Miracle-dz/UniFAR-FTUs huggingface dataset:
Likelihood ratios and Bayesian inference for Poisson channels

In recent years, infinite-dimensional methods have been introduced for the Gaussian channels estimation. The aim of this paper is to study the application of similar methods to Poisson channels. In particular we compute the Bayesian estimator of a Poisson channel using the likelihood ratio and the discrete Malliavin gradient. This algorithm is suitable for numerical implementation via the Monte-Carlo scheme. As an application we provide an new proof of the formula obtained recently by Guo, Shamai and Verdu\'u relating some derivatives of the input-output mutual information of a time-continuous Poisson channel and the conditional mean estimator of the input. These results are then extended to mixed Gaussian-Poisson channels.
Gait parameter adaptation for lower-limb exoskeletons

The field of exoskeletons and assistive orthotic devices is a multidisciplinary issue in the halfway between medicine and robotics. Within the robotic discipline, bipedal robot gaits are generated as a function of parameters such as stride length, foot clearance and body height. These features allow to adapt the gait to different surface characteristics. However, biped robot gaits do not look as natural as human gaits. On the other side, gaits imposed in actual active orthoses are fixed following the Clinical Gait Analysis (CGA) pattern. These CGA patterns ensure a comfortable, natural and safe gait, but do not exhibit flexibility to change some gait features to adapt to the characteristics of the terrain, such as slopes or small obstacles. This paper presents the development of an adaptable gait pattern for a full lower limb active orthosis. Based on an impedance control approach, gait parameters such as step height, body height or step length are modified online, providing a safe and smooth gait pattern. Two shoe insole pressure measurement systems provide ground reaction force and center of pressure to adapt these gait parameters online. To avoid abrupt movements that would affect user comfort, references on position and speed must be smooth, that is, derivable when the characteristics of the gait change. To ensure that position and speed references follow a derivable path, with a very low computational cost, the change from one reference path to another has been modulated with Gaussian windows. Thus, a more adaptable orthosis with soft motion is achieved.
Self-Organize Network Architecture for Multi-agent Cognitive Radio Systems

this paper presents self-organize network architecture for cognitive radio (CR). In order to increase the flexibility of CR, a division method and corresponding network keeping strategy is proposed in this paper, whose key part is finding the cutting edge in the interface graph. The TFCE (Tarjan-find-cutting-edge) algorithm is also given to find the cutting edge. On the basis of the division method a spectrum allocation model is introduced to research the effectiveness of the method for CR. The result shows the self-organize method could effectively add group's flexibility without affecting spectrum allocation utility or increasing system traffic.
Eecient Approximation of Spectral and Autocorrelation Coeecients

In this paper we provide polynomial time approximation techniques which allow us to calculate , to arbitrary levels of accuracy and with high probability of success, the spectral co-eecients and autocorrelation coeecients of Boolean functions, given that those functions are expressed in either Sum-of-Products or Product-of-Sums form.
Optimal Scheduling and Routing in Wireless Networks: A New Approach

A joint routing and scheduling algorithm for multi-hop wireless networks, based on a unified convex optimization framework, is proposed. Our approach is novel in that it integrates optimal scheduling with a modified version of distributed minimum delay routing. Accordingly, the algorithm performs packet routing based on a complete multi-hop view of the network and its traffic conditions. This stands in sharp contrast to joint routing and scheduling algorithms, such as the Tassiulas algorithm, that rely on per-session queue differential between adjacent nodes for channel scheduling and packet routing. Simulation results illustrate that the proposed algorithm performs much better than Tassiulas, in terms of packet delay and jitter, packet loss and misordering, and energy consumption. Moreover, in terms of capacity region, simulation results do not reflect any noticeable difference between the two algorithms.
Data fusion in 3D vision using a RGB-D data via switching observation model and its application to people tracking

In this paper, we propose a new method for 3D people tracking with RGB-D observations. The proposed method fuses RGB and depth data via a switching observation model. Specifically, the proposed switching observation model intelligently exploits both final detection results and raw signal intensity in a complementary manner in order to cope with missing detections. In real-world applications, the detector response to RGB data is frequently missing. When this occurs the proposed algorithm exploits the raw depth signal intensity. The fusion of detection result and raw signal intensity is integrated with the tracking task in a principled manner via the Bayesian paradigm and labeled random finite set (RFS). Our case study shows that the proposed method can reliably track people in a recently published 3D indoor data set.
Knowledge Representation, Learning, and Reasoning in WebDoc - A Web Document Classification System

This paper describe a novel approach to knowledge representation, learning, and reasoning in WebDoc, a system that classifies Web documents according to the Library of Congress classification system. We argue that an automatically constructed domain-independent knowledge base is indispensable. The WebDoc system builds a knowledge base (represented as a semantic network) that contains the Library of Congress subject headings and their relationships. Through training on human-indexed and NLP-parsed Web documents, WebDoc modifies the semantic network and generates rules for future index generation tasks.
Mechanisms of mouse spleen dendritic cell function in the generation of influenza-specific, cytolytic T lymphocytes

We have evaluated the capacity of dendritic cells to function as antigen-presenting cells (APCs) for influenza and have examined their mechanism of action. Virus-pulsed dendritic cells were 100 times more efficient than bulk spleen cells in stimulating cytotoxic T lymphocyte (CTL) formation. The induction of CTLs required neither exogenous lymphokines nor APCs in the responding T cell population. Infectious virus entered dendritic cells through intracellular acidic vacuoles and directed the synthesis of several viral proteins. If ultraviolet (UV)- inactivated or bromelain-treated viruses were used, viral protein synthesis could not be detected, and there was poor induction of CTLs. This indicated that dendritic cells were not capable of processing noninfectious virus onto major histocompatibility complex (MHC) class I molecules. However, UV-inactivated and bromelain-treated viruses were presented efficiently to class II-restricted CD4+ T cells. The CD4+ T cells crossreacted with different strains of influenza and markedly amplified CTL formation. Cell lines that lacked MHC class II, and consequently the capacity to stimulate CD4+ T cells, failed to induce CTLs unless helper lymphokines were added. Similarly, dendritic cells pulsed with the MHC class I-restricted nucleoprotein 147-155 peptide were poor stimulators in the absence of exogenous helper factors. We conclude that the function of dendritic cells as APCs for the generation of virus-specific CTLs in vitro depends measurably upon: (a) charging class I molecules with peptides derived from endogenously synthesized viral antigens, and (b) stimulating a strong CD4+ helper T cell response.
Animating work and time for debugging parallel programs foundation and experience

The task of debugging parallel programs often can be simplified by using appropriate notions of both time and work to animate the execution of a program on a parallel machine. The principle animations described in this paper are used as the foundation of the MAD monitoring system [3], which is the Makbilau visual debugger. The Makbilan is a shared memory machine which was constructed in the Hebrew University. In our animation system time and work are combined with a causaiity graph of the execution. This graph is constructed and maintained with the information collected during the execution and is used for both displaying the animations as well as the basis of control over the animations by the user. This graph is used for generating several views and animations of programs execution. In the system examined in this paper, there are several interesting animations and mechanisms providing to the user a significant control of many aspects of the debugging process, Almost all pieces of our system can be found in other
A theory of morphogenesis Universal Turing Machine

Many leading analysts would agree that, had it not been for I/O automata, the understanding of evolutionary programming might never have occurred. After years of technical research into systems, we verify the synthesis of red-black trees [114], [188], [62], [188], [114], [70], [179], [68], [95], [188], [54], [152], [191], [59], [168], [148], [99], [58], [70], [129]. In this work, we investigate how 802.11b can be applied to the exploration of linked lists.
CPS translating inductive and coinductive types

We investigate CPS translatability of typed λ-calculi with inductive and coinductive types. We show that tenable Plotkin-style call-by-name CPS translations exist for simply typed λ-calculi with a natural number type and stream types and, more generally, with arbitrary positive inductive and coinductive types. These translations also work in the presence of control operators and generalize for dependently typed calculi where case-like eliminations are only allowed in non-dependent forms. No translation is possible along the same lines for small Σ-types and sum types with dependent case.
Energy-efficient multi-objective power allocation for multi-user AF cooperative networks

In multi-user cooperative diversity systems, energy-efficient optimal power allocation (OPA) is an essential problem. However, most of the existing works only focus on optimizing the overall system energy efficiency (EE), rather than the individual EE for each user. In this paper, we investigate the OPA in multi-user amplify-and-forward cooperative systems aiming at maximizing the EE for each individual user while satisfying the quality-of-service (QoS) requirements. We utilize the multi-objective optimization framework to formulate the power allocation problem. Due to the NP-hardness of the problem, we further employ the non-dominated sorting genetic algorithm II (NSGA-II) to find the Pareto optimal solutions to the multi-objective optimization problem. Simulation results demonstrate that the proposed algorithm can obtain well-distributed Pareto optimal solutions, as well as yield fast convergence and flexible EE tradeoff.
Investigation of Boundary-Layer Separation for Lifting Surfaces

Flow separation from lifting surfaces such as airfoils is undesirable as it deteriorates performance. For example, when airfoils that are designed for large Reynolds numbers are operated at smaller off-design Reynolds numbers, laminar separation can occur. Laminar separation typically leads to transition and reattachment. Transition is influenced by factors, such as free-stream turbulence and wall roughness. Transition and reattachment affect the circulation and, thereby, separation itself. We are employing computational fluid dynamics for investigating the fundamental mechanisms of separation and transition for lifting surfaces. Using highly-resolved direct numerical simulations, we are investigating fundamental aspects of separation and transition in the presence of free-stream turbulence for canonical separation bubbles. In parallel, we are carrying out hybrid turbulence model simulations of an entire airfoil at a larger chord Reynolds number. The combined approach will advance both physical understanding and modeling capabilities, and thus provide a solid platform for the development of separation control strategies for practical applications.
EXPONENTIATED EXPONENTIAL FAMILY: AN ALTERNATIVE TO GAMMA AND WEIBULL DISTRIBUTIONS

Summary In this article we study some properties of a new family of distributions, namely Exponentiated Exponentialdistribution, discussed in Gupta, Gupta, and Gupta (1998). The Exponentiated Exponential family has two parameters (scale and shape) similar to a Weibull or a gamma family. It is observed that many properties of this new family are quite similar to those of a Weibull or a gamma family, therefore this distribution can be used as a possible alternative to a Weibull or a gamma distribution. We present two reall ife data sets, where it is observed that in one data set exponentiated exponential distribution has a better fit compared to Weibull or gamma distribution and in the other data set Weibull has a better fit than exponentiated exponential or gamma distribution. Some numerical experiments are performed to see how the maximum likelihood estimators and their asymptotic results work for finite sample sizes.
CD36/Fatty Acid Translocase, An Inflammatory Mediator, Is Involved in Hyperlipidemia-Induced Exacerbation in Ischemic Brain Injury

Hyperlipidemia with accompanying increase in peripheral inflammation is a risk factor for stroke. The effect of excess lipids on stroke-induced injury and the mechanism by which lipid-mediated inflammatory responses contribute to stroke are not known. We investigated these uncertainties by subjecting normal and hyperlipidemic mice to transient middle cerebral artery occlusion, followed by measurement of stroke severity and inflammatory response. Infarct size, swelling, and lipid contents were significantly increased in the high-fat fed ApoE knock-out mice, as was the expression of the inflammatory mediators CD36 and monocyte chemoattractant protein 1 (MCP-1) in the brain and periphery. Furthermore, the hyperlipidemic mice exhibited numerous foam cells, a probable cause of increased swelling and postischemic inflammation, in the peri-infarct area. Genetic deletion of cd36 in the hyperlipidemic condition reduced proinflammatory chemokine/receptor and cytokines (MCP-1, CC chemokine receptor 2, and interleukins 1β and 6), in the brain 6 h after ischemia. The reduced proinflammatory response also resulted in smaller ischemic injury, less swelling, and fewer foam cells at 3 d after ischemia. The results show that hyperlipidemia-induced inflammation is a negative factor for stroke outcomes and indicate that downregulating CD36 may be an effective therapeutic strategy for reducing the impact of stroke in hyperlipidemic subjects.
Classification of Cancer Data with Genetic Programming

Recent advances in DNA microarray technology allow scientists to measure expression levels of thousands of genes simultaneously in an biological organism. Since the cancer cells usually evolve from normal cells due to mutations in genomic DNA, comparison of the gene expression levels of cancerous and normal tissues or different cancerous tissues may be useful to identify those genes that might anticipate the clinical behavior of cancers. Widely used technique in cancer research to predict the classes of unknown samples is clustering that usually groups patient samples with similar clinical records and is used with or without gene selection [1, 2, 6]. Currently, machine learning techniques like k-nearest neighbor (kNN) or support vector machine (SVM) have been applied to the problem of classification of microarray data with or without gene selection [6, 3]. For gene selection, signal-to-noise ratio[2], RPMBGA [7], etc. have been used. In this paper, we use genetic programming (GP) [5] for gene expression based classification of patient samples. The main advantage of GP is that it can act as a classifier as well as a gene selection algorithm. In its typical implementation, a training set of gene expression data of disease samples are presented to GP to evolve an arithmetic expression of genes describing whether a given sample belongs to a given class or not. Then the evolved best rule (s) is (are) applied to the test samples to get the generalized accuracy on unknown samples. Unlike SVM and other classifiers, the use of GP as a classifier is transparent in the sense that the mechanism used to classify patient samples is available for inspection. In the next section, we describe GP and provide the experimental results on brain cancer data [6] and the data set used by Khan et al. [4].
Hox Targets and Cellular Functions

Hox genes are a group of genes that specify structures along the anteroposterior axis in bilaterians. Although in many cases they do so by modifying a homologous structure with a different (or no) Hox input, there are also examples of Hox genes constructing new organs with no homology in other regions of the body. Hox genes determine structures though the regulation of targets implementing cellular functions and by coordinating cell behavior. The genetic organization to construct or modify a certain organ involves both a genetic cascade through intermediate transcription factors and a direct regulation of targets carrying out cellular functions. In this review I discuss new data from genome-wide techniques, as well as previous genetic and developmental information, to describe some examples of Hox regulation of different cell functions. I also discuss the organization of genetic cascades leading to the development of new organs, mainly using Drosophila melanogaster as the model to analyze Hox function.
A General Mathematical Framework for Calculating Systems-Scale Efficiency of Energy Extraction and Conversion: Energy Return on Investment (EROI) and Other Energy Return Ratios

The efficiencies of energy extraction and conversion systems are typically expressed using energy return ratios (ERRs) such as the net energy ratio (NER) or energy return on investment (EROI). A lack of a general mathematical framework prevents inter-comparison of NER/EROI estimates between authors: methods used are not standardized, nor is there a framework for succinctly reporting results in a consistent fashion. In this paper we derive normalized mathematical forms of four ERRs for energy extraction and conversion pathways. A bottom-up (process model) formulation is developed for an n-stage energy harvesting and conversion pathway with various system boundaries. Formations with the broadest system boundaries use insights from life cycle analysis to suggest a hybrid process model/economic input output based framework. These models include indirect energy consumption due to external energy inputs and embodied energy in materials. Illustrative example results are given for simple energy extraction and conversion pathways. Lastly, we discuss the limitations of this approach and the intersection of this methodology with “top-down” economic approaches.
Analysis of inductive coupling coils for extending distances of efficient wireless power transmission

This paper proposed an approach to extend the transmission distances of wireless power transmission (WPT) systems under the constraint of the coil size and optimized delivered power and efficiency. Based on the reflected load theory, the inductive coupling coils can be modelled as an equivalent circuit for simple analysis and be extended to multiple-coil wireless power transmission systems. According to this proposed approach, power transmission efficiency (PTE), power delivered to load (PDL), and geometric factors of the coil are taken into consideration simultaneously as the proposed figure-of-merit (FoM) to optimize the performance of the wireless power transmission systems. Finally, a practical 4-coil WPT was implemented to verify the theory, and based on the FoM, the most efficient design parameters of WPT were identified. The optimal distance was increased by 50%, and the PTE remains more than 78%.
TV program detection in tweets

Posting comments on social networks using second screen devices (e.g., tablets) while watching TV is becoming very common. The simplicity of microblogs makes Twitter among the preferred social services used by the TV audience to share messages about TV shows and movies. Thus, users' comments about TV shows are considered a valuable indicator of the TV audience preferences. However, eliciting preferences from a tweet requires to understand if the tweet refers to a specific TV program, a task particularly challenging due to the nature of tweets - e.g., the limited length and the massive use of slangs and abbreviations. In this paper, we present a solution to identify whether a tweet posted by a user refers to one among a set of known TV programs. In such process, referred to as item detection, we assume the system is given a set of items (e.g., the TV shows or movies) together with some features (e.g., the title of the TV show). We tested the solution on a dataset composed by approximately 32000 tweets, where the optimal configuration reached a precision of about 92% with a recall equals to about 65%.
Off-line building block identification: detecting building blocks directly from fitness without genetic algorithms

This paper aims at detecting the existence of building blocks directly from the fitness function without performing genetic algorithms. To do so, this paper extends the convergence time model and the gambler's ruin model so they can be applied to a larger variety of problems. With proposed models, the number of fitness evaluations can be estimated for both of these two cases: (1) some genes are transferred together in crossover (treated as a building block); (2) the genes are transferred separately. Therefore, we can compare the number of fitness evaluations and detect the existence of building blocks for a large family of fitness functions without actually performing a genetic algorithm.
Motion Prediction Methods for Surrogate Safety Analysis

Despite the rise in interest in surrogate safety analysis, little work has been done to understand and test the impact of methods for motion prediction, which are needed to identify whether two road users are on a collision course, and to compute several surrogate safety indicators such as the time to collision. The default, unjustified method used in much of the literature is prediction at constant velocity. In this study, a generic framework is presented to predict road users' future positions depending on their current position and their choice of acceleration and direction. This method results in the possibility of generating many predicted trajectories by sampling distributions of acceleration and direction. Three safety indicators—the time to collision, an extended version of predicted post encroachment time, and a new indicator measuring the probability that the road user's attempted evasive actions will fail to avoid the collision—are computed over all predicted trajectories. These methods and indicators are illustrated in four case studies of lateral road user interactions. The evidence suggests that the prediction method based on the use of a set of initial positions seems to be the most robust. Another contribution of this study is to make all the data and code used available (the code as open source) to enable reproducibility and to start a collaborative effort to compare and improve the methods for surrogate safety analysis.