cssi/SciSciGPT-SciSciNet huggingface dataset:
Toward Semi-Automatic Misconception Discovery Using Code Embeddings

Understanding students’ misconceptions is important for effective teaching and assessment. However, discovering such misconceptions manually can be time-consuming and laborious. Automated misconception discovery can address these challenges by highlighting patterns in student data, which domain experts can then inspect to identify misconceptions. In this work, we present a novel method for the semi-automated discovery of problem-specific misconceptions from students’ program code in computing courses, using a state-of-the-art code classification model. We trained the model on a block-based programming dataset and used the learned embedding to cluster incorrect student submissions. We found these clusters correspond to specific misconceptions about the problem and would not have been easily discovered with existing approaches. We also discuss potential applications of our approach and how these misconceptions inform domain-specific insights into students’ learning processes.
Differential effects of redox conditions on the decomposition of litter and soil organic matter

Soil redox conditions exert substantial influence on biogeochemical processes in terrestrial ecosystems. Humid tropical forest soils are often characterized by fluctuating redox, yet how these dynamics affect patterns of organic matter decomposition and associated CO2 fluxes remains poorly understood. We used a 13C-label incubation experiment in a humid tropical forest soil to follow the decomposition of plant litter and soil organic matter (SOM) in response to four redox regimes—static oxic or anoxic, and two oscillating treatments. We used high-resolution mass spectrometry to characterize the relative composition of organic compound classes in the water extractable OM. CO2 production from litter and SOM showed different responses to redox treatments. While cumulative production of SOM-derived CO2 was positively correlated with the length of oxic exposure (r = 0.89, n = 20), cumulative 13C-litter-derived CO2 production was not linked to oxygen availability. Litter-derived CO2 production was highest under static anoxic conditions in the first half of the experiment, and later dropped to the lowest rate amongst the treatments. In anoxic soils, we observed depletion of more oxidized water-extractable OM (especially amino sugar-, carbohydrate-, and protein-like compounds) over the second half of the experiment, which likely served as substrates for anaerobic CO2 production. Results from two-pool kinetic modeling showed that more frequent anoxic exposure limited decomposition of a slow-cycling C pool, but not a fast-cycling pool. These results suggest that aerobic and anaerobic heterotrophs were equally effective at degrading labile substrates released from fresh plant litter in this humid tropical forest soil, while aerobic decomposers were more effective in breaking down the potentially refractory compounds found in SOM.
Impact of COVID-19 Behavioral Inertia on Reopening Strategies for New York City Transit

The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing A baseline model was previously developed and calibrated for pre-COVID conditions as MATSim-NYC A new COVID model is calibrated that represents travel behavior during the COVID-19 pandemic by recalibrating the population agendas to include work-from-home and re-estimating the mode choice model for MATSim-NYC to fit observed traffic and transit ridership data Assuming the change in behavior exhibits inertia during reopening, we analyze the increase in car traffic due to the phased reopen plan guided by the state government of New York Four reopening phases and two reopening scenarios (with and without transit capacity restrictions) are analyzed A Phase 4 reopening with 100% transit capacity may only see as much as 73% of pre-COVID ridership and an increase in the number of car trips by as much as 142% of pre-pandemic levels Limiting transit capacity to 50% would decrease transit ridership further from 73% to 64% while increasing car trips to as much as 143% of pre-pandemic levels While the increase appears small, the impact on consumer surplus is disproportionately large due to already increased traffic congestion Many of the trips also get shifted to other modes like micromobility The findings imply that a transit capacity restriction policy during reopening needs to be accompanied by (1) support for micromobility modes, particularly in non-Manhattan boroughs, and (2) congestion alleviation policies that focus on reducing traffic in Manhattan, such as cordon-based pricing
Universal scotogenic fermion masses in left-right gauge model

Abstract In the conventional left-right gauge model, if the Higgs scalar sector consists only of an S U ( 2 ) L doublet and an S U ( 2 ) R doublet, fermion masses are zero at tree level. There have been many studies on how they would become massive. With the help of a dark sector with U ( 1 ) D gauge symmetry, it is shown how all standard-model fermions may acquire realistic masses radiatively, including that of the top quark. In this context, the particle content of the model also implies the automatic conservation of baryon number B and lepton number L as in the standard model. Observable anomalous Higgs couplings are predicted.
One-Class Learning Towards Synthetic Voice Spoofing Detection

Human voices can be used to authenticate the identity of the speaker, but the automatic speaker verification (ASV) systems are vulnerable to voice spoofing attacks, such as impersonation, replay, text-to-speech, and voice conversion. Recently, researchers developed anti-spoofing techniques to improve the reliability of ASV systems against spoofing attacks. However, most methods encounter difficulties in detecting unknown attacks in practical use, which often have different statistical distributions from known attacks. Especially, the fast development of synthetic voice spoofing algorithms is generating increasingly powerful attacks, putting the ASV systems at risk of unseen attacks. In this work, we propose an anti-spoofing system to detect unknown synthetic voice spoofing attacks (i.e., text-to-speech or voice conversion) using one-class learning. The key idea is to compact the bona fide speech representation and inject an angular margin to separate the spoofing attacks in the embedding space. Without resorting to any data augmentation methods, our proposed system achieves an equal error rate (EER) of 2.19% on the evaluation set of ASVspoof 2019 Challenge logical access scenario, outperforming all existing single systems ( i.e. , those without model ensemble).
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling.

We consider the contextual bandit problem, where a player sequentially makes decisions based on past observations to maximize the cumulative reward. Although many algorithms have been proposed for contextual bandit, most of them rely on finding the maximum likelihood estimator at each iteration, which requires $O(t)$ time at the $t$-th iteration and are memory inefficient. A natural way to resolve this problem is to apply online stochastic gradient descent (SGD) so that the per-step time and memory complexity can be reduced to constant with respect to $t$, but a contextual bandit policy based on online SGD updates that balances exploration and exploitation has remained elusive. In this work, we show that online SGD can be applied to the generalized linear bandit problem. The proposed SGD-TS algorithm, which uses a single-step SGD update to exploit past information and uses Thompson Sampling for exploration, achieves $\tilde{O}(\sqrt{T})$ regret with the total time complexity that scales linearly in $T$ and $d$, where $T$ is the total number of rounds and $d$ is the number of features. Experimental results show that SGD-TS consistently outperforms existing algorithms on both synthetic and real datasets.
Online Forgetting Process for Linear Regression Models

Motivated by the EU's "Right To Be Forgotten" regulation, we initiate a study of statistical data deletion problems where users' data are accessible only for a limited period of time. This setting is formulated as an online supervised learning task with \textit{constant memory limit}. We propose a deletion-aware algorithm \texttt{FIFD-OLS} for the low dimensional case, and witness a catastrophic rank swinging phenomenon due to the data deletion operation, which leads to statistical inefficiency. As a remedy, we propose the \texttt{FIFD-Adaptive Ridge} algorithm with a novel online regularization scheme, that effectively offsets the uncertainty from deletion. In theory, we provide the cumulative regret upper bound for both online forgetting algorithms. In the experiment, we showed \texttt{FIFD-Adaptive Ridge} outperforms the ridge regression algorithm with fixed regularization level, and hopefully sheds some light on more complex statistical models.
Mind the Beat: Detecting Audio Onsets from EEG Recordings of Music Listening

We propose a deep learning approach to predicting audio event onsets in electroencephalogram (EEG) recorded from users as they listen to music. We use a publicly available dataset containing ten contemporary songs and concurrently recorded EEG. We generate a sequence of onset labels for the songs in our dataset and trained neural networks (a fully connected network (FCN) and a recurrent neural network (RNN)) to parse one second windows of input EEG to predict one second windows of onsets in the audio. We compare our RNN network to both the standard spectral-flux based novelty function and the FCN. We find that our RNN was able to produce results that reflected its ability to generalize better than the other methods.Since there are no pre-existing works on this topic, the numbers presented in this paper may serve as useful benchmarks for future approaches to this research problem.
Impartial achievement games on convex geometries

Abstract We study a game where two players take turns selecting points of a convex geometry until the convex closure of the jointly selected points contains all the points of a given winning set. The winner of the game is the last player able to move. We develop a structure theory for these games and use it to determine the nim number for several classes of convex geometries, including one-dimensional affine geometries, vertex geometries of trees, and games with a winning set consisting of extreme points.
The external effects of bank executive pay: Liquidity creation and systemic risk

Abstract We develop a conceptual framework that links the compensation incentives of bank executives to the risk and return externalities generated by banks but borne by society. Using 1994 to 2016 data from large U.S. commercial banks, we find that CEO pay-performance incentives reduce both negative systemic risk externalities and positive liquidity creation externalities, while pay-risk incentives increase both externalities. Our findings offer support for Federal Reserve guidelines that encourage greater reliance on long-term equity-based compensation, and they infer a regulatory tradeoff: Bank executive pay rules aimed at reducing systemic risk will result in reduced system-wide liquidity creation as well.
Exogenous extracellular matrix proteins decrease cardiac fibroblast activation in stiffening microenvironment through CAPG

Controlling fibrosis is an essential part of regenerating the post-ischemic heart. In the post-ischemic heart, fibroblasts differentiate to myofibroblasts that produce collagen-rich matrix to physically stabilize the infarct area. Infarct models in adult mice result in permanent scarring unlike newborn animals which fully regenerate. Decellularized extracellular matrix (dECM) hydrogels derived from early-aged hearts have been shown to be a transplantable therapy that preserves heart function and stimulates cardiomyocyte proliferation and vascularization. In this study, we investigate the anti-fibrotic effects of injectable dECM hydrogels in a cardiac explant model in the context of age-associated tissue compliance. Treatments with adult and fetal dECM hydrogels were tested for molecular effects on cardiac fibroblast activation and fibrosis. Altered sensitivity of fibroblasts to the mechanosignaling of the remodeling microenvironment was evaluated by manipulating the native extracellular matrix in explants and also with elastomeric substrates in the presence of dECM hydrogels. The injectable fetal dECM hydrogel treatment decreases fibroblast activation and contractility and lowers the stiffness-mediated increases in fibroblast activation observed in stiffened explants. The anti-fibrotic effect of dECM hydrogel is most observable at highest stiffness. Experiments with primary cells on elastomeric substrates with dECM treatment support this phenomenon. Transcriptome analysis indicated that dECM hydrogels affect cytoskeleton related signaling including Macrophage capping protein (CAPG) and Leupaxin (LPXN). CAPG was down-regulated by the fetal dECM hydrogel. LPXN expression was decreased by stiffening the explants; however, this effect was reversed by dECM hydrogel treatment. Pharmacological disruption of cytoskeleton polymerization lowered fibroblast activation and CAPG levels. Knocking down CAPG expression with siRNA inhibited fibroblast activation and collagen deposition. Collectively, fibroblast activation is dependent on cooperative action of extracellular molecular signals and mechanosignaling by cytoskeletal integrity.
Enabling AI-Accelerated Multiscale Modeling of Thrombogenesis at Millisecond and Molecular Resolutions on Supercomputers

We report the first congruent integration of HPC, AI, and multiscale modeling (MSM) for solving a mainstream biomechanical problem of thrombogenesis involving 6 million particles at record molecular-scale resolutions in space and at simulation rates of milliseconds per day. The two supercomputers, the IBM Summit-like AiMOS and our University’s SeaWulf, are used for scalability analysis of, and production runs with, the LAMMPS with our customization and AI augmentation and they attained optimal simulation speeds of 3,077 µs/day and 266 µs/day respectively. The long-time and large scales simulations enable the first study of the integrated platelet flowing, flipping, aggregating dynamics in one dynamically-coupled production run. The platelets’ angular and translational speeds, membrane particles’ speeds, and the membrane stress distributions are presented for the analysis of platelets’ aggregations.
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference

Methods that infer causal dependence from observational data are central to many areas of science, including medicine, economics, and the social sciences. A variety of theoretical properties of these methods have been proven, but empirical evaluation remains a challenge, largely due to the lack of observational data sets for which treatment effect is known. We describe and analyze observational sampling from randomized controlled trials (OSRCT), a method for evaluating causal inference methods using data from randomized controlled trials (RCTs). This method can be used to create constructed observational data sets with corresponding unbiased estimates of treatment effect, substantially increasing the number of data sets available for empirical evaluation of causal inference methods. We show that, in expectation, OSRCT creates data sets that are equivalent to those produced by randomly sampling from empirical data sets in which all potential outcomes are available. We then perform a large-scale evaluation of seven causal inference methods over 37 data sets, drawn from RCTs, as well as simulators, real-world computational systems, and observational data sets augmented with a synthetic response variable. We find notable performance differences when comparing across data from different sources, demonstrating the importance of using data from a variety of sources when evaluating any causal inference method.
Spaces of knots in the solid torus, knots in the thickened torus, and links in the 3-sphere

We recursively determine the homotopy type of the space of any irreducible framed link in the 3-sphere, modulo rotations. This leads us to the homotopy type of the space of any knot in the solid torus, thus answering a question posed by Arnold. We similarly study spaces of unframed links in the 3-sphere, modulo rotations, and spaces of knots in the thickened torus. The subgroup of meridional rotations splits as a direct factor of the fundamental group of the space of any framed link except the unknot. Its generators can be viewed as generalizations of the Gramain loop in the space of long knots. Taking the quotient by certain such rotations relates the spaces we study. All of our results generalize previous work of Hatcher and Budney. We provide many examples and explicitly describe generators of fundamental groups.
A generalized subspace theorem for closed subschemes in subgeneral position

Abstract In this paper, we extend the recent theorem of G. Heier and A. Levin [HL21] on the generalization of Schmidt's subspace theorem and Cartan's Second Main Theorem in Nevanlinna theory to closed subschemes located in l-subgeneral position, using the generic linear combination technique due to Quang (see [Quang19] ).
Data Poisoning Attacks to Local Differential Privacy Protocols

Local Differential Privacy (LDP) protocols enable an untrusted data collector to perform privacy-preserving data analytics. In particular, each user locally perturbs its data to preserve privacy before sending it to the data collector, who aggregates the perturbed data to obtain statistics of interest. In the past several years, researchers from multiple communities -- such as security, database, and theoretical computer science -- have proposed many LDP protocols. These studies mainly focused on improving the utility of the LDP protocols. However, the security of LDP protocols is largely unexplored. In this work, we aim to bridge this gap. We focus on LDP protocols for frequency estimation and heavy hitter identification, which are two basic data analytics tasks. Specifically, we show that an attacker can inject fake users into an LDP protocol and the fake users send carefully crafted data to the data collector such that the LDP protocol estimates high frequencies for arbitrary attacker-chosen items or identifies them as heavy hitters. We call our attacks data poisoning attacks. We theoretically and/or empirically show the effectiveness of our attacks. We also explore three countermeasures against our attacks. Our experimental results show that they can effectively defend against our attacks in some scenarios but have limited effectiveness in others, highlighting the needs for new defenses against our attacks.
Low-cost molded plastic sounding rocket motors

Sounding rockets motors with integrally molded plastic case and nozzle, discussing cost effectiveness and demonstration tests
How Does Local Economy Affect Commercial Property Performance

Local economy should be an important determinant of commercial real estate (CRE) performance. This paper empirically examines how the economic conditions of a metropolitan area drive the performance of CRE in the area. This paper shows that areas with better economic conditions provide a higher total return on commercial properties than those with worse economic conditions. Further analysis indicates that both the income return and capital appreciation of CRE are significantly affected by the size of the economy (proxied as GDP level), while the capital return (but not income return) is significantly affected by the growth of the economy (proxied as GDP growth). The results are largely consistent in the Fama–MacBeth regression, the portfolio analysis, and the propensity score matching model, providing solid evidence on the important effects of local economy on CRE.
Site-Resolved Imaging of Ultracold Fermions in a Triangular-Lattice Quantum Gas Microscope

A paradigmatic platform for the quantum simulation of fermions in frustrated lattice geometries are achieved, enabling further studies of quantum spin liquids.
Fair Dynamic Rationing

We study the allocative challenges that governmental and nonprofit organizations face when tasked with equitable and efficient rationing of a social good among agents whose needs (demands) realize sequentially and are possibly correlated. As one example, early in the COVID-19 pandemic, the Federal Emergency Management Agency faced overwhelming, temporally scattered, a priori uncertain, and correlated demands for medical supplies from different states. To better achieve their dual aims of equity and efficiency in such contexts, social planners intend to maximize the minimum fill rate across agents, where each agent's fill rate must be irrevocably decided upon its arrival. For an arbitrarily correlated sequence of demands, we establish upper bounds on both the expected minimum fill rate (ex-post fairness) and the minimum expected fill rate (ex-ante fairness) achievable by any policy. Our bounds are parameterized by the number of agents and the expected demand-to-supply ratio, and they shed light on the limits of attaining equity in dynamic rationing. Further, we show that for any set of parameters, a simple adaptive policy of projected proportional allocation achieves the best possible fairness guarantee, ex post as well as ex ante. Our policy is transparent and easy to implement, as it does not rely on distributional information beyond the first conditional moments. Despite its simplicity, we demonstrate that this policy provides significant improvement over the class of non-adaptive target-fill-rate policies by characterizing the performance of the optimal such policy, which relies on full distributional knowledge. We obtain the performance guarantees of (i) our proposed adaptive policy by inductively designing lower-bound functions on its corresponding value-to-go, and (ii) the optimal target-fill-rate policy by establishing an intriguing connection to a monopoly-pricing optimization problem. Further, we extend our results to considering alternative objective functions and to rationing multiple types of resources. We complement our theoretical developments with a numerical study motivated by the rationing of COVID-19 medical supplies based on a projected-demand model used by the White House. In such a setting, our simple adaptive policy significantly outperforms its theoretical guarantee as well as the optimal target-fill-rate policy.
Design oriented structural analysis

Desirable characteristics and benefits of design oriented analysis methods are described and illustrated by presenting a synoptic description of the development and uses of the Equivalent Laminated Plate Solution (ELAPS) computer code. ELAPS is a design oriented structural analysis method which is intended for use in the early design of aircraft wing structures. Model preparation is minimized by using a few large plate segments to model the wing box structure. Computational efficiency is achieved by using a limited number of global displacement functions that encompass all segments over the wing planform. Coupling with other codes is facilitated since the output quantities such as deflections and stresses are calculated as continuous functions over the plate segments. Various aspects of the ELAPS development are discussed including the analytical formulation, verification of results by comparison with finite element analysis results, coupling with other codes, and calculation of sensitivity derivatives. The effectiveness of ELAPS for multidisciplinary design application is illustrated by describing its use in design studies of high speed civil transport wing structures.
A Dataset of Dockerfiles

Dockerfiles are one of the most prevalent kinds of DevOps artifacts used in industry. Despite their prevalence, there is a lack of sophisticated semantics-aware static analysis of Dockerfiles. In this paper, we introduce a dataset of approximately 178,000 unique Dockerfiles collected from GitHub. To enhance the usability of this data, we describe five representations we have devised for working with, mining from, and analyzing these Dockerfiles. Each Dockerfile representation builds upon the previous ones, and the final representation, created by three levels of nested parsing and abstraction, makes tasks such as mining and static checking tractable. The Dockerfiles, in each of the five representations, along with metadata and the tools used to shepard the data from one representation to the next are all available at: https://doi.org/10.5281/zenodo.3628771.
Reference Pricing for Healthcare Services

Problem definition: The traditional payment system between an insurer and providers does not incentivize providers to limit their prices, nor patients to choose less expensive providers, hence cont...
A particle-in-cell code comparison for ion acceleration: EPOCH, LSP, and WarpX

There are now more Particle-in-Cell (PIC) codes than ever before that researchers use to simulate intense laser–plasma interactions. To date, there have been relatively few direct comparisons of these codes in the literature, especially for relativistic intensity lasers interacting with thin overdense targets. To address this, we perform a code comparison of three PIC codes: EPOCH, LSP, and WarpX for the problem of laser-driven ion acceleration in a 2D(3v) geometry for a 1020 W cm−2 intensity laser. We examine the plasma density, ion energy spectra, and laser–plasma coupling of the three codes and find strong agreement. We also run the same simulation 20 times with different random seeds to explore statistical fluctuations of the outputs. We then compare the execution times and memory usage of the codes (without “tuning” to improve performance) using between 1 and 48 processors on one node. We provide input files to encourage larger and more frequent code comparisons in this field.
SMBH seeds from dissipative dark matter

The existence of supermassive black holes (SMBHs) with masses greater than $\sim 10^{9}M_{\odot}$ at high redshift ($z\gtrsim 7$) is difficult to accommodate in standard astrophysical scenarios. We study the possibility that (nearly) totally dissipative self-interacting dark matter (tdSIDM)--in rare, high density dark matter fluctuations in the early Universe--produces SMBH seeds through catastrophic collapse. We use a semi-analytic model, tested and calibrated by a series of N-body simulations of isolated dark matter halos, to compute the collapse criteria and timescale of tdSIDM halos, where dark matter loses nearly all of its kinetic energy in a single collision in the center-of-momentum frame. Applying this model to halo merger trees, we empirically assign SMBH seeds to halos and trace the formation and evolution history of SMBHs. We make predictions for the quasar luminosity function, the $M_{\rm BH}$-$\sigma_{\rm v}^{\ast}$ relation, and cosmic SMBH mass density at high redshift and compare them to observations. We find that a dissipative dark matter interaction cross-section of $\sigma/m \sim 0.05~\rm cm^2/g$ is sufficient to produce the SMBHs observed in the early Universe while remaining consistent with ordinary SMBHs in the late Universe.
Improving vitamin D testing and supplementation in children with newly diagnosed cancer: A quality improvement initiative at Rady Children's Hospital San Diego.

Background Vitamin D deficiency and insufficiency have been associated with poorer health outcomes. Children with cancer are at high risk for vitamin D deficiency and insufficiency. At our institution, we identified high variability in vitamin D testing and supplementation in this population. Of those tested, 65% were vitamin D deficient/insufficient. We conducted a quality improvement (QI) initiative with aim to improve vitamin D testing and supplementation among children aged 2-18 years with newly diagnosed cancer to ≥80% over 6 months. Methods An inter-professional team reviewed baseline data, then developed and implemented interventions using Plan-Do-Study-Act (PDSA) cycles. Barriers were identified using QI tools, including lack of automated triggers for testing and inconsistent supplementation criteria and follow-up testing post supplementation. Interventions included an institutional vitamin D guideline, clinical decision-making tree for vitamin D deficiency, insufficiency and sufficiency, electronic medical record triggers, and automated testing options. Results Baseline: N = 26 patients, four (15%) had baseline vitamin D testing; two (8%) received appropriate supplementation. Postintervention: N = 33 patients; 32 (97%) had baseline vitamin D testing; 33 (100%) received appropriate supplementation and completed follow-up testing timely (6-8 weeks post supplementation). Change was sustained over 24 months. Conclusions We achieved and sustained our aim for vitamin D testing and supplementation in children with newly diagnosed cancer through inter-professional collaboration of hematology/oncology, endocrinology, hospital medicine, pharmacy, nursing, and information technology. Future PDSA cycles will address patient compliance with vitamin D supplementation and impact on patients' vitamin D levels.
A Uniform Min-Max Theorem with Applications in Cryptography.

We present a new, more constructive proof of von Neumann’s Min-Max Theorem for twoplayer zero-sum game — specifically, an algorithm that builds a near-optimal mixed strategy for the second player from several best-responses of the second player to mixed strategies of the first player. The algorithm extends previous work of Freund and Schapire (Games and Economic Behavior ’99) with the advantage that the algorithm runs in poly(n) time even when a pure strategy for the first player is a distribution chosen from a set of distributions over {0, 1}. This extension enables a number of additional applications in cryptography and complexity theory, often yielding uniform security versions of results that were previously only proved for nonuniform security (due to use of the non-constructive Min-Max Theorem). We describe several applications, including: a more modular and improved uniform version of Impagliazzo’s Hardcore Theorem (FOCS ’95); regularity theorems that provide efficient simulation of distributions within any sufficiently nice convex set (extending a result of Trevisan, Tulsiani and Vadhan (CCC ’09)); an improved version of the Weak Regularity Lemma of Frieze and Kannan; a Dense Model Theorem for uniform algorithms; and showing impossibility of constructing Succinct Non-Interactive Arguments (SNARGs) via black-box reductions under uniform hardness assumptions (using techniques from Gentry and Wichs (STOC ’11) for the nonuniform setting).
Classification of subbundles on the Fargues–Fontaine curve

We completely classify all subbundles of a given vector bundle on the Fargues-Fontaine curve. Our classification is given in terms of a simple and explicit condition on Harder-Narasimhan polygons. Our proof is inspired by the proof of the main theorem in [Hon19], but also involves a number of nontrivial adjustments.
Deep Feature CycleGANs: Speaker Identity Preserving Non-parallel Microphone-Telephone Domain Adaptation for Speaker Verification

With the increase in the availability of speech from varied domains, it is imperative to use such out-of-domain data to improve existing speech systems. Domain adaptation is a prominent pre-processing approach for this. We investigate it for adapt microphone speech to the telephone domain. Specifically, we explore CycleGAN-based unpaired translation of microphone data to improve the x-vector/speaker embedding network for Telephony Speaker Verification. We first demonstrate the efficacy of this on real challenging data and then, to improve further, we modify the CycleGAN formulation to make the adaptation task-specific. We modify CycleGAN's identity loss, cycle-consistency loss, and adversarial loss to operate in the deep feature space. Deep features of a signal are extracted from an auxiliary (speaker embedding) network and, hence, preserves speaker identity. Our 3D convolution-based Deep Feature Discriminators (DFD) show relative improvements of 5-10% in terms of equal error rate. To dive deeper, we study a challenging scenario of pooling (adapted) microphone and telephone data with data augmentations and telephone codecs. Finally, we highlight the sensitivity of CycleGAN hyper-parameters and introduce a parameter called probability of adaptation.
Data-driven protection levels for camera and 3D map-based safe urban localization

Reliably assessing the error in an estimated vehicle position is integral for ensuring the vehicle’s safety in urban environments. Many existing approaches use GNSS measurements to characterize protection levels (PLs) as probabilistic upper bounds on position error. However, GNSS signals might be reflected or blocked in urban environments, and thus additional sensor modalities need to be considered to determine PLs. In this paper, we propose an approach for computing PLs by matching camera image measurements to a LiDAR-based 3D map of the environment. We specify a Gaussian mixture model probability distribution of position error using deep neural-network-based data-driven models and statistical outlier weighting techniques. From the probability distribution, we compute PL by evaluating the position error bound using numerical line-search methods. Through experimental validation with real-world data, we demonstrate that the PLs computed from our method are reliable bounds on the position error in urban environments.
FBW7 couples structural integrity with functional output of primary cilia

Structural defects in primary cilia have robust effects in diverse tissues and systems. However, how disorders of ciliary length lead to functional outcomes are unknown. We examined the functional role of a ciliary length control mechanism of FBW7-mediated destruction of NDE1, in mesenchymal stem cell (MSC) differentiation. We show that FBW7 functions as a master regulator of both negative (NDE1) and positive (TALPID3) regulators of ciliogenesis, with an overall positive net effect on primary cilia formation, MSC differentiation to osteoblasts, and bone architecture. Deletion of Fbxw7 suppresses ciliation, Hedgehog activity, and differentiation, which are partially rescued in Fbxw7/Nde1-null cells. We also show that NDE1, despite suppressing ciliogenesis, promotes MSC differentiation by increasing the activity of the Hedgehog pathway by direct binding and enhancing GLI2 activity in a cilia-independent manner. We propose that FBW7 controls a protein-protein interaction network coupling ciliary structure and function, which is essential for stem cell differentiation.
Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for Localized Surface Temperature Forecasting in an Urban Environment

The rising temperature is one of the key indicators of a warming climate, capable of causing extensive stress to biological systems as well as built structures.Ambient temperature collected at ground level can have higher variability than regional weather forecasts, which fail to capture local dynamics. There remains a clear need for accurate air temperature prediction at the suburban scale at high temporal and spatial resolutions. This research proposed a framework based on a long short-term memory (LSTM) deep learning network to generate day-ahead hourly temperature forecasts with high spatial resolution. Air temperature observations are collected at a very fine scale (~150m) along major roads of New York City (NYC) through the Internet of Things (IoT) data for 2019-2020. The network is a stacked two layer LSTM network, which is able to process the measurements from all sensor locations at the same time and is able to produce predictions for multiple future time steps simultaneously. Experiments showed that the LSTM network outperformed other traditional time series forecasting techniques, such as the persistence model, historical average, AutoRegressive Integrated Moving Average (ARIMA), and feedforward neural networks (FNN). In addition, historical weather observations are collected from in situ weather sensors (i.e., Weather Underground, WU) within the region for the past five years. Experiments were conducted to compare the performance of the LSTM network with different training datasets: 1) IoT data alone, or 2) IoT data with the historical five years of WU data. By leveraging the historical air temperature from WU, the LSTM model achieved a generally increased accuracy by being exposed to more historical patterns that might not be present in the IoT observations. Meanwhile, by using IoT observations, the spatial resolution of air temperature predictions is significantly improved.
TransCT: Dual-Path Transformer for Low Dose Computed Tomography

Low dose computed tomography (LDCT) has attracted more and more attention in routine clinical diagnosis assessment, therapy planning, etc., which can reduce the dose of X-ray radiation to patients. However, the noise caused by low X-ray exposure degrades the CT image quality and then affects clinical diagnosis accuracy. In this paper, we train a transformer-based neural network to enhance the final CT image quality. To be specific, we first decompose the noisy LDCT image into two parts: high-frequency (HF) and low-frequency (LF) compositions. Then, we extract content features (\(X_{L_c}\)) and latent texture features (\(X_{L_t}\)) from the LF part, as well as HF embeddings (\(X_{H_f}\)) from the HF part. Further, we feed \(X_{L_t}\) and \(X_{H_f}\) into a modified transformer with three encoders and decoders to obtain well-refined HF texture features. After that, we combine these well-refined HF texture features with the pre-extracted \(X_{L_c}\) to encourage the restoration of high-quality LDCT images with the assistance of piecewise reconstruction. Extensive experiments on Mayo LDCT dataset show that our method produces superior results and outperforms other methods.
Low complexity online convolutional beamforming

Convolutional beamformers integrate the multichannel linear prediction model into beamformers, which provide good performance and optimality for joint dereverberation and noise reduction tasks. While longer filters are required to model long reverberation times, the computational burden of current online solutions grows fast with the filter length and number of microphones. In this work, we propose a low complexity convolutional beamformer using a Kalman filter derived affine projection algorithm to solve the adaptive filtering problem. The proposed solution is several orders of magnitude less complex than comparable existing solutions while slightly outperforming them on the REVERB challenge dataset.
Every Jordan curve inscribes uncountably many rhombi

We prove that every Jordan curve in $$\mathbb {R}^2$$ inscribes uncountably many rhombi. No regularity condition is assumed on the Jordan curve.
Non-Visual Cooking: Exploring Practices and Challenges of Meal Preparation by People with Visual Impairments

The reliance on vision for tasks related to cooking and eating healthy can present barriers to cooking for oneself and achieving proper nutrition. There has been little research exploring cooking practices and challenges faced by people with visual impairments. We present a content analysis of 122 YouTube videos to highlight the cooking practices of visually impaired people, and we describe detailed practices for 12 different cooking activities (e.g., cutting and chopping, measuring, testing food for doneness). Based on the cooking practices, we also conducted semi-structured interviews with 12 visually impaired people who have cooking experience and show existing challenges, concerns, and risks in cooking (e.g., tracking the status of tasks in progress, verifying whether things are peeled or cleaned thoroughly). We further discuss opportunities to support the current practices and improve the independence of people with visual impairments in cooking (e.g., zero-touch interactions for cooking). Overall, our findings provide guidance for future research exploring various assistive technologies to help people cook without relying on vision.
Longitudinal in vivo changes in radial peripapillary capillaries and optic nerve head structure in non-human primates with early experimental glaucoma

Purpose: There is conflicting evidence as to whether a loss of radial peripapillary capillaries (RPCs) precedes neuronal loss in glaucoma. We examined the time-course of in vivo changes in RPCs, optic nerve head (ONH) structure, and retinal nerve fiber layer thickness (RNFLT) in experimental glaucoma (EG). Methods: Spectral domain optical coherence tomography images were acquired before and approximately every 2 weeks after inducing unilateral EG in 9 rhesus monkeys to quantify mean anterior lamina cribrosa surface depth (ALCSD), minimum rim width (MRW), and RNFLT. Perfused RPC density was measured from adaptive optics scanning laser ophthalmoscope images acquired on the temporal half of the ONH. The time of first significant change was quantified as when values fell and remained outside of the 95% confidence interval established from control eyes. Results: Mean ALCSD and/or MRW were the first parameters to change in 8 EG eyes. RPC density changed first in the 9th. At their first points of change, mean ALCSD posteriorly deformed by 100.2 +- 101.2 microns, MRW thinned by 82.3 +- 65.9 microns, RNFLT decreased by 25 +- 14 microns, and RPC density decreased by 4.5 +- 2.1%. RPC density decreased before RNFL thinning in 5 EG eyes. RNFLT decreased before RPC density decreased in 2 EG eyes, while 2 EG eyes had simultaneous changes. Conclusions: In most EG eyes, RPC density decreased before (or simultaneous with) a change in RNFLT, suggesting that vascular factors may play a role in axonal loss in some eyes in early glaucoma.
Formalizing Galois Theory

We describe a project to formalize Galois theory using the Lean theorem prover, which is part of a larger effort to formalize all of the standard undergraduate mathematics curriculum in Lean. We di...
COVID-19 vaccine hesitancy in underserved communities of North Carolina.

Background In the United States, underserved communities including Blacks and Latinx are disproportionately affected by COVID-19. This study sought to estimate the prevalence of COVID-19 vaccine hesitancy, describe attitudes related to vaccination, and identify correlates among historically marginalized populations across 9 counties in North Carolina. Methods We conducted a cross-sectional survey distributed at free COVID-19 testing events in underserved rural and urban communities from August 27 -December 15, 2020. Vaccine hesitancy was defined as the response of "no" or "don't know/not sure" to whether the participant would get the COVID-19 vaccine as soon as it became available. Results The sample comprised 948 participants including 27.7% Whites, 59.6% Blacks, 12.7% Latinx, and 63% female. 32% earned Conclusions This study engaged the community to directly reach underserved minority populations at highest risk of COVID-19 that permitted assessment of vaccine hesitancy (which was much higher than national estimates), driven in part by distrust, and safety concerns.
Bringing discrete-time Langevin splitting methods into agreement with thermodynamics.

In light of the recently published complete set of statistically correct Gronbech-Jensen (GJ) methods for discrete-time thermodynamics, we revise a differential operator splitting method for the Langevin equation in order to comply with the basic GJ thermodynamic sampling features, namely, the Boltzmann distribution and Einstein diffusion, in linear systems. This revision, which is based on the introduction of time scaling along with flexibility of a discrete-time velocity attenuation parameter, provides a direct link between the ABO splitting formalism and the GJ methods. This link brings about the conclusion that any GJ method has at least weak second order accuracy in the applied time step. It further helps identify a novel half-step velocity, which simultaneously produces both correct kinetic statistics and correct transport measures for any of the statistically sound GJ methods. Explicit algorithmic expressions are given for the integration of the new half-step velocity into the GJ set of methods. Numerical simulations, including quantum-based molecular dynamics (QMD) using the QMD suite Los Alamos Transferable Tight-Binding for Energetics, highlight the discussed properties of the algorithms as well as exhibit the direct application of robust, time-step-independent stochastic integrators to QMD.
Lower-limb kinematics and kinetics during continuously varying human locomotion.

Human locomotion involves continuously variable activities including walking, running, and stair climbing over a range of speeds and inclinations as well as sit-stand, walk-run, and walk-stairs transitions. Understanding the kinematics and kinetics of the lower limbs during continuously varying locomotion is fundamental to developing robotic prostheses and exoskeletons that assist in community ambulation. However, available datasets on human locomotion neglect transitions between activities and/or continuous variations in speed and inclination during these activities. This data paper reports a new dataset that includes the lower-limb kinematics and kinetics of ten able-bodied participants walking at multiple inclines (±0°; 5° and 10°) and speeds (0.8 m/s; 1 m/s; 1.2 m/s), running at multiple speeds (1.8 m/s; 2 m/s; 2.2 m/s and 2.4 m/s), walking and running with constant acceleration (±0.2; 0.5), and stair ascent/descent with multiple stair inclines (20°; 25°; 30° and 35°). This dataset also includes sit-stand transitions, walk-run transitions, and walk-stairs transitions. Data were recorded by a Vicon motion capture system and, for applicable tasks, a Bertec instrumented treadmill. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16611523