cssi/SciSciGPT-SciSciNet huggingface dataset:
PRAGMA: Interactively Constructing Functional Brain Parcellations

A prominent goal of neuroimaging studies is mapping the human brain, in order to identify and delineate functionally-meaningful regions and elucidate their roles in cognitive behaviors. These brain regions are typically represented by atlases that capture general trends over large populations. Despite being indispensable to neuroimaging experts, population-level atlases do not capture individual differences in functional organization. In this work, we present an interactive visualization method, PRAGMA, that allows domain experts to derive scan-specific parcellations from established atlases. PRAGMA features a user-driven, hierarchical clustering scheme for defining temporally correlated parcels in varying granularity. The visualization design supports the user in making decisions on how to perform clustering, namely when to expand, collapse, or merge parcels. This is accomplished through a set of linked and coordinated views for understanding the user’s current hierarchy, assessing intra-cluster variation, and relating parcellations to an established atlas. We assess the effectiveness of PRAGMA through a user study with four neuroimaging domain experts, where our results show that PRAGMA shows the potential to enable exploration of individualized and state-specific brain parcellations and to offer interesting insights into functional brain networks.
Input-Distribution-Aware Successive Cancellation List Decoding of Polar Codes

Polar codes are linear block codes that can achieve channel capacity at infinite code length. Successive cancellation list (SCL) decoding relies on a set of parallel decoders; it yields good error-correction performance at finite code length, at the cost of increased implementation complexity and power consumption. Current efforts in literature focus on design-time decoder complexity reduction, while lacking practical run-time power reduction methods. In this work, input-distribution-aware SCL (IDA-SCL) decoding is proposed, that allows to determine the parallelism to adopt by performing simple observations on the input of the decoder. This technique guarantees fixed, short latency and allows hardware SCL decoders to dynamically shut down part of the internal parallelism before each decoding process. It can be combined with existing complexity- and power- reduction techniques. Simulation results show that IDA-SCL can reduce the run-time complexity of SCL of up to 50%.
Identifying Reform Priorities: The Role of Non-Linearities

Can countries improve their business climate through reforms in specific policy areas? Kraay and Tawara (2013) find that the answer depends on how we measure the business climate. When regressing seven different business climate indices on 38 policy indicators, they find little agreement among the seven models as to which of those policy indicators matter most. I revisit this puzzle using the same data but replacing their linear models with a Random Forest algorithm. I find a strong consensus across models on the importance ranking of policy indicators: No matter which business climate index is considered, the top ten contributors to a better business climate always include high recovery rates in insolvency proceedings (i.e., cents on the dollar for creditors), shorter border formalities for both importers and exporters, and low costs for starting a business. I show that the marginal effect of reforms is heterogeneous across countries and document how reform priorities depend on country specific circumstances.
Judgment Aggregation in Creative Production: Evidence from the Movie Industry

We study a novel, low-cost approach to aggregating judgment from a large number of industry experts on ideas that they encounter in their normal course of business. Our context is the movie industr...
Efficient 2D Tensor Network Simulation of Quantum Systems

Simulation of quantum systems is challenging due to the exponential size of the state space. Tensor networks provide a systematically improvable approximation for quantum states. 2D tensor networks such as Projected Entangled Pair States (PEPS) are well-suited for key classes of physical systems and quantum circuits. However, direct contraction of PEPS networks has exponential cost, while approximate algorithms require computations with large tensors. We propose new scalable algorithms and software abstractions for PEPS-based methods, accelerating the bottleneck operation of contraction and refactorization of a tensor subnetwork. We employ randomized SVD with an implicit matrix to reduce cost and memory footprint asymptotically. Further, we develop a distributed-memory PEPS library and study accuracy and efficiency of alternative algorithms for PEPS contraction and evolution on the Stampede2 supercomputer. We also simulate a popular near-term quantum algorithm, the Variational Quantum Eigensolver (VQE), and benchmark Imaginary Time Evolution (ITE), which compute ground states of Hamiltonians.
F-Seq2: improving the feature density based peak caller with dynamic statistics.

Genomic and epigenomic features are captured at a genome-wide level by using high-throughput sequencing (HTS) technologies. Peak calling delineates features identified in HTS experiments, such as open chromatin regions and transcription factor binding sites, by comparing the observed read distributions to a random expectation. Since its introduction, F-Seq has been widely used and shown to be the most sensitive and accurate peak caller for DNase I hypersensitive site (DNase-seq) data. However, the first release (F-Seq1) has two key limitations: lack of support for user-input control datasets, and poor test statistic reporting. These constrain its ability to capture systematic and experimental biases inherent to the background distributions in peak prediction, and to subsequently rank predicted peaks by confidence. To address these limitations, we present F-Seq2, which combines kernel density estimation and a dynamic 'continuous' Poisson test to account for local biases and accurately rank candidate peaks. The output of F-Seq2 is suitable for irreproducible discovery rate analysis as test statistics are calculated for individual candidate summits, allowing direct comparison of predictions across replicates. These improvements significantly boost the performance of F-Seq2 for ATAC-seq and ChIP-seq datasets, outperforming competing peak callers used by the ENCODE Consortium in terms of precision and recall.
A deep learning approach to predict Hamburg rutting curve

The Hamburg wheel tracking test (HWTT) is a widely used testing procedure designed to accelerate and simulate the rutting phenomena in the laboratory. Rut depth, as one of the outputs of the HWTT, ...
Predicting Drug Interactions to Unassociated Biomedical Implants Using Machine Learning Techniques and Model Polymers

Affinity based drug delivery mechanisms increase efficacy and minimalize off target effects when compared to non-specific methods due to the localization of drugs within target areas. While this is beneficial for targeted delivery, introduction of foreign polymeric medical devices into the body provide a potential area of localization due to high affinity between administered drugs and polymers. Previous attempts at creating models to predict affinity between small molecule drugs and polymers require a specific model be trained for each individual polymer failing to incorporate input features of both the polymer (host) and small molecule drug (guest). Within, we propose a universal model built using a neural network and quantitative structure activity relationships to predict the binding energy between guest and host molecules using input features. The trained model returned a correlation value, R2, of 0.9806 and 0.9958 between predicted and experimental binding affinity for the training and validation sets, respectively. This correlates to a mean absolute error of 0.951 kJ/mol and 0.771 kJ/mol for the training and validation sets, respectively. While limited to the current polymers used to train the model, the dataset can be expanded, and models retrained for further applications.
Analysis and Design Considerations for Achieving the Fundamental Limits of Phase Noise in mmWave Oscillators With On-Chip MEMS Resonator

Very small electromechanical coupling coefficient in micro-electromechanical systems (MEMS) or acoustic resonators is quite a concern for oscillator performance, specially at mmWave frequencies. This small coefficient is the manifestation of the small ratio of motional capacitance to static capacitance in the resonators. This work provides a general solution to overcome the problem of relatively high static capacitance at mmWave frequencies and presents analysis and design techniques for achieving extremely low phase noise and a very high figure-of-merit (FoM) in an on-chip MEMS resonator based mmWave oscillator. The proposed analysis and techniques are validated with design and simulation of a 30 GHz oscillator with MEMS resonator having quality factor of 10,000 in 14 nm GF technology. Post layout simulation results show that it achieves a phase noise of −132 dBc/Hz and FoM of 217 dBc/Hz at offset of 1 MHz.
Improved grey system models for predicting traffic parameters

Abstract In transportation applications such as real-time route guidance, ramp metering, congestion pricing and special events traffic management, accurate short-term traffic flow prediction is needed. For this purpose, this paper proposes several novel online Grey system models (GM): GM(1,1 | cos ( ω t ) ), GM(1,1 | sin ( ω t ) , cos ( ω t ) ), and GM(1,1 | e - at , sin ( ω t ) , cos ( ω t ) ). To evaluate the performance of the proposed models, they are compared against a set of benchmark models: GM(1,1) model, Grey Verhulst models with and without Fourier error corrections, linear time series model, and nonlinear time series model. The evaluation is performed using loop detector and probe vehicle data from California, Virginia, and Oregon. Among the benchmark models, the error corrected Grey Verhulst model with Fourier outperformed the GM(1,1) model, linear time series, and non-linear time series models. In turn, the three proposed models, GM(1,1 | cos ( ω t ) ), GM(1,1 | sin ( ω t ) , cos ( ω t ) ), and GM(1,1 | e - at , sin ( ω t ) , cos ( ω t ) ), outperformed the Grey Verhulst model in prediction by at least 65%, 16% and 11%, in terms of Root Mean Squared Error, and by 82%, 58% and 42%, in terms of Mean Absolute Percentage Error, respectively. It is observed that the proposed Grey system models are more adaptive to location (e.g., perform well for all roadway types) and traffic parameters (e.g., speed, travel time, occupancy, and volume), and they do not require as many data points for training (4 observations are found to be sufficient).
On duality of color and kinematics in (A)dS momentum space

We explore color-kinematic duality for tree-level AdS/CFT correlators in momentum space. We start by studying the bi-adjoint scalar in AdS at tree-level as an illustrative example. We follow this by investigating two forms of color-kinematic duality in Yang-Mills theory, the first for the integrated correlator in AdS4 and the second for the integrand in general AdSd+1. For the integrated correlator, we find color-kinematics does not yield additional relations among n-point, color-ordered correlators. To study color-kinematics for the AdSd+1 Yang-Mills integrand, we use a spectral representation of the bulk-to-bulk propagator so that AdS diagrams are similar in structure to their flat space counterparts. Finally, we study color KLT relations for the integrated correlator and double-copy relations for the AdS integrand. We find that double-copy in AdS naturally relates the bi-adjoint theory in AdSd+3 to Yang-Mills in AdSd+1. We also find a double-copy relation at three-points between Yang-Mills in AdSd+1 and gravity in AdSd−1 and comment on the higher-point generalization. By analytic continuation, these results on AdS/CFT correlators can be translated into statements about the wave function of the universe in de Sitter.
Deep Reinforcement Learning with embedded LQR Controllers

Abstract Reinforcement learning is a model-free optimal control method that optimizes a control policy through direct interaction with the environment. For reaching tasks that end in regulation, popular discrete-action methods are not well suited due to chattering in the goal state. We compare three different ways to solve this problem through combining reinforcement learning with classical LQR control. In particular, we introduce a method that integrates LQR control into the action set, allowing generalization and avoiding fixing the computed control in the replay memory if it is based on learned dynamics. We also embed LQR control into a continuous-action method. In all cases, we show that adding LQR control can improve performance, although the effect is more profound if it can be used to augment a discrete action set.
Hamiltonian systems, Toda lattices, solitons, Lax pairs on weighted Z-graded graphs

We consider discrete one-dimensional nonlinear equations and present the procedure of lifting them to Z-graded graphs. We identify conditions that allow one to lift one-dimensional solutions to solutions on graphs. In particular, we prove the existence of solitons for static potentials on graded fractal graphs. We also show that even for a simple example of a topologically interesting graph, the corresponding non-trivial Lax pairs and associated unitary transformations do not lift to a Lax pair on the Z-graded graph.
Serum selenium and non-alcoholic fatty liver disease (NAFLD) in U.S. adults: National Health and Nutrition Examination Survey (NHANES) 2011-2016.

Abstract Background Selenium is an essential trace element that shows beneficial or adverse health effects depending on the dose. Laboratory studies suggest that high selenium may contribute to the development of non-alcoholic fatty liver disease (NAFLD). However, human evidence is limited. We evaluated the associations of serum selenium level with serum alanine aminotransferase (ALT) activity and suspected NAFLD prevalence in U.S. adults. Methods We conducted the cross-sectional analysis in 3827 adults aged 20 years and older without viral hepatitis, hemochromatosis, or alcoholic liver disease who participated in the National Health and Nutrition Examination Survey (NHANES) 2011–2012, 2013–2014, and 2015–2016. Serum selenium was measured using inductively coupled plasma dynamic reaction cell mass spectrometry. Suspected NAFLD cases were defined in the presence of serum ALT >30 international units (IU)/L in men and >19 I.U./L in women in the absence of other identifiable causes of liver disease. Results The median (interquartile range) of serum selenium level was 127.9 (117.9, 139.4) μg/L. Non-linear associations of serum selenium with NAFLD prevalence and serum ALT activity were observed in the generalized additive models with penalized splines. After adjustment for sociodemographic variables, lifestyle factors, body mass index, and NHANES survey cycles, positive associations were found at > ~130 μg/L serum selenium with both NAFLD and ALT, whereas the associations were flattened at Conclusions Our findings provide evidence of non-linear associations of serum selenium with ALT activity and NAFLD prevalence. In particular, positive associations were found above serum selenium level of 130 μg/L, whereas no association was observed below this value. This finding requires confirmation in future prospective cohort studies.
Glucocorticoid Receptor Condensates Link DNA-Dependent Receptor Dimerization and Transcriptional Transactivation

Abstract The glucocorticoid receptor (GR) is a ligand-regulated transcription factor (TF) that controls the tissue- and gene-specific transactivation and transrepression of thousands of target genes. Distinct GR DNA binding sequences with activating or repressive activities have been identified, but how they modulate transcription in opposite ways is not known. We show that GR forms phase-separated condensates that specifically concentrate known co-regulators via their intrinsically disordered regions (IDRs) in vitro. A combination of dynamic, multivalent (between IDRs) and specific, stable interactions (between LxxLL motifs and the GR ligand binding domain) control the degree of recruitment. Importantly, GR DNA-binding directs the selective partitioning of co-regulators within GR condensates such that activating DNAs cause enhanced recruitment of co-activators. Our work shows that condensation controls GR function by modulating co-regulator recruitment and provides a mechanism for the up- and down-regulation of GR target genes controlled by distinct DNA recognition elements.
Bridging Between Material Properties Of Proteins And The Underlying Molecular Interactions

In this work, we develop a novel method that bridges between material properties of proteins, particularly the modulus of elasticity, and the underlying molecular interactions. To this end, we employ both an all-atom normal mode analysis (NMA) model with the CHARMM force field and an elastic solid model for proteins and protein interfaces. And the "bridge" between the two models is a common physical property predictable by both models: the magnitude of thermal vibrations. This connection allows one to calibrate the Young's moduli of proteins and protein interface regions. We find that the Young's moduli of proteins are in the range of a few Gpa to 10 Gpa, while the Young's moduli of the interface regions are several times smaller. The work is significant as it represents the first attempt to systematically compute the elastic moduli of proteins from molecular interactions.
Newly Identified Chemicals Preserve Mitochondrial Capacity and Decelerate Loss of Photoreceptor Cells in Murine Retinal Degeneration Models

Purpose: Metabolic stress and associated mitochondrial dysfunction are implicated in retinal degeneration irrespective of the underlying cause. We identified seven unique chemicals from a Chembridge DiverSET screen and tested their protection against photoreceptor cell death in cell- and animal-based approaches. Methods: Calcium overload (A23187) was triggered in 661W murine photoreceptor-derived cells, and changes in redox potential and real-time changes in cellular metabolism were assessed using the MTT and Seahorse Biosciences XF assay, respectively. Cheminformatics to compare structures, and biodistribution in the living pig eye aided in selection of the lead compound. In-situ, retinal organ cultures of rd1 mouse and S334ter-line-3 rat were tested, in-vivo the light-induced retinal degeneration in albino Balb/c mice was used, assessing photoreceptor cell numbers histologically. Results: Of the seven chemicals, six were protective against A23187- and IBMX-induced loss of mitochondrial capacity, as measured by viability and respirometry in 661W cells. Cheminformatic analyses identified a unique pharmacophore with 6 physico-chemical features based on two compounds (CB11 and CB12). The protective efficacy of CB11 was further shown by reducing photoreceptor cell loss in retinal explants from two retinitis pigmentosa rodent models. Using eye drops, CB11 targeting to the pig retina was confirmed. The same eye drops decreased photoreceptor cell loss in light-stressed Balb/c mice. Conclusions: New chemicals were identified that protect from mitochondrial damage and lead to improved mitochondrial function. Using ex-vivo and in-vivo models, CB11 decreased the loss of photoreceptor cells in murine models of retinal degeneration and may be effective as treatment for different retinal dystrophies.
ArCode: Facilitating the Use of Application Frameworks to Implement Tactics and Patterns

Software designers and developers are increasingly relying on application frameworks as first-class design concepts. They instantiate the services that frameworks provide to implement various architectural tactics and patterns. One of the challenges in employing frameworks for such tasks is the difficulty of learning and correctly using the APIs of the frameworks. This paper introduces a learning-based approach called ARCODE to help novice programmers correctly use frameworks’ APIs to implement architectural tactics and patterns. ARCODE has several novel components: a graph-based approach for learning specification of a framework from a limited number of training software, a program analysis algorithm to eliminate erroneous training data, and a recommender module to help programmers use APIs correctly and identify API misuses in their program. We evaluated our technique across two popular frameworks: JAAS security framework used for authentication and authorization tactic and Java RMI framework used to enable remote method invocation between client and server and other object oriented patterns. Our results demonstrate (i) the feasibility of using ARCODE to learn the specification of a framework; (ii) ARCODE generates accurate recommendations for finding the next API call to implement an architectural tactic/pattern based on the context of the programmer’s code; (iii) it accurately detects API misuses in the code that implements a tactic/pattern and provides fix recommendations. We also demonstrate that ArCode outperforms two famous techniques, MAPO and GrouMiner, on API recommendation and misuse detection tasks.
Learning to Blame: Localizing Novice Type Errors with Data-Driven Diagnosis

Localizing type errors is challenging in languages with global type inference, as the type checker must make assumptions about what the programmer intended to do. We introduce Nate, a data-driven approach to error localization based on supervised learning. Nate analyzes a large corpus of training data – pairs of ill-typed programs and their "fixed" versions – to automatically learn a model of where the error is most likely to be found. Given a new ill-typed program, Nate executes the model to generate a list of potential blame assignments ranked by likelihood. We evaluate Nate by comparing its precision to the state of the art on a set of over 5,000 ill-typed OCaml programs drawn from two instances of an introductory programming course. We show that when the top-ranked blame assignment is considered, Nate's data-driven model is able to correctly predict the exact sub-expression that should be changed 72% of the time, 28 points higher than OCaml and 16 points higher than the state-of-the-art SHErrLoc tool. Furthermore, Nate's accuracy surpasses 85% when we consider the top two locations and reaches 91% if we consider the top three.
The Benefit of Temporally-Strong Labels in Audio Event Classification

To reveal the importance of temporal precision in ground truth audio event labels, we collected precise (∼0.1 sec resolution) "strong" labels for a portion of the AudioSet dataset. We devised a temporally-strong evaluation set (including explicit negatives of varying difficulty) and a small strong-labeled training subset of 67k clips (compared to the original dataset’s 1.8M clips labeled at 10 sec resolution). We show that fine-tuning with a mix of weak- and strongly-labeled data can substantially improve classifier performance, even when evaluated using only the original weak labels. For a ResNet-50 architecture, d′ on the strong evaluation data including explicit negatives improves from 1.13 to 1.39. The new labels are available as an update to AudioSet.
Effects of water currents on fish migration through a Feynman-type path integral approach under $$\sqrt{8/3}$$ Liouville-like quantum gravity surfaces

A stochastic differential game theoretic model has been proposed to determine optimal behavior of a fish while migrating against water currents both in rivers and oceans. Then, a dynamic objective function is maximized subject to two stochastic dynamics, one represents its location and another its relative velocity against water currents. In relative velocity stochastic dynamics, a Cucker–Smale type stochastic differential equation is introduced under white noise. As the information regarding hydrodynamic environment is incomplete and imperfect, a Feynman type path integral under $$\sqrt{8/3}$$ Liouville-like quantum gravity surface has been introduced to obtain a Wick-rotated Schrodinger type equation to determine an optimal strategy of a fish during its migration. The advantage of having Feynman type path integral is that, it can be used in more generalized nonlinear stochastic differential equations where constructing a Hamiltonian–Jacobi–Bellman (HJB) equation is impossible. The mathematical analytic results show exact expression of an optimal strategy of a fish under imperfect information and uncertainty.
The molecular coupling between substrate recognition and atp turnover in a aaa+ hexameric helicase loader

In many bacteria and eukaryotes, replication fork establishment requires the controlled loading of hexameric, ring-shaped helicases around DNA by AAA+(ATPases Associated with various cellular Activities) ATPases. How loading factors use ATP to control helicase deposition is poorly understood. Here, we dissect how specific ATPase elements of Escherichia coli DnaC, an archetypal loader for the bacterial DnaB helicase, play distinct roles in helicase loading and the activation of DNA unwinding. We have identified a new element, the arginine-coupler, which regulates the switch-like behavior of DnaC to prevent futile ATPase cycling and maintains loader responsiveness to replication restart systems. Our data help explain how the ATPase cycle of a AAA+-family helicase loader is channeled into productive action on its target; comparative studies indicate that elements analogous to the Arg-coupler are present in related, switch-like AAA+ proteins that control replicative helicase loading in eukaryotes, as well as in polymerase clamp loading and certain classes of DNA transposases.
Charged Wormholes in Spacetimes of Embedding Class One

The existence of charged black holes has suggested that wormholes may also be charged. The purpose of this paper is to construct a general model of a charged wormhole that proves to be a natural extension of the original Morris-Thorne wormhole. This goal is achieved by means of the classical embedding theory that has played a major role in the general theory of relativity.
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming

Neural networks (NNs) have been extremely successful across many tasks in machine learning. Quantization of NN weights has become an important topic due to its impact on their energy efficiency, inference time and deployment on hardware. Although post-training quantization is well-studied, training optimal quantized NNs involves combinatorial non-convex optimization problems which appear intractable. In this work, we introduce a convex optimization strategy to train quantized NNs with polynomial activations. Our method leverages hidden convexity in two-layer neural networks from the recent literature, semidefinite lifting, and Grothendieck's identity. Surprisingly, we show that certain quantized NN problems can be solved to global optimality in polynomial-time in all relevant parameters via semidefinite relaxations. We present numerical examples to illustrate the effectiveness of our method.
Twenty Years of Unconventional Monetary Policies: Lessons and Way Forward for the Bank of Japan

The Bank of Japan has used unconventional monetary policies to fight deflation and stabilize the financial system since the late 1990s. While the Bank of Japan's reflation efforts have evolved over time, inflation and inflation expectations have remained stubbornly low. This paper examines the evolution of monetary policy in Japan over the past twenty years, in order to draw relevant lessons and propose ways to strengthen the Bank of Japan's policy framework. In doing so the analysis focuses on three aspects of monetary policy: objectives and goals; policy strategies; and the communication framework. Moreover, the paper discusses coordination between monetary, fiscal, and financial policies, and how the corresponding institutional design could be strengthened.
Biomapping of Microbial Indicators on Beef Subprimals Subjected to Spray or Dry Chilling over Prolonged Refrigerated Storage.

As the global meat market moves to never frozen alternatives, meat processors seek opportunities for increasing the shelf life of fresh meats by combinations of proper cold chain management, barrier technologies, and antimicrobial interventions. The objective of this study was to determine the impact of spray and dry chilling combined with hot water carcass treatments on the levels of microbial indicator organisms during the long-term refrigerated storage of beef cuts. Samples were taken using EZ-Reach™ sponge samplers with 25 mL buffered peptone water over a 100 cm2 area of the striploin. Sample collection was conducted before the hot carcass wash, after wash, and after the 24 h carcass chilling. Chilled striploins were cut into four sections, individually vacuum packaged, and stored to be sampled at 0, 45, 70, and 135 days (n = 200) of refrigerated storage and distribution. Aerobic plate counts, enterobacteria, Escherichia coli, coliforms, and psychrotroph counts were evaluated for each sample. Not enough evidence (p > 0.05) was found indicating the hot water wash intervention reduced bacterial concentration on the carcass surface. E. coli was below detection limits ( 0.05) was found between coliform counts throughout the sampling dates. Feed type did not seem to influence the (p > 0.25) microbial load of the treatments. Even though no immediate effect was seen when comparing spray or dry chilling of the samples at day 0, as the product aged, a significantly lower (p < 0.05) concentration of aerobic and psychrotrophic organisms in dry-chilled samples could be observed when compared to their spray-chilled counterparts. Data collected can be used to select alternative chilling systems to maximize shelf life in vacuum packaged beef kept over prolonged storage periods.
Mission options for rendezvous with the most accessible near-earth asteroid-1989 ML

The recent discovery of the Amor-class 1989 ML, the most accessible known asteroid for minimum-energy rendezvous missions, has expedited the search for frequent, low-cost Near-Earth Asteroid rendezvous and round-trip missions. This paper identifies trajectory characteristics and assesses mass performance for low Delta V ballistic rendezvous opportunities to 1989 ML during the period 1996-2010. This asteroid also offers occasional unique extended mission opportunities, such as the lowest known Delta V requirement for any asteroid sample return mission as well as pre-rendezvous asteroid flyby and post-rendezvous comet flyby opportunities requiring less than 5.25 km/sec total Delta V. This paper also briefly comments concerning mission opportunities for asteroid 1991 JW, which recently replaced other known asteroids as the most accessible Near-Earth Asteroid for fast rendezvous and round-trip missions.
Integrating Amdahl-like Laws and Divisible Load Theory

A simple means of integrating the characteristics of networked processors under divisible loads into Amdahl’s Law is presented. Amdahl’s Law serves as an upper bound to these speedup results. Amdah...
Learner-Private Convex Optimization

Convex optimization with feedback is a framework where a learner relies on iterative queries and feedback to arrive at the minimizer of a convex function. It has gained considerable popularity thanks to its scalability in large-scale optimization and machine learning. The repeated interactions, however, expose the learner to privacy risks from eavesdropping adversaries that observe the submitted queries. In this paper, we study how to optimally obfuscate the learner's queries in convex optimization with first-order feedback, so that their learned optimal value is provably difficult to estimate for an eavesdropping adversary. We consider two formulations of learner privacy: a Bayesian formulation in which the convex function is drawn randomly, and a minimax formulation in which the function is fixed and the adversary's probability of error is measured with respect to a minimax criterion. Suppose that the learner wishes to ensure the adversary cannot estimate accurately with probability greater than $1/L$ for some $L>0$. Our main results show that the query complexity overhead is additive in $L$ in the minimax formulation, but multiplicative in $L$ in the Bayesian formulation. Compared to existing learner-private sequential learning models with binary feedback, our results apply to the significantly richer family of general convex functions with full-gradient feedback. Our proofs learn on tools from the theory of Dirichlet processes, as well as a novel strategy designed for measuring information leakage under a full-gradient oracle.
The Curious Case of Hallucinations in Neural Machine Translation.

In this work, we study hallucinations in Neural Machine Translation (NMT), which lie at an extreme end on the spectrum of NMT pathologies. Firstly, we connect the phenomenon of hallucinations under source perturbation to the Long-Tail theory of Feldman, and present an empirically validated hypothesis that explains hallucinations under source perturbation. Secondly, we consider hallucinations under corpus-level noise (without any source perturbation) and demonstrate that two prominent types of natural hallucinations (detached and oscillatory outputs) could be generated and explained through specific corpus-level noise patterns. Finally, we elucidate the phenomenon of hallucination amplification in popular data-generation processes such as Backtranslation and sequence-level Knowledge Distillation. We have released the datasets and code to replicate our results.
A Step Toward More Inclusive People Annotations for Fairness

The Open Images Dataset contains approximately 9 million images and is a widely accepted dataset for computer vision research. As is common practice for large datasets, the annotations are not exhaustive, with bounding boxes and attribute labels for only a subset of the classes in each image. In this paper, we present a new set of annotations on a subset of the Open Images dataset called the MIAP (More Inclusive Annotations for People) subset, containing bounding boxes and attributes for all of the people visible in those images. The attributes and labeling methodology for the MIAP subset were designed to enable research into model fairness. In addition, we analyze the original annotation methodology for the person class and its subclasses, discussing the resulting patterns in order to inform future annotation efforts. By considering both the original and exhaustive annotation sets, researchers can also now study how systematic patterns in training annotations affect modeling.
Alternative Aircraft Fuels

In connection with the anticipated impossibility to provide on a long-term basis liquid fuels derived from petroleum, an investigation has been conducted with the objective to assess the suitability of jet fuels made from oil shale and coal and to develop a data base which will allow optimization of future fuel characteristics, taking energy efficiency of manufacture and the tradeoffs in aircraft and engine design into account. The properties of future aviation fuels are examined and proposed solutions to problems of alternative fuels are discussed. Attention is given to the refining of jet fuel to current specifications, the control of fuel thermal stability, and combustor technology for use of broad specification fuels. The first solution is to continue to develop the necessary technology at the refinery to produce specification jet fuels regardless of the crude source.
SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)

Understanding tables is an important and relevant task that involves understanding table structure as well as being able to compare and contrast information within cells. In this paper, we address this challenge by presenting a new dataset and tasks that addresses this goal in a shared task in SemEval 2020 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS). Our dataset contains 981 manually-generated tables and an auto-generated dataset of 1980 tables providing over 180K statement and over 16M evidence annotations. SEM-TAB-FACTS featured two sub-tasks. In sub-task A, the goal was to determine if a statement is supported, refuted or unknown in relation to a table. In sub-task B, the focus was on identifying the specific cells of a table that provide evidence for the statement. 69 teams signed up to participate in the task with 19 successful submissions to subtask A and 12 successful submissions to subtask B. We present our results and main findings from the competition.
Medical Disobedience and the Conscientious Provision of Prohibited Care

Should doctors ever be allowed to offer care that their state or employer forbids? What if their deeply held personal values or beliefs demand they treat patients in need? We’re used to hearing abo...
Expanding the armamentarium for reoperative coronary artery bypass grafting.

Expanding the armamentarium for reoperative coronary artery bypass graftingShort title : Expanding the armamentarium for redo CABGMaria Currie, MD PhD11 Department of Cardiothoracic Surgery, Stanford University School of Medicine
Application-driven design exploration for dense ferroelectric embedded non-volatile memories

The memory wall bottleneck is a key challenge across many data-intensive applications. Multi-level FeFET-based embedded non-volatile memories are a promising solution for denser and more energy-efficient on-chip memory. However, reliable multi-level cell storage requires careful optimizations to minimize the design overhead costs. In this work, we investigate the interplay between FeFET device characteristics, programming schemes, and memory array architecture, and explore different design choices to optimize performance, energy, area, and accuracy metrics for critical data-intensive workloads. From our cross-stack design exploration, we find that we can store DNN weights and social network graphs at a density of over 8MB/mm2 and sub-2ns read access latency without loss in application accuracy.
Extracellular Matrix Hydrogels Promote Expression of Muscle-Tendon Junction Proteins

Muscle and tendon injuries are prevalent and range from minor sprains and strains to traumatic, debilitating injuries. However, the interactions between these tissues during injury and recovery rem...
Interpolating ’t Hooft model between instant and front forms

The 't Hooft model, i.e. the two-dimensional quantum chromodynamics in the limit of infinite number of colors, is interpolated by an angle parameter $\delta$ between $\delta=0$ for the instant form dynamics (IFD) and $\delta=\pi/4$ for the light-front dynamics (LFD). With this parameter $\delta$, we formulate the interpolating mass gap equation which takes into account the non-trivial vacuum effect on the bare fermion mass to find the dressed fermion mass. Our interpolating mass gap solutions link the IFD and LFD results with the $\delta$ parameter. We find the interpolation angle independent characteristic energy function which satisfies the energy-momentum dispersion relation of the dressed fermion, identifying the renormalized fermion mass function and the wave function renormalization factor. Using the dressed fermion propagator interpolating between IFD and LFD, we derive the corresponding quark-antiquark bound-state equation in the interpolating formulation. The mass spectra of mesons bearing the feature of the Regge trajectories are found independent of the $\delta$-parameter for the equal mass quark and antiquark bound-states. The Gell-Mann - Oakes - Renner relation for the pionic ground-state in the zero fermion mass limit is confirmed indicating that the spontaneous breaking of the chiral symmetry occurs regardless of the quantization for $0 \le \delta \le \pi/4$. We obtain the corresponding bound-state wave functions and discuss their reference frame dependence. Applying them for the computation of the so-called quasi parton distribution functions, we note a possibility of utilizing not only the reference frame dependence but also the interpolation angle dependence to get an alternative effective approach to the LFD-like results.
Design and Performance Characterization of RADICAL-Pilot on Leadership-Class Platforms

Many extreme scale scientific applications have workloads comprised of a large number of individual high-performance tasks. The Pilot abstraction decouples workload specification, resource management, and task execution via job placeholders and late-binding. As such, suitable implementations of the Pilot abstraction can support the collective execution of large number of tasks on supercomputers. We introduce RADICAL-Pilot (RP) as a portable, modular and extensible pilot-enabled runtime system. We describe RP's design, architecture and implementation. We characterize its performance and show its ability to scalably execute workloads comprised of tens of thousands heterogeneous tasks on DOE and NSF leadership-class HPC platforms. Specifically, we investigate RP's weak/strong scaling with CPU/GPU, single/multi core, (non)MPI tasks and Python functions when using most of ORNL Summit and TACC Frontera. RADICAL-Pilot can be used stand-alone, as well as the runtime for third-party workflow systems.
A Rigorous Procedure for Generating a Well-ordered Set of Reals without use of Axiom of Choice/Well-ordering Theorem

Well-ordering of the Reals presents a major challenge in Set theory. Under the standard Zermelo Fraenkel Set theory with the Axiom of Choice (ZFC), a well-ordering of the Reals is indeed possible. However the Axiom of Choice (AC) had to be introduced to the original ZF theory which is then shown equivalent to the well-ordering theorem. Despite the result however, no way has still been found of actually constructing a well-ordered Set of Reals. In this paper the author attempts to generate a well ordered Set of Reals without using the i.e. under theory itself using the Axiom of the Power Set as the guiding principle.
Quantum Hoare Type Theory: Extended Abstract

As quantum computers become real, it is high time we come up with effective techniques that help programmers write correct quantum programs. In classical computing, formal verification and sound static type systems prevent several classes of bugs from being introduced. There is a need for similar techniques in the quantum regime. Inspired by Hoare Type Theory in the classical paradigm, we propose Quantum Hoare Types by extending the Quantum IO Monad by indexing it with pre- and post-conditions that serve as program specifications. In this paper, we introduce Quantum Hoare Type Theory (QHTT), present its syntax and typing rules, and demonstrate its effectiveness with the help of examples. QHTT has the potential to be a unified system for programming, specifying, and reasoning about quantum programs. This is a work in progress.
Conformal rigidity from focusing

The null curvature condition (NCC) is the requirement that the Ricci curvature of a Lorentzian manifold be nonnegative along null directions, which ensures the focusing of null geodesic congruences. In this note, we show that the NCC together with the causal structure signiﬁcantly constrain the metric. In particular, we prove that any conformal rescaling of a vacuum spacetime introduces either geodesic incompleteness or negative null curvature, provided the conformal factor is non-constant on at least one complete null geodesic. In the context of bulk reconstruction in AdS/CFT, our results combined with the technique of light-cone cuts can be used in vacuum spacetimes to reconstruct the full metric in regions probed by complete null geodesics reaching the boundary. For non-vacuum spacetimes, our results constrain the conformal factor, giving an approximate reconstruction of the metric.
Big fish in thin markets: Competing with the middlemen to increase market access in the Amazon

Abstract Middlemen are ubiquitous in supply chains. In developing countries they help bring products from remote communities to end markets but may exert strong market power. We study a cooperative intervention which organizes together poor fishing communities in the Amazon — one of the poorest and most remote regions of the world — to purchase large boats in order to partially bypass middlemen and deliver their fish directly to market. We find that the intervention increases income by 27%, largely through an increase in price received, and also increases consumption. Moreover, the intervention is highly cost effective with the projected stream of income gains easily covering the cost of the investment. Finally, we formalize a model in which the market power of middlemen itself can create a poverty trap, which can be eliminated with cooperative investment.
Correlating ZnSe Quantum Dot Absorption with Particle Size and Concentration

The focus on heavy metal-free semiconductor nanocrystals has increased interest in ZnSe semiconductor quantum dots (QDs) over the past decade. Reliable and consistent incorporation of ZnSe cores into core/shell heterostructures or devices requires empirical fit equations correlating the lowest energy electron transition (1S peak) to their size and molar extinction coefficients (e). While these equations are known and heavily used for CdSe, CdTe, CdS, PbS, etc., they are not well established for ZnSe and are non-existent for ZnSe QDs with diameters < 3.5 nm. In this study, a series of ZnSe QDs with diameters ranging from 2 to 6 nm were characterized with small angle X-ray scattering (SAXS), transmission electron microscopy (TEM), UV-Vis spectroscopy, and microwave plasma atomic emission spectroscopy (MP-AES). SAXS-based size analysis enabled practical inclusion of small particles in the evaluation, and elemental analysis with MP-AES elucidates a non-stoichiometric Zn:Se ratio consistent with zinc-terminated spherical ZnSe QDs. Using these combined results, empirical fit equations correlating QD size with its lowest energy electron transition (i.e., 1S peak position), Zn:Se ratio, and molar extinction coefficients for 1S peak, 1S integral, and high energy wavelengths are reported. Finally, the equations are used to track the evolution of a ZnSe core reaction. These results will enable the consistent and reliable use of ZnSe core particles in complex heterostructures and devices.
When products receive reviews across platforms: Studying the platform concentration of electronic word-of-mouth

Abstract A product can receive Electronic Word-of-Mouth (eWOM), e.g., user reviews, at both retail and review platforms, with considerable variance in volume and valence. We define such eWOM variation across platforms as the platform concentration of eWOM. A lower platform concentration is measured by a higher level of volume equality and valence equality, i.e., a more comparable number of user reviews and more consistent average ratings among multiple platforms, respectively. Results from multiple models over software data from retail and review platforms shows that volume equality and valence equality positively interact to affect online users’ product adoption decisions.
A Splitting into the Double Cover of SL(3,$\mathbb{R}$)

We provide a formula for the splitting of a congruence subgroup of SL $$(3,\mathbb {R})$$ into the double cover of SL $$(3,\mathbb {R})$$ in terms of Plucker coordinates. This new formula is then used to prove that the splitting satisfies a twisted multiplicativity. This formula has applications to the computation of Fourier coefficients of Eisenstein series on the double cover of SL(3) over $$\mathbb {Q}$$ .