cssi/SciSciGPT-SciSciNet huggingface dataset:
Almost Non-negative Scalar Curvature on Riemannian Manifolds Conformal to Tori

In this article we reduce the geometric stability conjecture for the scalar torus rigidity theorem to the conformal case via the Yamabe problem. Then we are able to prove the case where a sequence of Riemannian manifolds is conformal to a uniformly controlled sequence of flat tori and satisfies the geometric stability conjecture. We are also able to handle the case where a sequence of Riemannian manifolds is conformal to a sequence of constant negative scalar curvature Riemannian manifolds which converge to a flat torus in $$C^1$$ . The full conjecture from the conformal perspective is also discussed as a possible approach to resolving the conjecture.
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression

We study the convergence rates of the EM algorithm for learning two-component mixed linear regression under all regimes of signal-to-noise ratio (SNR). We resolve a long-standing question that many recent results have attempted to tackle: we completely characterize the convergence behavior of EM, and show that the EM algorithm achieves minimax optimal sample complexity under all SNR regimes. In particular, when the SNR is sufficiently large, the EM updates converge to the true parameter $\theta^{*}$ at the standard parametric convergence rate $\mathcal{O}((d/n)^{1/2})$ after $\mathcal{O}(\log(n/d))$ iterations. In the regime where the SNR is above $\mathcal{O}((d/n)^{1/4})$ and below some constant, the EM iterates converge to a $\mathcal{O}({\rm SNR}^{-1} (d/n)^{1/2})$ neighborhood of the true parameter, when the number of iterations is of the order $\mathcal{O}({\rm SNR}^{-2} \log(n/d))$. In the low SNR regime where the SNR is below $\mathcal{O}((d/n)^{1/4})$, we show that EM converges to a $\mathcal{O}((d/n)^{1/4})$ neighborhood of the true parameters, after $\mathcal{O}((n/d)^{1/2})$ iterations. Notably, these results are achieved under mild conditions of either random initialization or an efficiently computable local initialization. By providing tight convergence guarantees of the EM algorithm in middle-to-low SNR regimes, we fill the remaining gap in the literature, and significantly, reveal that in low SNR, EM changes rate, matching the $n^{-1/4}$ rate of the MLE, a behavior that previous work had been unable to show.
Classification of fNIRS Data Under Uncertainty: A Bayesian Neural Network Approach

Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive form of Brain-Computer Interface (BCI). It is used for the imaging of brain hemodynamics and has gained popularity due to the certain pros it poses over other similar technologies. The overall functionalities encompass the capture, processing and classification of brain signals. Since hemodynamic responses are contaminated by physiological noises, several methods have been implemented in the past literature to classify the responses in focus from the unwanted ones. However, the methods, thus far does not take into consideration the uncertainty in the data or model parameters. In this paper, we use a Bayesian Neural Network (BNN) to carry out a binary classification on an open-access dataset, consisting of unilateral finger tapping (left- and right-hand finger tapping). A BNN uses Bayesian statistics to assign a probability distribution to the network weights instead of a point estimate. In this way, it takes data and model uncertainty into consideration while carrying out the classification. We used Variational Inference (VI) to train our model. Our model produced an overall classification accuracy of 86.44% over 30 volunteers. We illustrated how the evidence lower bound (ELBO) function of the model converges over iterations. We further illustrated the uncertainty that is inherent during the sampling of the posterior distribution of the weights. We also generated a ROC curve for our BNN classifier using test data from a single volunteer and our model has an AUC score of 0.855.
Duration sensitivity of key moments.

Abstract The role of duration in the evaluation of experiences has been a topic of great interest. Extensive research finds “duration neglect,” defined as the duration of experiences exerting only a negligible direct effect on overall evaluations of experiences over that of key moments, usually peak and end. In contrast, we argue that the temporal location of key moments that are embedded in an experience affects how people experience these key moments. Thus, duration enters overall evaluations indirectly through the experience of key moments. Seven studies, as well as three supplementary studies, employing diverse designs and analyses find that temporal location affects the evaluation of peak and end, and consequently duration has a significant indirect effect on overall evaluations, while also replicating duration neglect. Establishing duration-dependent key moments in the evaluation of experiences, our account also uniquely predicts, and we subsequently test, that the temporal location of key moments matters for evaluations of experiences.
Efficient sympathetic cooling in mixed barium and ytterbium ion chains

We study sympathetic cooling of the radial ion motion in a linear RF trap in mixed barium-ytterbium chains. Barium ions are Doppler-cooled, while ytterbium ions are cooled through their interaction with cold barium ions. We estimate the efficiency of sympathetic cooling by measuring the average occupation quantum numbers, and thus the temperature, of all radial normal modes of motion in the ion chain. The full set of orderings in a chain of two barium and two ytterbium ions have been probed, and we show that the average thermal occupation numbers for all chain configurations strongly depend on the trap aspect ratio. We demonstrate efficient sympathetic cooling of all radial normal modes for the trap aspect ratio of approximately 2.9.
Greedy Scheduling: A Neural Network Method to Reduce Task Failure in Software Crowdsourcing.

Context: Highly dynamic and competitive crowdsourcing software development (CSD) marketplaces may experience task failure due to unforeseen reasons, such as increased competition over shared supplier resources, or uncertainty associated with a dynamic worker supply. Existing analysis reveals an average task failure ratio of 15.7\% in software crowdsourcing markets. Goal: The objective of this study is to provide a task scheduling recommendation model for software crowdsourcing platforms in order to improve the success and efficiency of software crowdsourcing. Method: We propose a task scheduling method based on neural networks, and develop a system that can predict and analyze task failure probability upon arrival. More specifically, the model uses a range of input variables, including the number of open tasks in the platform, the average task similarity between arriving tasks and open tasks, the winner's monetary prize, and task duration, to predict the probability of task failure on the planned arrival date and two surplus days. This prediction will offer the recommended day associated with the lowest task failure probability to post the task. The proposed model is based on the workflow and data of Topcoder, one of the primary software crowdsourcing platforms. Results: We present a model that suggests the best recommended arrival dates for any task in the project with surplus of two days per task in the project. The model on average provided 4\% lower failure ratio per project.
It’s Not Just the Movement: Experiential Information Needed for Stroke Telerehabilitation

Telerehabilitation systems for stroke survivors have been predominantly designed to measure and quantify movement in order to guide and encourage rehabilitation regular exercises at home. We set out to study what aspect of the movement data was essential, to better inform sensor design. We investigated face-to-face stroke rehabilitation sessions through a series of interviews and observations involving 16 stroke rehabilitation specialists including physiatrists, physical therapists, and occupational therapists. We found that specialists are not solely interested in movement data, and that experiential information about stroke survivors’ lived experience plays an essential role in specialists interpreting movement data and creating a rehabilitation plan. We argue for a reconceptualization in stroke telerehabilitation that is more inclusive of non-movement data, and present design implications to better account for experiential information in telerehabilitation systems.
Untying knots in 4D and Wedderburn's Theorem

It is proved that the Wedderburn Theorem on finite division rings implies that all knots and links in the smooth four-dimensional manifolds are trivial.Communicated by Igor Klep
Long-term results of simplified lymphatic microsurgical preventing healing approach (SLYMPHA) for the prevention of breast cancer-related clinical lymphedema after axillary lymph node dissection.

514Background: Lymphedema (LE) is a serious complication of axillary lymph node dissection (ALND) with an incidence rate of 16%. SLYMPHA is a safe and relatively simple method, which decreases inci...
Personal Jurisdiction in Comparative Context

This Article places the recent evolution of U.S. personal jurisdiction in comparative context. Comparativism helps illuminate and explain both the modest convergences and the more pervasive divergences. On the convergences side, the Supreme Court’s acknowledgment of transnational litigation and express invocation of European approaches to personal jurisdiction have helped move general jurisdiction away from the exorbitant “doing business” jurisdiction that seemed previously to be settled U.S. law. But persistent divergences tell the more interesting story. The Court’s refusal to deviate from its commitment to transient jurisdiction, its recent narrowing of specific jurisdiction since 2011, its implicit rejection of pendent-party personal jurisdiction, and its adherence to a strong form of consent-based personal jurisdiction all reveal a stark contrast with other countries’ approaches to personal jurisdiction. That contrast is founded on deep and stubborn ties to American history, political structure, and litigation norms, all of which make broader convergence difficult, if not implausible. For these reasons, U.S. personal-jurisdiction doctrine is more likely to continue to develop on an independent track rather than hew to global trends. Some areas of parallelism might still occur, but substantial convergence is likely to remain elusive.
Pseudouridine-free Ribosome Exhibits Distinct Inter-subunit Movements

Pseudouridine, the most abundant form of RNA modification, is known to play important roles in ribosome function. Mutations in human DKC1, the pseudouridine synthase responsible for catalyzing the ribosome RNA modification, cause translation deficiencies and are associated with a complex cancer predisposition. The structural basis for how pseudouridine impacts ribosome function remains uncharacterized. Here we report electron cryomicroscopy structures of a fully modified and a pseudouridine-free ribosome from Saccharomyces cerevisiae. In the modified ribosome, the rearranged N1 atom of pseudouridine is observed to stabilize key functional motifs by establishing predominately water-mediated close contacts with the phosphate backbone. The pseudouridine-free ribosome, however, is devoid of such interactions and displays conformations reflective of abnormal inter-subunit movements. The erroneous motions of the pseudouridine-free ribosome may explain its observed deficiencies in translation.
S-LIME: Stabilized-LIME for Model Explanation

An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are black boxes in nature which are hard to explain. There are growing efforts for researchers to develop methods to interpret these black-box models. Post hoc explanations based on perturbations, such as LIME [39], are widely used approaches to interpret a machine learning model after it has been built. This class of methods has been shown to exhibit large instability, posing serious challenges to the effectiveness of the method itself and harming user trust. In this paper, we propose S-LIME, which utilizes a hypothesis testing framework based on central limit theorem for determining the number of perturbation points needed to guarantee stability of the resulting explanation. Experiments on both simulated and real world data sets are provided to demonstrate the effectiveness of our method.
Why European Banks Are Undercapitalized and What Should Be Done About It

Major European banks are significantly undercapitalized as compared to large American banks, and, more importantly, as compared to the capital levels they would need to survive another severe financial crisis. Bank capital shortfalls in Italy, Spain, Germany, France and the United Kingdom, in particular, are largely the consequence of European bank regulations that: (1) apply static risk weights to assets like mortgages and sovereign debt; (2) fail to require an overall marketâ€ based capital ratio that is high enough to enable banks to survive a severe financial crisis; (3) fail to get banks to promptly write down their impaired assets to market value; (4) subject banks to weak stress tests that can create a false impression of capital adequacy; and (5) fail to compel banks to retain sufficient earnings and to raise sufficient capital externally to eliminate capital shortfalls promptly, all apparently out of fear that being tougher might cause investors and customers to lose confidence in the banks. This article summarizes important recent independent bank stress testing that has quantified the capital shortfalls in European banks. The recent highly publicized regulatory interventions to resolve failing European banks were inevitable due to these shortfalls. The authors recommend steps European bank regulators should take to address the problem and to eliminate the risk of serious capital shortfalls. In the absence of such steps, bank depositors, customers, and security holders should be prepared to expect further unwelcome surprises as the risks inherent in allowing undercapitalized banks to operate will continue to materialize in more bank failures.
Parametric oscillation and amplification with gate controlled capacitor-within-capacitor

Parametric oscillators and parametric amplifiers are known for their ‘quiet’ operation and find new applications in quantum circuitry. A Capacitor-within-Capacitor (CWC) is a nested electronic element that has two components: the cell (e.g., the outer capacitor) and the gate (e.g., the inner capacitor). Here we provide analysis and experiments on diode-interfaced, CWC that exhibit parametric oscillations and parametric amplifications. By replacing the diode with a doped nano-graphene junction, we demonstrated a new structure whose doping may be electronically and chemically controlled. Advantages of these elements are in their simplicity, large relative capacitance change (of the order of 50%), separation of pump and signal channels and possibility for large integration.
Contextualized Perturbation for Textual Adversarial Attack

Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness. Existing techniques of generating such examples are typically driven by local heuristic rules that are agnostic to the context, often resulting in unnatural and ungrammatical outputs. This paper presents CLARE, a ContextuaLized AdversaRial Example generation model that produces fluent and grammatical outputs through a mask-then-infill procedure. CLARE builds on a pre-trained masked language model and modifies the inputs in a context-aware manner. We propose three contextualized perturbations, Replace, Insert and Merge, that allow for generating outputs of varied lengths. CLARE can flexibly combine these perturbations and apply them at any position in the inputs, and is thus able to attack the victim model more effectively with fewer edits. Extensive experiments and human evaluation demonstrate that CLARE outperforms the baselines in terms of attack success rate, textual similarity, fluency and grammaticality.
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game

To achieve sample efficiency in reinforcement learning (RL), it necessitates efficiently exploring the underlying environment. Under the offline setting, addressing the exploration challenge lies in collecting an offline dataset with sufficient coverage. Motivated by such a challenge, we study the reward-free RL problem, where an agent aims to thoroughly explore the environment without any pre-specified reward function. Then, given any extrinsic reward, the agent computes the policy via a planning algorithm with offline data collected in the exploration phase. Moreover, we tackle this problem under the context of function approximation, leveraging powerful function approximators. Specifically, we propose to explore via an optimistic variant of the value-iteration algorithm incorporating kernel and neural function approximations, where we adopt the associated exploration bonus as the exploration reward. Moreover, we design exploration and planning algorithms for both single-agent MDPs and zero-sum Markov games and prove that our methods can achieve $\widetilde{\mathcal{O}}(1 /\varepsilon^2)$ sample complexity for generating a $\varepsilon$-suboptimal policy or $\varepsilon$-approximate Nash equilibrium when given an arbitrary extrinsic reward. To the best of our knowledge, we establish the first provably efficient reward-free RL algorithm with kernel and neural function approximators.
Deep Density: Circumventing the Kohn-Sham equations via symmetry preserving neural networks

Abstract The recently developed Deep Potential [Phys. Rev. Lett. 120 (2018) 143001 [27] ] is a powerful method to represent general inter-atomic potentials using deep neural networks. The success of Deep Potential rests on the proper treatment of locality and symmetry properties of each component of the network. In this paper, we leverage its network structure to effectively represent the mapping from the atomic configuration to the electron density in Kohn-Sham density function theory (KS-DFT). By directly targeting at the self-consistent electron density, we demonstrate that the adapted network architecture, called the Deep Density, can effectively represent the self-consistent electron density as the linear combination of contributions from many local clusters. The network is constructed to satisfy the translation, rotation, and permutation symmetries, and is designed to be transferable to different system sizes. We demonstrate that using a relatively small number of training snapshots, with each snapshot containing a modest amount of data-points, Deep Density achieves excellent performance for one-dimensional insulating and metallic systems, as well as systems with mixed insulating and metallic characters. We also demonstrate its performance for real three-dimensional systems, including small organic molecules, as well as extended systems such as water (up to 512 molecules) and aluminum (up to 256 atoms).
Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments

We present algorithms for aligning components of Abstract Meaning Representation (AMR) graphs to spans in English sentences. We leverage unsupervised learning in combination with heuristics, taking the best of both worlds from previous AMR aligners. Our unsupervised models, however, are more sensitive to graph substructures, without requiring a separate syntactic parse. Our approach covers a wider variety of AMR substructures than previously considered, achieves higher coverage of nodes and edges, and does so with higher accuracy. We will release our LEAMR datasets and aligner for use in research on AMR parsing, generation, and evaluation.
AgreeSum: Agreement-Oriented Multi-Document Summarization

We aim to renew interest in a particular multi-document summarization (MDS) task which we call AgreeSum: agreement-oriented multi-document summarization. Given a cluster of articles, the goal is to provide abstractive summaries that represent information common and faithful to all input articles. Given the lack of existing datasets, we create a dataset for AgreeSum, and provide annotations on article-summary entailment relations for a subset of the clusters in the dataset. We aim to create strong baselines for the task by applying the top-performing pretrained single-document summarization model PEGASUS onto AgreeSum, leveraging both annotated clusters by supervised losses, and unannotated clusters by T5-based entailment-related and language-related losses. Compared to other baselines, both automatic evaluation and human evaluation show better article-summary and cluster-summary entailment in generated summaries. On a separate note, we hope that our article-summary entailment annotations contribute to the community's effort in improving abstractive summarization faithfulness.
Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions

Cannabis legalization has been welcomed by many U.S. states but its role in escalation from tobacco e-cigarette use to cannabis vaping is unclear. Meanwhile, cannabis vaping has been associated with new lung diseases and rising adolescent use. To understand the impact of cannabis legalization on escalation, we design an observational study to estimate the causal effect of recreational cannabis legalization on the development of pro-cannabis attitude for e-cigarette users. We collect and analyze Twitter data which contains opinions about cannabis and JUUL, a very popular e-cigarette brand. We use weakly supervised learning for personal tweet filtering and classification for stance detection. We discover that recreational cannabis legalization policy has an effect on increased development of pro-cannabis attitudes for users already in favor of e-cigarettes.
Model Adaptation for Inverse Problems in Imaging

Deep neural networks have been applied successfully to a wide variety of inverse problems arising in computational imaging. These networks are typically trained using a forward model that describes the measurement process to be inverted, which is often incorporated directly into the network itself. However, these approaches are sensitive to changes in the forward model: if at test time the forward model varies (even slightly) from the one the network was trained for, the reconstruction performance can degrade substantially. Given a network trained to solve an initial inverse problem with a known forward model, we propose two novel procedures that adapt the network to a change in the forward model, even without full knowledge of the change. Our approaches do not require access to more labeled data (i.e., ground truth images). We show these simple model adaptation approaches achieve empirical success in a variety of inverse problems, including deblurring, super-resolution, and undersampled image reconstruction in magnetic resonance imaging.
The Role of E-Government in Promoting Foreign Direct Investment Inflows

The outbreak of the COVID-19 pandemic has helped accelerate the digitization of public services. The lockdown initiated by most governments to curb the spread of the coronavirus forced most public agencies to switch to online platforms to continue providing information and services to the public. It is widely recognized that information diffusion and communication technology play a large role in improving the quality of public services in terms of time, cost, and interface with the public, business, and other agencies. Potentially, e-government could enhance a country's locational advantages and attract more Foreign Direct Investment (FDI) inflows. This hypothesis is tested empirically using an unbalanced panel data analysis for 178 host countries over the period 2003-2018. The results suggest that e-government stimulates the inflow of FDI.
Policies for Multi-Agency Recovery of Physical Infrastructure After Disasters

We consider a scenario where multiple infrastructure components have been damaged after a disaster and the health value of each component continues to deteriorate if it is not being targeted by a repair agency, until it fails irreversibly. There are multiple agencies that seek to repair the components and there is an authority whose task is to allocate the components to the agencies within a given budget, so that the total number of components that are fully repaired by the agencies is maximized. We characterize the optimal policy for allocation and repair sequencing when the repair rates are sufficiently larger than the deterioration rates. For the case when the deterioration rates are larger than or equal to the repair rates, the rates are homogeneous across the components, and the costs charged by the entities for repair are equal, we characterize a policy for allocation and repair sequencing that permanently repairs at least half the number of components as that by an optimal policy.
Five-year pediatric use of a digital wearable fitness device: lessons from a pilot case study.

Author(s): Butte, Kimayani D; Bahmani, Amir; Butte, Atul J; Li, Xiao; Snyder, Michael P | Abstract: ObjectivesWearable fitness devices are increasingly being used by the general population, with many new applications being proposed for healthy adults as well as for adults with chronic diseases. Fewer, if any, studies of these devices have been conducted in healthy adolescents and teenagers, especially over a long period of time. The goal of this work was to document the successes and challenges involved in 5 years of a wearable fitness device use in a pediatric case study.Materials and methodsComparison of 5 years of step counts and minutes asleep from a teenaged girl and her father.ResultsAt 60 months, this may be the longest reported pediatric study involving a wearable fitness device, and the first simultaneously involving a parent and a child. We find step counts to be significantly higher for both the adult and teen on school/work days, along with less sleep. The teen walked significantly less towards the end of the 5-year study. Surprisingly, many of the adult's and teen's sleeping and step counts were correlated, possibly due to coordinated behaviors.DiscussionWe end with several recommendations for pediatricians and device manufacturers, including the need for constant adjustments of stride length and calorie counts as teens are growing.ConclusionWith periodic adjustments for growth, this pilot study shows these devices can be used for more accurate and consistent measurements in adolescents and teenagers over longer periods of time, to potentially promote healthy behaviors.
Explicit Artin maps into PGL2

Abstract Let G ⊂ PGL 2 ( F q ) where q is any prime power, and let Q ∈ F q ( x ) such that F q ( x ) / F q ( Q ) is a Galois extension with group G . By explicitly computing the Artin map on unramified degree-1 primes in F q ( Q ) for various groups G , interesting new results emerge about finite fields, additive polynomials, and conjugacy classes of PGL 2 ( F q ) . For example, by taking G to be a unipotent group, one obtains a new characterization for when an additive polynomial splits completely over F q . When G = PGL 2 ( F q ) , one obtains information about conjugacy classes of PGL 2 ( F q ) . When G is the group of order 3 generated by 1 1 − 1 0 , one obtains a natural tripartite symbol on F q with values in Z / 3 Z . Some of these results generalize to PGL 2 ( K ) for arbitrary fields K .
An In-Memory Analog Computing Co-Processor for Energy-Efficient CNN Inference on Mobile Devices

In this paper, we develop an in-memory analog computing (IMAC) architecture realizing both synaptic behavior and activation functions within non-volatile memory arrays. Spin-orbit torque magnetoresistive random-access memory (SOT-MRAM) devices are leveraged to realize sigmoidal neurons as well as binarized synapses. First, it is shown the proposed IMAC architecture can be utilized to realize a multilayer perceptron (MLP) classifier achieving orders of magnitude performance improvement compared to previous mixed-signal and digital implementations. Next, a heterogeneous mixed-signal and mixed-precision CPU-IMAC architecture is proposed for convolutional neural networks (CNNs) inference on mobile processors, in which IMAC is designed as a co-processor to realize fully-connected (FC) layers whereas convolution layers are executed in CPU. Architecture-level analytical models are developed to evaluate the performance and energy consumption of the CPU-IMAC architecture. Simulation results exhibit 6.5% and 10% energy savings for CPU-IMAC based realizations of LeNet and VGG CNN models, for MNIST and CIFAR-10 pattern recognition tasks, respectively.
Gradient-Enhanced Multifidelity Neural Networks for High-Dimensional Function Approximation

In this work, a novel multifidelity machine learning (ML) model, the gradient-enhanced multifidelity neural networks (GEMFNNs), is proposed. This model is a multifidelity version of gradient-enhanced neural networks (GENNs) as it uses both function and gradient information available at multiple levels of fidelity to make function approximations. Its construction is similar to multifidelity neural networks (MFNNs). This model is tested on three analytical function, a one, two, and a 20 variable function. It is also compared to neural networks (NNs), GENNs, and MFNNs, and the number of samples required to reach a global accuracy of 0.99 coefficient of determination (R^2) is measured. GEMFNNs required 18, 120, and 600 high-fidelity samples for the one, two, and 20 dimensional cases, respectively, to meet the target accuracy. NNs performed best on the one variable case, requiring only ten samples, while GENNs worked best on the two variable case, requiring 120 samples. GEMFNNs worked best for the 20 variable case, while requiring nearly eight times fewer samples than its nearest competitor, GENNs. For this case, NNs and MFNNs did not reach the target global accuracy even after using 10,000 high-fidelity samples. This work demonstrates the benefits of using gradient as well as multifidelity information in NNs for high-dimensional problems.
Leveraged ETF Pairs: An Empirical Evaluation of Portfolio Performance

We examine the performance of investment strategies involving leveraged and inverse leveraged ETF pairs. As in Jiang and Peterburgsky’s (2017) simulation analysis, the empirical analysis in this article indicates that simple portfolios of bull/bear short positions constructed to approximately mimic an underlying index (such as the S&P 500) outperform the index on a risk-adjusted basis. The outperformance asymptotically diminishes as the investor’s rebalance bounds tighten. Additionally, results indicate that the volatility effect dominates the trend effect for bull leveraged ETFs. This finding is relevant for the debate on the regulation of leveraged ETF markets. TOPICS:Exchange-traded funds and applications, simulations, performance measurement, legal/regulatory/public policy Key Findings ▪ Simple portfolios of bull/bear short positions in leveraged and inverse leveraged ETFs constructed to approximately mimic an underlying index (such as the S&P 500) outperform the index on a risk-adjusted basis. ▪ The outperformance asymptotically diminishes as the investor’s rebalance bounds tighten. ▪ The volatility effect dominates the trend effect for bull leveraged ETFs.
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge

Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability to support complex prediction problems using insurance claims data, such as readmission at 30 days, mainly due to data sparsity issue. Consequently, classical machine learning methods, especially those that embed domain knowledge in handcrafted features, are often on par with, and sometimes outperform, deep learning approaches. In this paper, we illustrate how the potential of deep learning can be achieved by blending domain knowledge within deep learning architectures to predict adverse events at hospital discharge, including readmissions. More specifically, we introduce a learning architecture that fuses a representation of patient data computed by a self-attention based recurrent neural network, with clinically relevant features. We conduct extensive experiments on a large claims dataset and show that the blended method outperforms the standard machine learning approaches.
Merit and Blame Assignment with Kind 2.

We introduce two new major features of the open-source model checker Kind 2 which provide traceability information between specification and design elements such as assumptions, guarantees, or other behavioral constraints in synchronous reactive system models. This new version of Kind 2 can identify minimal sets of design elements, known as Minimal Inductive Validity Cores, which are sufficient to prove a given set of safety properties, and also determine the set of MUST elements, design elements that are necessary to prove the given properties. In addition, Kind 2 is able to find minimal sets of design constraints, known as Minimal Cut Sets, whose violation leads the system to an unsafe state. We illustrate with an example how to use the computed information for tracking the safety impact of model changes, and for analyzing the tolerance and resilience of a system against faults.
Metabolomics of Lung Microdissections Reveals Region- and Sex-Specific Metabolic Effects of Acute Naphthalene Exposure in Mice.

Naphthalene is a ubiquitous environmental contaminant produced by combustion of fossil fuels and is a primary constituent of both mainstream and side stream tobacco smoke. Naphthalene elicits region-specific toxicity in airway club cells through cytochrome P450 (P450)-mediated bioactivation, resulting in depletion of glutathione and subsequent cytotoxicity. While effects of naphthalene in mice have been extensively studied, few experiments have characterized global metabolomic changes in the lung. In individual lung regions, we found metabolomic changes in microdissected mouse lung conducting airways and parenchyma obtained from animals sacrificed at three timepoints following naphthalene treatment. Data on 577 unique identified metabolites were acquired by accurate mass spectrometry-based assays focusing on lipidomics and non-targeted metabolomics of hydrophilic compounds. Statistical analyses revealed distinct metabolite profiles between the two lung regions. Additionally, the number and magnitude of statistically significant exposure-induced changes in metabolite abundance were different between airways and parenchyma for unsaturated lysophosphatidylcholines (LPCs), dipeptides, purines, pyrimidines, and amino acids. Importantly, temporal changes were found to be highly distinct for male and female mice, with males exhibiting predominant treatment-specific changes only at two hours post-exposure. In females, metabolomic changes persisted until six hours post-naphthalene treatment, which may explain the previously characterized higher susceptibility of female mice to naphthalene toxicity. In both males and females, treatment-specific changes corresponding to lung remodeling, oxidative stress response, and DNA damage were observed. Overall, this study provides insights into potential mechanisms contributing to naphthalene toxicity and presents a novel approach for lung metabolomic analysis that distinguishes responses of major lung regions.
A note on tight projective 2-designs

We study tight projective 2-designs in three different settings. In the complex setting, Zauner's conjecture predicts the existence of a tight projective 2-design in every dimension. Pandey, Paulsen, Prakash, and Rahaman recently proposed an approach to make quantitative progress on this conjecture in terms of the entanglement breaking rank of a certain quantum channel. We show that this quantity is equal to the size of the smallest weighted projective 2-design. Next, in the finite field setting, we introduce a notion of projective 2-designs, we characterize when such projective 2-designs are tight, and we provide a construction of such objects. Finally, in the quaternionic setting, we show that every tight projective 2-design for H^d determines an equi-isoclinic tight fusion frame of d(2d-1) subspaces of R^d(2d+1) of dimension 3.
Virtual Markov Chains

We introduce the space of virtual Markov chains (VMCs) as a projective limit of the spaces of all finite state space Markov chains (MCs), in the same way that the space of virtual permutations is the projective limit of the spaces of all permutations of finite sets.We introduce the notions of virtual initial distribution (VID) and a virtual transition matrix (VTM), and we show that the law of any VMC is uniquely characterized by a pair of a VID and VTM which have to satisfy a certain compatibility condition.Lastly, we study various properties of compact convex sets associated to the theory of VMCs, including that the Birkhoff-von Neumann theorem fails in the virtual setting.
SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks

In-memory computing (IMC) on a monolithic chip for deep learning faces dramatic challenges on area, yield, and on-chip interconnection cost due to the ever-increasing model sizes. 2.5D integration or chiplet-based architectures interconnect multiple small chips (i.e., chiplets) to form a large computing system, presenting a feasible solution beyond a monolithic IMC architecture to accelerate large deep learning models. This paper presents a new benchmarking simulator, SIAM, to evaluate the performance of chiplet-based IMC architectures and explore the potential of such a paradigm shift in IMC architecture design. SIAM integrates device, circuit, architecture, network-on-chip (NoC), network-on-package (NoP), and DRAM access models to realize an end-to-end system. SIAM is scalable in its support of a wide range of deep neural networks (DNNs), customizable to various network structures and configurations, and capable of efficient design space exploration. We demonstrate the flexibility, scalability, and simulation speed of SIAM by benchmarking different state-of-the-art DNNs with CIFAR-10, CIFAR-100, and ImageNet datasets. We further calibrate the simulation results with a published silicon result, SIMBA. The chiplet-based IMC architecture obtained through SIAM shows 130 and 72 improvement in energy-efficiency for ResNet-50 on the ImageNet dataset compared to Nvidia V100 and T4 GPUs.
Differentiable Surface Rendering via Non-Differentiable Sampling

We present a method for differentiable rendering of 3D surfaces that supports both explicit and implicit representations, provides derivatives at occlusion boundaries, and is fast and simple to implement. The method first samples the surface using non-differentiable rasterization, then applies differentiable, depth-aware point splatting to produce the final image. Our approach requires no differentiable meshing or rasterization steps, making it efficient for large 3D models and applicable to isosurfaces extracted from implicit surface definitions. We demonstrate the effectiveness of our method for implicit-, mesh-, and parametric-surface-based inverse rendering and neural-network training applications. In particular, we show for the first time efficient, differentiable rendering of an isosurface extracted from a neural radiance field (NeRF), and demonstrate surface-based, rather than volume-based, rendering of a NeRF.
Byzantine-Resilient Distributed Hypothesis Testing With Time-Varying Network Topology

We study the problem of distributed hypothesis testing over a network of mobile agents with limited communication and sensing ranges to infer the true hypothesis collaboratively. In particular, we consider a scenario where there is an unknown subset of compromised agents that may deliberately share altered information to undermine the team objective. We propose two distributed algorithms where each agent maintains and updates two sets of beliefs (i.e., probability distributions over the hypotheses), namely local and actual beliefs (LB and AB respectively for brevity). In both algorithms, at every time step, each agent shares its AB with other agents within its communication range and makes a local observation to update its LB. Then both algorithms can use the shared information to update ABs under certain conditions. One requires receiving a certain number of shared ABs at each time instant; the other accumulates shared ABs over time and updates after the number of shared ABs exceeds a prescribed threshold. Otherwise, both algorithms rely on the agents current LB and AB to update the new AB. We prove under mild assumptions that the AB for every non-compromised agent converges a
Extreme gaps between eigenvalues of Wigner matrices

This paper proves universality of the distribution of the smallest and largest gaps between eigenvalues of generalized Wigner matrices, under some smoothness assumption for the density of the entries. The proof relies on the Erd{\H o}s-Schlein-Yau dynamic approach. We exhibit a new observable that satisfies a stochastic advection equation and reduces local relaxation of the Dyson Brownian motion to a maximum principle. This observable also provides a simple and unified proof of universality in the bulk and at the edge, which is quantitative. To illustrate this, we give the first explicit rate of convergence to the Tracy-Widom distribution for generalized Wigner matrices.
Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective

Backdoor attacks have been considered a severe security threat to deep learning. Such attacks can make models perform abnormally on inputs with predefined triggers and still retain state-of-the-art performance on clean data. While backdoor attacks have been thoroughly investigated in the image domain from both attackers' and defenders' sides, an analysis in the frequency domain has been missing thus far. This paper first revisits existing backdoor triggers from a frequency perspective and performs a comprehensive analysis. Our results show that many current backdoor attacks exhibit severe high-frequency artifacts, which persist across different datasets and resolutions. We further demonstrate these high-frequency artifacts enable a simple way to detect existing backdoor triggers at a detection rate of 98.50% without prior knowledge of the attack details and the target model. Acknowledging previous attacks' weaknesses, we propose a practical way to create smooth backdoor triggers without high-frequency artifacts and study their detectability. We show that existing defense works can benefit by incorporating these smooth triggers into their design consideration. Moreover, we show that the detector tuned over stronger smooth triggers can generalize well to unseen weak smooth triggers. In short, our work emphasizes the importance of considering frequency analysis when designing both backdoor attacks and defenses in deep learning.
Reconstruction of Bloch wavefunctions of holes in a semiconductor.

A central goal of condensed-matter physics is to understand how the diverse electronic and optical properties of crystalline materials emerge from the wavelike motion of electrons through periodically arranged atoms. However, more than 90 years after Bloch derived the functional forms of electronic waves in crystals1 (now known as Bloch wavefunctions), rapid scattering processes have so far prevented their direct experimental reconstruction. In high-order sideband generation2–9, electrons and holes generated in semiconductors by a near-infrared laser are accelerated to a high kinetic energy by a strong terahertz field, and recollide to emit near-infrared sidebands before they are scattered. Here we reconstruct the Bloch wavefunctions of two types of hole in gallium arsenide at wavelengths much longer than the spacing between atoms by experimentally measuring sideband polarizations and introducing an elegant theory that ties those polarizations to quantum interference between different recollision pathways. These Bloch wavefunctions are compactly visualized on the surface of a sphere. High-order sideband generation can, in principle, be observed from any direct-gap semiconductor or insulator. We thus expect that the method introduced here can be used to reconstruct low-energy Bloch wavefunctions in many of these materials, enabling important insights into the origin and engineering of the electronic and optical properties of condensed matter. Bloch wavefunctions of two types of hole in gallium arsenide are reconstructed by measuring the polarization of light emitted by collisions of electrons and holes accelerated by a terahertz laser.
Rapid Prototype for Shifting HPC to the Cloud

With the growing adoption of cloud computing, we have watched the rapidly changing landscape looking for unique ways to leverage cloud resources in an on-premise batch scheduled HPC environment. Research Computing at Purdue University has a long history of providing low cost HPC compute and storage resources to researchers on campus. Two areas we have recently targeted for cloud adoption are large scale workload bursting and rapid resource prototyping. Utilizing the new HBv3 instance types in Microsoft Azure, we were able to access the most recent third generation of AMD EPYC CPUs sooner than the delivery and deployment of our equivalent on-site resource, the upcoming NSF funded Anvil supercomputer. This rapid access allowed our colleagues and system designers to complete various tasks needed to evaluate and verify the expected performance of the newer generation hardware that will be available with Anvil in parallel to the on-site deployment.
Multi-Branch Tensor Network Structure for Tensor-Train Discriminant Analysis

Higher-order data with high dimensionality arise in a diverse set of application areas such as computer vision, video analytics and medical imaging. Tensors provide a natural tool for representing these types of data. Two major challenges that confound current tensor based supervised learning algorithms are storage complexity and computational efficiency. In this paper, we address these problems by introducing a multi-branch tensor network structure. The multi-branch structure is a general tensor decomposition that includes Tucker and tensor-train (TT) as special cases and takes advantage of the flexibility of the tensor network to provide a better balance between storage and computational complexity. We then introduce a supervised discriminative tensor-train subspace learning approach referred to as tensor-train discriminant analysis (TTDA), and its implementations using the multi-branch tensor network structure. Multi-branch implementations of TTDA are shown to achieve lower storage and computational complexity while providing improved classification performance with respect to both Tucker and TT based supervised learning methods.