cssi/SciSciGPT-SciSciNet huggingface dataset:
Sensory disruption and sensory inequities in the Anthropocene

Anthropogenic disruptions to animal sensory ecology are as old as our species. But what about the effect on human sensory ecology? Human sensory dysfunction is increasing globally at great economic and health costs (mental, physical, and social). Contemporary sensory problems are directly tied to human behavioral changes and activity as well as anthropogenic pollution. The evolutionary sensory ecology and anthropogenic disruptions to three human senses (vision, audition, olfaction) are examined along with the economic and health costs of functionally reduced senses and demographic risk factors contributing to impairment. The primary goals of the paper are (a) to sew an evolutionary and ecological thread through clinical narratives on sensory dysfunction that highlights the impact of the built environment on the senses, and (b) to highlight structural, demographic, and environmental injustices that create sensory inequities in risk and that promote health disparities.
Secure Route Planning Using Dynamic Games with Stopping States

This paper studies a motion planning problem over a roadmap in which a vehicle aims to travel from a start to a destination in presence of an attacker who can launch a cyber-attack on the vehicle over any one edge of the roadmap. The vehicle (defender) has the capability to switch on/off a countermeasure that can detect and permanently disable the attack if it occurs concurrently. We first model the problem of traversing an edge as a zero-sum dynamic game with a stopping state, termed as an edge-game played between an attacker and defender. We characterize Nash equilibria of the edge-game and provide closed form expressions for the case of two actions per player. We further provide an analytic and approximate expression on the value of an edge-game and characterize conditions under which it grows sub-linearly with the length of the edge. We study the sensitivity of Nash equilibrium to the (i) cost of using the countermeasure, (ii) cost of motion and (iii) benefit of disabling the attack. The solution of the edge-game is used to formulate and solve the secure route planning problem. We design an efficient heuristic by converting the problem to a shortest path problem using the edge cost as the solution of corresponding edge-games. We illustrate our findings through several insightful simulations.
In vivo generation of BK and JC polyomavirus defective viral genomes in human urine samples associated with higher viral loads.

Defective viral genomes (DVGs) are parasitic viral sequences containing point mutations, deletions, or duplications that might interfere with replication. DVGs are often associated with viral passage at high multiplicities of infection in culture systems but have been increasingly reported in clinical specimens. To date however, only RNA viruses have been shown to contain DVGs in clinical specimens. Here, using direct deep sequencing with multiple library preparation strategies and confirmatory digital droplet PCR (ddPCR) of urine samples taken from immunosuppressed individuals, we show that clinical BK polyomavirus (BKPyV) and JC polyomavirus (JCPyV) strains contain widespread genomic rearrangements across multiple loci that likely interfere with viral replication. BKPyV DVGs were derived from BKPyV genotypes Ia, Ib-1, and Ic. The presence of DVGs was associated with specimens containing higher viral loads but never reached clonality, consistent with a model of parasitized replication. These DVGs persisted during clinical infection as evidenced in two separate pairs of samples containing BK virus collected from the same individual up to 302 days apart. In a separate individual, we observed the generation of DVGs after a 57.5-fold increase in viral load. In summary, by extending the presence of DVGs in clinical specimens to DNA viruses, we demonstrate the ubiquity of DVGs in clinical virology.IMPORTANCE Defective viral genomes (DVGs) can have a significant impact on the production of infectious virus particles. DVGs have only been identified in cultured viruses passaged at high multiplicities of infection and RNA viruses collected from clinical specimens; no DNA virus in the wild has been shown to contain DVGs. Here, we identified BK and JC polyomavirus DVGs in clinical urine specimens and demonstrated that these DVGs are more frequently identified in samples with higher viral loads. The strains containing DVGs had rearrangements throughout their genomes, with the majority affecting genes required for viral replication. Longitudinal analysis showed that these DVGs can persist during an infection but do not reach clonality within the chronically infected host. Our identification of polyomavirus DVGs suggests that these parasitic sequences exist across the many classes of viruses capable of causing human disease.
On COVID-19-safety ranking of seats in intercontinental commercial aircrafts: A preliminary multiphysics computational perspective.

The evolution of coronavirus disease (COVID-19) into a pandemic has severely hampered the usage of public transit systems. In a post-COVID-19 world, we may see an increased reliance on autonomous cars and personal rapid transit (PRT) systems, with inherent physical distancing, over buses, trains and aircraft for intracity, intercity, and interstate travel. However, air travel would continue to be the dominant mode of intercontinental transportation for humans. In this study, we perform a comprehensive computational analysis, using ANSYS Fluent, of typical intercontinental aircraft ventilation systems to determine the seat where environmental factors are most conducive to human comfort with regards to air quality, protection from orally or nasally released pollutants such as CO2 and coronavirus, and thermal comfort levels. Air velocity, temperature, and air pollutant concentration emitted from the nose/mouth of fellow travelers are considered for both Boeing and Airbus planes. In each plane, first class, business class, and economy class sections were analyzed. We present conclusions as to which is the optimum seat in each section of each plane and provide the data of the environmental conditions to support our inferences. The findings may be used by the general public to decide which seat to occupy for their next intercontinental flight. Alternatively, the commercial airliners can use such a model to plan the occupancy of the aircraft on long-duration intercontinental flights (viz., Airbus A380 and Boeing B747).
A study of DC electrical breakdown in liquid helium through analysis of the empirical breakdown field distributions

We report results from a study on electrical breakdown in liquid helium using near-uniform-field stainless steel electrodes with a stressed area of ∼ 0.7 cm 2. The distribution of the breakdown field is obtained for temperatures between 1.7 K and 4.0 K, pressures between the saturated vapor pressure and 626 Torr, and with electrodes of different surface polishes. A data-based approach for determining the electrode-surface-area scaling of the breakdown field is presented. The dependence of the breakdown probability on the field strength as extracted from the breakdown field distribution data is used to show that breakdown is a surface phenomenon closely correlated with Fowler–Nordheim field emission from asperities on the cathode. We show that the results from this analysis provide an explanation for the supposed electrode gap-size effect and also allow for a determination of the breakdown-field distribution for arbitrary shaped electrodes. Most importantly, the analysis method presented in this work can be extended to other noble liquids to explore the dependencies for electrical breakdown in those media.
Changes in Cell Size and Shape during 50,000 Generations of Experimental Evolution with Escherichia coli

Bacteria adopt a wide variety of sizes and shapes, with many species exhibiting stereotypical morphologies. How morphology changes, and over what timescales, is less clear. Previous work examining cell morphology in an experiment with Escherichia coli showed that populations evolved larger cells and, in some cases, cells that were less rod-like. That experiment has now run for over two more decades. Meanwhile, genome sequence data are available for these populations, and new computational methods enable high-throughput microscopic analyses. In this study, we measured stationary-phase cell volumes for the ancestor and 12 populations at 2,000, 10,000, and 50,000 generations, including measurements during exponential growth at the last time point. We measured the distribution of cell volumes for each sample using a Coulter counter and microscopy, the latter of which also provided data on cell shape. Our data confirm the trend toward larger cells while also revealing substantial variation in size and shape across replicate populations. Most populations first evolved wider cells but later reverted to the ancestral length-to-width ratio. All but one population evolved mutations in rod shape maintenance genes. We also observed many ghost-like cells in the only population that evolved the novel ability to grow on citrate, supporting the hypothesis that this lineage struggles with maintaining balanced growth. Lastly, we show that cell size and fitness remain correlated across 50,000 generations. Our results suggest that larger cells are beneficial in the experimental environment, while the reversion toward ancestral length-to-width ratios suggests partial compensation for the less favorable surface area-to-volume ratios of the evolved cells.IMPORTANCE Bacteria exhibit great morphological diversity, yet we have only a limited understanding of how their cell sizes and shapes evolve and of how these features affect organismal fitness. This knowledge gap reflects, in part, the paucity of the fossil record for bacteria. In this study, we revived and analyzed samples extending over 50,000 generations from 12 populations of experimentally evolving Escherichia coli to investigate the relation between cell size, shape, and fitness. Using this "frozen fossil record," we show that all 12 populations evolved larger cells concomitant with increased fitness, with substantial heterogeneity in cell size and shape across the replicate lines. Our work demonstrates that cell morphology can readily evolve and diversify, even among populations living in identical environments.
Spectral signatures of L-DOPA-induced dyskinesia depend on L-DOPA dose and are suppressed by ketamine.

Abstract L-DOPA-induced dyskinesias (LID) are debilitating motor symptoms of dopamine-replacement therapy for Parkinson's disease (PD) that emerge after years of L-DOPA treatment. While there is an abundance of research into the cellular and synaptic origins of LID, less is known about how LID impacts systems-level circuits and neural synchrony, how synchrony is affected by the dose and duration of L-DOPA exposure, or how potential novel treatments for LID, such as sub-anesthetic ketamine, alter this activity. Sub-anesthetic ketamine treatments have recently been shown to reduce LID, and ketamine is known to affect neural synchrony. To investigate these questions, we measured movement and local-field potential (LFP) activity from the motor cortex (M1) and the striatum of preclinical rodent models of PD and LID. In the first experiment, we investigated the effect of the LID priming procedures and L-DOPA dose on neural signatures of LID. Two common priming procedures were compared: a high-dose procedure that exposed unilateral 6-hydroxydopamine-lesioned rats to 12 mg/kg L-DOPA for 7 days, and a low-dose procedure that exposed rats to 7 mg/kg L-DOPA for 21 days. Consistent with reports from other groups, 12 mg/kg L-DOPA triggered LID and 80-Hz oscillations; however, these 80-Hz oscillations were not observed after 7 mg/kg administration despite clear evidence of LID, indicating that 80-Hz oscillations are not an exclusive signature of LID. We also found that weeks-long low-dose priming resulted in the emergence of non-oscillatory broadband gamma activity (> 30 Hz) in the striatum and theta-to-high-gamma cross-frequency coupling (CFC) in M1. In a second set of experiments, we investigated how ketamine exposure affects spectral signatures of low-dose L-DOPA priming. During each neural recording session, ketamine was delivered through 5 injections (20 mg/kg, i.p.) administered every 2 h. We found that ketamine exposure suppressed striatal broadband gamma associated with LID but enhanced M1 broadband activity. We also found that M1 theta-to-high-gamma CFC associated with the LID on-state was suppressed by ketamine. These results suggest that ketamine's therapeutic effects are region specific. Our findings also have clinical implications, as we are the first to report novel oscillatory signatures of the common low-dose LID priming procedure that more closely models dopamine replacement therapy in individuals with PD. We also identify neural correlates of the anti-dyskinetic activity of sub-anesthetic ketamine treatment.
Identifying novel forms of reluctance in commercially sexually exploited adolescents

Abstract Background Research has largely overlooked expressions of reluctance in commercially sexually exploited adolescent (“CSEA”) victims. This is problematic because gaining information from known victims is of the utmost importance in order to better serve the needs of current and potential future victims. Objective The current study proposes a novel conceptualization of reluctance based on CSEA victims’ transcripts from police interviews and courtroom examinations. Participants and setting The study examined police interviews (n = 8 victims, 1558 utterances) and courtroom transcripts (n = 6 victims, 1961 utterances) conducted with female CSEA victims aged 15–17 years old (Mage = 16.29). The victims were associated with the same trafficker and were thus interviewed by the same group of police officers, and for those who testified, were questioned by the same lawyers in court. Results Sixteen reluctance tactics were identified, including several that have been overlooked in previous literature. The current reluctance measure identified more reluctance than previous studies’ reluctance measures. Reluctance was much more common in police interviews (26.4%; p Conclusions These findings have implications for future conceptualizations of reluctance, and illustrate the importance of considering the age of the victim and the circumstances under which the victim is questioned in identifying reluctance.
Quantum circuit representation of Bayesian networks

Abstract Probabilistic graphical models such as Bayesian networks are widely used to model stochastic systems to perform various types of analysis such as probabilistic prediction, risk analysis, and system health monitoring, which can become computationally expensive in large-scale systems. While demonstrations of true quantum supremacy remain rare, quantum computing applications managing to exploit the advantages of amplitude amplification have shown significant computational benefits when compared against their classical counterparts. We develop a systematic method for designing a quantum circuit to represent a generic discrete Bayesian network with nodes that may have two or more states, where nodes with more than two states are mapped to multiple qubits. The marginal probabilities associated with root nodes (nodes without any parent nodes) are represented using rotation gates, and the conditional probability tables associated with non-root nodes are represented using controlled rotation gates. The controlled rotation gates with more than one control qubit are represented using ancilla qubits. The proposed approach is demonstrated for three examples: a 4-node oil company stock prediction, a 10-node network for liquidity risk assessment, and a 9-node naive Bayes classifier for bankruptcy prediction. The circuits were designed and simulated using Qiskit, a quantum computing platform that enables simulations and also has the capability to run on real quantum hardware. The results were validated against those obtained from classical Bayesian network implementations.
Static dipole polarizabilities of polyacenes using self-interaction-corrected density functional approximations

Density functional approximations (DFAs) are known to significantly overestimate the polarizabilities of long chain-like molecules. We study the static electric dipole polarizabilities and the vertical ionization potentials of polyacenes from benzene to pentacene using the Fermi–Lowdin orbital-based self-interaction corrected (FLOSIC) density functional method. The orbital by orbital self-interaction correction corrects for the overestimation tendency of DFAs. The polarizabilities calculated with FLOSIC-DFA are, however, overly corrected. We also tested the recently developed locally scaled self-interaction correction (LSIC) method on polyacenes. The local-scaling method applies full SIC in the one-electron regions and restores the proper behavior of the SIC exchange–correlation functionals in the uniform density limit. The results show that LSIC removes the overcorrection tendency of the FLOSIC-DFA and produces results that are in excellent agreement with reference coupled-cluster single and double values. The vertical ionization potentials with LSIC also show good agreement with available experimental values.
Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning

In this work, we introduce Dissipative SymODEN, a deep learning architecture which can infer the dynamics of a physical system with dissipation from observed state trajectories. To improve prediction accuracy while reducing network size, Dissipative SymODEN encodes the port-Hamiltonian dynamics with energy dissipation and external input into the design of its computation graph and learns the dynamics in a structured way. The learned model, by revealing key aspects of the system, such as the inertia, dissipation, and potential energy, paves the way for energy-based controllers.
Extracorporeal Shock-Wave Lithotripsy

SUMMARY Analysis of our data clearly demonstrates that morbidity, secondary treatment, post-treatment manipulations, and failure of treatment increase as the stone size increases because of the increased stone fragment burden. Patients whose stone burden was less than 2 cm clearly had less morbidity. Composition of the stone may also alter the success of treatment (with cystine, calcium oxalate monohydrate, and brushite stones being resistant to treatment). However, post-treatment morbidity and complications were observed in all categories. Perirenal hematomas are unpredictable by current preoperative testing. Obstruction with pain can occur any time after treatment but usually occurs within the first 48 hours in the majority of patients. Only 1 per cent of our patients required hospitalization in another institution after discharge. Ambulatory ESWL requires that staff at the facility or a urologist be able to observe and appropriately follow the patient for 24 to 48 hours after treatment.
Revisiting Membership Inference Under Realistic Assumptions

We study membership inference in settings where some of the assumptions typically used in previous research are relaxed. First, we consider skewed priors, to cover cases such as when only a small fraction of the candidate pool targeted by the adversary are actually members and develop a PPV-based metric suitable for this setting. This setting is more realistic than the balanced prior setting typically considered by researchers. Second, we consider adversaries that select inference thresholds according to their attack goals and develop a threshold selection procedure that improves inference attacks. Since previous inference attacks fail in imbalanced prior setting, we develop a new inference attack based on the intuition that inputs corresponding to training set members will be near a local minimum in the loss function, and show that an attack that combines this with thresholds on the per-instance loss can achieve high PPV even in settings where other attacks appear to be ineffective. Code for our experiments can be found here: this https URL.
The Divergence of Constitutional and Statutory Interpretation

There is a peculiar point of agreement between prominent defenders of originalist and dynamic interpretive methods, that their preferred interpretive approach applies not just to statutes or to the Constitution, but to both. In this Article, I challenge this shared position - as represented by Justice Antonin Scalia's originalist textualism and Professor William Eskridge's dynamic interpretive theory. I argue that the democratic and rule-of-law values that these theories invoke in fact suggest that different interpretive approaches govern constitutional and statutory interpretation. I contend, first, that disjunctures between the democratic justification for originalism in constitutional and statutory interpretation reveal the distinct democratic foundations of these two forms of enacted law, and provide reasons for interpreting them differently. I next argue that the rule-of-law virtue of stability developed in Professor Eskridge's theory pushes constitutional and statutory interpretation apart because, on Professor Eskridge's view, predictive judgments about how other institutions will respond to a decision are central to the Court's interpretive exercise, and those predictive judgments will differ depending on the interpretive domain at issue. Finally, I defend this interpretive particularism by suggesting reasons why interpretive approaches should vary depending on the theory of legal authority applicable to the type or class of law in question.
Impact of smoke-free ordinance strength on smoking prevalence and lung cancer incidence.

Background Smoke-free ordinances (SFO) have been shown to be effective public health interventions, but there is limited data on the impact SFO on lung cancer outcomes. We explored the effect of county-level SFO strength with smoking prevalence and lung cancer incidence in Indiana. Methods We obtained county-level lung cancer incidence from the Indiana State Cancer Registry and county-level characteristics from the Indiana Tobacco Prevention and Cessation Commission’s policy database between 1995 and 2016. Using generalized estimating equations, we performed multivariable analyses of smoking prevalence and age-adjusted lung cancer rates with respect to the strength of smoke-free ordinances at the county level over time. Results Of Indiana’s 92 counties, 24 had a SFO by 2011. In 2012, Indiana enacted a state-wide SFO enforcing at least moderate level SFO protection. Mean age-adjusted lung cancer incidence per year was 76.8 per 100,000 population and mean smoking prevalence per year was 25% during the study period. Counties with comprehensive or moderate SFO had a smoking prevalence 1.2% (95% CI [-1.88, -0.52]) lower compared with counties with weak or no SFO. Counties that had comprehensive or moderate SFO also had an 8.4 (95% CI [-11.5, -5.3]) decrease in new lung cancer diagnosis per 100,000 population per year compared with counties that had weak or no SFO. Conclusion Counties with stronger smoke-free air ordinances were associated with decreased smoking prevalence and fewer new lung cancer cases per year. Strengthening SFO is paramount to decreasing lung cancer incidence.
Survey of recent multi-agent reinforcement learning algorithms utilizing centralized training

Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution approach to improve human-like collaboration in cooperative tasks. Here, we introduce variations of centralized training to describe cases where shared/independent reward structure is utilized to improve learning by training agents in an intelligent way, and to analyze cooperative behavior in multi-agent systems. This work discusses implications for providing a classification of the recent MARL algorithmic approaches on the basis of their information sharing mechanism (e.g., reward, gradient, action, parameter, observation/state space sharing) on cooperative behavior in multi-agent systems.
Let's do the time warp again: non-linear time series matching as a tool for sequentially structured data in ecology

Abstract Ecological patterns are often fundamentally chronological. However, generalization of data is necessarily accompanied by a loss of detail or resolution. Temporal data in particular contains information not only in data values but in the temporal structure, which is lost when these values are aggregated to provide point estimates. Dynamic Time Warping (DTW) is a time series comparison method that is capable of efficiently comparing series despite temporal offsets that confound other methods. The DTW method is both efficient and remarkably flexible, capable of efficiently matching not only time series but any sequentially structured dataset, which has made it a popular technique in machine learning, artificial intelligence, and big data analytical tasks. DTW is rarely used in ecology despite the ubiquity of temporally structured data. As technological advances have increased the richness of small-scale ecological data, DTW may be an attractive analysis technique because it is able to utilize the additional information contained in the temporal structure of many ecological datasets. In this study we use an example dataset of high-resolution fish movement records obtained from otolith microchemistry to compare traditional analysis techniques with DTW clustering. Our results suggest that DTW is capable of detecting subtle behavioral patterns within otolith datasets which traditional data aggregation techniques cannot. These results provide evidence that the DTW method may be useful across many of the temporal data types commonly collected in ecology, as well other sequentially ordered “pseudo time series” data such as classification of species by shape. Keywords: classification, cluster analysis, data generalization, DTW, dynamic time warping, otolith chemistry, time series
Cluster Algebras and Binary Subwords

This paper establishes a connection between binary subwords and perfect matchings of a snake graph, an important tool in the theory of cluster algebras. Every binary expansion w can be associated to a piecewise-linear poset P and a snake graph G. We construct a tree structure called the antichain trie which is isomorphic to the trie of subwords introduced by Leroy, Rigo, and Stipulanti. We then present bijections from the subwords of w to the antichains of P and to the perfect matchings of G.
The neurokinin-1 receptor is expressed with gastrin-releasing peptide receptor in spinal interneurons and modulates itch

The neurokinin-1 receptor (NK1R, encoded by Tacr1) is expressed in spinal dorsal horn neurons and has been suggested to mediate itch. However, previous studies relied heavily on neurotoxic ablation of NK1R spinal neurons, which limited further dissection of their function in spinal itch circuitry. Thus, we leveraged a newly developed Tacr1-CreER mouse line to characterize the role of NK1R spinal neurons in itch. We show that pharmacological activation of spinal NK1R and chemogenetic activation of Tacr1-CreER spinal neurons increases itch behavior, whereas pharmacological inhibition of spinal NK1R suppresses itch behavior. We use fluorescence in situ hybridization to characterize the endogenous expression of Tacr1 throughout the superficial and deeper mouse dorsal horn, as well as the lateral spinal nucleus of mouse. Notably, TACR1 expression was evolutionarily conserved in the human spinal cord dorsal. Retrograde labeling studies from the parabrachial nucleus show that less than 20% of superficial Tacr1-CreER dorsal horn neurons are spinal projection neurons, and thus the majority of Tacr1-CreER are local interneurons. We then use a combination of in situ hybridization and ex vivo two-photon Ca2+ imaging of the spinal cord to establish that NK1R and the gastrin-releasing peptide receptor (GRPR) are coexpressed within a subpopulation of excitatory superficial dorsal horn neurons. These findings are the first to describe a role for NK1R interneurons in itch and extend our understanding of the complexities of spinal itch circuitry. NINDS T32NS086749 (TDS), NINDS F32NS110155 (TDS), NINDS T32 NS073548 (CAW), and NIH NINDS R01NS096705 (SER).
Online Learning in Unknown Markov Games

We study online learning in unknown Markov games, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable. We show that in this challenging setting, achieving sublinear regret against the best response in hindsight is statistically hard. We then consider a weaker notion of regret by competing with the \emph{minimax value} of the game, and present an algorithm that achieves a sublinear $\tilde{\mathcal{O}}(K^{2/3})$ regret after $K$ episodes. This is the first sublinear regret bound (to our knowledge) for online learning in unknown Markov games. Importantly, our regret bound is independent of the size of the opponents' action spaces. As a result, even when the opponents' actions are fully observable, our regret bound improves upon existing analysis (e.g., (Xie et al., 2020)) by an exponential factor in the number of opponents.
Discrimination in lending? Evidence from the Paycheck Protection Program

We assess the role of race in loans made through the Paycheck Protection Program (PPP). The PPP program, created by the U.S. government as a response to the Covid-19 pandemic, provides loans to small businesses so they can keep employees on their payroll. We argue that the historical record and PPP program design choices made it likely that many Black-owned businesses received smaller PPP loans than White-owned businesses. Using newly released data on the PPP program, we find that Black-owned businesses received loans that were approximately 50% lower than observationally similar White-owned businesses. The effect is marginally smaller in areas with more bank competition and disappeared over time as changes to the PPP program were implemented allowing for entry by fintechs and other non-traditional lenders. We find that Black-owned businesses received loans through the Paycheck Protection Program that were approximately 50 percent lower than White-owned businesses with similar characteristics. However, this difference in loan size shrank over time as more non-bank lenders such as fintechs were allowed to participate in the program and began approving PPP loans. Loan size differences were also slightly smaller in zip codes containing a larger number of bank branches. These results are consistent with prior research which shows lending discrimination by commercial banks against Black borrowers. It is also consistent with studies showing that greater access to and competition among banks and other lenders can reduce discrimination. In light of these results we recommend that policy makers account for existing racial inequalities within banking or other systems in their program design to produce more equitable outcomes.
Composer Style Classification of Piano Sheet Music Images Using Language Model Pretraining

This paper studies composer style classification of piano sheet music images. Previous approaches to the composer classification task have been limited by a scarcity of data. We address this issue in two ways: (1) we recast the problem to be based on raw sheet music images rather than a symbolic music format, and (2) we propose an approach that can be trained on unlabeled data. Our approach first converts the sheet music image into a sequence of musical "words" based on the bootleg feature representation, and then feeds the sequence into a text classifier. We show that it is possible to significantly improve classifier performance by first training a language model on a set of unlabeled data, initializing the classifier with the pretrained language model weights, and then finetuning the classifier on a small amount of labeled data. We train AWD-LSTM, GPT-2, and RoBERTa language models on all piano sheet music images in IMSLP. We find that transformer-based architectures outperform CNN and LSTM models, and pretraining boosts classification accuracy for the GPT-2 model from 46\% to 70\% on a 9-way classification task. The trained model can also be used as a feature extractor that projects piano sheet music into a feature space that characterizes compositional style.
Toward Diverse Precondition Generation

A typical goal for language understanding is to logically connect the events of a discourse, but often connective events are not described due to their commonsense nature. In order to address this deficit, we focus here on generating precondition events. Precondition generation can be framed as a sequence-to-sequence problem: given a target event, generate a possible precondition. However, in most real-world scenarios, an event can have several preconditions, which is not always suitable for standard seq2seq frameworks. We propose DiP, the Diverse Precondition generation system that can generate unique and diverse preconditions. DiP consists of three stages of the generative process – an event sampler, a candidate generator, and a post-processor. The event sampler provides control codes (precondition triggers) which the candidate generator uses to focus its generation. Post-processing further improves the results through re-ranking and filtering. Unlike other conditional generation systems, DiP automatically generates control codes without training on diverse examples. Analysis reveals that DiP improves the diversity of preconditions significantly compared to a beam search baseline. Also, manual evaluation shows that DiP generates more preconditions than a strong nucleus sampling baseline.
Probabilistic modeling of brittle fracture including 3-D effects on constraint loss and ductile tearing

This study presents a probabilistic, 3-D framework to describe brittle fracture in structural components which incorporates weakest link statistics and a micromechanics model reflecting local damage of material. The Weibull stress(σ w ) emerges as a fracture parameter to define conditions leading to local material failure. This parameter is correlated with the macroscopic loading and used to describe overall fracture conditions in a cracked solid exhibiting both loss of constraint and stable crack growth prior to cleavage fracture. The paper also describes an application of the proposed framework to predict the measured geometry and ductile tearing effects on the statistical distribution of cleavage fracture toughness for an HSLA steel.
Querying Encrypted Data (Tutorial)

Data security is a serious concern when we migrate data to a cloud DBMS. Database encryption, where sensitive columns are encrypted before they are stored in the cloud, has been proposed as a mechanism to address such data security concerns. The intuitive expectation is that an adversary cannot “learn” anything about the encrypted columns, since she does not have access to the encryption key. However, query processing becomes a challenge since it needs to “look inside” the data. This tutorial explores the space of designs studied in prior work on processing queries over encrypted data. We cover approaches based on both classic client-server and involving the use of a trusted hardware module where data can be securely decrypted. We discuss the security challenges that arise in both approaches and how they may be addressed. Briefly, supporting the full complexity of a modern DBMS including complex queries, transactions and stored procedures leads to significant challenges that we survey.
Effect of turbulence model uncertainty on scramjet strut injector flow field analysis

Abstract The effect of turbulence model closure coefficient uncertainty on the Reynolds-averaged Navier–Stokes solution of a scramjet strut fuel injector flow field is investigated with an uncertainty quantification and sensitivity analysis study for the Menter-BSL and Wilcox-2006 k- ω turbulence models. Simulations were performed using the VULCAN flow solver. Nonintrusive polynomial chaos theory was used for efficient propagation of uncertainty, and Sobol indices were utilized to quantify the sensitivity of various solution metrics to the variation of each closure coefficient. The output metrics of interest were evaluated at three crossflow planes, and included the integrated quantities of mixing efficiency, circulation, total pressure recovery, and one-dimensional Mach number, as well as the pointwise vorticity and eddy viscosity distributions. Influential sets of closure coefficients were identified for each turbulence model, with the Karman and diffusion constants being the most prominent. These results are expected to assist future efforts aimed at reducing the uncertainty in the numerical design of scramjet fuel injectors through the identification of closure coefficients and physical aspects of the flow that warrant further investigation.
Regional migration, entrepreneurship and university alumni

How does the migration of university graduates from rural to urban areas impact entrepreneurial performance? We propose that rural-to-urban migrants found larger firms than urban entrepreneurs beca...
Data-Driven Low-Rank Neural Network Compression

Despite many modern applications of Deep Neural Networks (DNNs), the large number of parameters in the hidden layers makes them unattractive for deployment on devices with storage capacity constraints. In this paper we propose a Data-Driven Low-rank (DDLR) method to reduce the number of parameters of pretrained DNNs and expedite inference by imposing low-rank structure on the fully connected layers, while controlling for the overall accuracy and without requiring any retraining. We pose the problem as finding the lowest rank approximation of each fully connected layer with given performance guarantees and relax it to a tractable convex optimization problem. We show that it is possible to significantly reduce the number of parameters in common DNN architectures with only a small reduction in classification accuracy. We compare DDLR with Net-Trim, which is another data-driven DNN compression technique based on sparsity and show that DDLR consistently produces more compressed neural networks while maintaining higher accuracy.
Is LaTeX use correlated with the number of equations in a manuscript

Astronomy papers are most frequently composed and submitted in either Microsoft Word or LaTeX format. While most journals accept submissions in Word format (and some, such as Meteoritics and Planetary Science, until recently required Word format), the majority of astronomy papers posted to the arXiV are submitted in LaTeX format. The wisdom of using LaTeX is sometimes questioned: for instance, a recent study demonstrated that a set of 40 researchers and graduate students were able to transcribe text and tables more quickly and with fewer errors using Word rather than LaTeX, although LaTeX proved similarly efficient when it came to transcribing equations and was more enjoyable to use. However, the study had all participants reproduce a given source text. We suspect that the use of Word may influence an author to include fewer equations when creating an original work. This is a difficult hypothesis to test directly, so we instead use a subset of arXiV submissions to probe whether the use of LaTeX is correlated with the number of standalone equations in a manuscript. We find that PDF-only manuscripts on the arXiV have roughly half as many equations as those with LaTeX source files.
Managing ML pipelines: feature stores and the coming wave of embedding ecosystems

The industrial machine learning pipeline requires iterating on model features, training and deploying models, and monitoring deployed models at scale. Feature stores were developed to manage and standardize the engineer's workflow in this end-to-end pipeline, focusing on traditional tabular feature data. In recent years, however, model development has shifted towards using self-supervised pretrained embeddings as model features. Managing these embeddings and the downstream systems that use them introduces new challenges with respect to managing embedding training data, measuring embedding quality, and monitoring downstream models that use embeddings. These challenges are largely unaddressed in standard feature stores. Our goal in this tutorial is to introduce the feature store system and discuss the challenges and current solutions to managing these new embedding-centric pipelines.
Sex difference in kidney electrolyte transport III: Impact of low K intake on thiazide-sensitive cation excretion in male and female mice

We compared the regulation of the NaCl cotransporter (NCC) in adaptation to a low-K (LK) diet in male and female mice. We measured hydrochlorothiazide (HCTZ)-induced changes in urine volume (UV), glomerular filtration rate (GFR), absolute (ENa, EK), and fractional (FENa, FEK) excretion in male and female mice on control-K (CK, 1% KCl) and LK (0.1% KCl) diets for 7 days. With CK, NCC-dependent ENa and FENa were larger in females than males as observed previously. However, with LK, HCTZ-induced ENa and FENa increased in males but not in females, abolishing the sex differences in NCC function as observed in CK group. Despite large diuretic and natriuretic responses to HCTZ, EK was only slightly increased in response to the drug when animals were on LK. This suggests that the K-secretory apparatus in the distal nephron is strongly suppressed under these conditions. We also examined LK-induced changes in Na transport protein expression by Western blotting. Under CK conditions females expressed more NCC protein, as previously reported. LK doubled both total (tNCC) and phosphorylated NCC (pNCC) abundance in males but had more modest effects in females. The larger effect in males abolished the sex-dependence of NCC expression, consistent with the measurements of function by renal clearance. LK intake did not change NHE3, NHE2, or NKCC2 expression, but reduced the amount of the cleaved (presumably active) form of γENaC. LK reduced plasma K to lower levels in females than males. These results indicated that males had a stronger NCC-mediated adaptation to LK intake than females.
Theoretical challenges for flavor physics

We discuss some highlights of the FCC-ee flavor physics program. It will help to explore various aspects of flavor physics: to test precision calculations, to probe nonperturbative QCD methods, and to increase the sensitivity to physics beyond the standard model. In some areas, FCC-ee will do much better than current and near-future experiments. We briefly discuss several probes that can be relevant for maximizing the gain from the FCC-ee flavor program.
Skeletal model reduction with forced optimally time dependent modes

Abstract Skeletal model reduction based on local sensitivity analysis of time dependent systems is presented in which sensitivities are modeled by forced optimally time dependent (f-OTD) modes. The f-OTD factorizes the sensitivity coefficient matrix into a compressed format as the product of two skinny matrices, i.e. f-OTD modes and f-OTD coefficients. The modes create a low-dimensional, time dependent, orthonormal basis which capture the directions of the phase space associated with most dominant sensitivities. These directions highlight the instantaneous active species, and reaction paths. Evolution equations for the f-OTD modes and coefficients are derived, and the implementation of f-OTD for skeletal reduction is described. For demonstration, skeletal reduction is conducted of the constant pressure ethylene-air burning in a zero-dimensional reactor, and new reduced models are generated. The laminar flame speed, the ignition delay, and the extinction curve as predicted by the models are compared against some existing skeletal models in literature for the same detailed model. The results demonstrate the capability of f-OTD to eliminate unimportant reactions and species in a systematic, efficient and accurate manner.
A genetic mouse model with postnatal Nf1 and p53 loss recapitulates the histology and transcriptome of human malignant peripheral nerve sheath tumor.

Background Malignant peripheral nerve sheath tumors (MPNST) are aggressive sarcomas. Somatic inactivation of NF1 and cooperating tumor suppressors, including CDKN2A/B, PRC2, and p53, is found in most MPNST. Inactivation of LATS1/2 of the Hippo pathway was recently shown to cause tumors resembling MPNST histologically, although Hippo pathway mutations are rarely found in MPNST. Because existing genetically engineered mouse (GEM) models of MPNST do not recapitulate some of the key genetic features of human MPNST, we aimed to establish a GEM-MPNST model that recapitulated the human disease genetically, histologically, and molecularly. Methods We combined 2 genetically modified alleles, an Nf1;Trp53 cis-conditional allele and an inducible Plp-CreER allele (NP-Plp), to model the somatic, possibly postnatal, mutational events in human MPNST. We also generated conditional Lats1;Lats2 knockout mice. We performed histopathologic analyses of mouse MPNST models and transcriptomic comparison of mouse models and human nerve sheath tumors. Results Postnatal Nf1;Trp53 cis-deletion resulted in GEM-MPNST that were histologically more similar to human MPNST than the widely used germline Nf1;Trp53 cis-heterozygous (NPcis) model and showed partial loss of H3K27me3. At the transcriptome level, Nf1;p53-driven GEM-MPNST were distinct from Lats-driven GEM-MPNST and resembled human MPNST more closely than do Lats-driven tumors. Conclusions The NP-Plp model recapitulates human MPNST genetically, histologically, and molecularly.
The local cohomology of a parameter ideal with respect to an arbitrary ideal

Abstract Let R be a regular ring, let J be an ideal generated by a regular sequence of codimension at least 2, and let I be an ideal containing J. We give an example of a module H I 3 ( J ) with infinitely many associated primes, answering a question of Hochster and Nunez-Betancourt in the negative. In fact, for i ≤ 4 , we show that under suitable hypotheses on R / J , Ass H I i ( J ) is finite if and only if Ass H I i − 1 ( R / J ) is finite. Our proof of this statement involves a novel generalization of an isomorphism of Hellus, which may be of some independent interest. The finiteness comparison between Ass H I i ( J ) and Ass H I i − 1 ( R / J ) tends to improve as our hypotheses on R / J become more restrictive. To illustrate the extreme end of this phenomenon, at least in the prime characteristic p > 0 setting, we show that if R / J is regular, then Ass H I i ( J ) is finite for all i ≥ 0 .
Comparing OpenMP Implementations with Applications Across A64FX Platforms

The development of the A64FX processor by Fujitsu has created a massive innovation in High-Performance Computing and the birth of Fugaku: the current world’s fastest supercomputer. A variety of tools are used to analyze the run-times and performances of several applications, and in particular, how these applications scale on the A64FX processor. We examine the performance and behavior of applications through OpenMP scaling and how their performance differs across different compilers both on the new Ookami cluster at Stony Brook University as well as the Fugaku supercomputer at RIKEN in Japan.