Miracle-dz/UniFAR-FTUs huggingface dataset:
Altruistic learning

Arguments against altruistic interpretations of experimentally observed behaviour include suggestions that individuals do not understand the rules of the game, are prone to misbelieve they (or their kin) will interact with opponents again in the future, or falsely infer they are being secretly observed and accordingly act to preserve their reputation in the eyes of experimenters (Smith, 1976). However, the widespread observation of altruism (both rewarding and punishing) across cultures (Henrich et al., 2001), and within meticulously designed experiments conducted by behavioural economists provide compelling support for its presence as a clear behavioural disposition. Furthermore, in fMRI experiments, altruistic actions correlate with brain activity, suggesting that they derive from some sort of intended or motivated behaviour and are not an expression of mere 'effector noise' (i.e. decision error) (de Quervain et al., 2004). The very existence of altruism raises the diffi cult question as to why evolution has allowed otherwise highly sophisticated brains to behave so selfl essly. This directs attention towards the decision-making systems that subserve economic and social behaviour (Lee, 2006, 2008; Behrens et al., 2009), and questions whether they are structured in such a way that yields altruism either inadvertently, or necessarily. The broader consequence is that if they do, then this reframes the question regarding the ultimate (evolutionary) causes of altruism towards the evolution of these very decision systems, and away from the phenomenological reality of altruism per se. In this paper, we fi rst review the structure of distinct human decision-making systems by considering a goal-directed (cognitive) system, a habitual system, and an innate (Pavlovian) action system and their interactions. We consider how these systems might operate in social contexts where the key problem is how to make optimal decisions when outcomes depend on the uncertainty associated with other agents and their motives. In the face of such computational complexity, we then consider how optimal actions can
ON RINGS WHERE LEFT PRINCIPAL IDEALS ARE LEFT PRINCIPAL ANNIHILATORS

The rings in the title are studied and related to right principally injective rings. Many properties of these rings (called left pseudo-morphic by Yang) are derived, and conditions are given that an endomorphism ring is left pseudo-morphic. Some particular results: (1) Commutative pseudo-morphic rings are morphic; (2) Semiprime left pseudo-morphic rings are semisimple; and (3) A left and right pseudo-morphic ring satisfying (equivalent) mild finiteness conditions is a morphic, quasi-Frobenius ring in which every onesided ideal is principal. Call a left ideal L a left principal annihilator if L = l(a) = {r ∈ R | ra = 0} for some a ∈ R. It is shown that if R is left pseudo-morphic, left mininjective ring with the ACC on left principal annihilators then R is a quasi-Frobenius ring in which every right ideal is principal and every left ideal is a left principal annihilator.
Asymmetry and Buoyancy Effects on Turbulent Multi-component Jets 3 a ) LIF LaserPIV Laser Energy Meter Laser Sheet Acetone Bubblers Water Bath LIF Camera PIV Camera Pressure Tranducers Thermocouples

Buoyancy effects and nozzle geometry can have a significant impact on turbulent jet dispersion. This work was motivated by applications involving hydrogen. Using helium as an experimental proxy, buoyant horizontal jets issuing from a round orifice on the side wall of a circular tube were analysed experimentally using particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF) techniques simultaneously to provide instantaneous and time-averaged flow fields of velocity and concentration. Effects of buoyancy and asymmetry on the resulting flow structure were studied over a range of Reynolds numbers and gas densities. Significant differences were found between the centreline trajectory, spreading rate, and velocity decay of conventional horizontal round axisymmetric jets issuing through flat plates and the pipeline leak-representative jets considered in the present study. The realistic pipeline jets were always asymmetric and found to deflect about the jet axis in the near field. In the far field, it was found that the realistic pipeline leak geometry causes buoyancy effects to dominate much sooner than expected compared to horizontal round jets issuing through flat plates.
Optimization With Penalized Point Probability Distance : An Alternative To Proximal Policy Optimization

As the most successful variant and improvement for Trust Region Policy Optimization (TRPO), proximal policy optimization (PPO) has been widely applied across various domains with several advantages: efficient data utilization, easy implementation, and good parallelism. In this paper, a firstorder gradient reinforcement learning algorithm called Policy Optimization with Penalized Point Probability Distance (POP3D), which is a lower bound to the square of total variance divergence is proposed as another powerful variant. Firstly, we talk about the shortcomings of several commonly used algorithms, by which our method is partly motivated. Secondly, we address to overcome these shortcomings by applying POP3D. Thirdly, we dive into its mechanism from the perspective of solution manifold. Finally, we make quantitative comparisons among several state-of-the-art algorithms based on common benchmarks. Simulation results show that POP3D is highly competitive compared with PPO. Besides, our code is released in https://github.com/paperwithcode/pop3d.
Early Anomaly Detection and Location in Distribution Network: A Data-Driven Approach

The online monitoring data in distribution networks contain rich information on the operating states of the networks. In this paper, by leveraging the data, a random matrix theory (RMT) based approach is proposed for early anomaly detection and location in distribution networks. First, a spatio-temporal matrix for each feeder line in the distribution network is formulated. { Based on the Marchenko-Pastur Law for the empirical spectral analysis of covariance `signal+noise' matrix, the linear eigenvalue statistics are introduced to indicate the anomaly, and the outliers and their corresponding eigenvectors are analyzed for locating the anomaly. As for the low observability feeders in the distribution network, an increasing data dimension algorithm is designed for the formulated low-dimensional matrices being more accurately analyzed. The proposed approach is capable of detecting and locating the early anomaly and robust against random fluctuations and measuring errors}. Case studies on both synthetic data from IEEE standard bus systems and real-world online monitoring data in a distribution network corroborate the feasibility and effectiveness of the proposed approach.
Predictions of short-term driving intention using recurrent neural network on sequential data

Predictions of driver's intentions and their behaviors using the road is of great importance for planning and decision making processes of autonomous driving vehicles. In particular, relatively short-term driving intentions are the fundamental units that constitute more sophisticated driving goals, behaviors, such as overtaking the slow vehicle in front, exit or merge onto a high way, etc. While it is not uncommon that most of the time human driver can rationalize, in advance, various on-road behaviors, intentions, as well as the associated risks, aggressiveness, reciprocity characteristics, etc., such reasoning skills can be challenging and difficult for an autonomous driving system to learn. In this article, we demonstrate a disciplined methodology that can be used to build and train a predictive drive system, therefore to learn the on-road characteristics aforementioned.
Multivariate Analysis of Orthogonal Range Searching and Graph Distances Parameterized by Treewidth

We show that the eccentricities, diameter, radius, and Wiener index of an undirected $n$-vertex graph with nonnegative edge lengths can be computed in time $O(n\cdot \binom{k+\lceil\log n\rceil}{k} \cdot 2^k k^2 \log n)$, where $k$ is the treewidth of the graph. For every $\epsilon>0$, this bound is $n^{1+\epsilon}\exp O(k)$, which matches a hardness result of Abboud, Vassilevska Williams, and Wang (SODA 2015) and closes an open problem in the multivariate analysis of polynomial-time computation. To this end, we show that the analysis of an algorithm of Cabello and Knauer (Comp. Geom., 2009) in the regime of non-constant treewidth can be improved by revisiting the analysis of orthogonal range searching, improving bounds of the form $\log^d n$ to $\binom{d+\lceil\log n\rceil}{d}$, as originally observed by Monier (J. Alg. 1980). We also investigate the parameterization by vertex cover number.
Appendix : Bayesian Uncertainty Estimation for Batch Normalized Deep Networks 1

Assume we were to come up with a family of distributions parameterized by θ in order to approximate the posterior, qθ(ω). Our goal is to set θ such that qθ(ω) is as similar to the true posterior p(ω|D) as possible. For clarity, qθ(ω) is a distribution over stochastic parameters ω that is determined by a set of learnable parameters θ and some source of randomness. The approximation is therefore limited by our choice of parametric function qθ(ω) as well as the randomness.1 Given ω and an input x, an output distribution p(y|x,ω) = p(y|fω(x)) = fω(x,y) is induced by observation noise (the conditionality of which is omitted for brevity).
Analysis of Network Lasso For Semi-Supervised Regression

We characterize the statistical properties of network Lasso for semi-supervised regression problems involving network- structured data. This characterization is based on the con- nectivity properties of the empirical graph which encodes the similarities between individual data points. Loosely speaking, network Lasso is accurate if the available label informa- tion is well connected with the boundaries between clusters of the network-structure datasets. We make this property precise using the notion of network flows. In particular, the existence of a sufficiently large network flow over the empirical graph implies a network compatibility condition which, in turn, en- sures accuracy of network Lasso.
Delineation of radiation therapy target volumes for patients with postoperative glioblastoma: a review

Glioblastoma is the most aggressive and lethal primary malignancy of the brain, and radiotherapy (RT) is a fundamental part of its treatment. However, the optimal radiation treatment conditions are still a matter of debate, and there is no clear consensus concerning the inclusion of peritumoral edema in the clinical target volume calculation. Target delineation calculations that use postoperative residual tumor and cavity volumes plus 2 cm margins result in smaller volumes of normal brain receiving high-dose irradiation, compared to calculations that include expanded edema. Smaller RT fields may be more appropriate than larger RT fields, possibly reducing the risk of late neurological deterioration, especially in patients with significant peritumoral edema. This review focuses on the factors influencing target delineation, such as peritumoral edema, failure patterns, and prognostic factors (clinical and pathological characteristics) of patients with glioblastoma. Based on this information, we make three suggestions for radiation oncologists to refer to in daily practice. Further study is necessary to investigate the unresolved problems related to routine clinical application of RT.
FC → Softmax Sieve I FuseFC Sieve II “ red luggage bottom left ” biLSTM → Attention FC →

We present an empirical analysis of the stateof-the-art systems for referring expression recognition – the task of identifying the object in an image referred to by a natural language expression – with the goal of gaining insight into how these systems reason about language and vision. Surprisingly, we find strong evidence that even sophisticated and linguistically-motivated models for this task may ignore the linguistic structure, instead relying on shallow correlations introduced by unintended biases in the data selection and annotation process. For example, we show that a system trained and tested on the input image without the input referring expression can achieve a precision of 71.2% in top-2 predictions. Furthermore, a system that predicts only the object category given the input can achieve a precision of 84.2% in top-2 predictions. These surprisingly positive results for what should be deficient prediction scenarios suggest that careful analysis of what our models are learning – and further, how our data is constructed – is critical as we seek to make substantive progress on grounded language tasks.
Familiar Strangers: A History of Muslims in Northwest China

List of MapsList of IllustrationsAcknowledgmentsPrefaceIntroduction: Purposes and Form of a Muslim History in ChinaThe Frontier Ground and Peoples of Northwest ChinaAcculturation and Accommodation: China's Muslims to the 17th CenturyConnections: Muslims in the Early Qing, 1644-1781Strategies of Resistance: Integration by ViolenceStrategies of Integration: Muslims in New ChinaConclusion: Familiar StrangersChinese Character GlossaryBibliographyIndex
On the transition between regular and irregular shock patterns of shock-wave/boundary-layer interactions

The reflection of strong oblique shock waves at turbulent boundary layers is studied numerically and analytically. A particular emphasis is put on the transition between regular shock-wave/boundary-layer interaction (SWBLI) and Mach reflection (irregular SWBLI). The classical two- and three-shock theory and a generalised form of the free interaction theory are used for the analysis of well-resolved large-eddy simulations (LES) and for the derivation of stability criteria. We found that at a critical deflection angle across the incident shock wave, the perturbations related to the turbulent boundary layer cause bi-directional transition processes between regular and irregular shock patterns for a free-stream Mach number of $\mathit{Ma}_{0}=2$ . Computational results show that the mean deflection angle across the separation shock is decoupled from the incident shock wave and can be accurately modelled by the generalised free interaction theory. On the basis of these observations, and the von Neumann and detachment criteria for the asymmetric intersection of shock waves, we derive the critical incident shock deflection angles at which the shock pattern may/must become irregular. Numerical data for a free-stream Mach number of $\mathit{Ma}_{0}=3$ confirm the existence of the dual-solution domain predicted by theory.
Diagnosis of spontaneous bacterial peritonitis in cirrhotic patients in northeastern Brazil by use of rapid urine-screening test Hospital Universitário

O R IG IN A L A R T IC L E Lucia Libanez Bessa Campelo Braga Marcellus Henrique Loiola Ponte de Souza Alzira Maria de Castro Barbosa Felipe Magalhães Furtado Paula Andréa Maia Campelo Antônio Haroldo de Araújo Filho Diagnosis of spontaneous bacterial peritonitis in cirrhotic patients in northeastern Brazil by use of rapid urine-screening test Hospital Universitário Walter Cantídio, Universidade Federal do Ceará, Ceará, Brazil INTRODUCTION Spontaneous bacterial peritonitis (SBP) is a frequent and severe complication of cirrhotic patients with ascites. The prevalence of SBP among unselected hospitalized cirrhotic patients with ascites ranges between 10% and 30%. Although antibiotic therapy produces a good response, the mortality rate due to SBP remains high: approximately 30%-50%. Improved survival in SBP episodes might be obtained through rapid diagnosis and treatment. The polymorphonuclear (PMN) leukocyte count in the ascitic fl uid has proven to be a very useful and sensitive method for diagnosing SBP. SBP is highly likely when the PMN cell count in the ascitic fl uid reaches a cutoff of 250/mm . Once this cut-off has been reached, antibiotic therapy must be started immediately, without waiting for a culture from the ascitic fl uid. However, total and PMN leukocyte counts from the ascitic fl uid are not always available everywhere, or it may be impossible to obtain them in emergency situations. Reagent strip testing for leukocyte esterase has been found to be a sensitive and accurate predictor for the presence of PMN in body fl uids such as urine, cerebrospinal fl uid, seminal and peritoneal fl uid. This test is based on the esterase activity of granulocytes present in the biological fl uid, which reacts with a chemical compound on the reagent strip to cause a color change in the azo dye (purple). It has been proposed that reagent strip testing for leukocyte esterase could be utilized to reduce the time between performing paracentesis and obtaining a presumptive diagnosis of SBP from a few hours to a few seconds. Moreover, such strips would be available everywhere, and could be a useful tool for diagnosing SBP, especially in developing countries. Therefore, it seems very important to validate the use of such reagent strips in a country like Brazil, and especially in the northeast of this country. OBJECTIVE The aim of this prospective study was to evaluate the accuracy of reagent strips for rapidly diagnosing SBP in cirrhotic patients with ascites in the university hospital in Fortaleza, State of Ceará, Brazil. PATIENTS AND METHODS
MHD couple stress nanofluid flow in a permeable wall channel with entropy generation and nonlinear radiative heat

Paper No.17-00252 © 2017 The Japan Society of Mechanical Engineers [DOI: 10.1299/jtst.2017jtst0033] Abstract In this paper, both first and second laws of thermodynamics are employed to examine the combined effects of nonlinear thermal radiation, buoyancy forces, thermophoresis and Brownian motion on entropy generation rate in hydromagnetic couple stress nanofluid flow through a vertical channel with permeable walls. The model equations of momentum, energy balance and nanoparticle concentration are obtained and tackled numerically using a shooting technique coupled with Runge-Kutta-Fehlberg integration scheme. The numerical results for velocity, temperature and nanoparticles concentration profiles are utilised to determine the skin friction, Nusselt number, Sherwood number, entropy generation rate and Bejan number. It is found that the entropy production in the flow system can be effectively minimized by regulating the values of the thermophysical parameters for efficient operation. Some other interesting results are displayed graphically and discussed quantitatively.
1 DARTS : Deceiving Autonomous Cars with Toxic Signs

Sign recognition is an integral part of autonomous cars. Any misclassication of trac signs can potentially lead to a multitude of disastrous consequences, ranging from a life-threatening accident to even a large-scale interruption of transportation services relying on autonomous cars. In this paper, we propose and examine realistic security aacks against sign recognition systems for Deceiving Autonomous caRs with Toxic Signs (we call the proposed aacks DARTS). Leveraging the concept of adversarial examples, we strategically modify innocuous signs/advertisements in the environment in such a way that they seem normal to human observers but are interpreted as the adversary’s desired trac sign by autonomous cars. Further, we pursue a fundamentally dierent perspective to aacking autonomous cars, motivated by the observation that the driver and vehicle-mounted camera see the environment from dierent angles (the camera commonly sees the road with a higher angle, e.g., from top of the car). Bridging concepts from optics (in particular, lenticular printing), security, and computer vision, we propose a novel aack against vehicular sign recognition systems: we create signs that change as they are viewed from dierent angles, and thus, can be interpreted dierently by the driver and sign recognition. We extensively evaluate the proposed aacks under various conditions: dierent distances, lighting conditions, and camera angles. We rst examine our aacks virtually, i.e., we check if the digital images of toxic signs can deceive the sign recognition system. Further, we investigate the eectiveness of aacks in real-world seings: we print toxic signs, install them in the environment, capture videos using a vehicle-mounted camera, and process them using our sign recognition pipeline. We nd our aacks to achieve aack success rates of over 90% in both the digital and real-world seings. We further suggest a countermeasure based on adversarial training to protect against adversarial example-based aacks. Our proof-of-concept aacks shed light on a fundamental security challenge associated with the use of sign recognition techniques in autonomous cars, paving the way for further investigation of overlooked security challenges of autonomous cars.
0 DARTS : Deceiving Autonomous Cars with Toxic Signs

Sign recognition is an integral part of autonomous cars. Any misclassication of trac signs can potentially lead to a multitude of disastrous consequences, ranging from a life-threatening accident to even a large-scale interruption of transportation services relying on autonomous cars. In this paper, we propose and examine realistic security aacks against sign recognition systems for Deceiving Autonomous caRs with Toxic Signs (we call the proposed aacks DARTS). Leveraging the concept of adversarial examples, we strategically modify innocuous signs/advertisements in the environment in such a way that they seem normal to human observers but are interpreted as the adversary’s desired trac sign by autonomous cars. Further, we pursue a fundamentally dierent perspective to aacking autonomous cars, motivated by the observation that the driver and vehicle-mounted camera see the environment from dierent angles (the camera commonly sees the road with a higher angle, e.g., from top of the car). Bridging concepts from optics (in particular, lenticular printing), security, and computer vision, we propose a novel aack against vehicular sign recognition systems: we create signs that change as they are viewed from dierent angles, and thus, can be interpreted dierently by the driver and sign recognition. We extensively evaluate the proposed aacks under various conditions: dierent distances, lighting conditions, and camera angles. We rst examine our aacks virtually, i.e., we check if the digital images of toxic signs can deceive the sign recognition system. Further, we investigate the eectiveness of aacks in real-world seings: we print toxic signs, install them in the environment, capture videos using a vehicle-mounted camera, and process them using our sign recognition pipeline. We nd our aacks to achieve aack success rates of over 90% in both the digital and real-world seings. We further suggest a countermeasure based on adversarial training to protect against adversarial example-based aacks. Our proof-of-concept aacks shed light on a fundamental security challenge associated with the use of sign recognition techniques in autonomous cars, paving the way for further investigation of overlooked security challenges of autonomous cars.
DINAMIKA ILMU

Developing professional madrasah teachers are increasingly popular in the teaching classrooms. But regarding developing professional madrasah teacher accuracy in teaching, mixed results are reported without clear reasons for this. This article addresses the impact of gender and length of certification on developing professional madrasah teacher accuracy in teaching classrooms. To develop the accuracy of the developing professional madrasah teacher, madrasah teacher length of certification and gender are used as a reference. A research project, which deals with the developing professional teacher in madrasah teachers using questionnaires, was carried out in two types of Madrasah Aliyah (public and private madrasah) at seventy-five madrasahs. Data from 325 madrasah teachers were collected and involved in this research. This analysis is differentiated based on the level, gender, and level of the length of certification of madrasah teachers in the