Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing equivalence between distributions using conditional samples

We study a recently introduced framework [7, 8] for property testing of probability distributions, by considering distribution testing algorithms that have access to a conditional sampling oracle. This is an oracle that takes as input a subset S ⊆ [N] of the domain [N] of the unknown probability distribution D and returns a draw from the conditional probability distribution D restricted to S. This model allows considerable flexibility in the design of distribution testing algorithms; in particular, testing algorithms in this model can be adaptive. In this paper we focus on algorithms for two fundamental distribution testing problems: testing whether D = D* for an explicitly provided D*, and testing whether two unknown distributions D1 and D2 are equivalent. For both problems, the sample complexity of testing in the standard model is at least Ω(√N). For the first problem we give an algorithm in the conditional sampling model that performs only poly(1/e)-queries (for the given distance parameter e) and has no dependence on N. This improves over the poly(log N, 1/e)-query algorithm of [8]. For the second, more difficult problem, we given an algorithm whose complexity is poly(log N, 1/e). For both problems we also give efficient algorithms that work under the restriction that the algorithm perform queries only on pairs of points and provide a lower bound that is polynomial in the upper bounds.
Quantification of copper in biological materials by use of electron spin resonance.

Plasma copper is reportedly increased in pregnancy, infections, and inflammation and decreased in corticosteroid and corticotropin treatment (1). The usual and convenient methods for measurement of metal ions are colorimetry and atomic absorption spectrophotometry (AAS). These methods, however, are not suitable for copper. For example, the molar absorptivity at 480 nm of copper complex with bathocuproine disulfonic acid, the most suitable reagent, is not strong (e = 1.3 × 104) (2)(3)(4), and the atomic absorption at 324.7 nm specific to copper is marginal. As a consequence, both methods require 2 mL of serum, although the copper concentration in plasma is fairly high, e.g., 15.7 μmol/L (1 μg/mL). A more sensitive but selective and convenient method is needed for quantification of small amounts of copper in blood as well as in polluted water, food, and tissues. Electron spin resonance (ESR) is a very sensitive method that requires only 109 spins for detection. We examined the use of ESR for measurement of copper because Cu2+ is paramagnetic, and thus can be measured by ESR, although Cu+ is diamagnetic. We confirmed the results of a previous report that diethyldithiocarbamate (DDC) reacts with Cu2+ at pH 0–10 (5). The Cu2+-DDC complex could be extracted with most organic solvents at these pHs (5)(6). We found that the addition of Na2SO4 facilitated the extraction of the complex with 1-butanol. As shown in Fig. 1A⇓ (spectrum a), Cu2+-DDC in 1-butanol shows a characteristic four-line ESR spectrum with hyperfine splitting of 80 gauss and a splitting factor (g) of 2.047. The height of the peak indicated by the arrow at the highest magnetic field of 3350 gauss was used for the quantification of copper. To liberate copper from bound protein …
Inferring relevant people from multi-modal user communication activity

Many network services such as email services and online social networking applications offer enhanced capabilities to standard communication services by mining user data. Examples of such services are friend suggestions, marketing recommendations, etc. In this paper, we look at enhancing related people or a simple contacts application by inferring a dynamic set of relevant people for users at any given time. We argue that this problem is non-trivial and different from finding friends and recommendation systems. We present models for influences of multi-modal communication units that determine the relevance score of people. We present our approach along with performance and evaluation details from a deployed service in a real enterprise communication network.
Simple Octree Solution for Multi-resolution LiDAR Processing and Visualisation

LiDAR (light detection and ranging) 3D points (both terrestrial and airborne) have recently become a prevalent type of geospatial data. However, this large-scale data poses significant technical challenges for efficient access and visualization. One approach is to generate points with different density levels, i.e. Multi-resolution data sets that can be used for various purposes. This paper presents a simple static tree-based approach and discusses implementation alternatives in favour of speeding up tree building and point sampling processes. This approach is generic and can be applied to other geographic data types as well.
Experimental characterization of conducted EMI in three-phase power electronics system using terminal model

This paper investigates the relevant issues for experimental characterization of conducted EMI resulting from three-phase power electronics system. Terminal model is used to describe the EMI behavior. The theoretical analysis and equivalent terminal model for three-phase power electronics system are conducted and proposed respectively. The measurement setup which fulfills the requirement of parameter extraction is then discussed. Measurement procedure is described and the obtained data is processed to calculate the desired parameters. Using the model and parameters from experimental extraction, conducted EMI level can be predicted and the results are compared with measurement to validate the correctness of the characterization.
Performance Estimation of Pipelined MultiProcessor System-on-Chips (MPSoCs)

The paradigm of pipelined MPSoC (processors connected in a pipeline) is well suited to data flow nature of multimedia applications. Often design space exploration is performed to optimize execution time, latency or throughput of a pipelined MPSoC where the variants in the system are processor configurations due to customizable options in each of the processors. Since there can be billions of combinations of processor configurations (design points), the challenge is to quickly provide estimates of performance metrics of those design points. Hence, in this article, we propose analytical models to estimate execution time, latency and throughput of a pipelined MPSoC's design points, avoiding slow full-system cycle accurate simulations of all the design points. For effective use of these analytical models, latencies of individual processor configurations should be available. We propose two estimation methods (PS and PSP) to quickly gather latencies of processor configurations with reduced number of simulations. The PS method simulates all the processor configurations once, while the PSP method simulates only a subset of processor configurations and then uses a processor analytical model to estimate the latencies of the remaining processor configurations. We experimented with several pipelined MPSoCs executing typical multimedia applications (JPEG encoder/decoder, MP3 encoder and H.264 encoder). Our results show that the analytical models with PS and PSP methods had maximum absolute error of 12.95 percent and 18.67 percent respectively, and minimum fidelity of 0.93 and 0.88 respectively. The design spaces of the pipelined MPSoCs ranged from 1012 to 1018 design points, and hence simulation of all design points will take years and is infeasible. Compared to PS method, the PSP method reduced simulation time from days to several hours.
Heterozygous Deficiency of Endoglin Decreases Insulin and Hepatic Triglyceride Levels during High Fat Diet

Endoglin is a transmembrane auxiliary receptor for transforming growth factor-beta (TGF-beta) that is predominantly expressed on proliferating endothelial cells. It plays a wide range of physiological roles but its importance on energy balance or insulin sensitivity has been unexplored. Endoglin deficient mice die during midgestation due to cardiovascular defects. Here we report for first time that heterozygous endoglin deficiency in mice decreases high fat diet-induced hepatic triglyceride content and insulin levels. Importantly, these effects are independent of changes in body weight or adiposity. At molecular level, we failed to detect relevant changes in the insulin signalling pathway at basal levels in liver, muscle or adipose tissues that could explain the insulin-dependent effect. However, we found decreased triglyceride content in the liver of endoglin heterozygous mice fed a high fat diet in comparison to their wild type littermates. Overall, our findings indicate that endoglin is a potentially important physiological mediator of insulin levels and hepatic lipid metabolism.
Distributed Priority Synthesis

Given a set of interacting components with non-deterministic variable update and given safety requirements, the goal of priority synthesis is to restrict, by means of priorities, the set of possible interactions in such a way as to guarantee the given safety conditions for all possible runs. In distributed priority synthesis we are interested in obtaining local sets of priorities, which are deployed in terms of local component controllers sharing intended next moves between components in local neighborhoods only. These possible communication paths between local controllers are specified by means of a communication architecture. We formally define the problem of distributed priority synthesis in terms of a multi-player safety game between players for (angelically) selecting the next transition of the components and an environment for (demonically) updating uncontrollable variables. We analyze the complexity of the problem, and propose several optimizations including a solution-space exploration based on a diagnosis method using a nested extension of the usual attractor computation in games together with a reduction to corresponding SAT problems. When diagnosis fails, the method proposes potential candidates to guide the exploration. These optimized algorithms for solving distributed priority synthesis problems have been integrated into the VissBIP framework. An experimental validation of this implementation is performed using a range of case studies including scheduling in multicore processors and modular robotics.
Real-Time Path Planning Tracing of Deformable Object by Robot

Abstract Edge tracing is considered important for deformable object manipulation in order to spread and reveal the original shape of an object before it can be sorted. This paper proposes a unique realtime path-planning tracing method for clothes manipulation by robots. Tracing in this paper context involves tracing the clothes edge, with the robot arm movement based on the calculated path and feedback from sensors. Using tracing method to find a second corner has been proven to be efficient in finding a second corner next to the first corner and not apposite it, resulting in the robot properly spreading the clothes. Adequate force control is also applied to the gripper during tracing so that it doesn't grip the clothes too hard or vice versa which can affect the performance of the robot. Vision sensor is used from time to time to check whether the static gripper has reached the second corner or not. Practical experiments were conducted to evaluate the proposed method. Experimental results have demonstrated the uniqueness and effectiveness of the proposed method.
A First Step Towards Parsing of Assamese Text

Assamese is a relatively free word order, morphologically rich and agglutinative language and has a strong case marking system stronger than other Indic languages such as Hindi and Bengali. Parsing a free word order language is still an open problem, though many different approaches have been proposed for this. This paper presents an introduction to the practical analysis of Assamese sentences from a computational perspective rather than from linguistics perspective. We discuss some salient features of Assamese syntax and the issues that simple syntactic frameworks cannot tackle. Keywords-Assamese, Indic, Parsing, Free word order.
Mathematisches Forschungsinstitut Oberwolfach Reassessing the Paradigms of Statistical Model-building

Statistical model-building is the science of constructing models from data and from information about the data-generation process, with the aim of analysing those data and drawing inference from that analysis. Many statistical tasks are undertaken during this analysis; they include classification , forecasting, prediction and testing. Model-building has assumed substantial importance, as new technologies enable data on highly complex phenomena to be gathered in very large quantities. This creates a demand for more complex models, and requires the model-building process itself to be adaptive. The word " paradigm " refers to philosophies, frameworks and methodolo-gies for developing and interpreting statistical models, in the context of data, and applying them for inference. In order to solve contemporary statistical problems it is often necessary to combine techniques from previously separate paradigms. The workshop addressed model-building paradigms that are at the frontiers of modern statistical research. It tried to create synergies, by delineating the connections and collisions among different paradigms. It also endeavoured to shape the future evolution of paradigms.
Information Structures of Maximizing Distributions of Feedback Capacity for General Channels with Memory & Applications

For any class of channel conditional distributions, with finite memory dependence on channel input RVs $A^n {\stackrel{\triangle}{=}} \{A_i: i=0, \ldots, n\}$ or channel output RVs $B^n {\stackrel{\triangle}{=}} \{B_i: i=0, \ldots, n\}$ or both, we characterize the sets of channel input distributions, which maximize directed information defined by $ I(A^n \rightarrow B^n) {\stackrel{\triangle}{=}} \sum_{i=0}^n I(A^i;B_i|B^{i-1}) $ and we derive the corresponding expressions, called "characterizations of Finite Transmission Feedback Information (FTFI) capacity". The main theorems state that optimal channel input distributions occur in subsets ${\cal P}_{[0,n]}^{CI}\subseteq {\cal P}_{[0,n]} {\stackrel{\triangle}{=}}\big\{ {\bf P}_{A_i|A^{i-1}, B^{i-1}}: i=0, \ldots, n\big\}$, which satisfy conditional independence on past information. We derive similar characterizations, when general transmission cost constraints are imposed. Moreover, we also show that the structural properties apply to general nonlinear and linear autoregressive channel models defined by discrete-time recursions on general alphabet spaces, and driven by arbitrary distributed noise processes. We derive these structural properties by invoking stochastic optimal control theory and variational equalities of directed information, to identify tight upper bounds on $I(A^n \rightarrow B^n)$, which are achievable over subsets of conditional distributions ${\cal P}_{[0,n]}^{CI} \subseteq {\cal P}_{[0,n]}$, which satisfy conditional independence and they are specified by the dependence of channel distributions and transmission cost functions on inputs and output symbols. We apply the characterizations to recursive Multiple Input Multiple Output Gaussian Linear Channel Models with limited memory and we show a separation principle between the computation of the elements of the optimal strategies.
Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules

We propose a method for comparing density forecasts that is based on weighted versions of the continuous ranked probability score. The weighting emphasizes regions of interest, such as the tails or the center of a variable’s range, while retaining propriety, as opposed to a recently developed weighted likelihood ratio test, which can be hedged. Threshold- and quantile-based decompositions of the continuous ranked probability score can be illustrated graphically and provide insight into the strengths and deficiencies of a forecasting method. We illustrate the use of the test and graphical tools in case studies on the Bank of England’s density forecasts of quarterly inflation rates in the United Kingdom, and probabilistic predictions of wind resources in the Pacific Northwest.
G R ] 1 6 Se p 20 05 Arithmetical Properties of Finite Groups ∗

Let G be a finite group and Chi(G) some quantitative sets. In this paper we study the influence of Chi(G) to the structure of G. We present a survey of author and his colleagues’ recent works. 2000 Mathematics Subject Classification: 20D60; 20D05; 20D06; 20D08; 20D25; 20E45; 20C15
A SAT-Based Implication Engine

This paper presents a flexible and efficient approach to evalu ating implications as well as deriving indirect implications in logic circuits. Evalu tion and derivation of implications are essential in ATPG, equivalence checking, and netl ist optimization. Contrary to other methods, our approach is based on a graph model of a circ uit’s clause description called the implication graph. It combines both the flexibili ty of SAT-based techniques and high efficiency of structure based methods. As the propos ed algorithms operate only on the implication graph, they are independent of the chosen logic. Evaluation of implications and computation of indirect implications are perfo rmed by simple and efficient graph algorithms. Experimental results for various applic ations relying on implication demonstrate the efficiency of our approach.
Measurement of Anomalous Resistance Induced by chaotic Motion of Electrons in a Magnetic Null point

Chaotic motion of particles in magnetic null regions can produce a large collisionless resistivity. In order to measure the macroscopic resistivity, a new instrument using a pockels crystal has been developed. This measurement can detect a high frequency electric field in plasmas. The pockels probe satisfies the frequency response with 13.56 MHz and the sensitivity as low as 3x102 V/m, which proves the capability of measuring the local electric fields in a plasma discharged by a radio-frequency method.
All-optical Signal Processing of Fiber Impairments in Dual-Polarization 112 Gbit/s m-ary QAM Coherent Transmission

We have numerically implemented a receiver side all-optical signal processing method, i.e. optical backward propagation (OBP), by dispersion compensating fiber (DCF) and non-linear compensator (NLC) devised by effective negative Kerr non-linear coefficient using two highly non-linear fibers (HNLFs). The method is implemented for the post-processing of fiber transmission impairments, i.e. chromatic dispersion (CD) and non-linearities (NL). The OBP module is evaluated for dual-polarization (DP) m-ary (m=4,16,32,64,256) quadrature amplitude modulation (QAM) in 112 Gbit/s coherent transmission over 1200 km standard single mode fiber (SMF). We have also investigated an intensity limited optical backward propagation module (IL-OBP) by using a self-phase modulation-based optical limiter with an appropriate pre-chirping to compensate for the intensity fluctuations in the transmission link. Our results show that in highly non-linear sensitive 256QAM transmission, we have observed a 66% increase in the transmission distance by implementing IL-OBP as compared to conventional OBP.
Optimal trajectory planning for autonomous driving integrating logical constraints: An MIQP perspective

This paper considers the problem of optimal trajectory generation for autonomous driving under both continuous and logical constraints. Classical approaches based on continuous optimization formulate the trajectory generation problem as a nonlinear program, in which vehicle dynamics and obstacle avoidance requirements are enforced as nonlinear equality and inequality constraints. In general, gradient-based optimization methods are then used to find the optimal trajectory. However, these methods are ill-suited for logical constraints such as those raised by traffic rules, presence of obstacles and, more generally, to the existence of multiple maneuver variants. We propose a new formulation of the trajectory planning problem as a Mixed-Integer Quadratic Program. This formulation can be solved efficiently using widely available solvers, and the resulting trajectory is guaranteed to be globally optimal. We apply our framework to several scenarios that are still widely considered as challenging for autonomous driving, such as obstacle avoidance with multiple maneuver choices, overtaking with oncoming traffic or optimal lane-change decision making. Simulation results demonstrate the effectiveness of our approach and its real-time applicability.
COMBINATION OF GENETIC ALGORITHM WITH LAGRANGE MULTIPLIERS FOR LOT-SIZE DETERMINATION IN CAPACITY CONSTRAINED MULTI-PERIOD, MULTI-PRODUCT AND MULTI-STAGE PROBLEMS

In this paper a meta-heuristic approach has been presented to solve lotsize determination problems in a complex multi-stage production planning problems with production capacity constraint. This type of problems has multiple products with sequential production processes which are manufactured in different periods to meet customer’s demand. By determining the decision variables, machinery production capacity and customer’s demand, an integer linear program with the objective function of minimization of total costs of set-up, inventory and production is achieved. In the first step, the original problem is decomposed to several subproblems using a heuristic approach based on the limited resource Lagrange multiplier. Thus, each sub-problem can be solved using one of the easier methods. In the second step, through combining the genetic algorithm with one of the neighborhood search techniques, a new approach has been developed for the subproblems. In the third step, to obtain a better result, resource leveling is performed for the smaller problems using a heuristic algorithm. Using this method, each product’s lot-size is determined through several steps. This paper’s propositions have been studied and verified through considerable empirical experiments.
Learning Observation - Introducing the Role of a Meta-Observer

Observation is a key skill in performing analysis and evaluation activities during development and maintenance of an information system. This paper looks at how this skill can be learnt by investigating the research question: “Can observation learning be improved by introducing a meta-observer? ”. This research consists of using a simulation of a real life situation of an observation scenario embracing three different roles: a user, an observer and a meta-observer. The user, while performing a task, is encouraged to ‘think aloud’ by the observer. The meta-observer’s task is to observe the observer and to provide feedback. Findings in terms of strengths and problems concerning the role of the meta-observer are presented.
Has Trade Openness Increased all Portuguese Public Expenditures? A Detailed Time-Series Study

This work aims at identifying the public outlays that has been influenced by the growth of Portuguese trade openness since the end of World War II. For the Portuguese reality, it is one of the first attempts to discuss a large set of simultaneously tested control variables. For this purpose, the methodology started from a model that tries to the public expenditures to a system of simultaneous macroeconomic forces and, for testing, it followed the steps associated with cointegration analysis. Using the most convenient techniques, a restrictive set of four expenditures (subsidies, interest payments, other current expenditures, and total public expenditures as a proportion of GDP) was found among the wider set suggested by the Literature. The nature of these expenditures supports the claim that, for the Portuguese case, a particular validity of the compensation hypothesis has been observed. The achieved evidence promotes an important rule: in addition to there being a long-term relation between (some) public expenditures and trade openness, short-term relations may also appear.