cssi/SciSciGPT-SciSciNet huggingface dataset:
Quantitative Assessment of Learning Behaviors for Oncology Providers.

How health care providers select topics and activities for learning is key to meeting their needs. The goal of this study was to investigate how oncology providers identify knowledge gaps and choose learning activities. An online focus group within a larger longitudinal study was conducted between November 2015 and August 2016. Participants were chosen by convenience and stratified random sampling of diverse types of oncology providers. Providers were asked monthly to identify learning needs, explain how they identified those needs, and describe the learning activity they chose to meet those needs. Thirty-two oncology providers recorded 201 learning needs via online journal entries (mean 6 entries per person). Needs were associated with practice setting and professional role (p 50% of identified learning needs across groups. Learning activity preferences were associated with practice setting (p < .05). Choice of learning activity was associated with practice setting, professional role, and geographic location. Colleague recommendation was important for APPs (p = .025). Over 75% of learner responses identify convenience and content quality as important factors in choosing an activity. This study represents a quantitative assessment of learning behaviors for oncology providers and shows that identification of learning needs and activity selection differ by provider demographics. Limitations include small size and underrepresentation of some groups. Our findings should be confirmed with larger samples. Future research should focus on assessment of cohort versus individual needs and learning priorities.
Further Evidence for the Cantor Dust Composition of Dark Matter

Recent experiments continue to falsify the hypothesis that Dark Matter (DM) consists of quantum-like particles resembling the content of the Standard Model (SM). Several arguments also exist against theories advocating that DM follows from various modifications or thermodynamic analogs of General Relativity (GR). These findings indirectly favor earlier interpretations of DM as Cantor Dust, an outcome of the minimal fractal structure of spacetime near or above the electroweak scale. Cantor Dust is consistent with the idea of Unmatter , an exotic phase containing fractional numbers of quanta per state, mixing particles and their antiparticles and carrying arbitrary non-integer spins.
On the number of simultaneous core partitions with d-distinct parts

Abstract We investigate the number N d , r ( s ) of ( s , s + r ) -core integer partitions with d -distinct parts. Our first main result is a proof of a recurrence relation conjectured by Sahin in 2018. We also derive generating functions, asymptotics, and exact formulas for N d , r ( s ) when r is within d of a multiple of s . Finally, we exhibit a surprising connection to A -restricted compositions.
The homotopy groups of a homotopy group completion

Let M be a topological monoid with homotopy group completion ΩBM. Under a strong homotopy commutativity hypothesis on M, we show that πk(ΩBM) is the quotient of the monoid of free homotopy classes [Sk, M] by its submonoid of nullhomotopic maps. We give two applications. First, this result gives a concrete description of the Lawson homology of a complex projective variety in terms of pointwise addition of spherical families of effective algebraic cycles. Second, we apply this result to monoids built from the unitary, or general linear, representation spaces of discrete groups, leading to results about lifting continuous families of characters to continuous families of representations.
The Dejudicialization of International Politics

For many, the growing judicialization of international relations is the next step in the process toward the complete legalization of international politics. We draw on the literature in comparative judicial politics to examine the limits of the phenomenon. The domestic literature on judicialization portrays the process as something of a one-way ratchet. In an increasingly juridified world, judges have been asked to take on greater roles in global governance, and seem to be doing so with aplomb. This in turn incentivizes individuals and interest groups to frame their policy claims in legal terms, providing ever-more fuel for judicial governance. Yet many courts and other legal institutions, both domestic and international, have had their jurisdiction constrained, with some areas of law removed from judicial purview. Might the dynamics of constraint and backlash lead to the dejuridification of an area that has been judicialized? We conceptualize the possibility of what we call dejudicialization, situate it in the context of the literature on backlash, and delimit its potential scope and implications. While dejudicialization is empirically rare, we argue that its very possibility suggests that judicialization should not be considered a teleological process.
Materials flow analysis in support of circular economy development: Plastics in Trinidad and Tobago

Can material flow analysis (MFA) support strategic decisions necessary for the development of circular economy (CE) in a developing country? An MFA can be an essential tool in providing necessary data inputs for decisions related to the development of CE, however, data-poor environments in developing economies, e.g. lack of data on physical flows in manufacturing, pose challenges to doing MFAs. Other data however, in particular trade statistics accounting for shipment mass, are often available. We undertake a case study to characterize plastic flows in Trinidad and Tobago (T&T) for 2016, demonstrating how leveraging such data enables MFA. A notable result from the MFA is that much (48%) of the landfilled plastic in T&T comes from plastic packaging for imported products rather than intentional domestic use. This is an example of what is probably a typical CE challenge for island nations: Importing materials with limited domestic demand at end-of-life. We use the MFA results to propose suggestions for a more circular flow of plastics in T&T. First, there is potential to divert plastic waste (including packaging plastic) for use as feedstock in a local cement plant. Second, the scale of PET plastic flows is of sufficient scale (26,000 metric tons annually) to make domestic recycle feasible. Techno-economic studies are needed to properly develop and evaluate these proposals, the role of the MFA here is to identify promising directions.
A comparative feasibility analysis for sensing swelling with textile-based soft strain sensors

Sensing edema or swelling in the body is a parameter of interest for many clinical and cosmetic applications of wearable technology. Edema creates heightened sensitivity to pressure points, making soft textile-based sensors particularly attractive. However, swelling induces slow, gradual changes in body circumferences, which can be difficult to sense accurately, especially with soft e-textile sensors. Anthropometric variability, sensor placement, and body movements further confound accuracy. Here, we explore the feasibility of accurately sensing ankle swelling through a comparative assessment of a set of sensors representing 3 soft sensing mechanisms in a linear-strain bench test under three frequency conditions. These sensors are then applied in a simulated swelling experiment to assess their performance relative to circumferential changes similar to those experienced in swelling. We find that two sensors (stitched and capacitive) demonstrate reliable performance that approximates or exceeds expert human measurement for swelling. Resistive polymer cord and capacitive sensors exhibited accurate response under medium- and high-frequency extensions.
A novel self-organizing embryonic stem cell system reveals signaling logic underlying the patterning of human ectoderm

During development, the ectoderm is patterned by a combination of BMP and WNT signaling. Research in model organisms has provided substantial insight into this process; however, there are currently no systems in which to study ectodermal patterning in humans. Further, the complexity of neural plate border specification has made it difficult to transition from discovering the genes involved to deeper mechanistic understanding. Here, we develop an in vitro model of human ectodermal patterning, in which human embryonic stem cells self-organize to form robust and quantitatively reproducible patterns corresponding to the complete medial-lateral axis of the embryonic ectoderm. Using this platform, we show that the duration of endogenous WNT signaling is a crucial control parameter, and that cells sense relative levels of BMP and WNT signaling in making fate decisions. These insights allowed us to develop an improved protocol for placodal differentiation. Thus, our platform is a powerful tool for studying human ectoderm patterning and for improving directed differentiation protocols.This article has an associated 'The people behind the papers' interview.
Homology cobordism and triangulations

The study of triangulations on manifolds is closely related to understanding the three-dimensional homology cobordism group. We review here what is known about this group, with an emphasis on the local equivalence methods coming from Pin(2)- equivariant Seiberg-Witten Floer spectra and involutive Heegaard Floer homology.
Guidance on strategic information: Investor-management disagreement and firm intrinsic value

Abstract We investigate the decision by corporate management to voluntarily disclose information that pertains to a firm's strategy. We find the likelihood of disclosure to be a tradeoff between the benefit of reducing information asymmetry and the cost of investors disagreeing with the strategy; this cost varies with the firm's intrinsic value. Higher levels of investor-management disagreement and of information asymmetry increase the likelihood that managers will disclose strategic information; those at firms with higher intrinsic value, however, are less likely to do so. Our results vary in statistical significance across different proxy specifications, but hold qualitatively. They are robust to the use of exogenous shocks to investor-management disagreement and information asymmetry. The evidence supports a causal link between investor-management disagreement and strategic disclosure.
Autonomous Discovery in the Chemical Sciences Part I: Progress.

This two-part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. Finally, we describe many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science. These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modeling. Part two reflects on these case studies and identifies a set of open challenges for the field.
D1-D5-P superstrata in 5 and 6 dimensions: separable wave equations and prepotentials

We construct the most general single-mode superstrata in 5 dimensions with ambipolar, two centered Gibbons Hawking bases, via dimensional reduction of superstrata in 6 dimensions. Previously, asymptotically AdS3 × 𝕊2 5-dimensional superstrata have been produced, giving microstate geometries of black strings in 5 dimensions. Our construction produces asymptotically AdS2 × 𝕊3 geometries as well, the first instances of superstrata describing the microstate geometries of black holes in 5 dimensions. New examples of superstrata with separable massless wave equations in both 5 and 6 dimensions are uncovered. A ℤ2 symmetry which identifies distinct 6-dimensional superstrata when reduced to 5 dimensions is found. Finally we use the mathematical structure of the underlying hyper-Kahler bases to produce prepotentials for the superstrata fluxes in 5 dimensions and uplift them to apply in 6 dimensions as well.
Wirtinger numbers for virtual links

The Wirtinger number of a virtual link is the minimum number of generators of the link group over all meridional presentations in which every relation is an iterated Wirtinger relation arising in a...
Non-Invasive Reverse Engineering of Finite State Machines Using Power Analysis and Boolean Satisfiability

In this paper, we present a non-invasive reverse engineering attack based on a novel approach that combines functional and power analysis to recover finite state machines from their synchronous sequential circuit implementations. The proposed technique formulates the machine exploration and state identification problem as a Boolean constraint satisfaction problem and solves it using a SMT (Satisfiability Modulo Theories) solver. It uses power measurements to achieve fast convergence. Experimental results using the LGSynth’91 benchmark suite show that the satisfiability-based approach is several times faster compared to existing techniques and can successfully recover 90%-100% of the transitions of a target machine.
Toward epidemic thresholds on temporal networks: a review and open questions

Epidemiological contact network models have emerged as an important tool in understanding and predicting spread of infectious disease, due to their capacity to engage individual heterogeneity that may underlie essential dynamics of a particular host-pathogen system. Just as fundamental are the changes that real-world contact networks undergo over time, both independently of and in response to pathogen spreading. These dynamics play a central role in determining whether a disease will die out or become epidemic within a population, known as the epidemic threshold. In this paper, we provide an overview of methods to predict the epidemic threshold for temporal contact network models, and discuss areas that remain unexplored.
Remote Estimation of Free-Flow Speeds

We propose an automated method to estimate a road segment’s free-flow speed from overhead imagery and road meta-data. The free-flow speed of a road segment is the average observed vehicle speed in ideal conditions, without congestion or adverse weather. Standard practice for estimating free-flow speeds depends on several road attributes, including grade, curve, and width of the right of way. Unfortunately, many of these fine-grained labels are not always readily available and are costly to manually annotate. To compensate, our model uses a small, easy to obtain subset of road features along with aerial imagery to directly estimate free-flow speed with a deep convolutional neural network (CNN). We evaluate our approach on a large dataset, and demonstrate that using imagery alone performs nearly as well as the road features and that the combination of imagery with road features leads to the highest accuracy.
The Limits of Partisan Loyalty

While partisan cues tend to dominate political choice, prior work shows that competing information can rival the effects of partisanship if it relates to salient political issues. But what are the limits of partisan loyalty? How much electoral leeway do co-partisan candidates have to deviate from the party line on important issues? We answer this question using conjoint survey experiments that characterize the role of partisanship relative to issues. We demonstrate a pattern of conditional party loyalty. Partisanship dominates electoral choice when elections center on low-salience issues. But while partisan loyalty is strong, it is finite: the average voter is more likely than not to vote for the co-partisan candidate until that candidate takes dissonant stances on four or more salient issues. These findings illuminate when and why partisanship fails to dominate political choice. They also suggest that, on many issues, public opinion minimally constrains politicians.
Structure and Properties of a-C:N Films Prepared by Pulsed Excimer Laser Deposition

We present results on the synthesis of a-C:N films deposited by pulsed excimer laser ablation of graphite in a nitrogen gas ambient. Analysis with RBS and forward recoil spectrometry show that the films are hydrogen-free and nitrogen can be incorporated upto 40% by varying the nitrogen gas pressure. Electron microscopy has identified these films to be amorphous. Raman spectroscopy, infrared spectroscopy, and electron energy loss spectroscopy have revealed carbon-nitrogen bonding structures. Compared to a-diamond films prepared by the same technique, the C:N films still possess the properties of high mechanical hardness, chemically inertness and good wear resistance, but incorporation of nitrogen into the films generally degrades their diamondlike properties. The results suggest that nitrogen doping effect is resulted in the films under the present process, instead of carbon-nitrogen compound formation.
AMON: annotation of metabolite origins via networks to integrate microbiome and metabolome data

Untargeted metabolomics of host-associated samples has yielded insights into mechanisms by which microbes modulate health. However, data interpretation is challenged by the complexity of origins of the small molecules measured, which can come from the host, microbes that live within the host, or from other exposures such as diet or the environment. We address this challenge through development of AMON: Annotation of Metabolite Origins via Networks. AMON is an open-source bioinformatics application that can be used to annotate which compounds in the metabolome could have been produced by bacteria present or the host, to evaluate pathway enrichment of host verses microbial metabolites, and to visualize which compounds may have been produced by host versus microbial enzymes in KEGG pathway maps. AMON empowers researchers to predict origins of metabolites via genomic information and to visualize potential host:microbe interplay. Additionally, the evaluation of enrichment of pathway metabolites of host versus microbial origin gives insight into the metabolic functionality that a microbial community adds to a host:microbe system. Through integrated analysis of microbiome and metabolome data, mechanistic relationships between microbial communities and host phenotypes can be better understood.
Combinatorial Sleeping Bandits With Fairness Constraints

The multi-armed bandit (MAB) model has been widely adopted for studying many practical optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with unknown parameters. The goal of the player (i.e., the decision maker) here is to maximize the cumulative reward in the face of uncertainty. However, the basic MAB model neglects several important factors of the system in many real-world applications, where multiple arms (i.e., actions) can be simultaneously played and an arm could sometimes be “sleeping” (i.e., unavailable). Besides reward maximization, ensuring fairness is also a key design concern in practice. To that end, we propose a new Combinatorial Sleeping MAB model with Fairness constraints , called CSMAB-F , aiming to address the aforementioned crucial modeling issues. The objective is now to maximize the reward while satisfying the fairness requirement of a minimum selection fraction for each individual arm. To tackle this new problem, we extend an online learning algorithm, called Upper Confidence Bound (UCB) , to deal with a critical tradeoff between exploitation and exploration and employ the virtual queue technique to properly handle the fairness constraints. By carefully integrating these two techniques, we develop a new algorithm, called Learning with Fairness Guarantee (LFG) , for the CSMAB-F problem. Further, we rigorously prove that not only LFG is feasibility-optimal , but it also has a time-average regret upper bounded by $\frac{N}{2 \eta } + \frac{\beta _1 \sqrt{m N T \log {T}}+ \beta _2~N}{T}$ , where $N$ is the total number of arms, $m$ is the maximum number of arms that can be simultaneously played, $T$ is the time horizon, $\beta _1$ and $\beta _2$ are constants, and $\eta$ is a design parameter that we can tune. Finally, we perform extensive simulations to corroborate the effectiveness of the proposed algorithm. Interestingly, the simulation results reveal an important tradeoff between the regret and the speed of convergence to a point satisfying the fairness constraints.
Controllable Water Vapor Assisted Chemical Vapor Transport Synthesis of WS2–MoS2 Heterostructure

The vapor phase synthesis of two-dimensional transition-metal dichalcogenides (MX2) and their heterostructures is often poorly reproducible and sensitive to uncontrolled environmental humidity. It ...
Modeling and optimization of Human-Machine Interaction Processes via the Maximum Entropy Principle

We propose a data-driven framework to enable the modeling and optimization of human-machine interaction processes, e.g., systems aimed at assisting humans in decision-making or learning, work-load allocation, and interactive advertising. This is a challenging problem for several reasons. First, humans’ behavior is hard to model or infer, as it may reflect biases, long term memory, and sensitivity to sequencing, i.e., transience and exponential complexity in the length of the interaction. Second, due to the interactive nature of such processes, the machine policy used to engage with human may bias possible data-driven inferences. Finally, in choosing machine policies that optimize interaction rewards, one must, on the one hand, avoid being overly sensitive to error/variability in the estimated human model, and on the other, being overly deterministic/predictable which may result in poor human ‘engagement’ in the interaction. To meet these challenges, we propose a robust approach, based on the maximum entropy principle, which iteratively estimates human behavior and optimizes the machine policy, namely Alternating Entropy-Reward Ascent (AREA) algorithm. We characterize AREA, in terms of its space and time complexity and convergence. We also provide an initial validation based on synthetic data generated by an established noisy nonlinear model for human decision-making.
Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli.

Abstract In this study, we developed a dynamic mathematical model of E. coli cell-free protein synthesis (CFPS). Model parameters were estimated from a dataset consisting of glucose, organic acids, energy species, amino acids, and protein product, chloramphenicol acetyltransferase (CAT) measurements. The model was successfully trained to simulate these measurements, especially those of the central carbon metabolism. We then used the trained model to evaluate the performance, e.g., the yield and rates of protein production. CAT was produced with an energy efficiency of 12%, suggesting that the process could be further optimized. Reaction group knockouts showed that protein productivity was most sensitive to the oxidative phosphorylation and glycolysis/gluconeogenesis pathways. Amino acid biosynthesis was also important for productivity, while overflow metabolism and TCA cycle affected the overall system state. In addition, translation was more important to productivity than transcription. Finally, CAT production was robust to allosteric control, as were most of the predicted metabolite concentrations; the exceptions to this were the concentrations of succinate and malate, and to a lesser extent pyruvate and acetate, which varied from the measured values when allosteric control was removed. This study is the first to use kinetic modeling to predict dynamic protein production in a cell-free E. coli system, and could provide a foundation for genome scale, dynamic modeling of cell-free E. coli protein synthesis.
Hydrogenation of the Martian Core by Hydrated Mantle Minerals With Implications for the Early Dynamo

Mars lacks an internally generated magnetic field today. Crustal remanent magnetism and meteorites indicate that a dynamo existed after accretion but died roughly four billion years ago. Standard m...
A problem in control of elastodynamics with piezoelectric effects

We consider an optimal control problem where the state equations are a coupled hyperbolic-elliptic system. This system arises in elastodynamics with piezoelectric effects -- the elastic stress tensor is a function of elastic displacement and electric potential. The electric flux acts as the control variable and, in addition to the state constraints, the bound constraints on the control are considered. We develop a complete analysis for the state equations and the control problem. The requisite regularity on the control, to show the well-posedness of state equations, is enforced using the cost functional. We rigorously derive the first order necessary and sufficient conditions using adjoint equations and further study their well-posedness. For spatially discrete (time continuous) problem, we show the convergence of our numerical scheme. Three dimensional numerical experiments are provided showing convergence properties of a fully discrete method and the practical applicability of our approach.
The Melanoma Risk Loci as Determinants of Melanoma Prognosis

ABSTRACT Background Genetic risk factors of human cancer emerge as promising markers of clinical outcome. Recent melanoma genome-wide scans (GWAS) have identified loci associated with disease risk, nevi or UV/pigmentation, but prognostic potential of these variants is yet to be determined. In this study, we performed the first-to-date systematic evaluation of the association between established melanoma risk loci and progression. Methods 891 melanoma patients prospectively accrued and followed up at NYU Medical Center were studied. We examined the association of 108 melanoma susceptibility single nucleotide polymorphisms (SNPs), selected or imputed from recent GWAS on melanoma, nevi or pigmentation, with recurrence-free survival (RFS) and overall survival (OS). The genotyping was performed using Sequenom I-plex. Univariate and multivariate Cox PH model were used to test association between each SNP and RFS and OS. Multivariate adjustments included age at diagnosis, gender, tumor stage, histological subtype, thickness, ulceration status, and mitotic index. ROC curves were used to measure utility of SNPs in predicting 3-year recurrence. Results Strong associations were observed from the multivariate analysis for rs7538876 (RCC2) with RFS (HR = 1.48, 95% CI 1.20 – 1.83, p = 0.0002) and rs9960018 (DLGAP1) with both RFS and OS (HR = 1.43, 95% CI = 1.07-1.91, p = 0.0159, HR = 1.52, 95% CI = 1.09-2.22, p = 0.0138, respectively). Subgroup analyses for ulceration and tumor thickness have identified several SNPs which show associations for RFS and OS among these different groups. In addition, we identified the classifiers rs7538876 and rs9960018, that when combined with stage and histological type, achieve a higher area under the ROC curve (AUC = 84%), compared to the baseline (AUC = 78%), in predicting 3-year recurrence. Conclusions Our data revealed associations between specific melanoma susceptibility variants and worse disease outcome. The strength of associations observed for rs7538876 and rs9960018 suggest biological implication of these loci in melanoma recurrence. The observed predictive patterns of associated variants with clinical outcome provide the evidence for the potential utilization of genetic markers in melanoma prognostication. Disclosure All authors have declared no conflicts of interest.
The Employment Effects of the Social Security Earnings Test

We investigate the impact of the Social Security Annual Earnings Test (AET) on the employment decisions of older Americans. The AET reduces Social Security benefits by one dollar for every two dollars earned above the exempt amount. Using a differences-in-differences design, we find that the employment rate of those predicted to become subject to the AET decreases substantially relative to those not predicted to become subject to it. The point estimates suggest that the AET reduces the employment rate of Americans aged 63-64 by at least 1.2 percentage points.
Algorithmic Design and Implementation of Unobtrusive Multistatic Serial LiDAR Image

To fully understand interactions between marine hydrokinetic (MHK) equipment and marine animals, a fast and effective monitoring system is required to capture relevant information whenever underwater animals appear. A new automated underwater imaging system composed of LiDAR (Light Detection and Ranging) imaging hardware and a scene understanding software module named Unobtrusive Multistatic Serial LiDAR Imager (UMSLI) to supervise the presence of animals near turbines. UMSLI integrates the front end LiDAR hardware and a series of software modules to achieve image preprocessing, detection, tracking, segmentation and classification in a hierarchical manner.
Gut microbiota differently contributes to intestinal immune phenotype and systemic autoimmune progression in female and male lupus-prone mice

The risk of developing systemic lupus erythematosus (SLE) is about 9 times higher in women as compared to men. Our recent report, which used (SWRxNZB) F1 (SNF1) mouse model of spontaneous lupus, showed a potential link between immune response initiated in the gut mucosa at juvenile age (sex hormone independent) and SLE susceptibility. Here, using this mouse model, we show that gut microbiota contributes differently to pro-inflammatory immune response in the intestine and autoimmune progression in lupus-prone males and females. We found that gut microbiota composition in male and female littermates are significantly different only at adult ages. However, depletion of gut microbes causes suppression of autoimmune progression only in females. In agreement, microbiota depletion suppressed the pro-inflammatory cytokine response of gut mucosa in juvenile and adult females. Nevertheless, microbiota from females and males showed, upon cross-transfer, contrasting abilities to modulate disease progression. Furthermore, orchidectomy (castration) not only caused changes in the composition of gut microbiota, but also a modest acceleration of autoimmune progression. Overall, our work shows that microbiota-dependent pro-inflammatory immune response in the gut mucosa of females initiated at juvenile ages and androgen-dependent protection of males contribute to gender differences in the intestinal immune phenotype and systemic autoimmune progression.
Governing Through Partnerships: Neoconservative Governance and State Reliance on Religious NGOs in Drug Policy

This article examines states’ pursuit of partnerships with non-governmental organizations (NGOs) as a strategy of governance in drug policy. State actors have used partnerships with religious NGOs to provide treatment services and disseminate messages about prevention. I investigate the emergence of such partnerships, drawing attention to neoconservatism as a political rationality associated with the rise of the New Right. I analyze officials’ justifications and strategies for including religious NGOs in such partnerships, using archival data on drug policies in Ohio and beginning with the formation of the statewide addiction services agency in 1989. The results demonstrate how officials have increasingly recognized the characteristics of the religious community by emphasizing their social service delivery and by framing religious leaders as health educators. Given the results, I consider the impacts of partnerships for the autonomy of organizations, the oversight of care, and the generation of images of an engaged community.
Off-line error prediction, diagnosis and recovery using virtual assembly systems

Automated assembly systems often stop their operation due to the unexpected failures occurred during their assembly process. Since these large-scale systems are composed of many parameters, it is difficult to anticipate all possible types of errors with their likelihood of occurrence. Several systems were developed in the literature, focussing on on-line diagnosing and recovery of the assembly process in an intelligent manner based on the predicted error scenarios. However, these systems do not cover all of the possible errors and they are deficient in dealing with the unexpected error situations. The proposed approach uses Monte Carlo simulation of the assembly process with the 3-D model of the assembly line to predict the possible errors in an off-line manner. After that, these predicted errors are diagnosed and recovered using Bayesian reasoning and genetic algorithms. Several case studies are performed on single-station and multi-station assembly systems and the results are discussed. It is expected that with this new approach, errors can be diagnosed and recovered accurately and costly downtimes of robotic assembly systems will be reduced.
Spatiotemporal dynamics of multi-vesicular release is determined by heterogeneity of release sites within central synapses

A synaptic active zone (AZ) can release multiple vesicles in response to an action potential. This multi-vesicular release (MVR) occurs at most synapses, but its spatiotemporal properties are unknown. Nanoscale-resolution detection of individual release events in hippocampal synapses revealed unprecedented heterogeneity among vesicle release sites within a single AZ, with a gradient of release probability decreasing from AZ center to periphery. Parallel to this organization, MVR events preferentially overlap with uni-vesicular release (UVR) events at sites closer to an AZ center. Pairs of fusion events comprising MVR are also not perfectly synchronized, and the earlier event tends to occur closer to AZ center. The spatial features of release sites and MVR events are similarly tightened by buffering intracellular calcium. These observations revealed a marked heterogeneity of release site properties within individual AZs, which determines the spatiotemporal features of MVR events and is controlled, in part, by non-uniform calcium elevation across the AZ.
Projectively coresolved Gorenstein flat and ding projective modules

We give necessary and sufficient conditions in order for the class of projectively coresolved Gorenstein flat modules, PGF (respectively that of projectively coresolved Gorenstein B flat modules, P...
The effect of smoking on sensory and attentional masking

This study examined the influence of smoking on low-level sensory and higher level visual cognitive abilities. Three groups (nonsmokers, deprived smokers, and nondeprived smokers) of 22 observers were tested using a visual target discrimination task. To assess sensory and visual cognitive differences, a masking task developed by Enns and Di Lollo (1997) was used. In the task, the target was masked by either a contour mask (contour masking) or an object mask (attentional masking by object substitution). The strongest masking effects were found in the group of nondeprived smokers, for both sensory and attentional masking. This pattern of results as well as results in the deprived smokers indicated changes in transient visual processing channels due to the acute actions of nicotine, not mediated by withdrawal relief.
Evolutionary games with environmental feedbacks.

Strategic interactions arise in all domains of life. This form of competition often plays out in dynamically changing environments. The strategies employed in a population may alter the state of the environment, which may in turn feedback to change the incentive structure of strategic interactions. Feedbacks between strategies and the environment are common in social-ecological systems, evolutionary-ecological systems, and even psychological-economic systems. Here we develop a framework of ‘eco-evolutionary game theory’ that enables the study of strategic and environmental dynamics with feedbacks. We consider environments governed either by intrinsic growth, decay, or tipping points. We show how the joint dynamics of strategies and the environment depend on the incentives for individuals to lead or follow behavioral changes, and on the relative speed of environmental versus strategic change. Our analysis unites dynamical phenomena that occur in settings as diverse as human decision-making, plant nutrient acquisition, and resource harvesting. We discuss implications in fields ranging from ecology to economics. Strategic game payoffs often depend on the state of the environment, which in turn can be influenced by game strategies. Here, Tilman et al. develop a general framework for modeling strategic games with environmental feedbacks and analyze case studies from decision-making, ecology, and economics.
A proximal-point algorithm with variable sample-sizes (PPAWSS) for monotone stochastic variational inequality problems

We consider a stochastic variational inequality (SVI) problem with a continuous and monotone mapping over a closed and convex set. In strongly monotone regimes, we present a variable sample-size averaging scheme (VS-Ave) that achieves a linear rate with an optimal oracle complexity. In addition, the iteration complexity is shown to display a muted dependence on the condition number compared with standard variance-reduced projection schemes. To contend with merely monotone maps, we develop amongst the first proximal-point algorithms with variable sample-sizes (PPAWSS), where increasingly accurate solutions of strongly monotone SVIs are obtained via (VS-Ave) at every step. This allows for achieving a sublinear convergence rate that matches that obtained for deterministic monotone VIs. Preliminary numerical evidence suggests that the schemes compares well with competing schemes.
Static loading of the knee joint results in modified single leg landing biomechanics.

Background External loading of the ligamentous tissues induces mechanical creep, which modifies neuromuscular response to perturbations. It is not well understood how ligamentous creep affects athletic performance and contributes to modifications of knee biomechanics during functional tasks. Hypothesis/purpose The purpose of this study was to examine the mechanical and neuromuscular responses to single leg drop landing perturbations before and after passive loading of the knee joint. Methods Descriptive laboratory study. Male (n = 7) and female (n = 14) participants’ (21.3 ± 2.1 yrs., 1.69 ± 0.09 m, 69.3 ± 13.0 kg) right hip, knee, and ankle kinematics were assessed during drop landings performed from a 30 cm height onto a force platform before and after a 10 min creep protocol. Electromyography (EMG) signals were recorded from rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), semimembranosus (SM), and biceps femoris (BF) muscles. The creep protocol involved fixing the knee joint at 35° during static loading with perpendicular loads of either 200 N (males) or 150 N (females). Maximum, minimum, range of motion (ROM), and angular velocities were assessed for the hip, knee, and ankle joints, while normalized EMG (NEMG), vertical ground reaction forces (VGRF), and rate of force development (RFD) were assessed at landing using ANOVAs. Alpha was set at 0.05. Results Maximum hip flexion velocity decreased (p < 0.01). Minimum knee flexion velocity increased (p < 0.02). Minimum knee ad/abduction velocity decreased (p < 0.001). Ankle ROM decreased (p < 0.001). aVGRF decreased (p < 0.02). RFD had a non-significant trend (p = 0.076). NAEMG was significant between muscle groups (p < 0.02). Conclusion Distinct changes in velocity parameters are attributed to the altered mechanical behavior of the knee joint tissues and may contribute to changes in the loading of the leg during landing.
Optimization of Passive Chip Components Placement with Self-Alignment Effect for Advanced Surface Mounting Technology

Abstract Surface mount technology (SMT) is an enhanced method in electronic packaging in which electronic components are placed directly on soldered printing circuit board (PCB) and are permanently attached on PCB with the aim of reflow soldering process. During reflow process, once deposited solder pastes start melting, electronic components move in a direction that achieve their highest symmetry. This motion is known as self-alignment since can correct potential mounting misalignment. In this study, two noticeable machine learning algorithms, including support vector regression (SVR) and random forest regression (RFR) are proposed as a prediction technique to (1) diagnose the relation among component self-alignment, deposited solder paste status and placement machining parameters, (2) predict the final component position on PCB in x, y, and rotational directions before entering in the reflow process. Based on the prediction result, a non-linear optimization model (NLP) is developed to optimize placement parameters at initial stage. Resultantly, RFR outperforms in terms of prediction model fitness and error. The optimization model is run for 6 samples in which the minimum Euclidean distance from component position after reflow process from ideal position (i.e., the center of pads) is outlined as 25.57 (μm) regarding defined boundaries in model.
Thickness of Deep Layers in the Fusiform Face Area Predicts Face Recognition.

People with superior face recognition have relatively thin cortex in face-selective brain areas, whereas those with superior vehicle recognition have relatively thick cortex in the same areas. We s...
Informational Shocks, Off-Label Prescribing, and the Effects of Physician Detailing

The relationship between pharmaceutical detailing and prescriptions for non-FDA-approved (off-label) use has been the subject of regulatory scrutiny, with more than $12 billion in regulatory settlements for off-label promotion since 2004. Using the case of AstraZeneca’s antipsychotic drug, Seroquel, I study the extent to which off-label prescriptions are caused by detailing. Using a physician panel that connects detailing exposure to medical charts, I exploit within-physician variation to identify detailing effects. I find the effect of detailing on off-label prescriptions is small in both absolute and relative terms. Detailing on net tilts the prescribing distribution toward on-label. This paper was accepted by Eric Anderson, marketing.
Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction

In this paper, we present an end-to-end future-prediction model that focuses on pedestrian safety. Specifically, our model uses previous video frames, recorded from the perspective of the vehicle, to predict if a pedestrian will cross in front of the vehicle. The long term goal of this work is to design a fully autonomous system that acts and reacts as a defensive human driver would — predicting future events and reacting to mitigate risk. We focus on pedestrian-vehicle interactions because of the high risk of harm to the pedestrian if their actions are miss-predicted. Our end-to-end model consists of two stages: the first stage is an encoder/decoder network that learns to predict future video frames. The second stage is a deep spatiotemporal network that utilizes the predicted frames of the first stage to predict the pedestrian’s future action. Our system achieves state-of-the-art accuracy on the Joint Attention for Autonomous Driving (JAAD) dataset on both future frames prediction, with a pixel-wise prediction l 1 error of 1.12, and pedestrian behavior prediction with an average precision of 86.7.
Strategy and the Strategist: How It Matters Who Develops the Strategy

This paper addresses primarily two questions. First, when (and why) should a company’s strategy be developed by its CEO versus by some outside analyst or other insider? Second, how does strategy interact with vision (in the sense of a strong belief about the right course of action)? The paper studies these questions using a functional definition of strategy as “the smallest set of choices to optimally guide other choices.” Among other things, the paper shows that strategy formulation by the CEO leads to both better strategy and better execution, and that a strategist’s vision may improve execution. In the process, the paper also identifies criteria that make a decision strategic and derives explanations why strategies often reflect the background of the strategist. This paper was accepted by Bruno Cassiman, business strategy.
Learning Numerical Observers using Unsupervised Domain Adaptation

Medical imaging systems are commonly assessed by use of objective image quality measures. Supervised deep learning methods have been investigated to implement numerical observers for task-based image quality assessment. However, labeling large amounts of experimental data to train deep neural networks is tedious, expensive, and prone to subjective errors. Computer-simulated image data can potentially be employed to circumvent these issues; however, it is often difficult to computationally model complicated anatomical structures, noise sources, and the response of real-world imaging systems. Hence, simulated image data will generally possess physical and statistical differences from the experimental image data they seek to emulate. Within the context of machine learning, these differences between the sets of two images is referred to as domain shift. In this study, we propose and investigate the use of an adversarial domain adaptation method to mitigate the deleterious effects of domain shift between simulated and experimental image data for deep learning-based numerical observers (DL-NOs) that are trained on simulated images but applied to experimental ones. In the proposed method, a DL-NO will initially be trained on computer-simulated image data and subsequently adapted for use with experimental image data, without the need for any labeled experimental images. As a proof of concept, a binary signal detection task is considered. The success of this strategy as a function of the degree of domain shift present between the simulated and experimental image data is investigated.
Character motion in function space

We address the problem of animated character motion representation and approximation by introducing a novel form of motion expression in a function space. For a given set of motions, our method extracts a set of orthonormal basis (ONB) functions. Each motion is then expressed as a vector in the ONB space or approximated by a subset of the ONB functions. Inspired by the static PCA, our approach works with the time-varying functions. The set of ONB functions is extracted from the input motions by using functional principal component analysis and it has an optimal coverage of the input motions for the given input set. We show the applications of the novel compact representation by providing a motion distance metric, motion synthesis algorithm, and a motion level of detail. Not only we can represent a motion by using the ONB; a new motion can be synthesized by optimizing connectivity of reconstructed motion functions, or by interpolating motion vectors. The quality of the approximation of the reconstructed motion can be set by defining a number of ONB functions, and this property is also used to level of detail. Our representation provides compression of the motion. Although we need to store the generated ONB that are unique for each set of input motions, we show that the compression factor of our representation is higher than for commonly used analytic function methods. Moreover, our approach also provides lower distortion rate.