cssi/SciSciGPT-SciSciNet huggingface dataset:
Trends in the use of administrative databases in urologic oncology: 2000–2019

Abstract Introduction and objective Administrative databases (AD) provide investigators with nationally representative study populations to answer research questions using large sample sizes. We aimed to quantify the trends and incidence of AD use in published manuscripts in urologic oncology. We examined 6 commonly used databases: National Cancer Database, surveillance, epidemiology, and end results database (SEER), SEER-Medicare, Nationwide Inpatient Sample, National Surgical Quality Improvement Program, and Premier Healthcare Database. Methods A literature review, powered by PubMed and DistillerSR, aggregated manuscripts that used the aforementioned databases to study a genitourinary malignancy between July 1, 2000 through June 30, 2019. Included publications were categorized by database used, corresponding author department affiliation, organ, journal, year, and contribution – defined as temporal treatment trends, outcomes and survival, comparative effectiveness research, or cost-effectiveness. Results There were 2,265 publications across 302 journals that met the inclusion criteria. Between 2000 and 2019 the compound annual growth rate of these publications was 18.7%. SEER use grew at a rate of 14.6% annually. National Cancer Database use grew 28.2% annually. Prostate cancer comprised the majority of publications (51.3%), followed by kidney (23.1%) and bladder (22.5%) cancer. Journals publishing these manuscripts had a median impact factor of 3.28 (IQR = 1.84–5.74) in 2019. Urologists published 52.5% of AD manuscripts over the study period. Conclusions Our results show substantial growth in the use of ADs for the study of urologic oncology. Given the broad use of ADs, investigators and specialty societies should advocate for continued improvement in the data captured by them.
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit

Modern deep learning models have achieved great success in predictive accuracy for many data modalities. However, their application to many real-world tasks is restricted by poor uncertainty estimates, such as overconfidence on out-of-distribution (OOD) data and ungraceful failing under distributional shift. Previous benchmarks have found that ensembles of neural networks (NNs) are typically the best calibrated models on OOD data. Inspired by this, we leverage recent theoretical advances that characterize the function-space prior of an infinitely-wide NN as a Gaussian process, termed the neural network Gaussian process (NNGP). We use the NNGP with a softmax link function to build a probabilistic model for multi-class classification and marginalize over the latent Gaussian outputs to sample from the posterior. This gives us a better understanding of the implicit prior NNs place on function space and allows a direct comparison of the calibration of the NNGP and its finite-width analogue. We also examine the calibration of previous approaches to classification with the NNGP, which treat classification problems as regression to the one-hot labels. In this case the Bayesian posterior is exact, and we compare several heuristics to generate a categorical distribution over classes. We find these methods are well calibrated under distributional shift. Finally, we consider an infinite-width final layer in conjunction with a pre-trained embedding. This replicates the important practical use case of transfer learning and allows scaling to significantly larger datasets. As well as achieving competitive predictive accuracy, this approach is better calibrated than its finite width analogue.
The Impact of the COVID-19 Pandemic on College Students' Health and Financial Stability in New York City: Findings from a Population-Based Sample of City University of New York (CUNY) Students.

Understanding the effect of the coronavirus disease 2019 (COVID-19) pandemic on students’ health and financial stability is important to establish effective interventions to mitigate these effects, which may have long-term consequences on their health and well-being. Public universities in urban centers represent a substantial proportion of college students in the USA. We implemented a cross-sectional population-based online survey of 2282 students in a large, public university in New York City in April 2020. We created weights to account for non-response and used Poisson regression with robust standard errors to estimate adjusted prevalence ratios (aPR) for factors associated with mental health outcomes. Students experienced high rates of anxiety/depression and financial instability due to the pandemic. Half of the students reported anxiety/depression (54.5%) and an increased need for mental health services (49.0%) as a result of the COVID-19 pandemic. The majority (81.1%) reported loss of household income, and half (49.8%) reported worries about losing housing. High levels of food (aPR = 1.4, 95% CI 1.2, 1.6) and housing (aPR = 1.3, 95% CI 1.2, 1.4) insecurity were the strongest predictors of anxiety/depression. Household and personal experiences with possible COVID-19 symptoms were also associated with anxiety/depression or the need for increased mental health services. Addressing student needs at public urban universities requires an integrated holistic approach that targets urgent mental health and economic needs related to the impact of COVID-19. Students who become infected need mental health services as well as health monitoring.
Multi-functional Coexistence of Radar-Sensing and Communication Waveforms

In this study, a novel transmission scheme is proposed to serve radar-sensing and communication objectives at the same time and allocated bandwidth. The proposed transmitted frame non-orthogonally superimposes two different waveforms, which are frequency modulated continuous-wave (FMCW) for radar-sensing and orthogonal frequency division multiplexing (OFDM) for communication. Also, the receiver scheme that performs channel estimation via radar-sensing functionality without degrading data rate of communication operation is introduced. As numerically evaluated, the proposed system achieves good sensing accuracy even if the signal-to-noise ratio (SNR) is low, and communication performance is only 0.6 dB less at the target bit-error rate (BER) of 1% compared to the assumption of perfect channel state information (CSI) without any pilot overhead over OFDM subcarriers.
Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime

We study the problem of policy optimization for infinite-horizon discounted Markov Decision Processes with softmax policy and nonlinear function approximation trained with policy gradient algorithms. We concentrate on the training dynamics in the mean-field regime, modeling e.g. the behavior of wide single hidden layer neural networks, when exploration is encouraged through entropy regularization. The dynamics of these models is established as a Wasserstein gradient flow of distributions in parameter space. We further prove global optimality of the fixed points of this dynamics under mild conditions on their initialization.
Functional prescription for EFT matching

We simplify the one-loop functional matching formalism to develop a streamlined prescription. The functional approach is conceptually appealing: all calculations are performed within the UV theory at the matching scale, and no prior determination of an Effective Field Theory (EFT) operator basis is required. Our prescription accommodates any relativistic UV theory that contains generic interactions (including derivative couplings) among scalar, fermion, and vector fields. As an example application, we match the singlet scalar extended Standard Model (SM) onto SMEFT.
On the spectral gap and the automorphism group of distance-regular graphs

Abstract We prove that a distance-regular graph with a dominant distance is a spectral expander. The key ingredient of the proof is a new inequality on the intersection numbers. We use the spectral gap bound to study the structure of the automorphism group . The minimal degree of a permutation group G is the minimum number of points not fixed by non-identity elements of G. Lower bounds on the minimal degree have strong structural consequences on G . In 2014 Babai proved that the automorphism group of a strongly regular graph with n vertices has minimal degree ≥ c n , with known exceptions. Strongly regular graphs correspond to distance-regular graphs of diameter 2. Babai conjectured that Hamming and Johnson graphs are the only primitive distance-regular graphs of diameter d ≥ 3 whose automorphism group has sublinear minimal degree. We confirm this conjecture for non-geometric primitive distance-regular graphs of bounded diameter. We also show if the primitivity assumption is removed, then only one additional family of exceptions arises, the crown graphs. We settle the geometric case in a companion paper.
Exploring and enhancing the accuracy of interior-scaled Perdew–Zunger self-interaction correction

The Perdew-Zunger self-interaction correction (PZ-SIC) improves the performance of density functional approximations for the properties that involve significant self-interaction error (SIE), as in stretched bond situations, but overcorrects for equilibrium properties where SIE is insignificant. This overcorrection is often reduced by local scaling self-interaction correction (LSIC) of the PZ-SIC to the local spin density approximation (LSDA). Here, we propose a new scaling factor to use in an LSIC-like approach that satisfies an additional important constraint: the correct coefficient of the atomic number Z in the asymptotic expansion of the exchange-correlation (xc) energy for atoms. LSIC and LSIC+ are scaled by functions of the iso-orbital indicator zσ, which distinguishes one-electron regions from many-electron regions. LSIC+ applied to the LSDA works better for many equilibrium properties than LSDA-LSIC and the Perdew, Burke, and Ernzerhof generalized gradient approximation (GGA), and almost close to the strongly constrained and appropriately normed (SCAN) meta-GGA. LSDA-LSIC and LSDA-LSIC+, however, fail to predict interaction energies involving weaker bonds, in sharp contrast to their earlier successes. It is found that more than one set of localized SIC orbitals can yield a nearly degenerate energetic description of the same multiple covalent bond, suggesting that a consistent chemical interpretation of the localized orbitals requires a new way to choose their Fermi orbital descriptors. To make a locally scaled down SIC to functionals beyond the LSDA requires a gauge transformation of the functional's energy density. The resulting SCAN-sdSIC, evaluated on SCAN-SIC total and localized orbital densities, leads to an acceptable description of many equilibrium properties including the dissociation energies of weak bonds.
Approximate Similarity Search Under Edit Distance Using Locality-Sensitive Hashing

Edit distance similarity search, also called approximate pattern matching, is a fundamental problem with widespread database applications. The goal of the problem is to preprocess n strings of length d, to quickly answer queries q of the form: if there is a database string within edit distance r of q, return a database string within edit distance cr of q. Previous approaches to this problem either rely on very large (superconstant) approximation ratios c, or very small search radii r. Outside of a narrow parameter range, these solutions are not competitive with trivially searching through all n strings. In this work we give a simple and easy-to-implement hash function that can quickly answer queries for a wide range of parameters. Specifically, our strategy can answer queries in time O(d3^rn^{1/c}). The best known practical results require c ≫ r to achieve any correctness guarantee; meanwhile, the best known theoretical results are very involved and difficult to implement, and require query time that can be loosely bounded below by 24^r. Our results significantly broaden the range of parameters for which there exist nontrivial theoretical bounds, while retaining the practicality of a locality-sensitive hash function.
A Computational Information Criterion for Particle-Tracking with Sparse or Noisy Data

Abstract Traditional probabilistic methods for the simulation of advection-diffusion equations (ADEs) often overlook the entropic contribution of the discretization, e.g., the number of particles, within associated numerical methods. Many times, the gain in accuracy of a highly discretized numerical model is outweighed by its associated computational costs or the noise within the data. We address the question of how many particles are needed in a simulation to best approximate and estimate parameters in one-dimensional advective-diffusive transport. To do so, we use the well-known Akaike Information Criterion (AIC) and a recently-developed correction called the Computational Information Criterion (COMIC) to guide the model selection process. Random-walk and mass-transfer particle tracking methods are employed to solve the model equations at various levels of discretization. Numerical results demonstrate that the COMIC provides an optimal number of particles that can describe a more efficient model in terms of parameter estimation and model prediction compared to the model selected by the AIC even when the data is sparse or noisy, the sampling volume is not uniform throughout the physical domain, or the error distribution of the data is non-IID Gaussian.
Spectrally encoded single-pixel machine vision using diffractive networks

We demonstrate optical networks composed of diffractive layers trained using deep learning to encode the spatial information of objects into the power spectrum of the diffracted light, which are used to classify objects with a single-pixel spectroscopic detector. Using a plasmonic nanoantenna-based detector, we experimentally validated this single-pixel machine vision framework at terahertz spectrum to optically classify the images of handwritten digits by detecting the spectral power of the diffracted light at ten distinct wavelengths, each representing one class/digit. We also coupled this diffractive network-based spectral encoding with a shallow electronic neural network, which was trained to rapidly reconstruct the images of handwritten digits based on solely the spectral power detected at these ten distinct wavelengths, demonstrating task-specific image decompression. This single-pixel machine vision framework can also be extended to other spectral-domain measurement systems to enable new 3D imaging and sensing modalities integrated with diffractive network-based spectral encoding of information.
Slip-Based Autonomous ZUPT Through Gaussian Process to Improve Planetary Rover Localization

The zero-velocity update (ZUPT) algorithm provides valuable state information to maintain the inertial navigation system (INS) reliability when stationary conditions are satisfied. Employing ZUPT along with leveraging non-holonomic constraints can greatly benefit wheeled mobile robot dead-reckoning localization accuracy. However, determining how often they should be employed requires consideration to balance localization accuracy and traversal rate for planetary rovers. To address this, we investigate when to autonomously initiate stops to improve wheel-inertial odometry (WIO) localization performance with ZUPT. To do this, we propose a 3D dead-reckoning approach that predicts wheel slippage while the rover is in motion and forecasts the appropriate time to stop without changing any rover hardware or major rover operations. We validate with field tests that our approach is viable on different terrain types and achieves a 3D localization accuracy of ${\sim}97{\bf \%}$ over 650 m drives on rough terrain.
Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning

We congratulate Kallus and Mo, Qi, and Liu for introducing thought-provoking new policy learning objective functions. Each of the proposed objectives involves an empirical estimate of the value fun...
A techno-social perspective of innovation jams: defining and characterising

Understanding technology’s influence on the implementation of new models of innovation is of increasing importance to research scholars. This study aims to understand through a techno-social perspe...
Coupled Relational Symbolic Execution for Differential Privacy.

Differential privacy is a de facto standard in data privacy with applications in the private and public sectors. Most of the techniques that achieve differential privacy are based on a judicious use of randomness. However, reasoning about randomized programs is difficult and error prone. For this reason, several techniques have been recently proposed to support designer in proving programs differentially private or in finding violations to it.
Capturing COVID-19-Like Symptoms at Scale Using Banner Ads on an Online News Platform: Pilot Survey Study.

Background: Identifying new COVID-19 cases is challenging. Not every suspected case undergoes testing, because testing kits and other equipment are limited in many parts of the world. Yet populations increasingly use the internet to manage both home and work life during the pandemic, giving researchers mediated connections to millions of people sheltering in place. Objective: The goal of this study was to assess the feasibility of using an online news platform to recruit volunteers willing to report COVID-19–like symptoms and behaviors. Methods: An online epidemiologic survey captured COVID-19–related symptoms and behaviors from individuals recruited through banner ads offered through Microsoft News. Respondents indicated whether they were experiencing symptoms, whether they received COVID-19 testing, and whether they traveled outside of their local area. Results: A total of 87,322 respondents completed the survey across a 3-week span at the end of April 2020, with 54.3% of the responses from the United States and 32.0% from Japan. Of the total respondents, 19,631 (22.3%) reported at least one symptom associated with COVID-19. Nearly two-fifths of these respondents (39.1%) reported more than one COVID-19–like symptom. Individuals who reported being tested for COVID-19 were significantly more likely to report symptoms (47.7% vs 21.5%; P<.001). Symptom reporting rates positively correlated with per capita COVID-19 testing rates (R2=0.26; P<.001). Respondents were geographically diverse, with all states and most ZIP Codes represented. More than half of the respondents from both countries were older than 50 years of age. Conclusions: News platforms can be used to quickly recruit study participants, enabling collection of infectious disease symptoms at scale and with populations that are older than those found through social media platforms. Such platforms could enable epidemiologists and researchers to quickly assess trends in emerging infections potentially before at-risk populations present to clinics and hospitals for testing and/or treatment.
In BRCA1 and BRCA2 breast cancers, chromosome breaks occur near herpes tumor virus sequences

Inherited mutations in BRCA1 and BRCA2 genes increase risks for breast, ovarian, and other cancers. Both genes encode proteins for accurately repairing chromosome breaks. If mutations inactivate this function, broken chromosome fragments get lost or reattach indiscriminately. These mistakes are characteristic of hereditary breast cancer. We tested the hypothesis that mistakes in reattaching broken chromosomes preferentially occur near viral sequences on human chromosomes. We tested millions of DNA bases around breast cancer breakpoints for similarities to all known viral DNA. DNA around breakpoints often closely matched the Epstein-Barr virus (EBV) tumor variants HKHD40 and HKNPC60. Almost all breakpoints were near EBV anchor sites, EBV tumor variant homologies, and EBV-associated regulatory marks. On chromosome 2, EBV binding sites accounted for 90% of breakpoints (p<0.0001). On chromosome 4, 51/52 inter-chromosomal breakpoints were close to EBV variant sequences. Five viral anchor sites at critical genes were near breast cancer breakpoints. Twenty-five breast cancer breakpoints were within 1.25% of breakpoints in model EBV cancers. EBV-like sequence patterns around breast cancer breakpoints resemble gene fusion breakpoints in model EBV cancers. All BRCA1 and BRCA2 breast cancers had mutated genes essential for immune responses. Because of this immune compromise, herpes viruses can attach and produce nucleases that break chromosomes. Alternatively, anchored viruses can retard break repairs, whatever the causes. The results imply proactive treatment and prevention of herpes viral infections may benefit BRCA mutation carriers.
A Concentration-Time Hybrid Modulation Scheme for Molecular Communications

Significant inter-symbol interference (ISI) challenges the achievement of reliable, high data-rate molecular communication via diffusion. In this paper, a hybrid modulation based on pulse position and concentration is proposed to mitigate ISI. By exploiting the time dimension, molecular concentration and position modulation (MCPM) increases the achievable data rate over conventional concentration and position-based modulations. In addition, unlike multi-molecule schemes, this hybrid scheme employs a single-molecule type and so simplifies transceiver implementations. In the paper, the optimal sequence detector of the proposed modulation is provided as well as a reduced complexity detector (two-stage, position-concentration detector, TPCD). A tractable cost function based on the TPCD detector is proposed and employed to optimize the design of the hybrid modulation scheme. In addition, the approximate probability of error for the MCPM-TPCD system is derived and is shown to be tight with respect to simulated performance. Numerically, MCPM shows improved performance over standard concentration and pulse position-based schemes in the low transmission power and high bit-rate regime. Furthermore, MCPM offers increased robustness against synchronization errors.
Time scales for molecule formation by ion-molecule reactions

Analytical solutions are obtained for nonlinear differential equations governing the time-dependence of molecular abundances in interstellar clouds. Three gas-phase reaction schemes are considered separately for the regions where each dominates. The particular case of CO, and closely related members of the Oh and CH families of molecules, is studied for given values of temperature, density, and the radiation field. Nonlinear effects and couplings with particular ions are found to be important. The time scales for CO formation range from 100,000 to a few million years, depending on the chemistry and regime. The time required for essentially complete conversion of C(+) to CO in the region where the H3(+) chemistry dominates is several million years. Because this time is longer than or comparable to dynamical time scales for dense interstellar clouds, steady-state abundances may not be observed in such clouds.
A Deep Learning Based Ternary Task Classification System Using Gramian Angular Summation Field in fNIRS Neuroimaging Data

Functional near-infrared spectroscopy (fNIRS) is a non-invasive, economical method used to study its blood flow pattern. These patterns can be used to classify tasks a subject is performing. Currently, most of the classification systems use simple machine learning solutions for the classification of tasks. These conventional machine learning methods, which are easier to implement and interpret, usually suffer from low accuracy and undergo a complex preprocessing phase before network training. The proposed method converts the raw fNIRS time series data into an image using Gramian Angular Summation Field. A Deep Convolutional Neural Network (CNN) based architecture is then used for task classification, including mental arithmetic, motor imagery, and idle state. Further, this method can eliminate the feature selection stage, which affects the traditional classifiers' performance. This system obtained 87.14% average classification accuracy higher than any other method for the dataset.
Writing Another as Other: A Retro-Intro-Extrospection

Autoethnographies such as Ajil and Blount-Hill’s (2020) in DCJ, 2(1), which demonstrate the disempowerment suffered by the othered at the hands of colonial systems are valuable contributions to decolonial literature. Still, as the othered gain in status, privilege, and power, narrative provides a worthy method of analyzing the othered and powerful.
Mala Prohibita and Proportionality

What is the proportionate punishment for conduct that is neither harmful nor wrongful? A likely response to that is that one ought not to be punished at all for such conduct. It is, however, common for the state to punish harmless conduct the wrongfulness of which is not always apparent. Take, for example, the requirement that those who give investment advice for compensation do so only after registering as an investment advisor. Advising a person on how to invest his or her funds and accepting a fee for the advice without registering with the government does not seem harmful or wrongful, so long as no fraud is involved, the relevant parties understand the relevant risks, and so on. But practicing investment advising without registering is a crime for which one may be convicted and punished. When one thinks of crimes, paradigmatic offenses are crimes like murder, rape, and robbery, but offenses like failure to register as an investment advisor are different. But in what way? One standard explanation is the distinction between two types of offenses, malum in se and malum prohibitum. Some offenses, like murder, are wrongs “in themselves” (“in se”) whereas other offenses, like investment advising without registering as an advisor, are wrongs because they have been prohibited (“prohibitum”). The question this Essay asks is how we should think about proportionality of punishment when punishing such mala prohibita offenses. This Essay presents a framework for such proportionality determinations and raises some challenges such a framework would need to confront.
No-Regret Algorithms for Time-Varying Bayesian Optimization

In this paper, we consider the time-varying Bayesian optimization problem. The unknown function at each time is assumed to lie in an RKHS (reproducing kernel Hilbert space) with a bounded norm. We adopt the general variation budget model to capture the time-varying environment, and the variation is characterized by the change of the RKHS norm. We adapt the restart and sliding window mechanism to introduce two GP-UCB type algorithms: R-GP-UCB and SW-GP-UCB, respectively. We derive the first (frequentist) regret guarantee on the dynamic regret for both algorithms. Our results not only recover previous linear bandit results when a linear kernel is used, but complement the previous regret analysis of time-varying Gaussian process bandit under a Bayesian-type regularity assumption, i.e., each function is a sample from a Gaussian process.
Geometric singular perturbation theory analysis of an epidemic model with spontaneous human behavioral change.

We consider a model due to Piero Poletti and collaborators that adds spontaneous human behavioral change to the standard SIR epidemic model. In its simplest form, the Poletti model adds one differential equation, motivated by evolutionary game theory, to the SIR model. The new equation describes the evolution of a variable x that represents the fraction of the population following normal behavior. The remaining fraction $$1-x$$ uses altered behavior such as staying home, social isolation, mask wearing, etc. Normal behavior offers a higher payoff when the number of infectives is low; altered behavior offers a higher payoff when the number is high. We show that the entry–exit function of geometric singular perturbation theory can be used to analyze the model in the limit in which behavior changes on a much faster time scale than that of the epidemic. In particular, behavior does not change as soon as a different behavior has a higher payoff; current behavior is sticky. The delay until behavior changes is predicted by the entry–exit function.
Observations and predictions from past lightcones

In a general Lorentzian manifold M, the past lightcone of a point is a proper subset of M that does not carry enough information to determine the rest of M. That said, if M is a globally hyperbolic Cauchy development of vacuum initial data on a Cauchy surface S and there is a point whose past lightcone contains S, then the contents of such a lightcone determines all of M (up to isometry). We show some results that describe what properties of M guarantee that past lightcones do indeed determine all or at least significant portions of M. Null lines and observer horizons, which are well known features of the de-Sitter spacetime, play a prominent role.
One-Shot Conditional Audio Filtering of Arbitrary Sounds

We consider the problem of separating a particular sound source from a single-channel mixture, based on only a short sample of the target source (from the same recording). Using SoundFilter, a wave-to-wave neural network architecture, we can train a model without using any sound class labels. Using a conditioning encoder model which is learned jointly with the source separation network, the trained model can be "configured" to filter arbitrary sound sources, even ones that it has not seen during training. Evaluated on the FSD50k dataset, our model obtains an SI-SDR improvement of 9.6 dB for mixtures of two sounds. When trained on Librispeech, our model achieves an SI-SDR improvement of 14.0 dB when separating one voice from a mixture of two speakers. Moreover, we show that the representation learned by the conditioning encoder clusters acoustically similar sounds together in the embedding space, even though it is trained without using any labels.
High-speed VLSI arithmetic processor architectures using hybrid number representation

This paper addresses design of high speed architectures for fixed-point, two's-complement, bit-parallel division, square-root, and multiplication operations. These architectures make use of hybrid number representations (i.e. the input and output numbers are represented using two's complement representation, and the internal numbers are represented using radix-2 redundant representation). We propose newshifted remainder conditioning, andsign multiplexing techniques in combination with novel circuit architecture approaches to obtain efficient divider and square-root architectures. Our divider exploits full dynamic range of operands and eliminates the need for on-line or off-line conversion of the result to binary (this is because our nonrestoring division and square-root operators output binary quotient). Furthermore, since the binary input set is a subset of the redundant digit set, no binary-to-redundant number conversion is necessary at the input of the divider and square-root operators. We also present a fast, new conversion scheme for converting radix-2 redundant numbers to two's complement binary numbers, and use this to design a bit-parallel multiplier. This multiplier architecture requires fewer pipelining latches than conventional two's complement multipliers, and reduces the latency of the multiplication operation from (2W---1) to aboutW (whereW is the word-length), when pipelined at the bit-level.
Thirty Years of The Network Scale-up Method

Estimating the size of hard-to-reach populations is an important problem for many fields. The network scale-up method (NSUM) is a relatively new approach to estimate the size of these hard-to-reach...
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature

Information overload is a prevalent challenge in many high-value domains. A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months. In general, biomedical literature expands by two papers every minute, totalling over a million new papers every year. Search in the biomedical realm, and many other vertical domains is challenging due to the scarcity of direct supervision from click logs. Self-supervised learning has emerged as a promising direction to overcome the annotation bottleneck. We propose a general approach for vertical search based on domain-specific pretraining and present a case study for the biomedical domain. Despite being substantially simpler and not using any relevance labels for training or development, our method performs comparably or better than the best systems in the official TREC-COVID evaluation, a COVID-related biomedical search competition. Using distributed computing in modern cloud infrastructure, our system can scale to tens of millions of articles on PubMed and has been deployed as Microsoft Biomedical Search, a new search experience for biomedical literature: https://aka.ms/biomedsearch.
Significance of the dihedral-effect for a combat aircraft in rapid fuselage-reorientation maneuvers

In the quest for understanding problems of supermaneuverability for combat aircraft, a study is presented about the role the dihedral-effect can have in fuselage-reorientation maneuvers that involve high angles of attack. A mathematical model for attitude maneuvers is developed, which accurately represents the High Angle-of-Attack Research Vehicle, including thrust-vectoring generated propulsive moments. The fuselage-reorientation problem is posed as an unconstrained time-optimal control problem. Results for a few families of extremal trajectories are obtained, and the global role of the dihedral effect in the course of the corresponding maneuvers is discussed. In addition, a detailed case-study is presented for an extremal trajectory which utilizes the dihedral effect with considerable benefit.
Nanotesla Magnetometry with the Silicon Vacancy in Silicon Carbide

Silicon carbide is a promising host material for spin-defect-based quantum sensors owing to its commercial availability and established techniques for electrical and optical microfabricated device integration. The negatively charged silicon vacancy is one of the leading spin defects studied in silicon carbide owing to its near-telecom photoemission, high spin number, and nearly temperature-independent ground-state zero-field splitting. We report the realization of nanotesla shot-noise-limited ensemble magnetometry based on optically detected magnetic resonance with the silicon vacancy in $4H$ silicon carbide. By coarsely optimizing the anneal parameters and minimizing power broadening, we achieve a sensitivity of $50\phantom{\rule{0.2em}{0ex}}\mathrm{nT}/\sqrt{\mathrm{Hz}}$ and a theoretical shot-noise-limited sensitivity of $3.5\phantom{\rule{0.2em}{0ex}}\mathrm{nT}/\sqrt{\mathrm{Hz}}$. This is accomplished without utilizing complex photonic engineering, control protocols, or applying excitation powers greater than a watt. This work demonstrates that the silicon vacancy in silicon carbide provides a low-cost and simple approach to quantum sensing of magnetic fields.
OMNILINGUAL ASPIRATIONS: THE CASE OF THE UNIVERSAL DECLARATION OF HUMAN RIGHTS

Adopted by the United Nations in 1948, the Universal Declaration of Human Rights is, according to the Guinness Book of World Records, the most widely translated document. However, versions in 419 languages are not conceived as translations but equivalences, alternate embodiments of identical tenets. The Bible has been rendered into numerous languages, but the Hebrew and Greek originals possess authority that English, Bengali, and Xhosa derivatives do not. The Bible is translated, but the UDHR is, through the theology of international governance, transubstantiated into multiple tongues. No version has priority; each is equally valid, transparent, and interchangeable. The utopian premise is not only that all humans possess inalienable rights but also that all languages express the same principles. The document’s title, the Universal Declaration of Human Rights, might seem a solecism, a misplaced modifier. Surely, it is human rights that are universal, not the declaration. However, the UN insists that all versions (at least in the original official languages) are equally binding. It rejects Whorfian notions that particular languages enable particular thoughts and embraces languages as neutral tools whose specific manifestation is irrelevant. Arguments against imprisoning writers in Burma could appeal equally to the authority of either the Universal Declaration of Human Rights or la Declaration universelle des droits de l’homme or Всеобщую декларацию прав человека or la Declaracion Universal de Derechos Humanos or 世界人权宣言. Rather than the Babelian myth of an Ur-Sprache before hubris scattered us into mutual unintelligibility, the UDHR endorses a Chomskyan belief that all languages can express the same thoughts. Yet differences among versions of Article 1 (“All human beings are born free and equal in dignity and rights”) are not trivial; dignity is incommensurable with Wurde, αξιοπρέπeια, dignidade, waardigheid, or достоинства. The UDHR is a translingual text shaped by the languages of framers and translators.
Self-Fulfilling Prophecies in the Classroom

Do teachers’ expectations directly impact student achievement? We draw on administrative data from North Carolina schools that report both student test scores and teachers’ expectations of students...
Near-Optimal Algorithms for Explainable k-Medians and k-Means

We consider the problem of explainable $k$-medians and $k$-means introduced by Dasgupta, Frost, Moshkovitz, and Rashtchian~(ICML 2020). In this problem, our goal is to find a threshold decision tree that partitions data into $k$ clusters and minimizes the $k$-medians or $k$-means objective. The obtained clustering is easy to interpret because every decision node of a threshold tree splits data based on a single feature into two groups. We propose a new algorithm for this problem which is $\tilde O(\log k)$ competitive with $k$-medians with $\ell_1$ norm and $\tilde O(k)$ competitive with $k$-means. This is an improvement over the previous guarantees of $O(k)$ and $O(k^2)$ by Dasgupta et al (2020). We also provide a new algorithm which is $O(\log^{3/2} k)$ competitive for $k$-medians with $\ell_2$ norm. Our first algorithm is near-optimal: Dasgupta et al (2020) showed a lower bound of $\Omega(\log k)$ for $k$-medians; in this work, we prove a lower bound of $\tilde\Omega(k)$ for $k$-means. We also provide a lower bound of $\Omega(\log k)$ for $k$-medians with $\ell_2$ norm.
Cuprate superconductors as viewed through a striped lens

Understanding the electron pairing in hole-doped cuprate superconductors has been a challenge, in particular because the “normal” state from which it evolves is unprecedented. Now, after three and ...
Cottonseed Extracts and Gossypol Regulate Diacylglycerol Acyltransferase Gene Expression in Mouse Macrophages

Plant bioactive polyphenols have been used for the prevention and treatment of various diseases since ancient history. Cotton (Gossypium hirsutum L.) seeds are classified as glanded or glandless depending on the presence or absence of pigment glands, which contain polyphenolic gossypol. Diacylglycerol acyltransferases (DGATs) are integral membrane proteins that catalyze the last step of triacylglycerol biosynthesis in eukaryotes. Understanding the regulation of DGATs will provide information for therapeutic intervention for obesity and related diseases. However, little was known if DGAT gene expression was regulated by natural products. The objective of this study was to investigate the effects of cottonseed extracts and gossypol on DGAT gene expression in mouse RAW264.7 macrophages. Mouse cells were treated with different concentrations of cottonseed extracts, gossypol, and lipopolysaccharides (LPS) for various times. Quantitative polymerase chain reaction assay showed that coat extract of glanded seeds ...
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins

We analyze the properties of gradient descent on convex surrogates for the zero-one loss for the agnostic learning of linear halfspaces. If $\mathsf{OPT}$ is the best classification error achieved by a halfspace, by appealing to the notion of soft margins we are able to show that gradient descent finds halfspaces with classification error $\tilde O(\mathsf{OPT}^{1/2}) + \varepsilon$ in $\mathrm{poly}(d,1/\varepsilon)$ time and sample complexity for a broad class of distributions that includes log-concave isotropic distributions as a subclass. Along the way we answer a question recently posed by Ji et al. (2020) on how the tail behavior of a loss function can affect sample complexity and runtime guarantees for gradient descent.
Mean excitation energy for molecules of hydrogen and carbon

The Gordon-Kim electron gas model of molecular bonding is used to calculate correction factors for the Bragg rule for molecules of hydrogen and carbon. General rules for molecular mean excitation energies are obtained that agree to about 4% with experimental values.
Vitamin D deficiency in cord plasma from multiethnic subjects living in the tropics

Background: Vitamin D deficiency is commonly reported in high-latitude areas and in dark-pigmented individuals. However, nothing is known about vitamin D in cord blood from multiethnic subjects living in the tropics. Objective: Our study objective was to determine the prevalence of vitamin D deficiency in summer and winter in cord blood from multiethnic individuals in Hawai’i where sufficient sun irradiance occurs year-round for cutaneous vitamin D production. Methods: 25-Hydroxyvitamin D (25(OH)D) levels were quantified by enzyme immunoassay in 100 cord plasma samples from apparently healthy full-term newborns and their mothers. Stratification was performed by birth season and ethnicity. Results: Mean 25(OH)D levels were 24.5 ng/mL (9.1–68.3 ng/mL). Overall, 28% of samples were vitamin D deficient (<20 ng/mL) and 50% were insufficient (20–30 ng/mL). 25(OH)D levels (ng/mL) were highest in Caucasians (30.5, n = 19), followed by Asians (25.1, n = 43), Hispanics (21.5, n = 3), Pacific Islanders (20.0, n = 25...
Which contributions count? Analysis of attribution in open source

Open source software projects usually acknowledge contributions with text files, websites, and other idiosyncratic methods. These data sources are hard to mine, which is why contributorship is most frequently measured through changes to repositories, such as commits, pushes, or patches. Recently, some open source projects have taken to recording contributor actions with standardized systems; this opens up a unique opportunity to understand how community-generated notions of contributorship map onto codebases as the measure of contribution. Here, we characterize contributor acknowledgment models in open source by analyzing thousands of projects that use a model called All Contributors to acknowledge diverse contributions like outreach, finance, infrastructure, and community management. We analyze the life cycle of projects through this model’s lens and contrast its representation of contributorship with the picture given by other methods of acknowledgment, including GitHub’s top committers indicator and contributions derived from actions taken on the platform. We find that community-generated systems of contribution acknowledgment make work like idea generation or bug finding more visible, which generates a more extensive picture of collaboration. Further, we find that models requiring explicit attribution lead to more clearly defined boundaries around what is and is not a contribution.
Massively Parallel Dantzig-Wolfe Decomposition Applied to Traffic Flow Scheduling

Optimal scheduling of air traffic over the entire National Airspace System is a computationally difficult task. To speed computation, Dantzig-Wolfe decomposition is applied to a known linear integer programming approach for assigning delays to flights. The optimization model is proven to have the block-angular structure necessary for Dantzig-Wolfe decomposition. The subproblems for this decomposition are solved in parallel via independent computation threads. Experimental evidence suggests that as the number of subproblems/threads increases (and their respective sizes decrease), the solution quality, convergence, and runtime improve. A demonstration of this is provided by using one flight per subproblem, which is the finest possible decomposition. This results in thousands of subproblems and associated computation threads. This massively parallel approach is compared to one with few threads and to standard (non-decomposed) approaches in terms of solution quality and runtime. Since this method generally provides a non-integral (relaxed) solution to the original optimization problem, two heuristics are developed to generate an integral solution. Dantzig-Wolfe followed by these heuristics can provide a near-optimal (sometimes optimal) solution to the original problem hundreds of times faster than standard (non-decomposed) approaches. In addition, when massive decomposition is employed, the solution is shown to be more likely integral, which obviates the need for an integerization step. These results indicate that nationwide, real-time, high fidelity, optimal traffic flow scheduling is achievable for (at least) 3 hour planning horizons.
PopFactor: Live-Streamer Behavior and Popularity

Live video-streaming platforms such as Twitch enable top content creators to reap significant profits and influence. To that effect, various behavioral norms are recommended to new entrants and those seeking to increase their popularity and success. Chiefly among them are to simply put in the effort and promote on social media outlets such as Twitter, Instagram, and the like. But does following these behaviors indeed have a relationship with eventual popularity? In this paper, we collect a corpus of Twitch streamer popularity measures---spanning social and financial measures---and their behavior data on Twitch and third party platforms. We also compile a set of community-defined behavioral norms. We then perform temporal analysis to identify the increased predictive value that a streamer's future behavior contributes to predicting future popularity. At the population level, we find that behavioral information improves the prediction of relative growth that exceeds that of the median streamer. At the individual level, we find that although it is difficult to quickly become successful in absolute terms, streamers that put in considerable effort are more successful than the rest. Ultimately, we find that studying the popularity and success of content creators in the long term is a promising and rich research area.
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective

In the classical multi-armed bandit problem, instance-dependent algorithms attain improved performance on "easy" problems with a gap between the best and second-best arm. Are similar guarantees possible for contextual bandits? While positive results are known for certain special cases, there is no general theory characterizing when and how instance-dependent regret bounds for contextual bandits can be achieved for rich, general classes of policies. We introduce a family of complexity measures that are both sufficient and necessary to obtain instance-dependent regret bounds. We then introduce new oracle-efficient algorithms which adapt to the gap whenever possible, while also attaining the minimax rate in the worst case. Finally, we provide structural results that tie together a number of complexity measures previously proposed throughout contextual bandits, reinforcement learning, and active learning and elucidate their role in determining the optimal instance-dependent regret. In a large-scale empirical evaluation, we find that our approach often gives superior results for challenging exploration problems. Turning our focus to reinforcement learning with function approximation, we develop new oracle-efficient algorithms for reinforcement learning with rich observations that obtain optimal gap-dependent sample complexity.