cssi/SciSciGPT-SciSciNet huggingface dataset:
Cell-Penetrating Peptides: Applications in Tumor Diagnosis and Therapeutics

Since their identification over twenty-five years ago, the plethora of cell-penetrating peptides (CPP) and their applications has skyrocketed. These 5 to 30 amino acid in length peptides have the unique property of breaching the cell membrane barrier while carrying cargoes larger than themselves into cells in an intact, functional form. CPPs can be conjugated to fluorophores, activatable probes, radioisotopes or contrast agents for imaging tissues, such as tumors. There is no singular mechanism for translocation of CPPs into a cell, and therefore, many CPPs are taken up by a multitude of cell types, creating the challenge of tumor-specific translocation and hindering clinical effectiveness. Varying strategies have been developed to combat this issue and enhance their diagnostic potential by derivatizing CPPs for better targeting by constructing specific cell-activated forms. These methods are currently being used to image integrin-expressing tumors, breast cancer cells, human histiocytic lymphoma and protease-secreting fibrosarcoma cells, to name a few. Additionally, identifying safe, effective therapeutics for malignant tumors has long been an active area of research. CPPs can circumvent many of the complications found in treating cancer with conventional therapeutics by targeted delivery of drugs into tumors, thereby decreasing off-target side effects, a feat not achievable by currently employed conventional chemotherapeutics. Myriad types of chemotherapeutics such as tyrosine kinase inhibitors, antitumor antibodies and nanoparticles can be functionally attached to these peptides, leading to the possibility of delivering established and novel cancer therapeutics directly to tumor tissue. While much research is needed to overcome potential issues with these peptides, they offer a significant advancement over current mechanisms to treat cancer. In this review, we present a brief overview of the research, leading to identification of CPPs with a comprehensive state-of-the-art review on the role of these novel peptides in both cancer diagnostics as well as therapeutics.
Designing Central Bank Digital Currencies

Abstract The optimal design of a central bank digital currency (CBDC) is analyzed in an environment where agents sort into cash, CBDC, and bank deposits according to their preferences over anonymity and security; and where network effects make the convenience of a payment instrument depend on the number of its users. A CBDC can be designed with attributes similar to cash or deposits, and can be interest bearing: a CBDC that closely competes with deposits depresses bank credit and output, while a cash-like CBDC may lead to the disappearance of cash. Then, the optimal CBDC design trades off bank intermediation against the social value of maintaining diverse payment instruments. When network effects matter, an interest-bearing CBDC alleviates the central bank’s tradeoff.
Prediction of Object Geometry from Acoustic Scattering Using Convolutional Neural Networks

Acoustic scattering is strongly influenced by boundary geometry of objects over which sound scatters. The present work proposes a method to infer object geometry from scattering features by training convolutional neural networks. The training data is generated from a fast numerical solver developed on CUDA. The complete set of simulations is sampled to generate multiple datasets containing different amounts of channels and diverse image resolutions. The robustness of our approach in response to data degradation is evaluated by comparing the performance of networks trained using the datasets with varying levels of data degradation. The present work has found that the predictions made from our models match ground truth with high accuracy. In addition, accuracy does not degrade when fewer data channels or lower resolutions are used.
Language Scaling for Universal Suggested Replies Model

We consider the problem of scaling automated suggested replies for a commercial email application to multiple languages. Faced with increased compute requirements and low language resources for language expansion, we build a single universal model for improving the quality and reducing run-time costs of our production system. However, restricted data movement across regional centers prevents joint training across languages. To this end, we propose a multi-lingual multi-task continual learning framework, with auxiliary tasks and language adapters to train universal language representation across regions. The experimental results show positive cross-lingual transfer across languages while reducing catastrophic forgetting across regions. Our online results on real user traffic show significant CTR and Char-saved gain as well as 65% training cost reduction compared with per-language models. As a consequence, we have scaled the feature in multiple languages including low-resource markets.
The unique cosmic event of 1979 March 5

The transient gamma-ray burst observed on March 5, 1979 by the nine spacecraft forming the interplanetary gamma-ray burst network is discussed. Measurements reveal the event to be unlike any previously observed gamma-ray burst or X-ray burster, with a maximum intensity greater than several thousandths erg/sq cm per sec, a rise time of less than 200 microsec, a narrow and featureless initial spike and a regular 8-sec oscillation with a compound pulse shape. The source of the transient has been localized to a region including the supernova remnant N 49 in the Large Magellanic Cloud, at a distance of 55 pc, and a ratio of X-ray point-source steady state to transient emission of less than 10 to the -9th was obtained. The identification of the burst source with the supernova remnant at that distance would require an enormous source output if it was radiated isotropically, and substantially less if beaming is present. Alternatively, the event could be explained by a nearby, invisible neutron star.
Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks.

Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős-Renyi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection) and which occur for a given community provided its size surpasses a detectability limit K* . When layers are aggregated via a summation, we obtain [Formula: see text], where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L, then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than 𝒪(L-1/2). Moreover, we find that thresholding the summation can, in some cases, cause K* to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.
Temporal stability of the ventral attention network and general cognition along the Alzheimer's disease spectrum.

Abstract Understanding the interrelationships of clinical manifestations of Alzheimer’s disease (AD) and functional connectivity (FC) as the disease progresses is necessary for use of FC as a potential neuroimaging biomarker. Degradation of resting-state networks in AD has been observed when FC is estimated over the entire scan, however, the temporal dynamics of these networks are less studied. We implemented a novel approach to investigate the modular structure of static (sFC) and time-varying (tvFC) connectivity along the AD spectrum in a two-sample Discovery/Validation design (n = 80 and 81, respectively). Cortical FC networks were estimated across 4 diagnostic groups (cognitively normal, subjective cognitive decline, mild cognitive impairment, and AD) for whole scan (sFC) and with sliding window correlation (tvFC). Modularity quality (across a range of spatial scales) did not differ in either sFC or tvFC. For tvFC, group differences in temporal stability within and between multiple resting state networks were observed; however, these differences were not consistent between samples. Correlation analyses identified a relationship between global cognition and temporal stability of the ventral attention network, which was reproduced in both samples. While the ventral attention system has been predominantly studied in task-evoked designs, the relationship between its intrinsic dynamics at-rest and general cognition along the AD spectrum highlights its relevance regarding clinical manifestation of the disease.
Applications of Controlled Sweeping Processes to Nonlinear Crowd Motion Models With Obstacles

This letter mainly focuses on solving the dynamic optimization of the planar controlled crowd motion models with obstacles which is an application of a class of optimal control problems governed by a general perturbed nonconvex sweeping process. This can be considered as a significant extension of the previous work regarding the controlled crowd motion models in [4] , [5] , where the obstacles have not been considered. The necessary optimality conditions for the problem under consideration are established based on the results in [4] , [5] and are illustrated by a nontrivial example of practical importance.
Biomedical Interpretable Entity Representations

Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable. This can be a barrier to model uptake in important domains such as biomedicine. There has been recent work on general interpretable representation learning (Onoe and Durrett, 2020), but these domain-agnostic representations do not readily transfer to the important domain of biomedicine. In this paper, we create a new entity type system and training set from a large corpus of biomedical texts by mapping entities to concepts in a medical ontology, and from these to Wikipedia pages whose categories are our types. From this mapping we derive Biomedical Interpretable Entity Representations(BIERs), in which dimensions correspond to fine-grained entity types, and values are predicted probabilities that a given entity is of the corresponding type. We propose a novel method that exploits BIER's final sparse and intermediate dense representations to facilitate model and entity type debugging. We show that BIERs achieve strong performance in biomedical tasks including named entity disambiguation and entity label classification, and we provide error analysis to highlight the utility of their interpretability, particularly in low-supervision settings. Finally, we provide our induced 68K biomedical type system, the corresponding 37 million triples of derived data used to train BIER models and our best performing model.
Launch Vehicle Propulsion Design with Multiple Selection Criteria

An analytical optimization and evaluation approach is presented for sizing a liquid hydrogen/liquid oxygen, single-stage-to-orbit system. The approach uses a unique merging of historical data for weight relationships, Monte Carlo simulations for uncertainty analysis, technology factors, thermochemical analysis, and genetic algorithm solvers for concept optimization. The method models and optimizes engine chamber pressure, area ratio, and oxidizer/fuel ratio to determine the best vehicle design based on seven separate cost/weight figures of merit Model results show that a 53% increase in design, development, test, and evaluation cost results in a 67% reduction in gross liftoff weight. The uncertainty range for the design, development, test, and evaluation costs is shown to be from -45 to +76% of the mean value for the 95th percentile uncertainty case. The oxidizer/fuel ratio has a significant impact on the overall weight of the vehicle and vehicle components due to the large percentage of the gross liftoff weight that is dedicated to propellants. Other results show the effects of propulsion parameters, technology factors, and cost factors on weight and cost under different overall design constraints.
Integration of Engineered "Spark-Cell" Spheroids for Optical Pacing of Cardiac Tissue

Optogenetic methods for pacing of cardiac tissue can be realized by direct genetic modification of the cardiomyocytes to express light-sensitive actuators, such as channelrhodopsin-2, ChR2, or by introduction of light-sensitized non-myocytes that couple to the cardiac cells and yield responsiveness to optical pacing. In this study, we engineer three-dimensional "spark cells" spheroids, composed of ChR2-expressing human embryonic kidney cells (from 100 to 100,000 cells per spheroid), and characterize their morphology as function of cell density and time. These "spark-cell" spheroids are then deployed to demonstrate site-specific optical pacing of human stem-cell-derived cardiomyocytes (hiPSC-CMs) in 96-well format using non-localized light application and all-optical electrophysiology with voltage and calcium small-molecule dyes or genetically encoded sensors. We show that the spheroids can be handled using liquid pipetting and can confer optical responsiveness of cardiac tissue earlier than direct viral or liposomal genetic modification of the cardiomyocytes, with 24% providing reliable stimulation of the iPSC-CMs within 6 h and >80% within 24 h. Moreover, our data show that the spheroids can be frozen in liquid nitrogen for long-term storage and transportation, after which they can be deployed as a reagent on site for optical cardiac pacing. In all cases, optical stimulation was achieved at relatively low light levels (<0.15 mW/mm2) when 5 ms or longer pulses were used. Our results demonstrate a scalable, cost-effective method with a cryopreservable reagent to achieve contactless optical stimulation of cardiac cell constructs without genetically modifying the myocytes, that can be integrated in a robotics-amenable workflow for high-throughput drug testing.
Inductive limits of quasi locally Baire spaces

Quasi-locally complete locally convex spaces are generalized to quasi-locally Baire locally convex spaces. It is shown that an inductive limit of strictly webbed spaces is regular if it is quasi-lo...
Replication of Quasi-Experimental Population Studies: Motivation and Guidance

Most quasi-experimental empirical analyses in accounting involve determining a set of sample selection criteria and then applying these criteria to obtain an exhaustive sample for analysis. By incl...
Algorithms and Learning for Fair Portfolio Design

In this paper we initiate the study of financial asset design with fairness as an explicit goal. We consider a variation on the classical problem of optimal portfolio design. In our setting, an individual consumer is specified by her risk tolerance, which corresponds to the variance in returns she is willing to accept in exchange for higher expected returns. We must design a (small) collection of portfolios and assign each consumer to a portfolio at lower or approximately equal risk than her tolerance. Fairness is imposed by demanding that the portfolios designed do not discriminate (in terms of expected returns) against less wealthy clients (or other specified protected groups). Our main results are algorithms for optimal and near-optimal portfolio design for both social welfare and fairness objectives, both with and without assumptions on the underlying group structure. We describe an efficient algorithm based on an internal two-player zero-sum game that learns near-optimal fair portfolios ex ante and show experimentally that it can be used to obtain a small set of fair portfolios ex post as well. For the special but natural case in which group structure coincides with risk tolerances (which models the reality that wealthy consumers generally tolerate greater risk), we give an efficient and optimal fair algorithm. We also provide generalization guarantees for the underlying risk distribution that has no dependence on the number of portfolios and illustrate the theory with simulation results.
Longitudinal shock waves in a class of semi-infinite stretch-limited elastic strings:

In this paper, we initiate the study of wave propagation in a recently proposed mathematical model for stretch-limited elastic strings. We consider the longitudinal motion of a simple class of unif...
Hierarchical integrated spatial process modeling of monotone West Antarctic snow density curves

Snow density estimates below the surface, used with airplane-acquired ice-penetrating radar measurements, give a site-specific history of snow water accumulation. Because it is infeasible to drill snow cores across all of Antarctica to measure snow density and because it is critical to understand how climatic changes are affecting the world’s largest freshwater reservoir, we develop methods that enable snow density estimation with uncertainty in regions where snow cores have not been drilled. In inland West Antarctica, snow density increases monotonically as a function of depth, except for possible microscale variability or measurement error, and it cannot exceed the density of ice. We present a novel class of integrated spatial process models that allow interpolation of monotone snow density curves. For computational feasibility we construct the space-depth process through kernel convolutions of log-Gaussian spatial processes. We discuss model comparison, model fitting and prediction. Using this model, we extend estimates of snow density beyond the depth of the original core and estimate snow density curves where snow cores have not been drilled. Along flight lines with ice-penetrating radar, we use interpolated snow density curves to estimate recent water accumulation and find predominantly decreasing water accumulation over recent decades.
The exterior tidal potential acting on a satellite

The tidal potential is related to small periodic variations in the geopotential which are caused by the formation of the tidal bulge and by the redistribution of masses in the earth's interior under the influence of lunisolar tidal attraction. The tidal effects in the motion of a satellite can be interpreted as the result of tidal periodic oscillations. An investigation of the tidal disturbing potential is conducted and the differential equations for the variations of the elements are considered. It is suggested that a future planning should include the tidal observations on the earth surface.
Vibration and flutter of mistuned bladed-disk assemblies

An analytical model for investigating vibration and flutter of mistuned bladed disk assemblies is presented. This model accounts for elastic, inertial and aerodynamic coupling between bending and torsional motions of each individual blade, elastic and inertial couplings between the blades and the disk, and aerodynamic coupling among the blades. The disk was modeled as a circular plate with constant thickness and each blade was represented by a twisted, slender, straight, nonuniform, elastic beam with a symmetric cross section. The elastic axis, inertia axis, and the tension axis were taken to be noncoincident and the structural warping of the section was explicitly considered. The blade aerodynamic loading in the subsonic and supersonic flow regimes was obtained from two-dimensional unsteady, cascade theories. All the possible standing wave modes of the disk and traveling wave modes of the blades were included. The equations of motion were derived by using the energy method in conjunction with the assumed mode shapes for the disk and the blades. Continuities of displacement and slope at the blade-disk junction were maintained. The equations were solved to investigate the effects of blade-disk coupling and blade frequency mistuning on vibration and flutter. Results showed that the flexibility of practical disks such as those used for current generation turbofans did not have a significant influence on either the tuned or mistuned flutter characteristics. However, the disk flexibility may have a strong influence on some of the system frequencies and on forced response.
Comparison of Minimization Methods for Rosenbrock Functions

This paper gives an in-depth review of the most common iterative methods for unconstrained optimization using two functions that belong to a class of Rosenbrock functions as a performance test. This study covers the Steepest Gradient Descent Method, the Newton-Raphson Method, and the Fletcher-Reeves Conjugate Gradient method. In addition, four different step-size selecting methods including fixed-step-size, variable step-size, quadratic-fit, and golden section method were considered. Due to the computational nature of solving minimization problems, testing the algorithms is an essential part of this paper. Therefore, an extensive set of numerical test results is also provided to present an insightful and a comprehensive comparison of the reviewed algorithms. This study highlights the differences and the trade-offs involved in comparing these algorithms.
Parity of the coefficients of certain eta-quotients

Abstract We investigate the parity of the coefficients of certain eta-quotients, extensively examining the case of m-regular partitions. Our theorems concern the density of their odd values, in particular establishing lacunarity modulo 2 for specified coefficients; self-similarities modulo 2; and infinite families of congruences in arithmetic progressions. For all m ≤ 28 , we either establish new results of these types where none were known, extend previous ones, or conjecture that such results are impossible. All of our work is consistent with a new, overarching conjecture that we present for arbitrary eta-quotients, greatly extending Parkin-Shanks' classical conjecture for the partition function. We pose several other open questions throughout the paper, and conclude by suggesting a list of specific research directions for future investigations in this area.
PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models

What should a malicious user write next to fool a detection model? Identifying malicious users is critical to ensure the safety and integrity of internet platforms. Several deep learning based detection models have been created. However, malicious users can evade deep detection models by manipulating their behavior, rendering these models of little use. The vulnerability of such deep detection models against adversarial attacks is unknown. Here we create a novel adversarial attack model against deep user sequence embedding-based classification models, which use the sequence of user posts to generate user embeddings and detect malicious users. In the attack, the adversary generates a new post to fool the classifier. We propose a novel end-to-end Personalized Text Generation Attack model, called PETGEN, that simultaneously reduces the efficacy of the detection model and generates posts that have several key desirable properties. Specifically, PETGEN generates posts that are personalized to the user's writing style, have knowledge about a given target context, are aware of the user's historical posts on the target context, and encapsulate the user's recent topical interests. We conduct extensive experiments on two real-world datasets (Yelp and Wikipedia, both with ground-truth of malicious users) to show that PETGEN significantly reduces the performance of popular deep user sequence embedding-based classification models. PETGEN outperforms five attack baselines in terms of text quality and attack efficacy in both white-box and black-box classifier settings. Overall, this work paves the path towards the next generation of adversary-aware sequence classification models.
A Deep Learning Approach To Multi-Context Socially-Aware Navigation

We present a context classification pipeline to allow a robot to change its navigation strategy based on the observed social scenario. Socially-Aware Navigation considers social behavior in order to improve navigation around people. Most of the existing research uses different techniques to incorporate social norms into robot path planning for a single context. Methods that work for hallway behavior might not work for approaching people, and so on. We developed a high-level decision-making subsystem, a model-based context classifier, and a multi-objective optimization-based local planner to achieve socially-aware trajectories for autonomously sensed contexts. Using a context classification system, the robot can select social objectives that are later used by Pareto Concavity Elimination Transformation (PaCcET) based local planner to generate safe, comfortable, and socially appropriate trajectories for its environment. This was tested and validated in multiple environments on a Pioneer mobile robot platform; results show that the robot could select and account for social objectives related to navigation autonomously.
The moral hazard of Lax FCC land use oversight for advanced network infrastructure

Abstract This article discusses issues that arise when service providers place network equipment on publicly owned lands in the United States. Based on land use policy at the Federal Communications Commission, this paper theorizes that the use of public lands for 5G network development will create a moral hazard, as service providers may be tempted to take risks in the way they use public lands. Per economic theory the firm could behave recklessly when knowing that the costs will be borne by someone else – in this case local citizens. This is an example of the moral hazard problem in economics.
A survey of size counting in population protocols

Abstract The population protocol model describes a network of n anonymous agents who cannot control with whom they interact. The agents collectively solve a computational problem through random pairwise interactions, each agent updating its state in response to seeing the state of the other agent. Population protocols are equivalent to the model of chemical reaction networks, describing abstract chemical reactions such as A + B → C + D , when the latter is subject to the restriction that all reactions have two reactants and two products, and all rate constants are 1. The counting problem is that of designing a protocol so that n agents, all starting in the same state, eventually converge to states where each agent encodes in its state an exact or approximate description of population size n. In this survey paper, we describe recent algorithmic advances on the counting problem.
Dark Pool Trading and Information Acquisition

Theory suggests that dark pools may facilitate or discourage information acquisition. We find that more dark pool trading leads to greater information acquisition. We measure information acquisition using stock price dynamics around earnings announcements. To overcome endogeneity concerns, we exploit a large exogenous decrease to dark pool trading that results from the implementation of the Security and Exchange Commission’s (SEC’s) Tick Size Pilot Program. The results cannot be explained by lit venue liquidity, algorithmic trading, or informational efficiency. A battery of additional tests, such as documenting a shift in SEC EDGAR searches, supports the information acquisition interpretation.
Dynamic Error-bounded Lossy Compression (EBLC) to Reduce the Bandwidth Requirement for Real-time Vision-based Pedestrian Safety Applications

As camera quality improves and their deployment moves to areas with limited bandwidth, communication bottlenecks can impair real-time constraints of an intelligent transportation systems application, such as video-based real-time pedestrian detection. Video compression reduces the bandwidth requirement to transmit the video which degrades the video quality. As the quality level of the video decreases, it results in the corresponding decreases in the accuracy of the vision-based pedestrian detection model. Furthermore, environmental conditions, such as rain and night-time darkness impact the ability to leverage compression by making it more difficult to maintain high pedestrian detection accuracy. The objective of this study is to develop a real-time error-bounded lossy compression (EBLC) strategy to dynamically change the video compression level depending on different environmental conditions to maintain a high pedestrian detection accuracy. We conduct a case study to show the efficacy of our dynamic EBLC strategy for real-time vision-based pedestrian detection under adverse environmental conditions. Our strategy dynamically selects the lossy compression error tolerances that maintain a high detection accuracy across a representative set of environmental conditions. Analyses reveal that for adverse environmental conditions, our dynamic EBLC strategy increases pedestrian detection accuracy up to 14% and reduces the communication bandwidth up to 14 × compared to the state-of-the-practice. Moreover, we show our dynamic EBLC strategy is independent of pedestrian detection models and environmental conditions allowing other detection models and environmental conditions to be easily incorporated.
Infrastructure recovery curve estimation using Gaussian process regression on expert elicited data

Abstract The U.S. National Institute of Standards and Technology (NIST)’s Community Resilience Planning Guide uses recovery times of infrastructure functions as key metrics for disaster resilience. The existing literature also widely uses the recovery curve and the area under it to measure infrastructure resilience. Therefore, infrastructure recovery curve estimation is critical to understanding and improving disaster resilience. Unfortunately, this process is challenging in the pre-event planning context due to lack of historical data. To bridge this gap, we consider a situation where infrastructure experts are asked to estimate the time for different infrastructure systems to recover to certain functionality levels after a scenario hazard event. We propose a methodological framework to use expert-elicited data to estimate the expected recovery time curve of a particular infrastructure system. This framework uses the Gaussian process regression (GPR) to capture the experts’ estimation-uncertainty and satisfy known physical constraints of recovery processes. The framework is designed to find a balance between the data collection cost of expert elicitation and the prediction accuracy of GPR. We evaluate the framework on simulated expert-elicited data concerning two case study events, the 1995 Great Hanshin-Awaji Earthquake and the 2011 Great East Japan Earthquake. It is shown that the framework is robust against different configurations such as the number of experts, how the quantities of interest are elicited, and uncertainty in the experts’ estimates.
Refraction of space-time wave packets: III. experiments at oblique incidence.

The refraction of space-time (ST) wave packets at planar interfaces between non-dispersive, homogeneous, isotropic dielectrics exhibits fascinating phenomena, even at normal incidence. Examples of such refractive phenomena include group-velocity invariance across the interface, anomalous refraction, and group-velocity inversion. Crucial differences emerge at oblique incidence with respect to the results established at normal incidence. For example, the group velocity of the refracted ST wave packet can be tuned simply by changing the angle of incidence. In the third paper, we present experimental verification of the refractive phenomena exhibited by ST wave packets at oblique incidence that were in the first paper of this sequence [J. Opt. Soc. Am. A38, 1409 (2021)10.1364/JOSAA.430105]. We also examine a proposal for “blind synchronization,” whereby identical ST wave packets arrive simultaneously at different receivers without a priori knowledge of their locations except that they are all located at the same depth beyond an interface between two media. A first proof-of-principle experimental demonstration of this effect is provided.
A Conditional Generative Matching Model for Multi-lingual Reply Suggestion.

We study the problem of multilingual automated reply suggestions (RS) model serving many languages simultaneously. Multilingual models are often challenged by model capacity and severe data distribution skew across languages. While prior works largely focus on monolingual models, we propose Conditional Generative Matching models (CGM), optimized within a Variational Autoencoder framework to address challenges arising from multilingual RS. CGM does so with expressive message conditional priors, mixture densities to enhance multilingual data representation, latent alignment for language discrimination, and effective variational optimization techniques for training multilingual RS. The enhancements result in performance that exceed competitive baselines in relevance (ROUGE score) by more than 10% on average, and 16%for low resource languages. CGM also shows remarkable improvements in diversity (80%) illustrating its expressiveness in representation of multi-lingual data.
Transformed Fay-Herriot model with measurement error in covariates

Statistical agencies are often asked to produce small area estimates (SAEs) for positively skewed variables. When domain sample sizes are too small to support direct estimators, effects of skewness of the response variable can be large. As such, it is important to appropriately account for the distribution of the response variable given available auxiliary information. Motivated by this issue and in order to stabilize the skewness and achieve normality in the response variable, we propose an area-level log-measurement error model on the response variable. Then, under our proposed modeling framework, we derive an empirical Bayes (EB) predictor of positive small area quantities subject to the covariates containing measurement error. We propose a corresponding mean squared prediction error (MSPE) of EB predictor using both a jackknife and a bootstrap method. We show that the order of the bias is $O(m^{-1})$, where $m$ is the number of small areas. Finally, we investigate the performance of our methodology using both design-based and model-based simulation studies.
Functional coupling between target selection and acquisition in the superior colliculus.

Survival in unpredictable environments requires that animals continuously evaluate their surroundings for behavioral targets, direct their movements towards those targets, and terminate movements o...
On dark matter as a geometric effect in the galactic halo

We obtain more straightforwardly some features of dark matter distribution in the halos of galaxies by considering the spherically symmetric space-time, which satisfies the flat rotational curve condition, and the geometric equation of state resulting from the modified gravity theory. In order to measure the equation of state for dark matter in the galactic halo, we provide a general formalism taking into account the modified $f(X)$ gravity theories. Here, $f(X)$ is a general function of $X \in \{ R, \mathcal{G}, T \}$ , where $R$ , $\mathcal{G}$ and $T$ are the Ricci scalar, the Gauss-Bonnet scalar and the torsion scalar, respectively. These theories yield that the flat rotation curves appear as a consequence of the additional geometric structure accommodated by those of modified gravity theories. Constructing a geometric equation of state $w_{{X}} \equiv p_{{X}} / \rho _{{X}}$ and inspiring by some values of the equation of state for the ordinary matter, we infer some properties of dark matter in galactic halos of galaxies.
Where Did the Web Archive Go

To perform a longitudinal investigation of web archives and detecting variations and changes replaying individual archived pages, or mementos, we created a sample of 16,627 mementos from 17 public web archives. Over the course of our 14-month study (November, 2017–January, 2019), we found that four web archives changed their base URIs and did not leave a machine-readable method of locating their new base URIs, necessitating manual rediscovery. Of the 1,981 mementos in our sample from these four web archives, 537 were impacted: 517 mementos were rediscovered but with changes in their time of archiving (or Memento-Datetime), HTTP status code, or the string comprising their original URI (or URI-R), and 20 of the mementos could not be found at all.
Dynamically Switching Human Prediction Models for Efficient Planning

As environments involving both robots and humans become increasingly common, so does the need to account for people during planning. To plan effectively, robots must be able to respond to and sometimes influence what humans do. This requires a human model which predicts future human actions. A simple model may assume the human will continue what they did previously; a more complex one might predict that the human will act optimally, disregarding the robot; whereas an even more complex one might capture the robot’s ability to influence the human. These models make different trade-offs between computational time and performance of the resulting robot plan. Using only one model of the human either wastes computational resources or is unable to handle critical situations. In this work, we give the robot access to a suite of human models and enable it to assess the performance-computation trade-off online. By estimating how an alternate model could improve human prediction and how that may translate to performance gain, the robot can dynamically switch human models whenever the additional computation is justified. Our experiments in a driving simulator showcase how the robot can achieve performance comparable to always using the best human model, but with greatly reduced computation.
Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections

Optimal transport maps between two probability distributions $\mu$ and $\nu$ on $\mathbb{R}^d$ have found extensive applications in both machine learning and statistics. In practice, these maps need to be estimated from data sampled according to $\mu$ and $\nu$. Plug-in estimators are perhaps most popular in estimating transport maps in the field of computational optimal transport. In this paper, we provide a comprehensive analysis of the rates of convergences for general plug-in estimators defined via barycentric projections. Our main contribution is a new stability estimate for barycentric projections which proceeds under minimal smoothness assumptions and can be used to analyze general plug-in estimators. We illustrate the usefulness of this stability estimate by first providing rates of convergence for the natural discrete-discrete and semi-discrete estimators of optimal transport maps. We then use the same stability estimate to show that, under additional smoothness assumptions of Besov type or Sobolev type, wavelet based or kernel smoothed plug-in estimators respectively speed up the rates of convergence and significantly mitigate the curse of dimensionality suffered by the natural discrete-discrete/semi-discrete estimators. As a by-product of our analysis, we also obtain faster rates of convergence for plug-in estimators of $W_2(\mu,\nu)$, the Wasserstein distance between $\mu$ and $\nu$, under the aforementioned smoothness assumptions, thereby complementing recent results in Chizat et al. (2020). Finally, we illustrate the applicability of our results in obtaining rates of convergence for Wasserstein barycenters between two probability distributions and obtaining asymptotic detection thresholds for some recent optimal-transport based tests of independence.
3DP3: 3D Scene Perception via Probabilistic Programming

We present 3DP3, a framework for inverse graphics that uses inference in a structured generative model of objects, scenes, and images. 3DP3 uses (i) voxel models to represent the 3D shape of objects, (ii) hierarchical scene graphs to decompose scenes into objects and the contacts between them, and (iii) depth image likelihoods based on real-time graphics. Given an observed RGB-D image, 3DP3's inference algorithm infers the underlying latent 3D scene, including the object poses and a parsimonious joint parametrization of these poses, using fast bottom-up pose proposals, novel involutive MCMC updates of the scene graph structure, and, optionally, neural object detectors and pose estimators. We show that 3DP3 enables scene understanding that is aware of 3D shape, occlusion, and contact structure. Our results demonstrate that 3DP3 is more accurate at 6DoF object pose estimation from real images than deep learning baselines and shows better generalization to challenging scenes with novel viewpoints, contact, and partial observability.
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination

A recurring theme in statistical learning, online learning, and beyond is that faster convergence rates are possible for problems with low noise, often quantified by the performance of the best hypothesis; such results are known as first-order or small-loss guarantees. While first-order guarantees are relatively well understood in statistical and online learning, adapting to low noise in contextual bandits (and more broadly, decision making) presents major algorithmic challenges. In a COLT 2017 open problem, Agarwal, Krishnamurthy, Langford, Luo, and Schapire asked whether first-order guarantees are even possible for contextual bandits and -- if so -- whether they can be attained by efficient algorithms. We give a resolution to this question by providing an optimal and efficient reduction from contextual bandits to online regression with the logarithmic (or, cross-entropy) loss. Our algorithm is simple and practical, readily accommodates rich function classes, and requires no distributional assumptions beyond realizability. In a large-scale empirical evaluation, we find that our approach typically outperforms comparable non-first-order methods. On the technical side, we show that the logarithmic loss and an information-theoretic quantity called the triangular discrimination play a fundamental role in obtaining first-order guarantees, and we combine this observation with new refinements to the regression oracle reduction framework of Foster and Rakhlin. The use of triangular discrimination yields novel results even for the classical statistical learning model, and we anticipate that it will find broader use.
AutoSVA: Democratizing Formal Verification of RTL Module Interactions

Modern SoC design relies on the ability to separately verify IP blocks relative to their own specifications. Formal verification (FV) using SystemVerilog Assertions (SVA) is an effective method to exhaustively verify blocks at unit-level. Unfortunately, FV has a steep learning curve and requires engineering effort that discourages hardware designers from using it during RTL module development. We propose AutoSVA, a framework to automatically generate FV testbenches that verify liveness and safety of control logic involved in module interactions. We demonstrate AutoSVA’s effectiveness and efficiency on deadlock-critical modules of widely-used open-source hardware projects.
Repo2Vec: A Comprehensive Embedding Approach for Determining Repository Similarity.

How can we identify similar repositories and clusters among a large online archive, such as GitHub? Determiningrepository similarity is an essential building block in studying the dynamics and the evolution of such software ecosystems. The key challenge is to determine the right representation for the diverse repository features in a way that: (a) it captures all aspects of the available information, and (b) it is readily usable by MLalgorithms. We propose Repo2Vec, a comprehensive embedding approach to represent a repository as a distributed vector by combining features from three types of information sources. As our key novelty, we consider three types of information: (a)metadata, (b) the structure of the repository, and (c) the source code. We also introduce a series of embedding approaches to represent and combine these information types into a single embedding. We evaluate our method with two real datasets from GitHub for a combined 1013 repositories. First, we show that our method outperforms previous methods in terms of precision (93%vs 78%), with nearly twice as many Strongly Similar repositories and 30% fewer False Positives. Second, we show how Repo2Vecprovides a solid basis for: (a) distinguishing between malware and benign repositories, and (b) identifying a meaningful hierarchical clustering. For example, we achieve 98% precision and 96%recall in distinguishing malware and benign repositories. Overall, our work is a fundamental building block for enabling many repository analysis functions such as repository categorization by target platform or intention, detecting code-reuse and clones, and identifying lineage and evolution.
The GTNP Breeding Bird Monitoring Project: Covariation Among Sites in Breeding Densities

The breeding densities of migrant birds are subject to a wide range of influences that may cause their variation, within a site from one year to another, within years but among habitats and sites from one location to another, and also among species with different migration strategies in terms of travel distances and wintering habitats. First measuring, and then understanding, this variation and its drivers is a substantial challenge for breeding bird monitors and population ecologists. Variation in breeding bird densities in Grand Teton National Park have been monitored since the early 1990's, following protocols instigated by M. Cody & S. Cain (1995 NPS Report). Of the thirty monitoring sites established by this report, one half to two-thirds have been censused yearly up to the present time, and a subset of the sites has been monitored yearly without discontinuities. Thus for many sites there is a census history of a decade or more, forming a data base that now approaches statistical adequacy for testing hypotheses about patterns of variation in breeding bird densities.This report presents preliminary data on the covariation of breeding densities, within and between species, over the various monitoring sites. Given that many of the breeding birds leave GTNP in the nonÂ­breeding season and overwinter elsewhere, early summer GTNP breeding densities are likely a consequence both of off-site conditions (winter survival and migration success) and on-site resources in the breeding habitat, likely in part weather-related. We ask questions such as: a) are there years when breeding densities are substantially higher than in other years? Are high-density years typical of many migrant species simultaneously, or do they occur independently over time among species? For a given species in a high-density year, are all breeding/monitoring sites occupied at higher density, with positive correlations among sites, or are some sites negatively correlated? Is the range of sites occupied by breeding birds greater in years when densities reach higher values in the most favored sites?