Miracle-dz/UniFAR-FTUs huggingface dataset:
EtherTrust: Sound Static Analysis of Ethereum bytecode

Ethereum has emerged as the most popular smart contract development platform, with hundreds of thousands of contracts stored on the blockchain and covering a variety of application scenarios, such as auctions, trading platforms, and so on. Given their financial nature, the security of these contracts is of paramount importance, as exemplified by recent attacks exploiting programming mistakes to freeze or steal millions of dollars (e.g., the DAO and Parity attacks). An automated security analysis of these contracts is thus of utmost interest, but it is challenging due to the EVM bytecode format in which contracts are uploaded on the blockchain, which exposes very little static information, and due to the specific transaction-oriented programming mechanisms, which feature a subtle semantics. In this work, we present the first sound and automated static analysis for EVM bytecode, which is practical and scales to large contracts. In particular, our static analysis supports reachability properties, which we show to be sufficient for capturing the most important security properties for smart contracts (e.g., single-entrancy and transaction environment dependency). The soundness of our analysis is proven against a complete and mechanized semantics of EVM bytecode. We implemented our analysis and tested our tool – EtherTrust – on real-world contracts from the Ethereum blockchain, comparing it with Oyente, the state-of-the-art bug finding tool for smart contracts: EtherTrust analyses real-life contracts in a few seconds, outperforming Oyente in efficiency and coverage by one order of magnitude. Furthermore, EtherTrust shows better precision on a benchmark, all of that despite being the first tool in the literature to provide formal security guarantees for EVM bytecode.
On Cumulative Slicing Estimation for High Dimensional Data

In the context of sufficient dimension reduction (SDR), sliced inverse regression (SIR) is the first and perhaps one of the most popular tools to reduce the covariate dimension for high-dimensional non-linear regressions. Despite the fact that the performance of SIR is very insensitive to the number of slices when the covariate is low or moderate dimensional, our empirical studies indicate that, the performance of SIR relies heavily upon the number of slices when the covariate is highor ultrahigh-dimensional. How to select the optimal number of slices for SIR is still a longstanding problem in the SDR literature, which is a crucial issue for SIR to be effective in highand ultrahigh-dimensional regressions. In this paper, we work with an improved version of SIR, the cumulative slicing estimation (CUME) method, which does not require selecting the optimal number of slices. We provide a general framework to analyze the phase transition phenomenon for the CUME method. We show that, without sparsity assumption, CUME is consistent if and only if p/n → 0, where p stands for the covariate dimension and n stands for the sample size. If we make certain sparsity assumpStatistica Sinica: Newly accepted Paper (accepted author-version subject to English editing)
A Re-examination of Ex-Dividend Day Price Movements: Evidence from ADR Market

This paper re-examines the tax-induced behavior on ex-dividend days for American depositary receipts (ADRs) since ADRs provide us unique settings where all ADR investors are subject to identical foreign tax rate for a given cash distribution. We can partition ADR samples by foreign tax liability levels to compare the excess returns on ex-dividend days for each sub-sample to versify the tax effect hypothesis. The purpose of this study is to analyze the influence of foreign tax costs and transaction costs on abnormal returns during ex-dividend periods. This paper employs a sample of 6,461 cash dividend distributions of 528 firms from 42 different countries from 1988 to 2004. The results exhibit prominent excess returns exactly on ADR ex-dividend days. Since double taxation, U.S. tax and foreign withholding tax, may apply on the ADR dividend income, the tax effect is apparent on ex-dividend day. Besides, the results of regressions exhibit that abnormal returns on ex-dividend day are positively associated with foreign tax costs, the products of foreign tax rates and dividend yields, as well as transaction costs. Because foreign dividend income liability is composed of foreign tax rate and dividend yield, our finding supports tax-induced ex-dividend day trading activity that is constrained by transaction costs.
Forecasting Bankruptcy with Incomplete Information

We propose new specifications that explicitly account for information noise in the input data of bankruptcy hazard models. The specifications are motivated by a theory of modeling credit risk with incomplete information (Duffie and Lando [2001]). Based on over 2 million firm-months of data during 1979-2012, we demonstrate that our proposed specifications significantly improve both insample model fit and out-of-sample forecasting accuracy. The improvements in forecasting accuracy are persistent throughout the 10-year holdout periods. The improvements are also robust to empirical setup, and are more substantial in cases where information quality is a more serious problem. Our findings provide strong empirical support for using our proposed hazard specifications in credit risk research and industry applications. They also reconcile conflicting empirical results in the literature. JEL Codes: C41, G17, G33.
Genetic Conflict and Gestational Diabetes

The mother-fetus relationship during pregnancy is based on a conflict of interests described in Haig’s paper [1] as the genetic conflict. The fetus utilizes mostly glucose for nutrition and has mechanisms that manipulate the mother’s physiology so that the amount of glucose produced is increased and the mother develops gestational diabetes. We have formulated a differential equations model for maternal, placental and fetal weight. Three additional differential equations from the Topp model [2] were added in order to describe the regulation of glucose, insulin and b-cell mass during pregnancy. The model successfully simulates fetus growth and pregnancy levels of the above variables. It can also be used to determine the caloric input a mother needs to take in order to maintain her weight throughout pregnancy. This research was supported by Duke research funds from Professor Thomas Witelski.
Successive collaborative SLAM for professional use cases: map learning aspects

Pedestrian navigation solely based on inertial measurement units in indoor environments is challenging if there is no signal of opportunity that can be exploited. In collaborative simultaneous localization and mapping (collaborative SLAM) the learned maps of several users can be combined in order to help indoor positioning. In this paper, we investigate the combination of the maps of several pedestrians and analyze the resulting position accuracy and the map. Depending on the number of users, on the sensor drifts, and on the amount of revisited area the degree of convergence of the maps varies. This paper provides a discussion about the map learning aspects and the advantages of successive collaborative mapping when there is no loop closure. The results show that even without loop closures the position error can be reduced to ~ 0.5m when applying successive collaborative mapping.
Running head : Building up biogeography : pattern to process

33 Linking pattern to process across spatial and temporal scales has been a key goal of the field of 34 biogeography. In January 2017, the 8 biennial conference of the International Biogeography 35 Society sponsored a symposium on “Building up biogeography—process to pattern” that aimed 36 to review progress towards this goal. Here we present a summary of the symposium, in which 37 we identified promising areas of current research and suggested future research directions. 38 We focus on (1) emerging types of data such as behavioral observations and ancient DNA, 39 (2) how to better incorporate historical data (such as fossils) to move beyond what we term 40 ‘footprint measures’ of past dynamics, and (3) the role that novel modeling approaches (e.g., 41 maximum entropy theory of ecology and approximate Bayesian computation) and conceptual 42 frameworks can play in the unification of disciplines. We suggest that the gaps separating 43 pattern and process are shrinking, and that we can better bridge these aspects by considering 44 the dimensions of space and time simultaneously. 45
Variance Reduced Advantage Estimation with δ Hindsight Credit Assignment

Hindsight Credit Assignment (HCA) refers to a recently proposed family of methods for producing more efficient credit assignment in reinforcement learning. These methods work by explicitly estimating the probability that certain actions were taken in the past given present information. Prior work has studied the properties of such methods and demonstrated their behaviour empirically. We extend this work by introducing a particular HCA algorithm which has provably lower variance than the conventional Monte-Carlo estimator when the necessary functions can be estimated exactly. This result provides a strong theoretical basis for how HCA could be broadly useful.
N ov 2 01 9 Property Testing of LP-Type Problems

Given query access to a set of constraints S, we wish to quickly check if some objective function φ subject to these constraints is at most a given value k. We approach this problem using the framework of property testing where our goal is to distinguish the case φ(S) ≤ k from the case that at least an ǫ fraction of the constraints in S need to be removed for φ(S) ≤ k to hold. We restrict our attention to the case where (S, φ) are LP-Type problems which is a rich family of combinatorial optimization problems with an inherent geometric structure. By utilizing a simple sampling procedure which has been used previously to study these problems, we are able to create property testers for any LP-Type problem whose query complexities are independent of the number of constraints. To the best of our knowledge, this is the first work that connects the area of LP-Type problems and property testing in a systematic way. Among our results is a tight upper bound on the query complexity of testing clusterability with one cluster considered by Alon, Dar, Parnas, and Ron (FOCS 2000). We also supply a corresponding tight lower bound for this problem and other LP-Type problems using geometric constructions.
Extensive Piano Practicing has Regionally Specific Effects on White Matter Development in Autism?

The pervasive developmental disorders are a group of neurodevelopmental disorders that include autistic disorder, Asperger’s disorder, pervasive developmental disorder not otherwise specified (PDD-NOS), childhood disintegrative disorder (CDD), and Rett’s disorder. All feature childhood onset with a constellation of symptoms spanning social interaction and communication and including atypical behavior patterns.
Preliminary and Incomplete PUBLIC EDUCATION AND THE DYNAMICS OF INCOME DISTRIBUTION: A QUANTITATIVE EVALUATION OF EDUCATION FINANCE REFORM

Many states are considering school finance reforms aimed at lessening inequality in the provision of public education across communities. These reforms will tend to have aggregate effects on income distribution, intergenerational income mobility, and welfare. This paper constructs a dynamic general equilibrium model of public education provision, calibrates it using US data, and examines the quantitative effects of a major school finance reforms. The policy reform we analyze is one in which all funding is done at the federal level (instead of substantially at the local level as is now the case). We find that this policy increases average income, intergenerational income mobility, and total spending on education as a fraction of income. Moreover, there are large welfare gains associated with this policy: we find that steady-state welfare increases by 7t of steadystate income.
Hospital Alignment with Physicians as a Bargaining Response to Commercial Insurance Markets∗

The relationship between physicians and hospitals has dramatically changed over the last decade, with the employer-employee model supplanting the traditional model of private physicians with hospital admitting privileges. We examine the motivations for this increase in physician-hospital alignment, focusing on alignment as a tool to increase bargaining power with private insurers. We find a positive and significant effect of private insurance concentration on hospital-physician alignment. We also find differential effects of private insurance markets and physician-hospital alignment across hospital ownership type and competition in the local hospital market. Sensitivity analysis suggests that our results are robust to mismeasured alignment and endogenous private insurance market concentration. ∗We thank Bob Town, Jim Burgess, Gautam Gowrisankaran, Guy David, Avi Dor, and Jim Marton for insightful comments and suggestions, as well as the participants at the 2015 American Health Economics Conference. †Correspondence to: Ian McCarthy, Emory University, Department of Economics, 1602 Fishburne Dr., Atlanta, GA 30322, U.S.A. E-mail: ian.mccarthy@emory.edu
Subsidising Renewables but Taxing Storage? Second-Best Policies with Imperfect Carbon Pricing

We consider an economy in which competitive firms use three technologies for electricity production: pollutive fossils, intermittent renewables like wind or solar, and storage. We determine optimal subsidies for renewables and storage capacities when carbon pricing is imperfect. This policy is efficient for low market shares of intermittent renewables in the energy system, but it turns inefficient once there are sucient renewables to partly displace fossil electricity production at times of high availability. Moreover, the subsidy scheme is substantially more complex than a first-best Pigouvian tax. The optimal renewable subsidy is always positive but tends to decrease as electricity production becomes less reliant on fossils. The optimal storage subsidy even changes its sign. It is usually negative as long as fossils contribute to lling the storage, but turns positive if fossils are used only during times of low availability of renewables. This is because more storage capacity reduces the price during times of destorage, but raises it when electricity is taken from the market to fill the storage. This has countervailing effects on firms' incentives to invest in fossil capacities, and these effects are more pronounced the higher the round-trip effciency losses during a storage cycle.
Learning Safety-Aware Policy with Imitation Learning for Context-Adaptive Navigation

This paper presents an Imitation Learning (IL) based visual navigation system, which could guide the robots navigating from some start position to a goal location without any explicit map. We pay close attention to the safety issue due to partially-observability and data distribution mismatching—when the robot meets some incomplete or unfamiliar states, it probably performs an unsafe action, making it hard to work on lifelong robot navigation. In this paper, a sequenceto-sequence (Seq2seq) deep neural network is built to enhance the agent’s context-awareness in partially-observable conditions and boost the model’s adaptability to unseen scenarios. Additionally, we propose Uncertainty-Aware Imitation Learning (UAIL) by explicitly estimating model uncertainty and actively request experts for labeling samples according to the uncertainty with On-Policy IL. Simulations demonstrated that the combined method—Safety-Aware Imitation Learning (SAIL) in goal-driven visual navigation achieves 35.6% shorter expected moving steps and 22% fewer collisions compared with current counterparts. With the learned safer policy, SAIL had be successfully adapted to unseen environments with minimal navigation performance loss.
HMER_A_201630 79..88

First Department of Medicine, Paracelsus Medical University, Salzburg, Austria Abstract: Lysosomal acid lipase deficiency (LAL-D) is an ultra-rare lysosomal storage disease that may present from infancy to late adulthood depending on residual enzyme activity. While the severe form manifests as a rapidly progressive disease with near universal mortality within the first 6 months of life, milder forms frequently go undiagnosed for prolonged periods and typically present with progressive fatty liver disease, enlarged spleen, atherogenic dyslipidemia and premature atherosclerosis. The adult variant of LAL-D is typically diagnosed late or even overlooked due to the unspecific nature of the presenting symptoms, which are similar to common changes observed in the context of the metabolic syndrome. This review is aimed at delineating clinically useful scenarios in which pediatric or adult medicine clinicians should be aware of LAL-D as a differential diagnosis for selected patients. This is particularly relevant as a potentially life-saving enzyme replacement therapy has become available and the diagnosis can easily be ruled out or confirmed using a dried blood spot test.
The Mixed Reality Simulation Platform (MIXR)

The Mixed Reality Simulation Platform (MIXR) is an open-source software project designed to support the development of robust, scalable, virtual and constructive, and stand-alone and distributed simulation applications. Its most common use case is to support the development of executable simulation applications used to assemble realtime, interactive, distributed virtual environments (DVEs). MIXR’s core infrastructure is architected to favor the development of applications that can be executed in a deterministic manner to meet real-time interaction/response time requirements, yet provide a degree of configuration flexibility for scenario definition, I/O, and standard interoperability interfaces (e.g., DIS, HLA, etc.). Within the military, MIXR has been used as a basis develop applications that include mixtures of Live, Virtual, and Constructive (LVC) entities; more generally known as “mixed reality” simulations [1]. This paper describes the modeling and simulation software platform itself, and how it partitions code to take advantage of multi-core/multi-CPU PCs to support the development of these type applications.
Accuracy of Pinch Force Sense in Elite Female Adolescent Weightlifters

Objective: Force sense is an aspect of proprioception, which is most commonly assessed by reproducing a percentage of maximal voluntary isometric contraction. Exercise improves other aspects of proprioception such as joint position sense and kinesthesia, but there are contradictory findings regarding the effects of exercise on force sense. The purpose of this study is to assess the effects of long-term weightlifting training on pinch force sense of elite adolescent female athletes. Materials and Methods: Maximum key and tip pinch strength of 25 elite female adolescent weightlifters and 22 sedentary peers were measured. Participants were asked to press the pinchmeter with 50% of their maximal strength 3 times while looking at the scale and try to learn how much force they apply. Participants then tried to reproduce 50% of their maximum key and tip pinch strength without any visual feedback 3 times. Absolute error, constant error, root mean square error and coefficient of variation were calculated and these values were normalized by dividing to the target values. Accuracy was evaluated by absolute error and root mean square error; directionality of errors was evaluated by constant error and the precision was evaluated by coefficient of variation. Results: Weightlifter adolescents had higher key and tip pinch strength values compared to sedentary (p=0.03 and p=0.02, respectively). Normalized absolute error was lower in weightlifters in comparison with sedentary in both key and tip pinch force sense (p<0.01). Conclusions: These results demonstrate that regular weightlifting training improves accuracy in pinch force sense, without affecting precision or directionality of error. In future studies, it would be appropriate to investigate whether higher accuracy in force sense would also be evident in adult or senior weightlifters.
Referenda and initiatives Excerpt from draft SAB Committee report, Valuing the Protection of Ecological Systems and Services: Referenda or initiatives can provide information about how members of the voting

Services and has been approved by the chartered SAB, All other text was provided by individual committee members and is offered to extend and elaborate the very brief descriptions provided in chapter 4 of the SAB Report, Valuing the Protection of Ecological Systems and Service and to encourage further deliberation within EPA and the broader scientific community about how to meet the need for an integrated and expanded approach for valuing the protection of ecological systems and services.
Functional quality of optimized peach‐based beverage developed by application of ultrasonic processing

Abstract The influence of thermal treatment (at 90°C for 10 min) and sonication (at 20 kHz and 130 W for 30, 60, and 90 min on room temperature) on the physicochemical properties, bioactive compounds, antioxidant activity, and organic acids of fresh formulated functional peach beverage was investigated. The results indicated that conventional pasteurization and sonication treatment did not show any significant changes in pH value and Brix amount of juice, and however, a rise in cloud value was observed under all processing conditions. The thermal treatment caused the decrement in total phenolic content (TPC), total flavonoid content (TFC), antioxidant activity (assessed by diphenyl dipicryl hydrazyl (DPPH), ferric ion reducing antioxidant power (FRAP) and 2,2'‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulfonic acid) (ABTS)), and organic acids of juice, whereas sonication treatment for 90 min increased maximum the activity of bioactive compounds (TPC: 600.61 µg/100 ml; TFC: 177 µg CE/100 ml), antioxidants (DPPH: 51.87%; FRAP: 506.13 µmol Trolox/L; ABTS: 1,507.375 µmol Trolox/L), and organic acids (malic acid: 998; citric acid: 128; oxalic acid: 145; shikimic acid: 63 µg/100 ml) as compared to other treatment conditions and control. Multivariate data analysis was done by principal component analysis as it identifies patterns in data by comparing data sets which is further expressed based on their similarities and discriminations, respectively.
Les lésions histopathologiques du placenta au cours de retard de croissance intra utérine sévère: à propos d'un cas clinique

L'examen du placenta est indispensable dans le diagnostic du retard de croissance intra-utérin (RCIU). Son intérêt réside dans la recherche étiologique de cette pathologie, et les conséquences materno-fœtal qui peuvent en découler, ainsi dans la mise en place des stratégies préventive lors des grossesses ultérieures dans le cas des pathologies récidivantes. Nous rapportons les lésions anatomopathologiques possibles retrouvées au niveau du placenta de cette pathologie à travers une observation de RCIU sévère suivi au sein du Service de Néonatologie du CHU Ibn Sina de Rabat.