of the epidermal barrier. These effects are mediated by microbiota signaling through the aryl hydrocarbon receptor (AHR) in keratinocytes, a xenobiotic receptor also implicated in epidermal differentiation. Mice lacking keratinocyte AHR are more susceptible to barrier damage and infection, during steady-state and epicutaneous sensitization. Colonization with a defined consortium of human skin isolates restored barrier competence in an AHR-dependent manner. We reveal a fundamental mechanism whereby the microbiota regulates skin barrier formation and repair, which has far-reaching implications for the numerous skin disorders characterized by epidermal barrier dysfunction.
Effective mechanical properties of lattice material fabricated by material extrusion additive manufacturing

Abstract In this paper, a two-step homogenization method is proposed and implemented for evaluating effective mechanical properties of lattice structured material fabricated by the material extrusion additive manufacturing process. In order to consider the characteristics of the additive manufacturing process in estimation procedures, the levels of scale for homogenization are divided into three stages – the levels of layer deposition, structural element, and lattice structure. The method consists of two transformations among stages. In the first step, the transformation between layer deposition and structural element levels is proposed to find the geometrical and material effective properties of structural elements in the lattice structure. In the second step, the method to estimate effective mechanical properties of lattice material is presented, which uses a unit cell and is based on the discretized homogenization method for periodic structure. The method is implemented for cubic lattice structure and compared to experimental results for validation purposes.
Grothendieck Rings of Towers of Twisted Generalized Weyl Algebras

Twisted generalized Weyl algebras (TGWAs) A(R,σ,t) are defined over a base ring R by parameters σ and t, where σ is an n-tuple of automorphisms, and t is an n-tuple of elements in the center of R. We show that, for fixed R and σ, there is a natural algebra map $A(R,\sigma ,tt^{\prime })\to A(R,\sigma ,t)\otimes _{R} A(R,\sigma ,t^{\prime })$ . This gives a tensor product operation on modules, inducing a ring structure on the direct sum (over all t) of the Grothendieck groups of the categories of weight modules for A(R,σ,t). We give presentations of these Grothendieck rings for n = 1,2, when $R=\mathbb {C}[z]$ . As a consequence, for n = 1, any indecomposable module for a TGWA can be written as a tensor product of indecomposable modules over the usual Weyl algebra. In particular, any finite-dimensional simple module over $\mathfrak {sl}_{2}$ is a tensor product of two Weyl algebra modules.
High-fidelity state transfer between leaky quantum memories

Employing the scattering-Lindblad-Hamiltonian formalism description of quantum network theory, we model the general problem of quantum state transfer between two disparate quantum memory blocks in an open quantum system. We derive an analytical expression for the fidelity of quantum state transfer between the memory blocks under the action of a specific phase space trajectory for each of the relevant classical control fields. We find a set of trajectories that maximize the state transfer fidelity between asymmetric systems. We show that, for the example where the mechanical modes of two optomechanical oscillators act as the quantum memory blocks, their optical modes and a waveguide channel connecting them can be used to achieve a quantum state transfer fidelity of 96% with realistic parameters using our optimal control solution. The effects of the intrinsic losses and the asymmetries in the physical memory parameters are discussed quantitatively.
Chronic wireless neural population recordings with common marmosets

Marmosets are an increasingly important model system for neuroscience in part due to genetic tractability and enhanced cortical accessibility, due to a lissencephalic neocortex. However, many of the techniques generally employed to record neural activity in primates inhibit the expression of natural behaviors in marmosets precluding neurophysiological insights. To address this challenge, we have developed methods for recording neural population activity in unrestrained marmosets across multiple ethological behaviors, multiple brain states, and over multiple years. Notably, our flexible methodological design allows for replacing electrode arrays and removal of implants providing alternative experimental endpoints. We validate the method by recording sensorimotor cortical population activity in freely moving marmosets across their natural behavioral repertoire and during sleep.
Periodic behaviors and graceful transitions between them for bimodal systems: Example of a jumper on a trampoline

This paper sets up a simple model for the controlled jumps of a jumper on a trampoline. Such a model is a paradigm for the impulsive control of more general multi-mode multi-dimensional (M3D) systems. First, necessary and sufficient conditions for a motion to be periodic are set up and different types of periodic orbits, characterized by timing diagrams, are identified. Next, we characterize and identify graceful transitions between such orbits, using our raccordation framework. This generalizes notions of persistency of behavior to the class of impulsively controlled multi-mode multi-dimensional systems.
Who Bears the Greatest Responsibility for International Crimes

This short symposium contribution discusses the concept of greatest responsibility for international crimes in the context of Charles Jalloh's excellent book on the legal legacy of the Special Court for Sierra Leone.
Breaking the Symmetry in Queues with Delayed Information

Giving customers queue length information about a service system has the potential to influence the decision of a customer to join a queue. Thus, it is imperative for managers of queueing systems t...