cssi/SciSciGPT-SciSciNet huggingface dataset:
Analyzing Machine Learning Approaches for Online Malware Detection in Cloud

The variety of services and functionality offered by various cloud service providers (CSP) have exploded lately. Utilizing such services has created numerous opportunities for enterprises infrastructure to become cloud-based and, in turn, assisted the enterprises to easily and flexibly offer services to their customers. The practice of renting out access to servers to clients for computing and storage purposes is known as Infrastructure as a Service (IaaS). The popularity of IaaS has led to serious and critical concerns with respect to the cyber security and privacy. In particular, malware is often leveraged by malicious entities against cloud services to compromise sensitive data or to obstruct their functionality. In response to this growing menace, malware detection for cloud environments has become a widely researched topic with numerous methods being proposed and deployed. In this paper, we present online malware detection based on process level performance metrics, and analyze the effectiveness of different baseline machine learning models including, Support Vector Classifier (SVC), Random Forest Classifier (RFC), K-Nearest Neighbor (KNN), Gradient Boosted Classifier (GBC), Gaussian Naive Bayes (GNB) and Convolutional Neural Networks (CNN). Our analysis conclude that neural network models can most accurately detect the impact malware have on the process level features of virtual machines in the cloud, and therefore are best suited to detect them. Our models were trained, validated, and tested by using a dataset of 40,680 malicious and benign samples. The dataset was complied by running different families of malware (collected from VirusTotal) in a live cloud environment and collecting the process level features.
Toward the Next Generation of News Recommender Systems

This paper proposes a vision and research agenda for the next generation of news recommender systems (RS), called the table d’hote approach. A table d’hote (translates as host’s table) meal is a sequence of courses that create a balanced and enjoyable dining experience for a guest. Likewise, we believe news RS should strive to create a similar experience for the users by satisfying the news-diet needs of a user. While extant news RS considers criteria such as diversity and serendipity, and RS bundles have been studied for other contexts such as tourism, table d’hote goes further by ensuring the recommended articles satisfy a diverse set of user needs in the right proportions and in a specific order. In table d’hote, available articles need to be stratified based on the different ways that news can create value for the reader, building from theories and empirical research in journalism and user engagement. Using theories and empirical research from communication on the uses and gratifications (U&G) consumers derive from media, we define two main strata in a table d’hote news RS, each with its own substrata: 1) surveillance, which consists of information the user needs to know, and 2) serendipity, which are the articles offering unexpected surprises. The diversity of the articles according to the defined strata and the order of the articles within the list of recommendations are also two important aspects of the table d’hote in order to give the users the most effective reading experience. We propose our vision, link it to the existing concepts in the RS literature, and identify challenges for future research.
Asset-Level Transparency and the (E)valuation of Asset-Backed Securities

As of November 2016, SEC Regulation (“Reg”) AB II requires issuers of certain types of asset-backed securities (“ABS”) to disclose the credit-risk attributes of each asset in the underlying pool, a substantial expansion of prior disclosure requirements. We examine how ABS issuers’ asset-level disclosures under Reg AB II affect the (e)valuation of ABS by investors and credit rating agencies. Using difference-in-differences models that compare affected and unaffected types of ABS, we find that these disclosures improve the ability of initial ABS yields and credit ratings to predict the performance of the underlying assets. These results are concentrated in deals with above-median risk layering in the underlying assets and complexity in the tranching of credit risk. We further find that asset-level disclosures are associated with lower yields. Lastly, we provide evidence that most prospective ABS investors download asset-level information during the price formation period prior to ABS issuance.
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing

Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product. It is desirable for this pricing policy to be simple and interpretable, so it can be verified, checked for fairness, and easily implemented. However, efforts to incorporate machine learning into a pricing framework often lead to complex pricing policies which are not interpretable, resulting in slow adoption in practice. We present a customized, prescriptive tree-based algorithm that distills knowledge from a complex black-box machine learning algorithm, segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability. We quantify the regret of a resulting policy and demonstrate its efficacy in applications with both synthetic and real-world datasets.
Rotor wake characteristics of a transonic axial-flow fan

State of the art turbomachinery flow analysis codes are not capable of predicting the viscous flow features within turbomachinery blade wakes. Until efficient 3D viscous flow analysis codes become a reality there is therefore a need for models which can describe the generation and transport of blade wakes and the mixing process within the wake. To address the need for experimental data to support the development of such models, high response pressure measurements and laser anemometer velocity measurements were obtained in the wake of a transonic axial flow fan rotor.
Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning

A growing body of literature has focused on detailing the linguistic knowledge embedded in large, pretrained language models. Existing work has shown that non-linguistic biases in models can drive model behavior away from linguistic generalizations. We hypothesized that competing linguistic processes within a language, rather than just non-linguistic model biases, could obscure underlying linguistic knowledge. We tested this claim by exploring a single phenomenon in four languages: English, Chinese, Spanish, and Italian. While human behavior has been found to be similar across languages, we find cross-linguistic variation in model behavior. We show that competing processes in a language act as constraints on model behavior and demonstrate that targeted fine-tuning can re-weight the learned constraints, uncovering otherwise dormant linguistic knowledge in models. Our results suggest that models need to learn both the linguistic constraints in a language and their relative ranking, with mismatches in either producing non-human-like behavior.
Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models.

Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision process of a trained classifier, existing techniques typically rely on saliency maps to quantify the voxel-wise or feature-level importance for classification through partial derivatives. Despite providing some level of localization, these maps are not human-understandable from the neuroscience perspective as they often do not inform the specific type of morphological changes linked to the brain disorder. Inspired by the image-to-image translation scheme, we propose to train simulator networks to inject (or remove) patterns of the disease into a given MRI based on a warping operation, such that the classifier increases (or decreases) its confidence in labeling the simulated MRI as diseased. To increase the robustness of training, we propose to couple the two simulators into a unified model based on conditional convolution. We applied our approach to interpreting classifiers trained on a synthetic dataset and two neuroimaging datasets to visualize the effect of Alzheimer’s disease and alcohol dependence. Compared to the saliency maps generated by baseline approaches, our simulations and visualizations based on the Jacobian determinants of the warping field reveal meaningful and understandable patterns related to the diseases.
A discontinuous Galerkin method for a diffuse-interface model of immiscible two-phase flows with soluble surfactant

A numerical method using discontinuous polynomial approximations is formulated for solving a phase-field model of two immiscible fluids with a soluble surfactant. The proposed scheme is shown to decay the total free Helmholtz energy at the discrete level, which is consistent with the continuous model dynamics. The scheme recovers the Langmuir adsorption isotherms at equilibrium. Simulations of spinodal decomposition, flow through a cylinder and flow through a sequence of pore throats show the dynamics of the flow with and without surfactant. Finally the numerical method is used to simulate fluid flows in the pore space of Berea sandstone obtained by micro-CT imaging.
Audit Committee Accounting Expertise and the Mitigation of Strategic Auditor Behavior

Our study is motivated by the theory of credence goods in the auditing setting. We propose that audit committee accounting expertise should reduce information asymmetries between the auditor and the client, thereby limiting auditors’ ability to over-audit and under-audit. Consistent with this notion, our results indicate that when audit committees have accounting expertise, clients (1) pay lower fees when changes in standards decrease required audit effort; (2) pay a smaller fee premium in the presence of remediated material weaknesses; and (3) have a reduced likelihood of restatement when audit market competition is high. Our findings in the under-auditing setting generally are strongest among non-Big 4 engagements, consistent with non-Big 4 auditors being less sensitive to market-wide disciplining mechanisms such as reputation, legal liability, and professional regulation. We also provide evidence that the nature of audit committee members’ accounting expertise differentially impacts the committee’s ability to curtail over- and under-auditing.
Prophylactic Application of Tailocins Prevents Infection by Pseudomonas syringae.

Tailocins are phage-derived bacteriocins that demonstrate great potential as agricultural antimicrobials given their high killing efficiency and their precise strain-specific targeting ability. Our group has recently categorized and characterized tailocins produced by and tailocin sensitivities of the phytopathogen Pseudomonas syringae, and here we extend these experiments to test whether prophylactic tailocin application can prevent infection of Nicotiana benthamiana by P. syringae pv. syringae B728a. Specifically, we demonstrate that multiple strains can produce tailocins that prevent infection by strain B728a and engineer a deletion mutant to prove that tailocin targeting is responsible for this protective effect. Lastly, we provide evidence that heritable resistance mutations do not explain the minority of cases where tailocins fail to prevent infection. Our results extend previous reports of prophylactic use of tailocins against phytopathogens, and establish a model system with which to test and optimize tailocin application for prophylactic treatment to prevent phytopathogen infection.
Endocardial/endothelial angiocrines regulate cardiomyocyte development and maturation and induce features of ventricular non-compaction

Aims Non-compaction cardiomyopathy is a devastating genetic disease caused by insufficient consolidation of ventricular wall muscle that can result in inadequate cardiac performance. Despite being the third most common cardiomyopathy, the mechanisms underlying the disease, including the cell types involved, are poorly understood. We have previously shown that endothelial cell-specific deletion of the chromatin remodeller gene Ino80 results in defective coronary vessel development that leads to ventricular non-compaction in embryonic mouse hearts. We aimed to identify candidate angiocrines expressed by endocardial and endothelial cells (ECs) in wildtype and LVNC conditions in Tie2Cre;Ino80fl/fltransgenic embryonic mouse hearts, and test the effect of these candidates on cardiomyocyte proliferation and maturation. Methods and results We used single-cell RNA-sequencing to characterize endothelial and endocardial defects in Ino80-deficient hearts. We observed a pathological endocardial cell population in the non-compacted hearts and identified multiple dysregulated angiocrine factors that dramatically affected cardiomyocyte behaviour. We identified Col15a1 as a coronary vessel-secreted angiocrine factor, downregulated by Ino80-deficiency, that functioned to promote cardiomyocyte proliferation. Furthermore, mutant endocardial and endothelial cells up-regulated expression of secreted factors, such as Tgfbi, Igfbp3, Isg15, and Adm, which decreased cardiomyocyte proliferation and increased maturation. Conclusions These findings support a model where coronary endothelial cells normally promote myocardial compaction through secreted factors, but that endocardial and endothelial cells can secrete factors that contribute to non-compaction under pathological conditions.
The META tool optimizes metagenomic analyses across sequencing platforms and classifiers.

A major challenge in the field of metagenomics is the selection of the correct combination of sequencing platform and downstream metagenomic analysis algorithm, or classifier. Here, we present the Metagenomic Evaluation Tool Analyzer (META), which produces simulated data and facilitates platform and algorithm selection for any given metagenomic use case. META-generated in silico read data are modular, scalable, and reflect user-defined community profiles, while the downstream analysis is done using a variety of metagenomic classifiers. Reported results include information on resource utilization, time-to-answer, and performance. Real-world data can also be analyzed using selected classifiers and results benchmarked against simulations. To test the utility of the META software, simulated data was compared to real-world viral and bacterial metagenomic samples run on four different sequencers and analyzed using 12 metagenomic classifiers. Lastly, we introduce META Score: a unified, quantitative value which rates an analytic classifiers ability to both identify and count taxa in a representative sample.
Titan Explorer Mission Trades from the Perspective of Aerocapture

This paper reviews the launch vehicle, propulsion, and trajectory options to reach Titan in the 2010-2015 time frame using aerocapture and all-propulsive vehicles.
Sequential auctions with ambiguity

Abstract This paper studies sequential auctions in the presence of ambiguity about the distribution of values. Using the multi-self approach to accommodate the possible time inconsistency that arises with dynamic bidding for maxmin bidders, we characterize the unique symmetric and monotone equilibrium in closed form. We show that the equilibrium prices are a supermartingale if the worst-case beliefs reverse hazard rate dominate the true distribution of values, providing an explanation for the well-documented puzzle “declining price anomaly” in sequential auctions. Importantly, the sufficient condition for declining prices allows for dynamic inconsistency. We also propose a non-parametric procedure to identify bidders' values and beliefs from auction data. The model delivers rich testable implications: on the practical side, declining prices imply that bidders' worst-case beliefs first-order stochastically dominate the true distribution of values; on the theoretical side, dynamic inconsistency generates history dependence in bidding strategies and possibly higher revenues for the seller.
A Causal Bootstrap

The bootstrap, introduced by The Jackknife, the Bootstrap and Other Resampling Plans ((1982), SIAM), has become a very popular method for estimating variances and constructing confidence intervals. A key insight is that one can approximate the properties of estimators by using the empirical distribution function of the sample as an approximation for the true distribution function. This approach views the uncertainty in the estimator as coming exclusively from sampling uncertainty. We argue that for causal estimands the uncertainty arises entirely, or partially, from a different source, corresponding to the stochastic nature of the treatment received. We develop a bootstrap procedure for inference regarding the average treatment effect that accounts for this uncertainty, and compare its properties to that of the classical bootstrap. We consider completely randomized and observational designs as well as designs with imperfect compliance.
U.S. welding technology - Constraints to space implementation

U.S. and European efforts to develop welding techniques for satellite solar arrays are described. Soldering practices in the U.S. have benefitted from Mo solders, which are well fitted to Si solar cell material thermal characteristics. Analyses have indicated that welds are the preferred method for interconnections and bonds. Extensive work has been done with the parallel gap resistance method (RW), which involves process heat generated by passing a current through resistive layers. Confining the primary heat input to the interconnector/cell contact interface results in welds being formed beneath both electrodes. Pulse welding has become the dominant RW technique in Europe, while ultrasonic welding is used in the U.S.; silver is employed as the interconnect material on both continents. The bonding techniques have been developed empirically instead of theoretically. An IR inspection technique has been produced for monitoring the weld temperature.
The basis of easy controllability in Boolean networks.

Effective control of biological systems can often be achieved through the control of a surprisingly small number of distinct variables. We bring clarity to such results using the formalism of Boolean dynamical networks, analyzing the effectiveness of external control in selecting a desired final state when that state is among the original attractors of the dynamics. Analyzing 49 existing biological network models, we find strong numerical evidence that the average number of nodes that must be forced scales logarithmically with the number of original attractors. This suggests that biological networks may be typically easy to control even when the number of interacting components is large. We provide a theoretical explanation of the scaling by separating controlling nodes into three types: those that act as inputs, those that distinguish among attractors, and any remaining nodes. We further identify characteristics of dynamics that can invalidate this scaling, and speculate about how this relates more broadly to non-biological systems. Boolean networks allow a simplified representation of interactions. Here, the authors systematically analyze regulation in dozens of biological Boolean networks, finding mathematical regularities that suggest biological systems could be controlled through a relatively small number of components.
Linear Shannon Capacity of Cayley Graphs

The Shannon capacity of a graph is a fundamental quantity in zero-error information theory measuring the rate of growth of independent sets in graph powers. Despite being well-studied, this quantity continues to hold several mysteries. Lovasz famously proved that the Shannon capacity of $C$ 5 (the 5-cycle) is at most $\sqrt{5}$ via his theta function. This bound is achieved by a simple linear code over $\mathbb{F}_{5}$ mapping x → 2x. This motivates the notion of linear Shannon capacity of graphs, which is the largest rate achievable when restricting oneself to linear codes. We give a simple proof based on the polynomial method that the linear Shannon capacity of $C$ 5 is $\sqrt{5}$ . Our method applies more generally to Cayley graphs over the additive group of finite fields $\mathbb{F}_{q}$ , giving an upper bound on the linear Shannon capacity. We compare this bound to the Lovasz theta function, showing that they match for self-complementary Cayley graphs (such as C 5 ), and that the bound is smaller in some cases. We also exhibit a quadratic gap between linear and general Shannon capacity for some graphs.
Bott vanishing for elliptic surfaces

We explore Bott Vanishing for elliptic surfaces over $${\mathbb {P}}^1$$ . We show that Bott Vanishing is significantly affected by the geometric properties of fibers. For example, whether there exist certain types of singular fibers on the elliptic fibration such as cuspidal fibers. For an ample line bundle on the surface with large self-intersection, these geometric properties give criteria for Bott Vanishing.
Alternative Data in Investment Management: Usage, Challenges, and Valuation

Alternative data in finance is an umbrella term for diverse nontraditional datasets used by quantitative and fundamental institutional investors to enhance portfolio returns. Although the use of alternative data is a recent phenomenon, it was not until the last five years that it gained widespread acceptance and the sector started to evolve into a complex ecosystem of data originators, intermediaries, and investors. The alternative data industry faces several obstacles, including difficulty estimating a dataset’s value to investors and technical challenges in leveraging these datasets efficiently at a large scale. In this article, the authors provide an up-to-date description of the alternative data space as it relates to the institutional investment industry. The authors elaborate on what alternative data are and how they are used in investment management. The authors identify and discuss some of the key challenges that arise when working with alternative data. In particular, they address issues such as entity mapping, ticker-tagging, panel stabilization, and debiasing with modern statistical and machine learning approaches. The authors advance several methodologies for the valuation of alternative datasets, including an event study methodology they refer to as the golden triangle, the application of report cards, and the relationship between a dataset’s structure of information content and its potential to enhance investment returns. To illustrate the effectiveness of these methods, they apply them to a case study analysis of real-world healthcare data, delivering an improvement in revenue prediction accuracy from an 88% mean absolute error to a 2.6% mean absolute error. TOPICS:Big data/machine learning, quantitative methods, performance measurement Key Findings ▪ The authors describe the alternative data industry in an investment context. They discuss the commercial incentives for different categories of organizations within the industry and methodologies to valuate alternative datasets. ▪ They identify key technical challenges in working with alternative data, including entity mapping, ticker-tagging, panel stabilization, and debiasing. ▪ Through a case study, they demonstrate how to use alternative data combined with imputation and optimization techniques to predict revenues of publicly traded companies within the healthcare sector.
An Assessment of Prisoner Reentry, Legal Financial Obligations and Family Financial Support: A Focus on Fathers

Scholars have found that family support is an important facilitator of successful reentry from prison to the community. At the same time, they have argued that owing court-ordered fines or fees, also called legal financial obligations (LFOs), can act as an additional barrier to reentry, especially for parents. There remains a need to test how LFOs impact the financial support formerly incarcerated parents receive from their families. The current study responds to this gap by employing logistic regression analyses of the Serious and Violent Offender Reentry Initiative (SVORI) data to test whether owing court fees is associated with formerly incarcerated fathers’ (1) perceptions of available financial support from family and (2) receipt of financial support from family. We find that owing court fees is not associated with perceptions of available financial support. However, owing court fees has a positive, statistically significant association with receiving financial support from family during the first three months after prison release. This relationship remains after accounting for whether the person owes child support or sees their children monthly. Our results suggest that LFOs may create a greater need for financial support among formerly incarcerated fathers, making the financial challenges of reentry a consequence not just for those who were incarcerated but for their loved ones as well.
Fibronectin: Molecular Structure, Fibrillar Structure and Mechanochemical Signaling

The extracellular matrix (ECM) plays a key role as both structural scaffold and regulator of cell signal transduction in tissues. In times of ECM assembly and turnover, cells upregulate assembly of the ECM protein, fibronectin (FN). FN is assembled by cells into viscoelastic fibrils that can bind upward of 40 distinct growth factors and cytokines. These fibrils play a key role in assembling a provisional ECM during embryonic development and wound healing. Fibril assembly is also often upregulated during disease states, including cancer and fibrotic diseases. FN fibrils have unique mechanical properties, which allow them to alter mechanotransduction signals sensed and relayed by cells. Binding of soluble growth factors to FN fibrils alters signal transduction from these proteins, while binding of other ECM proteins, including collagens, elastins, and proteoglycans, to FN fibrils facilitates the maturation and tissue specificity of the ECM. In this review, we will discuss the assembly of FN fibrils from individual FN molecules; the composition, structure, and mechanics of FN fibrils; the interaction of FN fibrils with other ECM proteins and growth factors; the role of FN in transmitting mechanobiology signaling events; and approaches for studying the mechanics of FN fibrils.
Edge-Unfolding Prismatoids: Tall or Rectangular Base.

We show how to edge-unfold a new class of convex polyhedra, specifically a new class of prismatoids (the convex hull of two parallel convex polygons, called the top and base), by constructing a nonoverlapping "petal unfolding" in two new cases: (1) when the top and base are sufficiently far from each other; and (2) when the base is a rectangle and all other faces are nonobtuse triangles. The latter result extends a previous result by O'Rourke that the petal unfolding of a prismatoid avoids overlap when the base is a triangle (possibly obtuse) and all other faces are nonobtuse triangles. We also illustrate the difficulty of extending this result to a general quadrilateral base by giving a counterexample to our technique.
The Koopman representation and positive Rokhlin entropy

In a prior paper, the author generalized the classical factor theorem of Sinai to actions of arbitrary countably infinite groups. In the present paper, we use this theorem and the techniques of its proof in order to study connections between positive entropy phenomena and the Koopman representation. Following the line of work initiated by Hayes for sofic entropy, we show in a certain precise manner that all positive entropy must come from portions of the Koopman representation that embed into the left-regular representation. By combining this result with the generalized factor theorem of the previous paper, we conclude that for actions having completely positive outer entropy, the Koopman representation must be isomorphic to the countable direct sum of the left-regular representation. This generalizes a theorem of Dooley--Golodets for countable amenable groups. As a final consequence, we observe that actions with completely positive outer entropy must be mixing, and when the group is non-amenable they must be strongly ergodic and have spectral gap. We also prove generalizations of these results that apply relative to sub-$\sigma$-algebras and apply to non-free actions.
Spatiotemporal dynamics of PIEZO1 localization controls keratinocyte migration during wound healing

Keratinocytes, the predominant cell type of the epidermis, migrate to reinstate the epithelial barrier during wound healing. Mechanical cues are known to regulate keratinocyte re-epithelialization and wound healing; however, the underlying molecular transducers and biophysical mechanisms remain elusive. Here, we show through molecular, cellular, and organismal studies that the mechanically activated ion channel PIEZO1 regulates keratinocyte migration and wound healing. Epidermal-specific Piezo1 knockout mice exhibited faster wound closure while gain-of-function mice displayed slower wound closure compared to littermate controls. By imaging the spatiotemporal localization dynamics of endogenous PIEZO1 channels, we find that channel enrichment at some regions of the wound edge induces a localized cellular retraction that slows keratinocyte collective migration. In migrating single keratinocytes, PIEZO1 is enriched at the rear of the cell, where maximal retraction occurs, and we find that chemical activation of PIEZO1 enhances retraction during single as well as collective migration. Our findings uncover novel molecular mechanisms underlying single and collective keratinocyte migration that may suggest a potential pharmacological target for wound treatment. More broadly, we show that nanoscale spatiotemporal dynamics of Piezo1 channels can control tissue-scale events, a finding with implications beyond wound healing to processes as diverse as development, homeostasis, disease, and repair.
Crystal structure of silver pentazolates AgN5 and AgN6.

Silver pentazolate, a high energy density compound containing the cyclo-N5− anion, has recently been synthesized under ambient conditions. However, due to high sensitivity to irradiation, its crystal structure has not been determined. In this work, silver–nitrogen crystalline compounds under ambient conditions and at high pressures, up to 100 GPa, are predicted and characterized by performing first-principles evolutionary crystal structure searching with variable stoichiometry. It is found that newly discovered AgN5 and AgN6 are the only thermodynamically stable silver–nitrogen compounds at pressures between 42 and 80 GPa. In contrast to AgN5, the pentazolate AgN6 compound contains N2 diatomic molecules in addition to cyclo-N5−. These AgN5 and AgN6 crystals are metastable under ambient conditions with positive formation enthalpies of 54.95 kJ mol−1 and 46.24 kJ mol−1, respectively. The underlying cause of the stability of cyclo-N5− silver pentazolates is the enhanced aromaticity enabled by the charge transfer from silver atoms to nitrogen rings. To aid in the experimental identification of these materials, calculated Raman spectra are reported at ambient pressure: the frequencies of N5− vibrational modes of AgN5 are in good agreement with those measured in the experiment.
Contact Mode Guided Sampling-Based Planning for Quasistatic Dexterous Manipulation in 2D

The discontinuities and multi-modality introduced by contacts make manipulation planning challenging. Many previous works avoid this problem by pre-designing a set of high-level motion primitives like grasping and pushing. However, such motion primitives are often not adequate to describe dexterous manipulation motions. In this work, we propose a method for dexterous manipulation planning at a more primitive level. The key idea is to use contact modes to guide the search in a sampling-based planning framework. Our method can automatically generate contact transitions and motion trajectories under the quasistatic assumption. In the experiments, this method sometimes generates motions that are often pre-designed as motion primitives, as well as dexterous motions that are more task-specific 1.
Evaluating representations by the complexity of learning low-loss predictors

We consider the problem of evaluating representations of data for use in solving a downstream task. We propose to measure the quality of a representation by the complexity of learning a predictor on top of the representation that achieves low loss on a task of interest. To this end, we introduce two measures: surplus description length (SDL) and e sample complexity (eSC). To compare our methods to prior work, we also present a framework based on plotting the validation loss versus dataset size (the "loss-data" curve). Existing measures, such as mutual information and minimum description length, correspond to slices and integrals along the data-axis of the loss-data curve, while ours correspond to slices and integrals along the loss-axis. This analysis shows that prior methods measure properties of an evaluation dataset of a specified size, whereas our methods measure properties of a predictor with a specified loss. We conclude with experiments on real data to compare the behavior of these methods over datasets of varying size.
Explainable AI-based clinical decision support system for hearing disorders

In clinical system design, human-computer interaction and explainability are important topics of research. Clinical systems need to provide users with not only results but also an account of their behaviors. In this research, we propose a knowledge-based clinical decision support system (CDSS) for the diagnosis and therapy of hearing disorders, such as tinnitus, hyperacusis, and misophonia. Our prototype eTRT system offers an explainable output that we expect to increase its trustworthiness and acceptance in the clinical setting. Within this paper, we: (1) present the problem area of tinnitus and its treatment; (2) describe our data-driven approach based on machine learning, such as association- and action rule discovery; (3) present the evaluation results from the inference on the extracted rule-based knowledge and chosen test cases of patients; (4) discuss advantages of explainable output incorporated into a graphical user interface; (5) conclude with the results achieved and directions for future work.
How do investors trade R&D-intensive Stocks? Evidence from hedge funds and other institutional investors

Abstract We examine how institutional investors trade stocks with high research and development (R&D) expenses and investigate whether they can detect value-relevant R&D. We document significant differences between hedge funds and other institutional investors in their trading in high R&D stocks. We find that hedge funds (other institutional investors) invest more (less) in high R&D stocks compared to all other stocks. Moreover, hedge funds exhibit strong stock-picking ability in high R&D stocks, and hedge funds with larger allocations to high R&D stocks generate higher future fund returns. Our findings suggest that hedge funds have superior skill in identifying value-relevant R&D.
Pb-Oxo Interactions in Uranyl Hybrid Materials: A Combined Experimental and Computational Analysis of Bonding and Spectroscopic Properties.

Reported are the syntheses and characterization of six new heterometallic UO22+/Pb2+ compounds. These materials feature rare instances of M-oxo interactions, which influence the bonding properties of the uranyl cation. The spectroscopic effects of these interactions were measured using luminescence and Raman spectroscopy. Computational density functional theory-based natural bonding orbital and quantum theory of atoms in molecules methods indicate interactions arise predominantly through charge transfer between cationic units via the electron-donating uranyl O spx lone pair orbitals and electron-accepting Pb2+ p orbitals. The interaction strength varies as a function of Pb-oxo interaction distance and angle with energy values ranging from 0.47 kcal/mol in the longer contacts to 21.94 kcal/mol in the shorter contacts. Uranyl units with stronger interactions at the oxo display an asymmetric bond weakening and a loss of covalent character in the U═O bonds interacting closely with the Pb2+ ion. Luminescence quenching is observed in cases in which strong Pb-oxo interactions are present and is accompanied by red-shifting of the uranyl symmetric Raman stretch. Changes to inner sphere uranyl bonding manifest as a weakening of the U═O bond as a result of interaction with the Pb2+ ion. Comprehensive evaluation of the effects of metal ions on uranyl spectra supports modeling efforts probing uranyl bonding and may inform applications such as forensic signatures.
Modeling Ion Flux Coefficient in High Ion Concentrated Dusty Plasmas Using Langevin Dynamics Simulations

The influence of dense and strongly coupled ions on the ion current to a particle is modeled in this work. Langevin Dynamics is used to simulate the motion of multiple ions around a negatively charged grain in a periodic domain. The ion flux coefficient is calculated using the grain-ion collision time distribution. In addition to the ion-ion electrostatic coupling strength Γ=e 2 /(4πe 0 k B T i n i -1/3 ), the ion flux coefficient is also influenced by the diffusive Knudsen number Kn D =(m i k B T i ) 1/2 /f i a p that characterizes the ion-neutral gas interactions (through the scalar friction factor f i =k B T i /D i , D i is the ion diffusion coefficient), a concentration parameter χ=a p /n i -1/3 that compares the size of the grain a p to the mean inter-ion spacing n i -1/3 , where n i is the ion number concentration and Ψ E =-z p Γ/χ that denotes the strength of particle-ion interaction. The influence of Kn D on the structure of the ions and their self-organization is demonstrated by analyzing the ion-ion pair correlation function g 2 (r) and ion velocity distribution function. It is seen that depending on the regime defined by Γ, Kn D , χ, Ψ E the ion velocity distribution function is Maxwellian for Γ<10 and the state resembles that of a gas. For higher values of Γ (strongly coupled states), liquid or solid like behavior is seen and the use of a simple screened Coulomb potential may not be valid in such regimes. The ion flux coefficient model is presented as the ratio of the computed value to that obtained using a dilute charging model to understand the relative increase or decrease due to ion correlated motion. The developed model will be validated by comparing with suitable experimental data from the literature with an intention to extend previously published dilute charging models 1 , 2 for non-thermal plasmas into the high ion concentration regime.
Optimal Operation of Power Systems with Energy Storage under Uncertainty: A Scenario based Method with Strategic Sampling

The multi-period dynamics of energy storage (ES), intermittent renewable generation and uncontrollable power loads, make the optimization of power system operation (PSO) challenging. A multi-period optimal PSO under uncertainty is formulated using the chance-constrained optimization (CCO) modeling paradigm, where the constraints include the nonlinear energy storage and AC power flow models. Based on the emerg-ing scenario optimization method which does not rely on pre-known probability distribution, this paper develops a novel solu-tion method for this challenging CCO problem. The proposed method is computationally effective for mainly two reasons. First, the original AC power flow constraints are approximated by a set of learning-assisted quadratic convex inequalities based on a generalized least absolute shrinkage and selection operator (LASSSO). Second, considering the physical patterns of data and driven by the learning-based sampling, the strategic sampling method is developed to significantly reduce the required number of scenarios by different sampling strategies. The simulation re-sults on IEEE standard systems indicate that 1) the proposed strategic sampling significantly improves the computational effi-ciency of the scenario-based approach for solving the chance-constrained optimal PSO problem, 2) the data-driven convex approximation of power flow can be promising alternatives of nonlinear and nonconvex AC power flow.
Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes

Optimization algorithms such as projected Newton's method, FISTA, mirror descent and its variants enjoy near-optimal regret bounds and convergence rates, but suffer from a computational bottleneck of computing "projections'' in potentially each iteration (e.g., $O(T^{1/2})$ regret of online mirror descent). On the other hand, conditional gradient variants solve a linear optimization in each iteration, but result in suboptimal rates (e.g., $O(T^{3/4})$ regret of online Frank-Wolfe). Motivated by this trade-off in runtime v/s convergence rates, we consider iterative projections of close-by points over widely-prevalent submodular base polytopes $B(f)$. We develop a toolkit to speed up the computation of projections using both discrete and continuous perspectives. We subsequently adapt the away-step Frank-Wolfe algorithm to use this information and enable early termination. For the special case of cardinality based submodular polytopes, we improve the runtime of computing certain Bregman projections by a factor of $\Omega(n/\log(n))$. Our theoretical results show orders of magnitude reduction in runtime in preliminary computational experiments.
Graded Poisson algebras on bordism groups of garlands

Abstract Let M m be an oriented manifold and let N be a set consisting of oriented closed manifolds of the same odd dimension n . We consider a topological space G N , M of commutative diagrams. Each commutative diagram consists of a few manifolds from N that are mapped to M and of one point spaces pt that are each mapped to a pair of manifolds from N . We consider the oriented bordism group Ω ⁎ ( G N , M ) = ⊕ i = 0 ∞ Ω i ( G N , M ) . We introduce the operations ⋆ and [ ⋅ , ⋅ ] on Ω ⁎ ( G N , M ) ⊗ Q , that make Ω ⁎ ( G N , M ) ⊗ Q into a Z -graded ( m − 2 n ) -Poisson algebra. For N = { S 1 } and a surface M = F 2 , the subalgebra Ω 0 ( G { S 1 } , F 2 ) ⊗ Q of our algebra is related to the Andersen-Mattes-Reshetikhin Poisson algebra of chord-diagrams.
Phase I single center trial of Ketogenic Diet for Adults with Traumatic Brain Injury

Summary Background Traumatic Brain injury (TBI) is a major cause of mortality and morbidity in the United States. Ketogenic diet (KD) has been shown to have neuroprotective effects in acute brain injury, but limited data about its use in adult TBI patients is available. The objective of this study is to investigate the feasibility and safety of ketogenic diet (KD) for adult TBI patients in the Neuroscience Intensive Care Unit (NSICU). Methods TBI patients admitted to NSICU between June 2019 to March 2021 were enrolled in this single-center, open label, single-arm prospective intervention study. The primary feasibility outcome was achievement of ketosis (detection and maintenance of serum beta-hydroxybutyrate (BOB) levels above normal); secondary outcomes included laboratory and clinical adverse effects related to KD. Results 10 adults with TBI with Abbreviated Injury Score (AIS)-Head ≥ 3 and ventriculostomy catheter to monitor intracranial pressure met inclusion/exclusion criteria and were placed on KD. Mean age was 47 years, and all patients were male. Eight out of 10 patients achieved ketosis within mean 2.2 days. KD was initiated within 8-33 hours (average 23 hours) of hospital admission. No clinical adverse effects were noted, 2 patients developed hypertriglyceridemia and 1 patient developed hypoglycemia. Serum glucose showed a decreasing trend in most patients. Conclusions This pilot study shows that KD is feasible in the management of TBI patients. A randomized controlled trial (RCT) is justified to further understand the optimal serum BOB levels, dose and duration of KD in TBI and its effect on the outcome. ClinicalTrials.gov Identifier NCT03982602, Registered 06/11/2019, https://clinicaltrials.gov/ct2/show/NCT03982602?term=brain+injury&cond=ketogenic+diet&draw=2&rank=3 .
Are Transformers More Robust Than CNNs

Transformer emerges as a powerful tool for visual recognition. In addition to demonstrating competitive performance on a broad range of visual benchmarks, recent works also argue that Transformers are much more robust than Convolutions Neural Networks (CNNs). Nonetheless, surprisingly, we find these conclusions are drawn from unfair experimental settings, where Transformers and CNNs are compared at different scales and are applied with distinct training frameworks. In this paper, we aim to provide the first fair & in-depth comparisons between Transformers and CNNs, focusing on robustness evaluations. With our unified training setup, we first challenge the previous belief that Transformers outshine CNNs when measuring adversarial robustness. More surprisingly, we find CNNs can easily be as robust as Transformers on defending against adversarial attacks, if they properly adopt Transformers' training recipes. While regarding generalization on out-of-distribution samples, we show pre-training on (external) large-scale datasets is not a fundamental request for enabling Transformers to achieve better performance than CNNs. Moreover, our ablations suggest such stronger generalization is largely benefited by the Transformer's self-attention-like architectures per se, rather than by other training setups. We hope this work can help the community better understand and benchmark the robustness of Transformers and CNNs. The code and models are publicly available at this https URL.
Modeling of some coplanar waveguide discontinuities

Lumped equivalent circuit models for several coplanar waveguide (CPW) discontinuities such as an open circuit, a series gap in the center conductor, and a symmetric step in the center conductor are presented, and their element value are given as a function of the physical dimensions of the discontinuity. The model element values are de-embedded from measured S-parameters, and the effects of the center conductor width and the substrate thickness on the equivalent circuit element values are obtained. The characteristics of a CPW right-angle bend using a compensation technique are presented. The frequency dependence of the effective dielectric constant is measured and compared to computed values. >
Development of a volume H/sup -/ ion source for LAMPF

We have continued the development of a toroidal-filter volume H/sup -/ ion source with the goal of providing a better ion source for the proton storage ring at the Los Alamos Neutron Scattering Center (LANSCE). A prototype source has produced H/sup -/ beam currents of 6.3 mA from a 3-mm-diameter emission aperture (89 mA/cm/sup 2/) and 18 mA from a 10-mm-diameter emission aperture (23 mA/cm/sup 2/). These results were achieved when cesium was added to tile discharge. The cesium increased the H/sup -/ current by a factor of 2-3 while the extracted electron current was strongly suppressed. The magnetic filter can be configured so that the electron to H/sup -/ ratio is 2:1 and is weakly dependent on operating parameters, with only a moderate loss of H/sup -/ current. Tests indicate there is essentially no explicit dependence of extracted beam current on duty factor up to 10% duty-factor. Emittance data were taken for various operating conditions in a mass-analyzed beam line and a comparison is made to the surface conversion H/sup -/ ion source now in operation for the storage ring.