cssi/SciSciGPT-SciSciNet huggingface dataset:
A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries

We present a novel deep learning framework for flow field predictions in irregular domains when the solution is a function of the geometry of either the domain or objects inside the domain. Grid vertices in a computational fluid dynamics (CFD) domain are viewed as point clouds and used as inputs to a neural network based on the PointNet architecture, which learns an end-to-end mapping between spatial positions and CFD quantities. Using our approach, (i) the network inherits desirable features of unstructured meshes (e.g., fine and coarse point spacing near the object surface and in the far field, respectively), which minimizes network training cost; (ii) object geometry is accurately represented through vertices located on object boundaries, which maintains boundary smoothness and allows the network to detect small changes between geometries and (iii) no data interpolation is utilized for creating training data; thus accuracy of the CFD data is preserved. None of these features are achievable by extant methods based on projecting scattered CFD data into Cartesian grids and then using regular convolutional neural networks. Incompressible laminar steady flow past a cylinder with various shapes for its cross section is considered. The mass and momentum of predicted fields are conserved. We test the generalizability of our network by predicting the flow around multiple objects as well as an airfoil, even though only single objects and no airfoils are observed during training. The network predicts the flow fields hundreds of times faster than our conventional CFD solver, while maintaining excellent to reasonable accuracy.
The effective action of superrotation modes

Starting from an analysis of four-dimensional asymptotically flat gravity in first order formulation, we show that superrotation reparametrization modes are governed by an Alekseev-Shatashvili action on the celestial sphere. This two-dimensional conformal theory describes spontaneous symmetry breaking of Virasoro superrotations together with the explicit symmetry breaking of more general Diff( $$ {\mathcal{S}}^2 $$ ) superrotations. We arrive at this result by first reformulating the asymptotic field equations and symmetries of the radiative vacuum sector in terms of a Chern-Simons theory at null infinity, and subsequently performing a Hamiltonian reduction of this theory onto the celestial sphere.
Weight changes according to treatment in a diverse cohort of breast cancer patients.

Background Weight changes are common among breast cancer patients. The majority of studies to date have focused on weight gain after a breast cancer diagnosis and its implications on health in survivors. Fewer studies have examined weight loss and its related characteristics. Weight changes have been reported to be influenced by several factors such as age, treatment, stage and pre-diagnostic weight. We evaluated weight changes during key treatment time points in early stage breast cancer patients. Methods We characterized 389 female patients diagnosed in Hawaii with early stage breast cancer from 2003 to 2017 in the Multiethnic Cohort (MEC) linked with Kaiser Permanente Hawaii electronic medical record data. We evaluated weight changes from surgery to 4 years post-diagnosis with six time points along a patient's treatment trajectory (chemotherapy, radiation, endocrine, or surgery alone) and annually thereafter, adjusting for age, race/ethnicity and initial body mass index (BMI). Results We found key time points of significant weight change for breast cancer patients according to their adjuvant treatment. In patients who had surgery alone (S), surgery-radiation (SR), or surgery-endocrine therapy (SE), the majority of patients had stable weight, although this consistently decreased over time. However, the percentages of patients with weight loss and weight gain during this time steadily increased up to 4 years after initial surgery. Weight loss was more common than weight gain by about 2 fold in these treatment groups. For patients with surgery-chemotherapy (SC), there was significant weight loss seen within the first 3 months after surgery, during the time when patients receive chemotherapy. And this weight loss persisted until year 4. Weight gain was less commonly seen in this treatment group. Conclusions We identified key time points during breast cancer treatment that may provide a therapeutic window to positively influence outcomes. Tailored weight management interventions should be utilized to promote overall health and long term survivorship.
Kawamata-Viehweg vanishing fails for log del Pezzo surfaces in characteristic 3

Abstract We construct a klt del Pezzo surface in characteristic three violating the Kawamata-Viehweg vanishing theorem. As a consequence we show that there exists a Kawamata log terminal threefold singularity which is not Cohen-Macaulay in characteristic three.
Molecular Basis for the Evolved Instability of a Human G-Protein Coupled Receptor

Membrane proteins are prone to misfolding and degradation. This is particularly true for mammalian forms of the gonadotropin-releasing hormone receptor (GnRHR). Though they function at the plasma membrane, mammalian GnRHRs tend to accumulate within the secretory pathway. Their apparent instability is believed to have evolved in response to selection for attenuated GnRHR activity. Nevertheless, the structural basis of this adaptation remains unclear. We find that this adaptation coincides with a C-terminal truncation and an increase in the polarity of its transmembrane (TM) domains. This enhanced polarity compromises the translocon-mediated cotranslational folding of two TM domains. Moreover, replacing a conserved polar residue in TM6 with an ancestral hydrophobic residue partially restores GnRHR expression with minimal impact on function. An evolutionary analysis suggests variations in the polarity of this residue are associated with reproductive differences. Our findings suggest the marginal energetics of cotranslational folding can be exploited to tune membrane protein fitness.
From Optimizing Engagement to Measuring Value

Most recommendation engines today are based on predicting user engagement, e.g. predicting whether a user will click on an item or not. However, there is potentially a large gap between engagement signals and a desired notion of value that is worth optimizing for. We use the framework of measurement theory to (a) confront the designer with a normative question about what the designer values, (b) provide a general latent variable model approach that can be used to operationalize the target construct and directly optimize for it, and (c) guide the designer in evaluating and revising their operationalization. We implement our approach on the Twitter platform on millions of users. In line with established approaches to assessing the validity of measurements, we perform a qualitative evaluation of how well our model captures a desired notion of "value".
From Hi-C Contact Map to Three-Dimensional Organization of Interphase Human Chromosomes

A new computational method solves a major problem in biology---how to obtain 3D coordinates of positions on chromosomes from 2D ``contact maps'' that encode distances between the positions.
Fairness Metrics: A Comparative Analysis

Algorithmic fairness is receiving significant attention in the academic and broader literature due to the increasing use of predictive algorithms. One benefit of this trend is that algorithm designers and users have a growing set of fairness measures to choose from. However, this choice comes with the challenge of identifying how the different fairness measures relate to one another, as well as the extent to which they are compatible or mutually exclusive. We describe some of the most widely used fairness metrics using a common mathematical framework and present new results on the relationships among them. The results presented herein can help place both specialists and non-specialists in a better position to identify the metric best suited for their application and goals.
Generalized difference sets and autocorrelation integrals

In 2010, Cilleruelo, Ruzsa, and Vinuesa established a surprising connection between the maximum possible size of a generalized Sidon set in the first $N$ natural numbers and the optimal constant in an ``analogous'' problem concerning nonnegative-valued functions on $[0,1]$ with autoconvolution integral uniformly bounded above. Answering a recent question of Barnard and Steinerberger, we prove the corresponding dual result about the minimum size of a so-called generalized difference set that covers the first $N$ natural numbers and the optimal constant in an analogous problem concerning nonnegative-valued functions on $\mathbb{R}$ with autocorrelation integral bounded below on $[0,1]$. These results show that the correspondence of Cilleruelo, Ruzsa, and Vinuesa is representative of a more general phenomenon relating discrete problems in additive combinatorics to questions in the continuous world.
Portfolio Strategies for Volatility Investing

The VIX premium, the difference between VIX futures and VIX Index levels, has been shown to have predictive power over volatility returns and investment risk. This article examines a conditional strategy applied within a portfolio construct allocating equally to market and volatility risk. Although it is predominantly short volatility, the strategy owns volatility during much of the financial crisis. Both long and short volatility allocations prove to be profitable over the sample period. They produce a portfolio with more consistent profits than the S&P 500 Index and several related volatility strategies developed in previous literature and those available as volatility-based strategy indexes. TOPICS:Futures and forward contracts, mutual funds/passive investing/indexing, financial crises and financial market history, performance measurement Key Findings ▪ From April 2007–2018, a portfolio that invests in the S&P 500 Index and VIX futures earns 1.79%, on average, each month, with a 1.02 Sharpe ratio, more than doubling the absolute and risk-adjusted returns of the S&P 500 Index. ▪ The VIX premium, the difference between VIX futures and VIX Index levels, foretells investment risk and VIX futures returns. Conditioning a long or short VIX futures allocation on the VIX premium enables the portfolio to hold short VIX futures positions for most of the time and long VIX futures positions during turbulent periods. Both long and short VIX futures investments earned positive returns, and the portfolio outperformed related strategies over the entire period and each subsample. ▪ The portfolio posts positive returns during the financial crisis by holding long VIX futures positions. In 2008, the portfolio earns 39.87%, while the S&P 500 Index lost 37.00%.
Additive manufacturing of fibrous sound absorbers

Abstract We investigate the possibility of additively manufacturing fibrous sound absorbers using fused deposition modeling. Two methods for 3D printing fibers are proposed. The fiber bridging method involves extruding the filament between two points with no underlying supports. The extrude-and-pull method involves extruding a filament droplet before pulling away the extruder rapidly to generate thin fibers. Both methods can produce fibers with aspect ratios greater than 100. Optical microscopy is used to investigate the effect of various printing parameters on the fiber characteristics. The sound absorption coefficient of samples printed using the two techniques are measured using a two-microphone normal incidence impedance tube setup. Effects of printing parameters and fiber density variables are experimentally studied. The experimental studies are supported by the Johnson-Champoux-Allard semi-empirical analytical model informed using an inverse characterization approach. The analytical model is then utilized to understand the effect of fiber parameters on the acoustical transport parameters. It is observed that the two methods result in individual fibers with distinct characteristics. On average, the fiber bridging method results in thicker fibers, which results in comparatively higher sound absorption. However, the extrude-and-pull method results in fibers with hair-like characteristics (thick base with progressively decreasing thickness) and one may easily incorporate it within existing additive manufacturing routines to add fibers to a base surface, thus opening up a new route towards fiber-enhanced multifunctional structures.
Modular zero modes and sewing the states of QFT

We point out an important difference between continuum relativistic quantum field theory (QFT) and lattice models with dramatic consequences for the theory of multi-partite entanglement. On a lattice given a collection of density matrices ρ(1), ρ(2), ⋯, ρ(n) there is no guarantee that there exists an n-partite pure state |Ω〉12⋯n that reduces to these marginals. The state |Ω〉12⋯n exists only if the eigenvalues of the density matrices ρ(i) satisfy certain polygon inequalities. We show that in QFT, as opposed to lattice systems, splitting the space into n non-overlapping regions any collection of local states ω(1), ω(2), ⋯ ω(n) come from the restriction of a global pure state. The reason is that rotating any local state ω(i) by unitary Ui localized in the ith region we come arbitrarily close to any other local state ψ(i). We construct explicit examples of such local unitaries using the cocycle.
Reproductive Proteins Evolve Faster Than Non-reproductive Proteins Among Solanum Species.

Elevated rates of evolution in reproductive proteins are commonly observed in animal species, and are thought to be driven by the action of sexual selection and sexual conflict acting specifically on reproductive traits. Whether similar patterns are broadly observed in other biological groups is equivocal. Here, we examine patterns of protein divergence among wild tomato species (Solanum section Lycopersicon), to understand forces shaping the evolution of reproductive genes in this diverse, rapidly evolving plant clade. By comparing rates of molecular evolution among loci expressed in reproductive and non-reproductive tissues, our aims were to test if: (a) reproductive-specific loci evolve more rapidly, on average, than non-reproductive loci; (b) ‘male’-specific loci evolve at different rates than ‘female’-specific loci; (c) genes expressed exclusively in gametophytic (haploid) tissue evolve differently from genes expressed in sporophytic (diploid) tissue or in both tissue types; and (d) mating system variation (a potential proxy for the expected strength of sexual selection and/or sexual conflict) affects patterns of protein evolution. We observed elevated evolutionary rates in reproductive proteins. However, this pattern was most evident for female- rather than male-specific loci, both broadly and for individual loci inferred to be positively selected. These elevated rates might be facilitated by greater tissue-specificity of reproductive proteins, as faster rates were also associated with more narrow expression domains. In contrast, we found little evidence that evolutionary rates are consistently different in loci experiencing haploid selection (gametophytic-exclusive loci), or in lineages with quantitatively different mating systems. Overall while reproductive protein evolution is generally elevated in this diverse plant group, some specific patterns of evolution are more complex than those reported in other (largely animal) systems, and include a more prominent role for female-specific loci among adaptively evolving genes.
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML

Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models. A critical, final step of AutoML is human selection of a final model from dozens of candidates. In current AutoML systems, selection is supported only by performance metrics. Prior work has shown that in practice, people evaluate ML models based on additional criteria, such as the way a model makes predictions. Comparison may happen at multiple levels, from types of errors, to feature importance, to how the model makes predictions of specific instances. We developed Model LineUpper to support interactive model comparison for AutoML by integrating multiple Explainable AI (XAI) and visualization techniques. We conducted a user study in which we both evaluated the system and used it as a technology probe to understand how users perform model comparison in an AutoML system. We discuss design implications for utilizing XAI techniques for model comparison and supporting the unique needs of data scientists in comparing AutoML models.
Some Extremal Functions in Fourier Analysis, III

We obtain the best approximation in L 1(ℝ), by entire functions of exponential type, for a class of even functions that includes e −λ|x|, where λ>0, log |x| and |x| α , where −1<α<1. We also give periodic versions of these results where the approximating functions are trigonometric polynomials of bounded degree.
Special Lagrangian submanifolds of log Calabi-Yau manifolds

We study the existence of special Lagrangian submanifolds of log Calabi–Yau manifolds equipped with the complete Ricci-flat Kahler metric constructed by Tian and Yau. We prove that if X is a Tian–Yau manifold and if the compact Calabi–Yau manifold at infinity admits a single special Lagrangian, then X admits infinitely many disjoint special Lagrangians. In complex dimension 2, we prove that if Y is a del Pezzo surface or a rational elliptic surface and D∈|−KY| is a smooth divisor with D2=d, then X=Y∖D admits a special Lagrangian torus fibration, as conjectured by Strominger–Yau–Zaslow and Auroux. In fact, we show that X admits twin special Lagrangian fibrations, confirming a prediction of Leung and Yau. In the special case that Y is a rational elliptic surface or Y= P2, we identify the singular fibers for generic data, thereby confirming two conjectures of Auroux. Finally, we prove that after a hyper-Kahler rotation, X can be compactified to the complement of a Kodaira type Id fiber appearing as a singular fiber in a rational elliptic surface πˇ:Yˇ→P1.
Method G: Uncertainty Quantification for Distributed Data Problems Using Generalized Fiducial Inference

It is not unusual for a data analyst to encounter datasets distributed across several computers. This can happen for reasons such as privacy concerns, efficiency of likelihood evaluations, or just ...
Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation

The timing of individual neuronal spikes is essential for biological brains to make fast responses to sensory stimuli. However, conventional artificial neural networks lack the intrinsic temporal coding ability present in biological networks. We propose a spiking neural network model that encodes information in the relative timing of individual spikes. In classification tasks, the output of the network is indicated by the first neuron to spike in the output layer. This temporal coding scheme allows the supervised training of the network with backpropagation, using locally exact derivatives of the postsynaptic spike times with respect to presynaptic spike times. The network operates using a biologically plausible synaptic transfer function. In addition, we use trainable pulses that provide bias, add flexibility during training, and exploit the decayed part of the synaptic function. We show that such networks can be successfully trained on multiple data sets encoded in time, including MNIST. Our model outperforms comparable spiking models on MNIST and achieves similar quality to fully connected conventional networks with the same architecture. The spiking network spontaneously discovers two operating modes, mirroring the accuracy-speed tradeoff observed in human decision-making: a highly accurate but slow regime, and a fast but slightly lower accuracy regime. These results demonstrate the computational power of spiking networks with biological characteristics that encode information in the timing of individual neurons. By studying temporal coding in spiking networks, we aim to create building blocks toward energy-efficient, state-based biologically inspired neural architectures. We provide open-source code for the model.
Relaxed Locally Correctable Codes in Computationally Bounded Channels

Error-correcting codes that admit local decoding and correcting algorithms have been the focus of much recent research due to their numerous applications. An important goal is to obtain the best possible tradeoffs between the number of symbols of the codeword that the local decoding algorithm must examine (the locality ), and the amount of redundancy in the encoding (the information rate ). In Hamming’s classical adversarial channel model, the current tradeoffs are dramatic, allowing either small locality but superpolynomial blocklength, or small blocklength but high locality. However, in the computationally bounded adversarial channel model, proposed by Lipton (STACS 1994), constructions of locally decodable codes suddenly exhibit small locality and small blocklength, but these constructions require strong trusted setup assumptions. We study variants of locally decodable and locally correctable codes in computationally bounded, adversarial channels, in a setting with no trusted setup. The only assumption we require is the selection of the public parameters (seed) for a collision-resistant hash function. Specifically, we provide constructions of relaxed locally correctable and relaxed locally decodable codes over the binary alphabet, with constant information rate, and poly-logarithmic locality. Our constructions, which compare favorably with their classical analogs, crucially employ collision-resistant hash functions and local expander graphs , extending ideas from recent cryptographic constructions of memory-hard functions.
Deep Generative Storm Model for Dynamic Imaging

We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed generative framework represents the image time series as a smooth non-linear function of low-dimensional latent vectors that capture the cardiac and respiratory phases. The nonlinear function is represented using a deep convolutional neural network (CNN). Unlike the popular CNN approaches that require extensive fully-sampled training data that is not available in this setting, the parameters of the CNN generator as well as the latent vectors are jointly estimated from the undersampled measurements using stochastic gradient descent. We penalize the norm of the gradient of the generator to encourage the learning of a smooth surface/manifold, while temporal gradients of the latent vectors are penalized to encourage the time series to be smooth. The main benefits of the proposed scheme are (a) the quite significant reduction in memory demand compared to the analysis based SToRM model, and (b) the spatial regularization brought in by the CNN model. We also introduce efficient progressive approaches to minimize the computational complexity of the algorithm.
Adaptable automation with modular deep reinforcement learning and policy transfer

Abstract Future industrial automation systems are anticipated to be shaped by intelligent technologies that allow for the adaptability of machines to the variations and uncertainties in processes and work environments. This paper is motivated by the need for devising new intelligent methods that enable efficient and scalable training of collaborative robots on a variety of tasks that foster their adaptability to new tasks and environments. Recent advances in deep Reinforcement Learning (RL) provide new possibilities to realize this vision. The state-of-the-art in deep RL offers proven algorithms that enable autonomous learning and mastery of a variety of robotic manipulation tasks with minimal human intervention. However, current deep RL algorithms predominantly specialize in a narrow range of tasks, are sample inefficient, and lack sufficient stability, which hinders their adoption in real-life, industrial settings. This paper develops and tests a Hyper-Actor Soft Actor–Critic (HASAC) deep RL framework based on the notions of task modularization and transfer learning to tackle this limitation. The goal of the proposed HASAC is to enhance an agent’s adaptability to new tasks by transferring the learned policies of former tasks to the new task through a ”hyper-actor”. The HASAC framework is tested on the virtual robotic manipulation benchmark, Meta-World. Numerical experiments indicate superior performance by HASAC over state-of-the-art deep RL algorithms in terms of reward value, success rate, and task completion time.
3D Printed Metallic Dual-Polarized Vivaldi Arrays on Square and Triangular Lattices

We report the first Vivaldi arrays monolithically fabricated exclusively using commercial, low-cost, 3D metal printing (direct metal laser sintering). Furthermore, we developed one of the first dual-polarized Vivaldi arrays on a triangular lattice, and compare it to a square lattice array. The triangular lattice is attractive because it has a 15.5% larger cell size compared to the square lattice and can be more naturally truncated into a wide range of aperture shapes such as a rectangle, hexagon, or triangle. Both arrays operate at 3-20 GHz and scan angles out to 60o from normal. The fabrication process is significantly simplified compared to previously published Vivaldi arrays since the antenna is ready for use directly after the standard printing process is complete. This rapid manufacturing is further expedited by printing the “Sub-Miniature Push-on, Micro” (SMPM) connectors directly onto the radiating elements, which simplifies assembly and reduces cost compared to utilizing discrete RF connectors. The arrays have a modular design that allow for combining multiple sub-arrays together for arbitrarily increasing the aperture size. Simulations and measurement show that our arrays have similar performance as previously published Vivaldi arrays, but with simpler fabrication.
The Sooner (and the Smarter), the Better: COVID-19 Containment Measures and Fiscal Responses

This paper finds empirical evidence that faster and smarter containment measures were associated with lower fiscal responses to the COVID-19 shock. We also find that initial conditions, such as fiscal space, income, health preparedness and budget transparency were important in shaping the amount and design of the COVID-19 fiscal response.
SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data

Extracting detailed 3D information of objects from video data is an important goal for holistic scene understanding. While recent methods have shown impressive results when reconstructing meshes of objects from a single image, results often remain ambiguous as part of the object is unobserved. Moreover, existing image-based datasets for mesh reconstruction don’t permit to study models which integrate temporal information. To alleviate both concerns we present SAIL-VOS 3D: a synthetic video dataset with frame-by-frame mesh annotations which extends SAIL-VOS. We also develop first baselines for reconstruction of 3D meshes from video data via temporal models. We demonstrate efficacy of the proposed baseline on SAIL-VOS 3D and Pix3D, showing that temporal information improves reconstruction quality. Resources and additional information are available at http://sailvos.web.illinois.edu.
Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment

Non-autoregressive encoder-decoder models greatly improve decoding speed over autoregressive models, at the expense of generation quality. To mitigate this, iterative decoding models repeatedly infill or refine the proposal of a non-autoregressive model. However, editing at the level of output sequences limits model flexibility. We instead propose *iterative realignment*, which by refining latent alignments allows more flexible edits in fewer steps. Our model, Align-Refine, is an end-to-end Transformer which iteratively realigns connectionist temporal classification (CTC) alignments. On the WSJ dataset, Align-Refine matches an autoregressive baseline with a 14x decoding speedup; on LibriSpeech, we reach an LM-free test-other WER of 9.0% (19% relative improvement on comparable work) in three iterations. We release our code at https://github.com/amazon-research/align-refine.
Fluctuating Environments Maintain Genetic Diversity through Neutral Fitness Effects and Balancing Selection.

Genetic variation is the raw material upon which selection acts. The majority of environmental conditions change over time and therefore may result in variable selective effects. How temporally fluctuating environments impact the distribution of fitness effects and in turn population diversity is an unresolved question in evolutionary biology. Here, we employed continuous culturing using chemostats to establish environments that switch periodically between different nutrient limitations and compared the dynamics of selection to static conditions. We used the pooled Saccharomyces cerevisiae haploid gene deletion collection as a synthetic model for populations comprising thousands of unique genotypes. Using barcode sequencing (barseq), we find that static environments are uniquely characterized by a small number of high fitness genotypes that rapidly dominate the population leading to dramatic decreases in genetic diversity. By contrast, fluctuating environments are enriched in genotypes with neutral fitness effects and an absence of extreme fitness genotypes contributing to the maintenance of genetic diversity. We also identified a unique class of genotypes whose frequencies oscillate sinusoidally with a period matching the environmental fluctuation. Oscillatory behavior corresponds to large differences in short term fitness that are not observed across long timescales pointing to the importance of balancing selection in maintaining genetic diversity in fluctuating environments. Our results are consistent with a high degree of environmental specificity in the distribution of fitness effects and the combined effects of reduced and balancing selection in maintaining genetic diversity in the presence of variable selection.
Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality.

In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting the ability to effectively leverage transformers. Here, we provide a systematic study on the role of dimension reduction methods (principal components analysis, factorization techniques, or multi-layer auto-encoders) as well as the dimensionality of embedding vectors and sample sizes as a function of predictive performance. We first find that fine-tuning large models with a limited amount of data pose a significant difficulty which can be overcome with a pre-trained dimension reduction regime. RoBERTa consistently achieves top performance in human-level tasks, with PCA giving benefit over other reduction methods in better handling users that write longer texts. Finally, we observe that a majority of the tasks achieve results comparable to the best performance with just 1/12 of the embedding dimensions.
Dementia Diagnosis Disparities by Race and Ethnicity.

BACKGROUND Dementia is often underdiagnosed and this problem is more common among some ethnoracial groups. OBJECTIVE The objective of this study was to examine racial and ethnic disparities in the timeliness of receiving a clinical diagnosis of dementia. RESEARCH DESIGN This was a prospective cohort study. SUBJECTS A total of 3966 participants age 70 years and above with probable dementia in the Health and Retirement Study, linked with their Medicare and Medicaid claims. MEASURES We performed logistic regression to compare the likelihood of having a missed or delayed dementia diagnosis in claims by race/ethnicity. We analyzed dementia severity, measured by cognition and daily function, at the time of a dementia diagnosis documented in claims, and estimated average dementia diagnosis delay, by race/ethnicity. RESULTS A higher proportion of non-Hispanic Blacks and Hispanics had a missed/delayed clinical dementia diagnosis compared with non-Hispanic Whites (46% and 54% vs. 41%, P<0.001). Fully adjusted logistic regression results suggested more frequent missed/delayed dementia diagnoses among non-Hispanic Blacks (odds ratio=1.12; 95% confidence interval: 0.91-1.38) and Hispanics (odds ratio=1.58; 95% confidence interval: 1.20-2.07). Non-Hispanic Blacks and Hispanics had a poorer cognitive function and more functional limitations than non-Hispanic Whites around the time of receiving a claims-based dementia diagnosis. The estimated mean diagnosis delay was 34.6 months for non-Hispanic Blacks and 43.8 months for Hispanics, compared with 31.2 months for non-Hispanic Whites. CONCLUSIONS Non-Hispanic Blacks and Hispanics may experience a missed or delayed diagnosis of dementia more often and have longer diagnosis delays. When diagnosed, non-Hispanic Blacks and Hispanics may have more advanced dementia. Public health efforts should prioritize racial and ethnic underrepresented communities when promoting early diagnosis of dementia.
Neural semi-Markov CRF for Monolingual Word Alignment

Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc. In this paper, we present a novel neural semi-Markov CRF alignment model, which unifies word and phrase alignments through variable-length spans. We also create a new benchmark with human annotations that cover four different text genres to evaluate monolingual word alignment models in more realistic settings. Experimental results show that our proposed model outperforms all previous approaches for monolingual word alignment as well as a competitive QA-based baseline, which was previously only applied to bilingual data. Our model demonstrates good generalizability to three out-of-domain datasets and shows great utility in two downstream applications: automatic text simplification and sentence pair classification tasks.
Strong asymptotics of Jacobi-type kissing polynomials

We investigate asymptotic behaviour of polynomials pnω(z) satisfying varying non-Hermitian orthogonality relations ∫−11xkpnω(x)h(x)eiωxdx=0,k∈{0,…,n−1}, where h(x)=h∗(x)(1−x)α(1+x)β, ω=λn, λ≥ 0 and...
Man-in-the-Middle Attack Resistant Secret Key Generation via Channel Randomization

Physical-layer based key generation schemes exploit the channel reciprocity for secret key extraction, which can achieve information-theoretic secrecy against eavesdroppers. Such methods, although practical, have been shown to be vulnerable against man-in-the-middle (MitM) attacks, where an active adversary, Mallory, can influence and infer part of the secret key generated between Alice and Bob by injecting her own packet upon observing highly correlated channel/RSS measurements from Alice and Bob. As all the channels remain stable within the channel coherence time, Mallory's injected packets cause Alice and Bob to measure similar RSS, which allows Mallory to successfully predict the derived key bits. To defend against such a MitM attack, we propose to utilize a reconfigurable antenna at one of the legitimate transceivers to proactively randomize the channel state across different channel probing rounds. The randomization of the antenna mode at every probing round breaks the temporal correlation of the channels from the adversary to the legitimate devices, while preserving the reciprocity of the channel between the latter. This prevents key injection from the adversary without affecting Alice and Bob's ability to measure common randomness. We theoretically analyze the security of the protocol and conduct extensive simulations and real-world experiments to evaluate its performance. Our results show that our approach eliminates the advantage of an active MitM attack by driving down the probability of successfully guessing bits of the secret key to a random guess.
A Finite-Volume Euler Solver for Computing Rotary-Wing Aerodynamics on Unstructured Meshes

An unstructured-grid solver for the unsteady Euler equations has been developed for predicting the aerodynamics of helicopter rotor blades. This flow solver is a finite-volume scheme that computes flow quantities at the vertices of the mesh. Special treatments are used for the flux differencing and boundary conditions in order to compute rotary-wing flowfields, and these are detailed in the paper. The unstructured-grid solver permits adaptive grid refinement in order to improve the resolution of flow features such as shocks, rotor wakes and acoustic waves. These capabilities are demonstrated in the paper. Example calculations are presented for two hovering rotors. In both cases, adaptive-grid refinement is used to resolve high gradients near the rotor surface and also to capture the vortical regions in the rotor wake. The computed results show good agreement with experimental results for surface airloads and wake geometry.
Initial Investigation into the Psychoacoustic Properties of Small Unmanned Aerial System Noise

For the past several years, researchers at NASA Langley have been engaged in a series of projects to study the degree to which existing facilities and capabilities, originally created for work on full-scale aircraft, are extensible to smaller scales --those of the small unmanned aerial systems (sUAS, also UAVs and, colloquially, `drones') that have been showing up in the nation's airspace of late. This paper follows an e ort that has led to an initial human{subject psychoacoustic test regarding the annoyance generated by sUAS noise. This e ort spans three phases: 1. The collection of the sounds through field recordings. 2. The formulation and execution of a psychoacoustic test using those recordings. 3. The initial analysis of the data from that test. The data suggests a lack of parity between the noise of the recorded sUAS and that of a set of road vehicles that were also recorded and included in the test, as measured by a set of contemporary noise metrics. Future work, including the possibility of further human subject testing, is discussed in light of this suggestion.
Computing the partition function of the Sherrington–Kirkpatrick model is hard on average

We establish the average-case hardness of the algorithmic problem of exact computation of the partition function associated with the Sherrington–Kirkpatrick model of spin glasses with Gaussian couplings and random external field. In particular, we establish that unless P=#P, there does not exist a polynomial-time algorithm to exactly compute the partition function on average. This is done by showing that if there exists a polynomial time algorithm, which exactly computes the partition function for inverse polynomial fraction (1/nO(1)) of all inputs, then there is a polynomial time algorithm, which exactly computes the partition function for all inputs, with high probability, yielding P=#P. The computational model that we adopt is finite-precision arithmetic, where the algorithmic inputs are truncated first to a certain level N of digital precision. The ingredients of our proof include the random and downward self-reducibility of the partition function with random external field; an argument of Cai et al. (In STACS 99 (Trier) (1999) 90–99 Springer) for establishing the average-case hardness of computing the permanent of a matrix; a list-decoding algorithm of Sudan (In 37th Annual Symposium on Foundations of Computer Science (Burlington, VT, 1996) (1996) 164–172 IEEE Comput. Soc. Press), for reconstructing polynomials intersecting a given list of numbers at sufficiently many points; and near-uniformity of the log-normal distribution, modulo a large prime p. To the best of our knowledge, our result is the first one establishing a provable hardness of a model arising in the field of spin glasses. Furthermore, we extend our result to the same problem under a different real-valued computational model, for example, using a Blum–Shub–Smale machine (In [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science (1988) 387–397 IEEE) operating over real-valued inputs. We establish that, if there exists a polynomial time algorithm which exactly computes the partition function for 34+1nO(1) fraction of all inputs, then there exists a polynomial time algorithm, which exactly computes the partition function for all inputs, with high probability, yielding P=#P. Our proof uses the random self-reducibility of the partition function, together with a control over the total variation distance for log-normal random variables in presence of a convex perturbation, and the Berlekamp–Welch algorithm.
Why do countries in financial distress strategically delay seeking help

Abstract The paper differs from current literature by providing a systematic analysis of the relationship between sovereign debt, financial distress and political career concerns via a novel game-theoretic model, in order to analyze the strategic behavior of governments in revealing financial distress and requesting a rescue program from international organizations. The analysis shows that, in equilibrium, a rent-seeking government will ”gamble for resurrection” by strategically delaying the revelation of financial distress through the creation of ”hidden debt,” hoping that things will improve in the future which will enable getting away with hiding the unfavorable news. This is so when the electorate only has short-term commitment power, if the government expects sufficient rents, provided a re-elected government adds to national output through experience, or international rescue organizations charge a relatively low interest rate, or the electorate discounts the future sufficiently. The analysis discusses incentive systems, such as differential interest rates when the government has been caught cheating, credit limiting and austerity, that can deter strategic delays, and a precommitment mechanism to get around time inconsistency.
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration

This paper studies regret minimization with randomized value functions in reinforcement learning. In tabular finite-horizon Markov Decision Processes, we introduce a clipping variant of one classical Thompson Sampling (TS)-like algorithm, randomized least-squares value iteration (RLSVI). Our $\tilde{\mathrm{O}}(H^2S\sqrt{AT})$ high-probability worst-case regret bound improves the previous sharpest worst-case regret bounds for RLSVI and matches the existing state-of-the-art worst-case TS-based regret bounds.
Free-Tip Rotor Wind Tunnel Test Results

Seven controller mechanisms and several tip geometries were proposed and evaluated in the course of a free tip rotor development program which integrated those components into a model rotor system for wind tunnel testing. After a rigid whirl test was run to determine controller mechanism performance and structural integrity, a second whirl test was performed with a model rotor incorporating a selected controller design and a selected tip platform to determine the transient behavior of the tips in response to aerodynamic excitation. The results obtained demonstrate that a swept, tapered tip with a tension-torsion strap controller furnished satisfactory mechanical operation, achieved the specified output torque, possessed a low spring rate, and had fast and stable response to excitation.
The Complexity of Pacing for Second-Price Auctions

Budget constraints are ubiquitous in online advertisement auctions. To manage these constraints and smooth out the expenditure across auctions, the bidders (or the platform on behalf of them) often employ pacing: each bidder is assigned a multiplier between 0 and 1, and her bid on each item is multiplicatively scaled down by the multiplier. This naturally gives rise to a game in which each bidder strategically selects a multiplier. The appropriate notion of equilibrium in this game is the pacing equilibrium. In this work, we show that the problem of finding an approximate pacing equilibrium is PPAD-complete for second-price auctions. This resolves an open question of[2]. As a consequence of our hardness result, we show that the tatonnement-style budget-management dynamics introduced by[1] is unlikely to converge efficiently for repeated second-price auctions. Our hardness result also implies the existence of a refinement of supply-aware market equilibria which is hard to compute with simple linear utilities.
A General Framework for Distributed Inference With Uncertain Models

This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best describes a sequence of observations. The problem is first abstracted to a hypothesis-testing framework, where we assume that the agents seek to agree on the hypothesis (target class) that best matches the distribution of observations. Non-Bayesian social learning theory provides a framework that solves this problem in an efficient manner by allowing the agents to sequentially communicate and update their beliefs for each hypothesis over the network. Most existing approaches assume that agents have access to exact statistical models for each hypothesis. However, in many practical applications, agents learn the likelihood models based on limited data, which induces uncertainty in the likelihood function parameters. In this work, we build upon the concept of uncertain models to incorporate the agents’ uncertainty in the likelihoods by identifying a broad set of parametric distribution that allows the agents’ beliefs to converge to the same result as a centralized approach. Furthermore, we empirically explore extensions to non-parametric models to provide a generalized framework of uncertain models in non-Bayesian social learning.
Information limits for detecting a subhypergraph

We consider the problem of recovering a subhypergraph based on an observed adjacency tensor corresponding to a uniform hypergraph. The uniform hypergraph is assumed to contain a subset of vertices called as subhypergraph. The edges restricted to the subhypergraph are assumed to follow a different probability distribution than other edges. We consider both weak recovery and exact recovery of the subhypergraph, and establish information-theoretic limits in each case. Specifically, we establish sharp conditions for the possibility of weakly or exactly recovering the subhypergraph from an information-theoretic point of view. These conditions are fundamentally different from their counterparts derived in hypothesis testing literature.
Steady Thermocapillary Flows and Their Stability

Thermocapillarity can drive steady flows in float-zone configurations. These flows can become unstable and exhibit time-periodic oscillations. In order to obtain insight into such phenomena, studies are made of a simplified system consisting of liquid in a two-dimensional slot. Steady flows and their instabilities are examined.