cssi/SciSciGPT-SciSciNet huggingface dataset:
Hypersonic Wake Diagnostics Using Laser Induced Fluorescence Techniques

A review of recent research performed in iodine that involves a two photon absorption of light at 193 nm will be discussed, and it’s potential application to velocimetry measurements in a hypersonic flow field will be described. An alternative seed atom, krypton, will be presented as a good candidate for performing nonintrusive hypersonic flow diagnostics. Krypton has a metastable state with a lifetime of approximately 43 s which would prove useful for time of flight measurement (TOF) and a sensitivity to collisions that can be utilized for density measurements. Calculations using modest laser energies and experimental values show an efficiency of excited state production to be on the order of 10 −6 for a two photon absorption at 193 nm to the 6p[3/2]2 state.
Approximation Schemes for Multiperiod Binary Knapsack Problems

An instance of the multiperiod binary knapsack problem (MPBKP) is given by a horizon length T, a non-decreasing vector of knapsack sizes \((c_1, \ldots , c_T)\) where \(c_t\) denotes the cumulative size for periods \(1,\ldots ,t\), and a list of n items. Each item is a triple (r, q, d) where r denotes the reward or value of the item, q its size, and d denotes its time index (or, deadline). The goal is to choose, for each deadline t, which items to include to maximize the total reward, subject to the constraints that for all \(t=1,\ldots ,T\), the total size of selected items with deadlines at most t does not exceed the cumulative capacity of the knapsack up to time t. We also consider the multiperiod binary knapsack problem with soft capacity constraints (MPBKP-S) where the capacity constraints are allowed to be violated by paying a penalty that is linear in the violation. The goal of MPBKP-S is to maximize the total profit, which is the total reward of the selected items less the total penalty. Finally, we consider the multiperiod binary knapsack problem with soft stochastic capacity constraints (MPBKP-SS), where the non-decreasing vector of knapsack sizes \((c_1, \ldots , c_T)\) follows an arbitrary joint distribution with the set of possible sample paths (realizations) of knapsack sizes and the probability of each sample path given to the algorithm.
Local rules for fabricating allosteric networks

Mechanical properties of disordered networks can be significantly tailored by modifying a small fraction of their bonds. This procedure has been used to design and build mechanical metamaterials with a variety of responses. A long-range ``allosteric'' response, where a localized input strain at one site gives rise to a localized output strain at a distant site, has been of particular interest. This work presents an approach to incorporating allosteric responses in experimental systems by pruning disordered networks in situ. Previous work has relied on computer simulations to design and predict the response of such systems using a cost function where the response of the entire network to each bond removal is used at each step to determine which bond to prune. It is not feasible to follow such a design protocol in experiments where one has access only to local response at each site. This paper presents design algorithms that allow determination of what bonds to prune based purely on the local forces in the network without employing a cost function; using only local information, allosteric networks are designed in simulations and then built out of real materials. The results show that some pruning strategies work better than others when translated into an experimental system. A method is presented to measure local stresses experimentally in disordered networks. This approach is then used to implement pruning methods to design desired responses in situ. Results from these experiments confirm that the pruning methods are robust and work in a real laboratory material.
Aerodynamic Comparison of Hyper-Elliptic Cambered Span (HECS) Wings with Conventional Configurations

An experimental study was conducted to examine the aerodynamic and flow field characteristics of hyper-elliptic cambered span (HECS) wings and compare results with more conventional configurations used for induced drag reduction. Previous preliminary studies, indicating improved L/D characteristics when compared to an elliptical planform prompted this more detailed experimental investigation. Balance data were acquired on a series of swept and un-swept HECS wings, a baseline elliptic planform, two winglet designs and a raked tip configuration. Seven-hole probe wake surveys were also conducted downstream of a number of the configurations. Wind tunnel results indicated aerodynamic performance levels of all but one of the HECS wings exceeded that of the other configurations. The flow field data surveys indicate the HECS configurations displaced the tip vortex farther outboard of the wing than the Baseline configuration. Minimum drag was observed on the raked tip configuration and it was noted that the winglet wake lacked the cohesive vortex structure present in the wakes of the other configurations.
Interplay between hysteresis and nonlocality during onset and arrest of flow in granular materials.

The jamming transition in granular materials is well-known for exhibiting hysteresis, wherein the level of shear stress required to trigger flow is larger than that below which flow stops. Although such behavior is typically modeled as a simple non-monotonic flow rule, the rheology of granular materials is also nonlocal due to cooperativity at the grain scale, leading for instance to increased strengthening of the flow threshold as system size is reduced. We investigate how these two effects – hysteresis and nonlocality – couple with each other by incorporating non-monotonicity of the flow rule into the nonlocal granular fluidity (NGF) model, a nonlocal constitutive model for granular flows. By artificially tuning the strength of nonlocal diffusion, we demonstrate that both ingredients are key to explaining certain features of the hysteretic transition between flow and arrest. Finally, we assess the ability of the NGF model to quantitatively predict material behavior both around the transition and in the flowing regime, through stress-driven discrete element method (DEM) simulations of flow onset and arrest in various geometries. Along the way, we develop a new methodology to compare deterministic model predictions with the stochastic behavior exhibited by the DEM simulations around the jamming transition.
Papillary fibroelastoma of posterior mitral leaflet, confused as vegetation or calcification

Abstract Papillary fibroelastoma (PFE) is one of the most common tumour of heart valves and found to be second common primary cardiac tumour. Only less than 50 cases were reported in the literature( Remadi et al., 2004) . 1 Due to the advancement in 3D echocardiography, its diagnosis in living patient is become easy. The PFE could be found on both ventricles, valvular leaflets, and chordae tendinea. We present an interesting case of the CPF originating from the posterior mitral leaflet confusing with vegetation or annular calcification. Successful surgical excision was done of the papillary fibroelastoma.
CUT&Tag-BS: an efficient and low-cost method for simultaneous profiling of histone modification and DNA methylation

Summary It remains a challenge to decipher the complex relationship between DNA methylation, histone modification, and the underlying DNA sequence with limited input material. Here, we developed an efficient, low-input, and low-cost method for simultaneous profiling of genomic localization of histone modification and methylation status of the underlying DNA at single-base resolution from the same cells in a single experiment by integrating CUTT it further reveals that H3K4me1-marked regions are mostly CpG-poor, lack methylation concordance, and exhibit prevalent DNA methylation heterogeneity among the cells. We anticipate that CUT&Tag-BS will be widely applied to directly address the genomic relationship between DNA methylation and histone modification, especially in low-input scenarios with precious biological samples.
Outcomes of robotic surgery in morbidly obese patients with endometrial cancer: a retrospective study.

With advances in minimal invasive surgery, robotic surgery has become the widespread approach for surgical staging of endometrial cancer in the obese population. This study aimed to evaluate safety and surgical outcomes of robotic surgery in the morbidly obese and extremely morbidly obese patients with endometrial cancer. Retrospective cohort study. A total of 391 obese women undergoing robotic-assisted surgical staging were identified and included in the study. Surgical outcomes for obese patients (BMI > 30 kg/m2) who underwent surgical staging between 2011 and 2019 were retrospectively collected. Preoperative characteristics, perioperative outcomes and postoperative complications were analyzed among the categories of obesity (BMI ≥ 30–34.9 kg/m2, ≥ 35–39.9 kg/m2, ≥ 40–49.9 kg/m2) including the extremely morbid obese (BMI > 50 kg/m2). Comparative analysis revealed a higher percentage of postoperative complications with increasing BMI, although the results were not statistically significant. Postoperative complications are observed at higher rates among women with increasing BMI.
On Weil Sums, Conjectures of Helleseth, and Niho Exponents

Abstract Let F be a finite field, μ be a fixed additive character and s be an integer coprime to | F × | . For any a ∈ F , the corresponding Weil sum is defined to be W F , s ( a ) = ∑ x ∈ F μ ( x s − a x ) . The Weil spectrum counts distinct values of the Weil sum as a runs through the invertible elements in the finite field. The value of these sums and the size of the Weil spectrum are of particular interest, as they link problems in coding and information theory to other areas of math such as number theory and arithmetic geometry. In the setting of Niho exponents, we examine the Weil sum, its bounds and its spectrum. As a consequence, we give a new proof to the Vanishing Conjecture of Helleseth (1971) on the presence of zero in the Weil spectrum in the case of Niho exponents. We also state a conjecture for when the Weil spectrum contains at least five elements, and prove it for a certain class of Weil sums.
Escaping the 'Impossibility of Fairness': From Formal to Substantive Algorithmic Fairness

In the face of compounding crises of social and economic inequality, many have turned to algorithmic decision-making to achieve greater fairness in society. As these efforts intensify, reasoning within the burgeoning field of “algorithmic fairness” increasingly shapes how fairness manifests in practice. This paper interrogates whether algorithmic fairness provides the appropriate conceptual and practical tools for enhancing social equality. I argue that the dominant, “formal” approach to algorithmic fairness is ill-equipped as a framework for pursuing equality, as its narrow frame of analysis generates restrictive approaches to reform. In light of these shortcomings, I propose an alternative: a “substantive” approach to algorithmic fairness that centers opposition to social hierarchies and provides a more expansive analysis of how to address inequality. This substantive approach enables more fruitful theorizing about the role of algorithms in combatting oppression. The distinction between formal and substantive algorithmic fairness is exemplified by each approach’s responses to the “impossibility of fairness” (an incompatibility between mathematical definitions of algorithmic fairness). While the formal approach requires us to accept the “impossibility of fairness” as a harsh limit on efforts to enhance equality, the substantive approach allows us to escape the “impossibility of fairness” by suggesting reforms that are not subject to this false dilemma and that are better equipped to ameliorate conditions of social oppression.
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization

In this paper, we propose and analyze SQuARM-SGD, a communication-efficient algorithm for decentralized training of large-scale machine learning models over a network. In SQuARM-SGD, each node performs a fixed number of local SGD steps using Nesterov's momentum and then sends sparsified and quantized updates to its neighbors regulated by a locally computable triggering criterion. We provide convergence guarantees of our algorithm for general smooth objectives, which, to the best of our knowledge, is the first theoretical analysis for compressed decentralized SGD with momentum updates. We show that SQuARM-SGD converges at rate $\mathcal{O}({{}^{1}}\!/\!_{\sqrt{nT}})$ , matching that of vanilla distributed SGD. We empirically show that SQuARM-SGD saves significantly in total communicated bits over state-of-the-art without sacrificing much on accuracy.
Efficient treatment of molecular excitations in the liquid phase environment via stochastic many-body theory.

Accurate predictions of charge excitation energies of molecules in the disordered condensed phase are central to the chemical reactivity, stability, and optoelectronic properties of molecules and critically depend on the specific environment. Herein, we develop a stochastic GW method for calculating these charge excitation energies. The approach employs maximally localized electronic states to define the electronic subspace of a molecule and the rest of the system, both of which are randomly sampled. We test the method on three solute–solvent systems: phenol, thymine, and phenylalanine in water. The results are in excellent agreement with the previous high-level calculations and available experimental data. The stochastic calculations for supercells containing up to 1000 electrons representing the solvated systems are inexpensive and require ≤1000 central processing unit hrs. We find that the coupling with the environment accounts for ∼40% of the total correlation energy. The solvent-to-solute feedback mechanism incorporated in the molecular correlation term causes up to 0.6 eV destabilization of the quasiparticle energy. Simulated photo-emission spectra exhibit red shifts, state-degeneracy lifting, and lifetime shortening. Our method provides an efficient approach for an accurate study of excitations of large molecules in realistic condensed phase environments.
Minimum-Time Earth-to-Mars Interplanetary Orbit Transfer Using Adaptive Gaussian Quadrature Collocation

The problem of minimum-time, low-thrust, Earth-to-Mars interplanetary orbital trajectory optimization is considered. The minimum-time orbital transfer problem is modeled as a four-phase optimal con...
Generalized Zero-Shot Learning Using Multimodal Variational Auto-Encoder With Semantic Concepts

With the ever-increasing amount of data, the central challenge in multimodal learning involves limitations of labelled samples. For the task of classification, techniques such as meta-learning, zero-shot learning, and few-shot learning showcase the ability to learn information about novel classes based on prior knowledge. Recent techniques try to learn a cross-modal mapping between the semantic space and the image space. However, they tend to ignore the local and global semantic knowledge. To overcome this problem, we propose a Multimodal Variational Auto-Encoder (M-VAE) which can learn the shared latent space of image features and the semantic space. In our approach we concatenate multimodal data to a single embedding before passing it to the VAE for learning the latent space. We propose the use of a multi-modal loss during the reconstruction of the feature embedding through the decoder. Our approach is capable to correlating modalities and exploit the local and global semantic knowledge for novel sample predictions. Our experimental results using a MLP classifier on four benchmark datasets show that our proposed model outperforms the current state-of-the-art approaches for generalized zero-shot learning.
A practical workflow for land seismic wavefield recovery with weighted matrix factorization

While wavefield reconstruction through weighted low-rank matrix factorizations has been shown to perform well on marine data, out-of-the-box application of this technology to land data is hampered by ground roll. The presence of these strong surface waves tends to dominate the reconstruction at the expense of the weaker body waves. Because ground roll is slow, it also suffers more from aliasing. To overcome these challenges, we introduce a practical workflow where the ground roll and body wave components are recovered separately and combined. We test the proposed approach blindly on a subset of the 3D SEAM Barrett dataset. With our technique, we recover densely sampled data from 25 percent randomly subsampled receivers. Independent comparisons on a single shot demonstrate significant improvements achievable with the presented workflow.
Integrating Biomedical Informatics Training into Existing High School Curricula

Growing demand for biomedical informaticists and expertise in areas related to this discipline has accentuated the need to integrate biomedical informatics training into high school curricula. The K-12 Bioinformatics professional development project educates high school teachers about data analysis, biomedical informatics and mobile learning, and partners with them to expose high school students to health and environment-related issues using biomedical informatics knowledge and current technologies. We designed low-cost pollution sensors and created interactive web applications that teachers from six Philadelphia public high schools used during the 2019-2020 school year to successfully implement a problem-based mobile learning unit that included collecting and interpreting air pollution data, as well as relating this data to asthma. Through this project, we sought to improve data and health literacy among the students and teachers, while inspiring student engagement by demonstrating how biomedical informatics can help address problems relevant to communities where students live.
Electronic Structure and Superconductivity of Compressed Metal Tetrahydrides.

Tetrahydrides crystallizing in the ThCr2 Si2 structure type have been predicted to become stable for a plethora of metals under pressure, and some have recently been synthesized. Through detailed first-principles investigations we show that the metal atoms within these I4/mmm symmetry MH4 compounds may be divalent, trivalent or tetravalent. The valence of the metal atom and its radius govern the bonding and electronic structure of these phases, and their evolution under pressure. The factors important for enhancing superconductivity include a large number of hydrogenic states at the Fermi level, and the presence of quasi-molecular H 2δ- units whose bonds have been stretched and weakened (but not broken) via electron transfer from the electropositive metal, and via a Kubas-like interaction with the metal. Analysis of the microscopic mechanism of superconductivity in MgH4 , ScH4 and ZrH4 reveals that phonon modes involving a coupled libration and stretch of the H 2δ- units leading to the formation of more complex hydrogenic motifs are important contributors towards the electron phonon coupling mechanism. In the divalent hydride MgH4 , modes associated with motions of the hydridic hydrogen atoms are also key contributors, and soften substantially at lower pressures.
A Comparison between Human and NLP-based Annotation of Clinical Trial Eligibility Criteria Text Using The OMOP Common Data Model

Human annotations are the established gold standard for evaluating natural language processing (NLP) methods. The goals of this study are to quantify and qualify the disagreement between human and NLP. We developed an NLP system for annotating clinical trial eligibility criteria text and constructed a manually annotated corpus, both following the OMOP Common Data Model (CDM). We analyzed the discrepancies between the human and NLP annotations and their causes (e.g., ambiguities in concept categorization and tacit decisions on inclusion of qualifiers and temporal attributes during concept annotation). This study initially reported complexities in clinical trial eligibility criteria text that complicate NLP and the limitations of the OMOP CDM. The disagreement between and human and NLP annotations may be generalizable. We discuss implications for NLP evaluation.
Assessing Disparity Using Measures of Racial and Educational Isolation.

We develop a local, spatial measure of educational isolation (EI) and characterize the relationship between EI and our previously developed measure of racial isolation (RI). EI measures the extent to which non-college educated individuals are exposed primarily to other non-college educated individuals. To characterize how the RI-EI relationship varies across space, we propose a novel measure of local correlation. Using birth records from the State of Michigan (2005–2012), we estimate associations between RI, EI, and birth outcomes. EI was lower in urban communities and higher in rural communities, while RI was highest in urban areas and parts of the southeastern United States (US). We observed greater heterogeneity in EI in low RI tracts, especially in non-urban tracts; residents of high RI tracts are likely to be both educationally and racially isolated. Associations were also observed between RI, EI, and gestational length (weeks) and preterm birth (PTB). For example, moving from the lowest to the highest quintile of RI was associated with a 1.11 (1.07, 1.15) and 1.16 (1.10, 1.22) increase in odds of PTB among NHB and NHW women, respectively. Moving from the lowest to the highest quintile of EI was associated with a 1.07 (1.02, 1.12) and 1.03 (1.00, 1.05) increase in odds of PTB among NHB and NHW women, respectively. This work provides three tools (RI, EI, and the local correlation measure) to researchers and policymakers interested in how residential isolation shapes disparate outcomes.
Settling the Sharp Reconstruction Thresholds of Random Graph Matching

This paper studies the problem of recovering the hidden vertex correspondence between two edge-correlated random graphs. We focus on the Gaussian model where the two graphs are complete graphs with correlated Gaussian weights and the Erdős-Renyi model where the two graphs are subsampled from a common parent Erdős-Renyi graph $\mathcal{G}(n, p)$ . For dense graphs with $p=n^{-o(1)}$ , we prove that there exists a sharp threshold, above which one can correctly match all but a vanishing fraction of the vertices and below which correctly matching any positive fraction is impossible, a phenomenon known as the “all-or-nothing” phase transition. Even more strikingly, in the Gaussian setting, above the threshold all vertices can be exactly matched with high probability. In contrast, for sparse Erdős-Renyi graphs with $p=n^{-\Theta(1)}$ , we show that the all-or-nothing phenomenon no longer holds and we determine the thresholds up to a constant factor. Along the way, we also derive the sharp threshold for exact recovery, sharpening the existing results in Erdős-Renyi graphs [1], [2]. The proof of the negative results builds upon a tight characterization of the mutual information based on the truncated second-moment computation in [3] and an “area theorem” that relates the mutual information to the integral of the reconstruction error. The positive results follows from a tight analysis of the maximum likelihood estimator that takes into account the cycle structure of the induced permutation on the edges.
ADVANCED PATERNAL AGE AND SPERM DNA FRAGMENTATION: A SYSTEMATIC REVIEW

Purpose Male ageing is often associated with defective sperm DNA remodeling mechanisms that result in poorly packaged chromatin and a decreased ability to repair DNA strand breaks. However, the impact of advanced paternal age on DNA fragmentation remains inconclusive. The aim of the present systematic review was to investigate the impact of advancing paternal age (APA) on DNA fragmentation. Materials and methods We conducted a thorough search of listed publications in Scopus, PubMed, and EMBASE, in accordance with the PRISMA guidelines. Results We identified 3,120 articles, of which nineteen were selected for qualitative analysis, resulting in a sample of 40,668 men. Of the 19 articles evaluating the impact of APA on DFI% (DNA fragmentation Index) included, 4 were on Normozoospermic and subfertile men, 3 on normozoospermic, Oligoasthenoteratozoospermic and Teratozoospermic, 6 on fertile and infertile men, 4 on just infertile men, and 2 evaluated a general population. Seventeen of the ninrnteen studies demonstrated APA's effect and impact on DFI%. Conclusions Although there was no universal definition for APA, the present review suggests that older age is associated with increased DFI. In elderly men with normal semen parameters, further studies should be performed to assess the clinical implications of DFI, as a conventional semen analysis can often fail to detect an etiology for infertility.
A Case Study of LLVM-Based Analysis for Optimizing SIMD Code Generation

This paper presents a methodology for using LLVM-based tools to tune the DCA++ (dynamical cluster approximation) application that targets the new ARM A64FX processor. The goal is to describe the changes required for the new architecture and generate efficient single instruction/multiple data (SIMD) instructions that target the new Scalable Vector Extension instruction set. During manual tuning, the authors used the LLVM tools to improve code parallelization by using OpenMP SIMD, refactored the code and applied transformation that enabled SIMD optimizations, and ensured that the correct libraries were used to achieve optimal performance. By applying these code changes, code speed was increased by 1.98\(\times \) and 78 GFlops were achieved on the A64FX processor. The authors aim to automatize parts of the efforts in the OpenMP Advisor tool, which is built on top of existing and newly introduced LLVM tooling.
AI Choreographer: Music Conditioned 3D Dance Generation With AIST++

We present AIST++, a new multi-modal dataset of 3D dance motion and music, along with FACT, a Full-Attention Cross-modal Transformer network for generating 3D dance motion conditioned on music. The proposed AIST++ dataset contains 5.2 hours of 3D dance motion in 1408 sequences, covering 10 dance genres with multi-view videos with known camera poses -- the largest dataset of this kind to our knowledge. We show that naively applying sequence models such as transformers to this dataset for the task of music conditioned 3D motion generation does not produce satisfactory 3D motion that is well correlated with the input music. We overcome these shortcomings by introducing key changes in its architecture design and supervision: FACT model involves a deep cross-modal transformer block with full-attention that is trained to predict $N$ future motions. We empirically show that these changes are key factors in generating long sequences of realistic dance motion that are well-attuned to the input music. We conduct extensive experiments on AIST++ with user studies, where our method outperforms recent state-of-the-art methods both qualitatively and quantitatively.
Synergistic Enzyme Mixtures to Realize Near-Complete Depolymerization in Biodegradable Polymer/Additive Blends.

Embedding catalysts inside of plastics affords accelerated chemical modification with programmable latency and pathways. Nanoscopically embedded enzymes can lead to near-complete degradation of polyesters via chain-end mediated processive depolymerization. The overall degradation rate and pathways have a strong dependence on the morphology of semicrystalline polyesters. Yet, most studies to date focus on pristine polymers instead of mixtures that contain additives and other components despite their nearly universal use in plastic production. Here, additives are introduced to purposely change the morphology of polycaprolactone (PCL) by increasing the bending and twisting of crystalline lamellae. These morphological changes immobilize chain ends preferentially at the crystalline/amorphous interfaces and limit chain-end accessibility by the embedded processive enzyme. This chain-end redistribution reduces the polymer-to-monomer conversion from >95% to less than 50%, causing formation of highly crystalline plastic pieces, including microplastics. By synergizing both random chain scission and processive depolymerization, it is feasible to navigate morphological changes in polymer/additive blends and to achieve near-complete depolymerization. The random scission enzymes in the amorphous domains create new chain ends that are subsequently bound and depolymerized by processive enzymes. Present studies further highlight the importance to consider how the host polymer's morphologies affect the reactions catalyzed by embedded catalytic species.
Chasing Sparsity in Vision Transformers:An End-to-End Exploration

Vision transformers (ViTs) have recently received explosive popularity, but their enormous model sizes and training costs remain daunting. Conventional post-training pruning often incurs higher training budgets. In contrast, this paper aims to trim down both the training memory overhead and the inference complexity, without sacrificing the achievable accuracy. We launch and report the first-of-its-kind comprehensive exploration, on taking a unified approach of integrating sparsity in ViTs "from end to end". Specifically, instead of training full ViTs, we dynamically extract and train sparse subnetworks, while sticking to a fixed small parameter budget. Our approach jointly optimizes model parameters and explores connectivity throughout training, ending up with one sparse network as the final output. The approach is seamlessly extended from unstructured to structured sparsity, the latter by considering to guide the prune-and-grow of self-attention heads inside ViTs. For additional efficiency gains, we further co-explore data and architecture sparsity, by plugging in a novel learnable token selector to adaptively determine the currently most vital patches. Extensive results on ImageNet with diverse ViT backbones validate the effectiveness of our proposals which obtain significantly reduced computational cost and almost unimpaired generalization. Perhaps most surprisingly, we find that the proposed sparse (co-)training can even improve the ViT accuracy rather than compromising it, making sparsity a tantalizing "free lunch". For example, our sparsified DeiT-Small at (5%, 50%) sparsity for (data, architecture), improves 0.28% top-1 accuracy, and meanwhile enjoys 49.32% FLOPs and 4.40% running time savings. Our codes are available at this https URL.
ZeRO-infinity: breaking the GPU memory wall for extreme scale deep learning

In the last three years, the largest dense deep learning models have grown over 1000x to reach hundreds of billions of parameters, while the GPU memory has only grown by 5x (16 GB to 80 GB). Therefore, the growth in model scale has been supported primarily though system innovations that allow large models to fit in the aggregate GPU memory of multiple GPUs. However, we are getting close to the GPU memory wall. It requires 800 NVIDIA V100 GPUs just to fit a trillion parameter model for training, and such clusters are simply out of reach for most data scientists. In addition, training models at that scale requires complex combinations of parallelism techniques that puts a big burden on the data scientists to refactor their model. In this paper we present ZeRO-Infinity, a novel heterogeneous system technology that leverages GPU, CPU, and NVMe memory to allow for unprecedented model scale on limited resources without requiring model code refactoring. At the same time it achieves excellent training throughput and scalability, unencumbered by the limited CPU or NVMe bandwidth. ZeRO-Infinity can fit models with tens and even hundreds of trillions of parameters for training on current generation GPU clusters. It can be used to fine-tune trillion parameter models on a single NVIDIA DGX-2 node, making large models more accessible. In terms of training throughput and scalability, it sustains over 25 petaflops on 512 NVIDIA V100 GPUs (40% of peak), while also demonstrating super linear scalability. An open source implementation of ZeRO-Infinity is available through DeepSpeed 1.
Non-Monotone Energy-Aware Information Gathering for Heterogeneous Robot Teams

This paper considers the problem of planning trajectories for a team of sensor-equipped robots to reduce uncertainty about a dynamical process. Optimizing the trade-off between information gain and energy cost (e.g., control effort, distance travelled) is desirable but leads to a non-monotone objective function in the set of robot trajectories. Therefore, common multi-robot planning algorithms based on techniques such as coordinate descent lose their performance guarantees. Methods based on local search provide performance guarantees for optimizing a non-monotone submodular function, but require access to all robots’ trajectories, making it not suitable for distributed execution. This work proposes a distributed planning approach based on local search and shows how lazy/greedy methods can be adopted to reduce the computation and communication of the approach. We demonstrate the efficacy of the proposed method by coordinating robot teams composed of both ground and aerial vehicles with different sensing/control profiles and evaluate the algorithm’s performance in two target tracking scenarios. Compared to the naive distributed execution of local search, our approach saves up to 60% communication and 80–92% computation on average when coordinating up to 10 robots, while outperforming the coordinate descent based algorithm in achieving a desirable trade-off between sensing and energy cost.
On certain partition bijections related to Euler's partition problem

Abstract We give short elementary expositions of combinatorial proofs of some variants of Euler's partition identity that were first addressed analytically by George Andrews, and later combinatorially by others. The method using certain matrices to concisely explain these bijections, based on ideas first used in a previous manuscript by the author, enables us to also give new generalizations of two of these results.
A call to embrace jural dualism

In this paper I explore the importance of embracing two jural relationships, which I call jural dualism. Jural dualism is a concept originating from my study of Adam Smith’s work. I first establish that Adam Smith is part of a larger tradition utilizing jural dualism. I then discuss how humankind’s evolution has made us predisposed to revert from jural dualism to jural monism. I suggest a deep-seated human unease with the superior-inferior relationship, and a corresponding urge to return to one jural relationship, the equal-equal. I then walk through how two modern ideologies strive to dispense with the superior-inferior jural relationship and to imagine jural monism. Anarchistic libertarianism construes the government as essentially a criminal jural equal, and points us to a world without a jural superior. Democratic socialism or social democracy embraces government but effectively construes it as a voluntary institution, a formation existing within the realm of the equal-equal jural relationship. I close by suggesting that we embrace jural dualism. Doing so puts the coercive nature of the superior-inferior jural relationship out in the open, and enhances our aptitude to assess and argue for less bad forms of it. Reconciling ourselves to jural dualism, we practice more useful political discourse.
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning

We study risk-sensitive reinforcement learning (RL) based on the entropic risk measure. Although existing works have established non-asymptotic regret guarantees for this problem, they leave open an exponential gap between the upper and lower bounds. We identify the deficiencies in existing algorithms and their analysis that result in such a gap. To remedy these deficiencies, we investigate a simple transformation of the risk-sensitive Bellman equations, which we call the exponential Bellman equation. The exponential Bellman equation inspires us to develop a novel analysis of Bellman backup procedures in risk-sensitive RL algorithms, and further motivates the design of a novel exploration mechanism. We show that these analytic and algorithmic innovations together lead to improved regret upper bounds over existing ones.
Will this question be answered? Question filtering via answer model distillation for efficient question answering

In this paper we propose a novel approach towards improving the efficiency of Question Answering (QA) systems by filtering out questions that will not be answered by them. This is based on an interesting new finding: the answer confidence scores of state-of-the-art QA systems can be approximated well by models solely using the input question text. This enables preemptive filtering of questions that are not answered by the system due to their answer confidence scores being lower than the system threshold. Specifically, we learn Transformer-based question models by distilling Transformer-based answering models. Our experiments on three popular QA datasets and one industrial QA benchmark demonstrate the ability of our question models to approximate the Precision/Recall curves of the target QA system well. These question models, when used as filters, can effectively trade off lower computation cost of QA systems for lower Recall, e.g., reducing computation by ~60%, while only losing ~3-4% of Recall.
Provably efficient, succinct, and precise explanations

We consider the problem of explaining the predictions of an arbitrary blackbox model $f$: given query access to $f$ and an instance $x$, output a small set of $x$'s features that in conjunction essentially determines $f(x)$. We design an efficient algorithm with provable guarantees on the succinctness and precision of the explanations that it returns. Prior algorithms were either efficient but lacked such guarantees, or achieved such guarantees but were inefficient. We obtain our algorithm via a connection to the problem of {\sl implicitly} learning decision trees. The implicit nature of this learning task allows for efficient algorithms even when the complexity of $f$ necessitates an intractably large surrogate decision tree. We solve the implicit learning problem by bringing together techniques from learning theory, local computation algorithms, and complexity theory. Our approach of "explaining by implicit learning" shares elements of two previously disparate methods for post-hoc explanations, global and local explanations, and we make the case that it enjoys advantages of both.
WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres.

Previous work on Entity Linking has focused on resources targeting non-nested proper named entity mentions, often in data from Wikipedia, i.e. Wikification. In this paper, we present and evaluate WikiGUM, a fully wikified dataset, covering all mentions of named entities, including their non-named and pronominal mentions, as well as mentions nested within other mentions. The dataset covers a broad range of 12 written and spoken genres, most of which have not been included in Entity Linking efforts to date, leading to poor performance by a pretrained SOTA system in our evaluation. The availability of a variety of other annotations for the same data also enables further research on entities in context.
Model-based assessment of SARS-CoV-2 Delta variant transmission dynamics within partially vaccinated K-12 school populations

Background We examined school reopening policies amidst ongoing transmission of the highly transmissible Delta variant, accounting for vaccination among individuals ≥12 years. Methods We collected data on social contacts among school-aged children in the California Bay Area and developed an individual-based transmission model to simulate transmission of the Delta variant of SARS-CoV-2 in schools. We evaluated the additional infections in students and teachers/staff resulting over a 128-day semester from in-school instruction compared to remote instruction when various NPIs (mask use, cohorts, and weekly testing of students/teachers) were implemented, across various community-wide vaccination coverages (50%, 60%, 70%), and student (≥12 years) and teacher/staff vaccination coverages (50% - 95%). Findings At 70% vaccination coverage, universal masking reduced infections by >57% among students. Masking plus 70% vaccination coverage enabled achievement of <50 excess cases per 1,000 students/teachers, but stricter risk tolerances, such as <25 excess infections per 1,000 students/teachers, required a cohort approach in elementary and middle school populations. In the absence of NPIs, increasing the vaccination coverage of community members from 50% to 70% or elementary teachers from 70% to 95% reduced the excess rate of infection among elementary school students attributable to school transmission by 24% and 37%, respectively. Interpretations Amidst Delta variant circulation, we found that schools are not inherently low risk, yet can be made so with high community vaccination coverages and masking. Vaccination of adults protects unvaccinated children. Funding National Science Foundation grant no. 2032210; National Institutes of Health grant nos. R01AI125842 and R01AI148336; MIDAS Coordination Center (MIDASSUP2020-4).
Neural Attention for Image Captioning: Review of Outstanding Methods

Image captioning is the task of automatically generating sentences that describe an input image in the best way possible. The most successful techniques for automatically generating image captions have recently used attentive deep learning models. There are variations in the way deep learning models with attention are designed. In this survey, we provide a review of literature related to attentive deep learning models for image captioning. Instead of offering a comprehensive review of all prior work on deep image captioning models, we explain various types of attention mechanisms used for the task of image captioning in deep learning models. The most successful deep learning models used for image captioning follow the encoder-decoder architecture, although there are differences in the way these models employ attention mechanisms. Via analysis on performance results from different attentive deep models for image captioning, we aim at finding the most successful types of attention mechanisms in deep models for image captioning. Soft attention, bottom-up attention, and multi-head attention are the types of attention mechanism widely used in state-of-the-art attentive deep learning models for image captioning. At the current time, the best results are achieved from variants of multi-head attention with bottom-up attention.
The role of parasitoid and predator production in technology transfer of field crop biological control

The immediate goals for improving natural enemy production are to reduce costs, increase efficacy and provide additional species for pest management. This paper describes expanding markets for natural enemies that are or could be produced commercially, gives operational and experimental examples of parasitoid and predator production for use in field crop biological control, defines some of the obstacles and makes recommendations for producing and using natural enemies. Additionally, it provides recent published guidance for implementing biological control in Integrated Pest Management.
The Salience of Adolescent Romantic Experiences for Romantic Relationship Qualities in Young Adulthood

Conceptual links between aspects of adolescents' dating experiences (i.e., involvement and quality; ages 15–17.5) and qualities of their romantic relationships in young adulthood (ages 20–21) were examined in a prospective longitudinal design. Even after accounting for earlier relationship experiences with parents and peers, aspects of adolescent dating experiences predicted romantic relationship qualities in young adulthood. Adolescents who dated fewer partners in mid-adolescence and who experienced a better quality dating relationship at age 16 demonstrated romantic partner interactions characterized by smoother relationship process in young adulthood (e.g., negotiating conflict to mutual satisfaction, effective and timely caregiving/seeking); adolescents who dated more partners in mid-adolescence displayed greater negative affect in romantic partner interactions in young adulthood.
TURBULENT NATURAL CONVECTION IN AN ENCLOSURE WITH LOCALIZED HEATING FROM BELOW

Natural-convection heat transfer in a cylindrical enclosure, heated partilly from below by a disk-shaped heating surface and cooled from the top and the side, was investigated experimentally and numerically. Heat transfer measurements are presented for the range of Rayleigh numbers from 10 8 to 2 x 10 10 . The total acceleration normal to the heating surface was varied from 1 to 100 times the standard gravitational acceleration. The heat flux was varied from 0.19 MW /m 2 to 1.5 MW /m 2 . The test results were correlated by an equation of the form NU D = 0.2 Ra d 0.325 for Prandtl number 2 and aspect ratio 1. The flow field was studied numerically using FLUENT code.
The effect of household wealth on educational attainment : demographic and health survey evidence

In this paper are interested not just in countries' average educational enrollment and attainment, for which there has been a great deal of examination both from official and academic sources, but in how educational attainment differs by household wealth within countries. How much schooling are children from poor households India, Brazil, or Kenya receiving, both absolutely and relative to the rich in the same country? Answering this question, especially in a way that produces valid comparisons across countries is hampered by the limited availability, difficulty of use, and comparability of household survey data. The Demographic and Health Surveys (DHS), having applied essentially the same survey instrument in 35 countries potentially overcomes these problems. One potential limitation of the DHS is that it lacks questions on household income or consumption expenditures, which are conventionally used as indicators of households' economic status. However, in a separate methodological paper the authors shows that an index constructed from the questions asked in the DHS about household assets and housing characteristics (e.g. construction materials, drinking water and toilet facilities) works as well, and arguably better, than consumption expenditures as a proxy for household long-run wealth. This finding allows us to use a comparable method, principal components, in constructing a ranking of households within each country. The 'poor' are simply defined as the bottom 40 percent in each country, so while levels of poverty are not comparable across countries, the rankings are constructed using a similar method. An analysis of this data on education and wealth reveals three key findings. First, very low primary attainment by the poor is driven by two distinct patterns of enrollment and dropout. There is a South Asian and Western/Central African pattern in which many of the poor never enroll in school.
Arthroscopic Suture Fixation in Femoral-Sided Avulsion Fracture of Anterior Cruciate Ligament

A femoral-sided avulsion fracture of the anterior cruciate ligament (ACL) is a rare and challenging condition. Most reported cases have occurred in childhood or adolescence. Many techniques of ACL repair have been reported, and in recent years, techniques in arthroscopic surgery have been developed and have become ever more popular with orthopaedic surgeons. We created a technique of arthroscopic ACL repair with suture anchor fixation for a femoral-sided ACL avulsion fracture. This technique saves the natural ACL stump. It is available for cases in which creation of a tibial tunnel is not allowed. Moreover, it does not require a skin incision for fixation on the far femoral cortex and, therefore, does not require a second operation to remove the fixation device. The arthroscopic technique also has a good cosmetic outcome.