Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections

This paper investigates how a Bayesian reinforcement learning method can be used to create a tactical decision-making agent for autonomous driving in an intersection scenario, where the agent can estimate the confidence of its decisions. An ensemble of neural networks, with additional randomized prior functions (RPF), are trained by using a bootstrapped experience replay memory. The coefficient of variation in the estimated Q-values of the ensemble members is used to approximate the uncertainty, and a criterion that determines if the agent is sufficiently confident to make a particular decision is introduced. The performance of the ensemble RPF method is evaluated in an intersection scenario and compared to a standard Deep Q-Network method, which does not estimate the uncertainty. It is shown that the trained ensemble RPF agent can detect cases with high uncertainty, both in situations that are far from the training distribution, and in situations that seldom occur within the training distribution. This work demonstrates one possible application of such a confidence estimate, by using this information to choose safe actions in unknown situations, which removes all collisions from within the training distribution, and most collisions outside of the distribution.
Computer-assisted proofs for Lyapunov stability via Sums of Squares certificates and Constructive Analysis

We provide a computer-assisted approach to ensure that a given continuous or discrete-time polynomial system is (asymptotically) stable. Our framework relies on constructive analysis together with formally certified sums of squares Lyapunov functions. The crucial steps are formalized within of the proof assistant Minlog. We illustrate our approach with various examples issued from the control system literature.
Diode laser phase noise influence on the ultimate performance of its frequency stabilization to a Mach-Zehnder interferometer fringe

We report a detailed investigation on the effect of semiconductor laser phase noise on the achievable frequency stability when locked to a Mach-Zehnder interferometer fringe. We show that the modulation-demodulation operation produces in the presence of laser phase noise two kinds of excess noise, which could be much above the shot noise limit, namely: conversion noise (PM-to-AM) and intermodulation noise. We show that in typical stabilization conditions, the frequency stability of the locked laser is limited by the intermodulation excess noise. This effect, reported initially in the microwave domain, can be considerably reduced by a convenient choice of the modulation frequency. To our knowledge, this is the first time such a phenomenon is reported in the optical domain.
A Tweet-based Dataset for Company-Level Stock Return Prediction

Public opinion influences events, especially related to stock market movement, in which a subtle hint can influence the local outcome of the market. In this paper, we present a dataset that allows for company-level analysis of tweet based impact on one-, two-, three-, and seven-day stock returns. Our dataset consists of 862, 231 labelled instances from twitter in English, we also release a cleaned subset of 85, 176 labelled instances to the community. We also provide baselines using standard machine learning algorithms and a multi-view learning based approach that makes use of different types of features. Our dataset, scripts and models are publicly available at: https://github.com/ImperialNLP/stockreturnpred.
Incidence of Major Complications of Neurolytic Coeliac Plexus Block

The number of neurolytic coeliac plexus blocks carried out in England and Wales over a 5 year period (1986–1990) was ascertained. The number of cases of the major complications of permanent paraplegia and/or loss of anal and bladder sphincter function following on from such blocks, over the same period of time, was also ascertained. The information was obtained by means of a questionnaire which was sent to most of the pain clinics in England and Wales. There were 2730 neurolytic blocks carried out over the 5 year period. The number of cases of permanent paraplegia following on from the blocks was four. Of these four cases, three of them also had loss of anal and bladder sphincter function — loss of sphincter function never occurred in isolation. The incidence of major complications following neurolytic coeliac plexus block was thus one case per 683 blocks.
On-chip temperature and process variation sensing using a reconfigurable Ring Oscillator

This paper proposes a temperature monitoring scheme using a reconfigurable ring oscillator that has been proposed to estimate process variation. New circuit configurations, whose delay characteristics are sensitive to leakage current, are proposed to exploit the exponential dependence of the leakage current to temperature. Based on transistor-level simulation assuming a 65 nm process technology, the oscillation frequency of the proposed circuit topology shows the temperature sensitivity of 5.0 %/°C at 20 °C and 2.9 %/°C at 80 °C with low voltage sensitivity of 0.28 °C/10 mV at 25 °C and a supply voltage of 0.9 V. Estimation error of a 65 nm test chip ranges from −0.6 °C to 0.4 °C after two-point calibration. We also proposed a method to estimate the process variation and the temperature at the same time.
Persistent replication of a hepatitis C virus genotype 1b‐based chimeric clone carrying E1, E2 and p6 regions from GB virus B in a New World monkey

The development of effective hepatitis C virus (HCV) vaccines is essential for the prevention of further HCV dissemination, especially in developing countries. Therefore the aim of this study is to establish a feasible and immunocompetent surrogate animal model of HCV infection that will help in evaluation of the protective efficacy of newly developing HCV vaccine candidates. To circumvent the narrow host range of HCV, an HCV genotype 1b‐based chimeric clone carrying E1, E2 and p6 regions from GB virus B (GBV‐B), which is closely related to HCV, was generated. The chimera between HCV and GBV‐B, named HCV/G, replicated more efficiently as compared with the HCV clone in primary marmoset hepatocytes. Furthermore, it was found that the chimera persistently replicated in a tamarin for more than 2 years after intrahepatic inoculation of the chimeric RNA. Although relatively low (<200 copies/mL), the viral RNA loads in plasma were detectable intermittently during the observation period. Of note, the chimeric RNA was found in the pellet fraction obtained by ultracentrifugation of the plasma at 73 weeks, indicating production of the chimeric virus. Our results will help establish a novel non‐human primate model for HCV infection on the basis of the HCV/G chimera in the major framework of the HCV genome.
Ultrastructural studies of normal and alloxan‐treated islet cells of the pancreas of the lizard, Eumeces fasciatus

Alpha and beta cells can be distinguished by differences in mitochondrial and secretion granule structure. Many mitochondria of alpha cells possess “tubular” or prismatic cristae oriented longitudinally and having triangular profiles in cross‐section. The matrix is particulate, with the roughly spherical particles measuring about 100 A in diameter. Evidence is presented indicating that alpha and beta granules are sequestrated in association with Golgi elements. Fully‐condensed beta granules, assumed to be insulin, appear homogeneously dense and crystalline. Recurrent profiles of crystalline beta granules suggest that they possess an octahedral configuration. Alpha cell granules also appear homogeneously dense but have round profiles.
Stereo camera — Based 3D object reconstruction utilizing Semi-Global Matching Algorithm

a 3D reconstruction using stereo cameras still becomes an issue among researchers specialized in computer vision. The corresponding pixel between two images from stereo camera needs to be estimated accurately. One of the widely used methods is Semi-Global Matching (SGM), which uses mutual information (MI) in the form of entropy between two pixels to determine the level of similarity based on the smallest energy (lower cost). The reconstruction result shows the percentage of registered pointcloud is equal to 62.11% where the observation distance ranges are between 1 to 4 meters. In this research, a nearest-neighbor filter is utilized to improve the pointcloud quality where the variations of the neighbor's number are 4 to 128 pixels. The results show that this technique can eliminate the outliers up to 4.9% with the standard deviation of nearest-neighbor distances means equals to 1.0.
Graph Partitioning Methods for Fast Parallel Quantum Molecular Dynamics

We study a graph partitioning problem motivated by the simulation of the physical movement of multi-body systems on an atomistic level, where the forces are calculated from a quantum mechanical description of the electrons. Several advanced algorithms have been published in the literature for such simulations that are based on evaluations of matrix polynomials. We aim at efficiently parallelizing these computations by using a special type of graph partitioning. For this, we represent the zero-nonzero structure of a thresholded matrix as a graph and partition that graph into several components. The matrix polynomial is then evaluated for each separate submatrix corresponding to the subgraphs and the evaluated submatrix polynomials are used to assemble the final result for the full matrix polynomial. The paper provides a rigorous definition as well as a mathematical justification of this partitioning problem. We use several algorithms to compute graph partitions and experimentally evaluate their performance with respect to the quality of the partition obtained with each method and the time needed to produce it.
Convex Synthesis of Accelerated Gradient Algorithms for Optimization and Saddle Point Problems using Lyapunov functions

This paper considers the problem of designing accelerated gradient-based algorithms for optimization and saddle-point problems. The class of objective functions is defined by a generalized sector condition. This class of functions contains strongly convex functions with Lipschitz gradients but also non-convex functions, which allows not only to address optimization problems but also saddle-point problems. The proposed design procedure relies on a suitable class of Lyapunov functions and on convex semi-definite programming. The proposed synthesis allows the design of algorithms that reach the performance of state-of-the-art accelerated gradient methods and beyond.
DCAF: A Dynamic Computation Allocation Framework for Online Serving System

Modern large-scale systems such as recommender system and online advertising system are built upon computation-intensive infrastructure. The typical objective in these applications is to maximize the total revenue, e.g. GMV~(Gross Merchandise Volume), under a limited computation resource. Usually, the online serving system follows a multi-stage cascade architecture, which consists of several stages including retrieval, pre-ranking, ranking, etc. These stages usually allocate resource manually with specific computing power budgets, which requires the serving configuration to adapt accordingly. As a result, the existing system easily falls into suboptimal solutions with respect to maximizing the total revenue. The limitation is due to the face that, although the value of traffic requests vary greatly, online serving system still spends equal computing power among them. In this paper, we introduce a novel idea that online serving system could treat each traffic request differently and allocate "personalized" computation resource based on its value. We formulate this resource allocation problem as a knapsack problem and propose a Dynamic Computation Allocation Framework~(DCAF). Under some general assumptions, DCAF can theoretically guarantee that the system can maximize the total revenue within given computation budget. DCAF brings significant improvement and has been deployed in the display advertising system of Taobao for serving the main traffic. With DCAF, we are able to maintain the same business performance with 20\% computation resource reduction.
AdvMind: Inferring Adversary Intent of Black-Box Attacks

Deep neural networks (DNNs) are inherently susceptible to adversarial attacks even under black-box settings, in which the adversary only has query access to the target models. In practice, while it may be possible to effectively detect such attacks (e.g., observing massive similar but non-identical queries), it is often challenging to exactly infer the adversary intent (e.g., the target class of the adversarial example the adversary attempts to craft) especially during early stages of the attacks, which is crucial for performing effective deterrence and remediation of the threats in many scenarios. In this paper, we present AdvMind, a new class of estimation models that infer the adversary intent of black-box adversarial attacks in a robust and prompt manner. Specifically, to achieve robust detection, AdvMind accounts for the adversary adaptiveness such that her attempt to conceal the target will significantly increase the attack cost (e.g., in terms of the number of queries); to achieve prompt detection, AdvMind proactively synthesizes plausible query results to solicit subsequent queries from the adversary that maximally expose her intent. Through extensive empirical evaluation on benchmark datasets and state-of-the-art black-box attacks, we demonstrate that on average AdvMind detects the adversary intent with over 75% accuracy after observing less than 3 query batches and meanwhile increases the cost of adaptive attacks by over 60%. We further discuss the possible synergy between AdvMind and other defense methods against black-box adversarial attacks, pointing to several promising research directions.
Approach to analyze the organizational characteristics for being an innovative organization

Innovation has become an important weapon for an organization to sustain its competitiveness under today's business dynamic. This paper aims to propose the idea how to develop a model to assess the organizational characteristics for being an innovative organization. With the development of the model, it would help the top management of any organization determines proper strategies to fill in the strategic gaps between the current and future requirements for becoming an innovative organization.
LSD-C: Linearly Separable Deep Clusters

We present LSD-C, a novel method to identify clusters in an unlabeled dataset. Our algorithm first establishes pairwise connections in the feature space between the samples of the minibatch based on a similarity metric. Then it regroups in clusters the connected samples and enforces a linear separation between clusters. This is achieved by using the pairwise connections as targets together with a binary cross-entropy loss on the predictions that the associated pairs of samples belong to the same cluster. This way, the feature representation of the network will evolve such that similar samples in this feature space will belong to the same linearly separated cluster. Our method draws inspiration from recent semi-supervised learning practice and proposes to combine our clustering algorithm with self-supervised pretraining and strong data augmentation. We show that our approach significantly outperforms competitors on popular public image benchmarks including CIFAR 10/100, STL 10 and MNIST, as well as the document classification dataset Reuters 10K. Our code is available at https://github.com/srebuffi/lsd-clusters.
Two-riccati H∞ controllers for descriptor systems

H∞ formulae based on the 2-Riccati procedure are presented for plants in descriptor form. It is expected that this would lead to a method which is more numerically reliable method than those which currently exist for state-space realizations. The theory is developed first for the case where the E-matrix is nonsingular and the possibility of applying these formulae to cases where E is singular is discussed.
The upgrade I of LHCb VELO—towards an intelligent monitoring platform

The Large Hadron Collider beauty (LHCb) detector is designed to detect decays of b- and c- hadrons for the study of CP violation and rare decays. At the end of the LHC Run 2, many of the LHCb measurements remained statistically dominated. In order to increase the trigger yield for purely hadronic channels, the hardware trigger will be removed, and the detector will be read out at 40 MHz. This, in combination with the five-fold increase in luminosity, requires radical changes to LHCb's electronics, and, in some cases, the replacement of entire sub-detectors with state-of-the-art detector technologies. The Vertex Locator (VELO) surrounding the interaction region is used to reconstruct the collision points (primary vertices) and decay vertices of long-lived particles (secondary vertices). The upgraded VELO will be composed of 52 modules placed along the beam axis divided into two retractable halves. The modules will each be equipped with 4 silicon hybrid pixel tiles, each read out by 3 VeloPix ASICs. The total output data rate anticipated for the whole detector will be around 1.6 Tbit/s. The highest occupancy ASICs will have pixel hit rates of approximately 900 Mhit/s, with the corresponding output data rate of 15 Gbit/s. The LHCb upgrade detector will be the first detector to read out at the full LHC rate of 40 MHz. The VELO upgrade will utilize the latest detector technologies to read out at this rate while maintaining the required radiation-hard profile and minimizing the detector material.
From Ancient Contemplative Practice to the App Store: Designing a Digital Container for Mindfulness

Hundreds of popular mobile apps today market their ties to mindfulness. What activities do these apps support and what benefits do they claim? How do mindfulness teachers, as domain experts, view these apps? We first conduct an exploratory review of 370 mindfulness-related apps on Google Play, finding that mindfulness is presented primarily as a tool for relaxation and stress reduction. We then interviewed 15 U.S. mindfulness teachers from the therapeutic, Buddhist, and Yogic traditions about their perspectives on these apps. Teachers expressed concern that apps that introduce mindfulness only as a tool for relaxation neglect its full potential. We draw upon the experiences of these teachers to suggest design implications for linking mindfulness with further contemplative practices like the cultivation of compassion. Our results also speak to the importance of coherence in design: that the metaphors and mechanisms of a technology align with the underlying principles it follows.
An Iterative Approach for Noncausal Feedforward Tuning

A new iterative approach for the determination of a noncausal feedforward control law suitable for set-point regulation is presented in this paper. The technique aims at estimating recursively the parameters of the system to be controlled in order to determine the exact noncausal command input to be applied to the closed-loop system in order to achieve a predefined output transition. In this context, a gradient based minimization of the integrated square error cost function is performed. Simulation results show the effectiveness of the methodology for different system characteristics.
Changing trends in osteoporosis care from specialty to primary care physicians

Abstract Background: Inadequate treatment by non-specialty primary care physicians (PCPs) has been a concern in improving osteoporosis care. An increase in outpatient visits by older patients seeking osteoporosis care has been reported. But what percentages of these visits are made to PCPs are unknown. We investigated recent trends of outpatient visits and treatment for osteoporosis in older adults (>50 years) by physician type (PCPs vs. specialty care non-PCPs). Methods: A cross-sectional analysis used National Hospital Ambulatory Medical Care Survey (NHAMCS) data from 2002 to 2008 in the USA. Patients ≥50 years with osteoporosis were included. Descriptive and logistic analysis reported trends of visits and treatment for osteoporosis care by physician type. Results: About 9 million weighted visits were reported between 2002 and 2008. An increase in osteoporosis-related visits was noted from 5% in 2002 to 20% in 2008. The majority of patients in these visits were women (89%), between 65 and 84 years of age (56%), of non-Hispanic (88%) and White (81%) ethnic/racial backgrounds and had Medicare (51%) as the source of payment. Almost half (49.5%) of the visits were made to the PCPs. About 74% visits recorded a prescription for osteoporosis treatment. Calcium supplementation was low in the study population, and the lack of data on vitamin D supplements prevented us commenting on the supplementary aspect of osteoporosis management. Non-PCPs were more likely to have prescribed bisphosphonates (p < 0.05) during an osteoporosis-related visit compared to PCPs (odds ratio:1.81, 95% CI: 1.12–2.92). Conclusions: We report a change with increasing trend of outpatient visits to PCPs for osteoporosis care since the 1990s. Despite a lower percentage of prescriptions by PCPs than non-PCPs, there is an increasing trend of prescribing osteoporosis treatment by PCPs. Growing demand for managing the chronic bone disease mandates a need for assessment of educational interventions to better prepare PCPs for improving skeletal health of the elderly.
Distinguishing excitotoxic from apoptotic neurodegeneration in the developing rat brain

Much confusion has arisen recently over the question of whether excitotoxic neuronal degeneration can be considered an apoptotic phenomenon. Here, we addressed this question by using ultrastructural methods and DNA fragmentation analysis to compare a prototypic apoptotic in vivo central nervous system cell death process (physiologic cell death in the developing rat brain) with several central nervous system cell death processes in the in vivo infant rat brain that are generally considered excitotoxic (degeneration of hypothalamic neurons after subcutaneous administration of glutamate and acute neurodegeneration induced by hypoxia/ischemia or by concussive head trauma). We found by ultrastructural analysis that glutamate induces neurodegenerative changes in the hypothalamus that are identical to acute changes induced in the infant rat brain by either hypoxia/ischemia or head trauma, and that these changes are fundamentally different both in type and sequence from those associated with physiologic cell death (apoptosis). In addition, we show by ultrastructural analysis that concussive head trauma induces both excitotoxic and apoptotic neurodegeneration, the excitotoxic degeneration being very acute and localized to the impact site, and the apoptotic degeneration being delayed and occurring in regions distant from the impact site. Thus, in the head trauma model, excitotoxic and apoptotic degeneration can be distinguished not only by ultrastructural criteria but by their temporal and spatial patterns of expression. Whereas ultrastructural analysis provided an unambiguous means of distinguishing between excitotoxic and apoptotic neurodegeneration in each example analysed in this study, DNA fragmentation analysis (TUNEL staining or gel electrophoresis) was of no value because these tests were positive for both processes. J. Comp. Neurol. 408:461–476, 1999. © 1999 Wiley‐Liss, Inc.