Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustered regression with unknown clusters

We consider a collection of prediction experiments, which are clustered in the sense that groups of experiments ex- hibit similar relationship between the predictor and response variables. The experiment clusters as well as the regres- sion relationships are unknown. The regression relation- ships define the experiment clusters, and in general, the predictor and response variables may not exhibit any clus- tering. We call this prediction problem clustered regres- sion with unknown clusters (CRUC) and in this paper we focus on linear regression. We study and compare several methods for CRUC, demonstrate their applicability to the Yahoo Learning-to-rank Challenge (YLRC) dataset, and in- vestigate an associated mathematical model. CRUC is at the crossroads of many prior works and we study several prediction algorithms with diverse origins: an adaptation of the expectation-maximization algorithm, an approach in- spired by K-means clustering, the singular value threshold- ing approach to matrix rank minimization under quadratic constraints, an adaptation of the Curds and Whey method in multiple regression, and a local regression (LoR) scheme reminiscent of neighborhood methods in collaborative filter- ing. Based on empirical evaluation on the YLRC dataset as well as simulated data, we identify the LoR method as a good practical choice: it yields best or near-best prediction performance at a reasonable computational load, and it is less sensitive to the choice of the algorithm parameter. We also provide some analysis of the LoR method for an asso- ciated mathematical model, which sheds light on optimal parameter choice and prediction performance.
A social-cognitive model of animal cruelty

ABSTRACT Recent decades have seen the emergence of social cognitive models of interpersonal aggression. These models, which have focused primarily on childhood aggression, have been the subject of extensive theoretical, methodological, and empirical attention. More recently, researchers have used social cognitive models to address adult interpersonal aggression as well as child abuse and neglect. This article will discuss the ways in which researchers can potentially extend this model to understand the abuse and neglect of animals. After a brief review of the literature on social-information processing, schemas, and normative beliefs about aggression, I will apply these mechanisms and findings to what we know about animal cruelty. I will also use current social cognitive models of aggression to describe potential directions for future research within the field of animal abuse and neglect scholarship.
Defining the Parameters of Incidental Learning on a Serial Reaction Time (SRT) Task: Do Conscious Rules Apply?

There is ongoing debate about the contribution of explicit processes to incidental learning, particularly attention, working memory and control mechanisms. Studies generally measure explicit process contributions to incidental learning by comparing dual- to single-task sequence learning on some variant of a Serial Reaction Time (SRT), usually adopting an auditory tone counting task as the secondary task/memory load. Few studies have used secondary working memory stimuli with the SRT task, those that have typically presented secondary stimuli, before, after or between primary task stimuli. Arguably, this design is problematic because participants may potentially “switch” attention between sequential stimulus sources limiting the potential of both tasks to simultaneously index shared cognitive resources. In the present study secondary Visual and Verbal, memory tasks were temporally synchronous and spatially embedded with the primary SRT task for Visual and Verbal dual-task conditions and temporally synchronous but spatially displaced for Visual-Spatial and Verbal-Spatial Above/Below conditions, to investigate modality specific contributions of visual, verbal and spatial memory to incidental and explicit sequence learning. Incidental learning scores were not different as an effect of condition but explicit scores were. Explicit scores significantly and incrementally diminished from the Single-task through Visual-Spatial Below conditions; percentage accuracy scores on secondary tasks followed a significant corresponding pattern suggesting an explicit learning/secondary memory task trade-off as memory demands of tasks increased across condition. Incidental learning boundary conditions are unlikely to substantially comprise working memory processes.
Auxin Transport Promotes Arabidopsis Lateral Root Initiation

Lateral root development in Arabidopsis provides a model for the study of hormonal signals that regulate postembryonic organogenesis in higher plants. Lateral roots originate from pairs of pericycle cells, in several cell files positioned opposite the xylem pole, that initiate a series of asymmetric, transverse divisions. The auxin transport inhibitor N-1-naphthylphthalamic acid (NPA) arrests lateral root development by blocking the first transverse division(s). We investigated the basis of NPA action by using a cell-specific reporter to demonstrate that xylem pole pericycle cells retain their identity in the presence of the auxin transport inhibitor. However, NPA causes indoleacetic acid (IAA) to accumulate in the root apex while reducing levels in basal tissues critical for lateral root initiation. This pattern of IAA redistribution is consistent with NPA blocking basipetal IAA movement from the root tip. Characterization of lateral root development in the shoot meristemless1 mutant demonstrates that root basipetal and leaf acropetal auxin transport activities are required during the initiation and emergence phases, respectively, of lateral root development.
A Novel Histology-directed Strategy for MALDI-MS Tissue Profiling That Improves Throughput and Cellular Specificity in Human Breast Cancer*

We describe a novel tissue profiling strategy that improves the cellular specificity and analysis throughput of protein profiles obtained by direct MALDI analysis. The new approach integrates the cellular specificity of histology, the accuracy and reproducibility of robotic liquid dispensing, and the speed and objectivity of automated spectra acquisition. Traditional methodologies for preparing and analyzing tissue samples rely heavily on manual procedures, which for various reasons discussed, restrict cellular specificity and sample throughput. Here, a robotic spotter deposits micron-sized droplets of matrix precisely onto foci of normal mammary epithelium, ductal carcinoma in situ, invasive mammary cancer, and peritumoral stroma selected by a pathologist from high resolution histological images of sectioned human breast cancer samples. The location of each matrix spot was then determined and uploaded into the instrument to facilitate automated profile acquisition by MALDI-TOF. In the example shown, the different lesions were clearly differentiated using mass profiling. Further, the workflow permits a visual projection of any information produced from the profile analyses directly on the histological image for a unique combination of proteomic and histological assessment of sample regions. The higher performance characteristics offered by the new workflow promises to be a significant advancement toward the next generation of tissue profiling studies.
Three Stories on Automated Reasoning for Natural Language Understanding

Three recent applications of computerised reasoning in natural language processing are presented. The first is a text understanding system developed in the late 1990s, the second is a spoken-dialogue interface to a mobile robot and automated home, and the third is a system that determines textual entailment. In all of these applications, off-the-shelf tools for reasoning with first-order logic (theorem provers as well as model builders) are employed to assist in natural language understanding. This overview is not only an attempt to identify the added value of computerised reasoning in natural language understanding, but also to point out the limitations of the first-order inference techniques currently used in natural language processing.
A Different Learning Way For Pupils With Specific Learning Disabilities

Several factors may contribute to a child's failure to learn, some possible causes of school failure are specific learning disabilities. Dyslexia is one of the most common learning disabilities; dyslexia is to fail to learn to read in spite of adequate environment, normal intelligence, and decent educational opportunities. Various studies have recently focused on dyslexia on different scientific areas. Our main focus was to explore the use of Information and Communication Technology (ICT) in education to improve learning for pupils with learning disabilities. In this paper we present a positive approach which considers dyslexia as a different learning style rather than a disability.
Searching in space and time: a system for forensic analysis of large video repositories

The use of surveillance cameras to monitor public buildings and urban areas is becoming increasingly widespread. Each camera delivers a continuous stream of video data, which, once archived, is a valuable source of information for forensic analysis. However, current video analysis tools are primarily based on searching backwards and forwards in time at a single location (i.e. camera), which does not account for events or people of interest that change location over time. In this paper we describe a practical system for tracking a target backwards and forwards in both space and time, effectively following a feature of interest as it moves within and between cameras in a surveillance network. This provides a video analysis tool that is target-centred rather than camera-centred, and thus allows rapid access to the footage that matters for forensic analysis.
Genetic braid optimization: A heuristic approach to compute quasiparticle braids

In topologically-protected quantum computation, quantum gates can be carried out by adiabatically braiding two-dimensional quasiparticles, reminiscent of entangled world lines. Bonesteel et al. [Phys. Rev. Lett. 95, 140503 (2005)], as well as Leijnse and Flensberg [Phys. Rev. B 86, 104511 (2012)] recently provided schemes for computing quantum gates from quasiparticle braids. Mathematically, the problem of executing a gate becomes that of finding a product of the generators (matrices) in that set that approximates the gate best, up to an error. To date, efficient methods to compute these gates only strive to optimize for accuracy. We explore the possibility of using a generic approach applicable to a variety of braiding problems based on evolutionary (genetic) algorithms. The method efficiently finds optimal braids while allowing the user to optimize for the relative utilities of accuracy and/or length. Furthermore, when optimizing for error only, the method can quickly produce efficient braids.
High-quality prosodic modification of speech signals

The aim of the work was to develop a procedure that allows prosodic modifications of speech signals without impairing the quality. The developed procedure is based on the Fourier analysis/synthesis technique with several improvements on the analysis side, such as the analysis of signals with rapidly changing F/sub 0/ and the analysis of weak spectral components. Also for the modification of the short time spectrum and for the reconstruction of the speech signal some new methods have been introduced. The most important one, in terms of speech quality, is the method of phase compensation that limits the absolute time shift to half the pitch period. The developed procedure is used in our high quality text to speech synthesis system that is based on concatenation of prosodically modified diphones.
Design of piecewise linear LQ control for linear systems with actuator rate saturations using LMIs optimization

A model of the servomechanism used for high-powered actuators in mechanical systems consists of a position feedback loop around the cascade connection of a memoryless saturation function with an integrator with a large time constant. The saturation function in the servomechanism has a linear high gain characteristics for a small input and so the equivalent time constant of the actuator servomechanism becomes small. As the input to the saturation function becomes larger than the small linear range for a drastic control demand, the output of the saturation function becomes constant irrespective of the input magnitude and the actuator response has a rate saturation determined by the large time constant of the integrator and so the time lag of the actuator response behind the demand results in the actual plant input much different from the demand and the plant may exhibit an undesirable behavior of the system. Therefore in this paper we consider a control method for a system with rate saturations in the actuator servomechanisms to keep stable by switching the controller gain according to the input magnitudes to the saturation functions so that the inputs to the saturation functions are controlled within the permissible maximum absolute values which are decided according to each level of LQ controller gain groups determined beforehand to ensure the local absolute stability of the total system whose conditions are expressed as a linear matrix inequalities optimization problem by introducing a Lure-Postnikov-type Lyapunov function. In a piecewise linear control the switching function selects a controller gain group by on-line monitoring the inputs to the saturation functions in the actuator servomechanisms. The effectiveness of the design method is illustrated with a practical example of dynamic positioning(DP) system.
Development of face detection in preschool children

We investigated the ability to detect a face among other visual objects in a complex visual array in 3-, 4-, and 5-year-old children, as well as in adults. To this end, we used a visual search paradigm implemented on a touch-tablet device. Subjects (N = 100) saw up to eighty 3 × 3 visual search arrays and had to find and tap upon a target—a face or a car—among eight objects that served as distractors. Our data revealed a relative face detection advantage, which did not differ in its extent between children and adults. This suggests that, beginning in young childhood and ending in adulthood, face detection performance advances as a consequence of other cognitive functions such as a general advance in visual search performance. Our study closes a gap in the knowledge about the development of face detection—as a prototype for social stimuli and their capacity to attract attention—from early to middle childhood.
Performance visualization: a new challenge to music through visualization

This paper describes performance visualization with its purposes, significance, and our prototype system. Performance visualization provides us with a new user interface for music systems, a non-subjective explanation and judgment for musical expression, and a visual aid to understand, analyze, and compare performances and their musical structures. It will also introduce a more challenging research on designing a musical data mining interface not by tags nor by contents but by the mood. Other challenges include finding qualitative characteristics of music with analysis methods for information visualization and synesthesia between visualized and sonified information. We will describe which information should be shown on the visualized figures for expressive performance. Then we will show our prototype system to visualize expressive performance consisting of several instruments.
Solving the Hamiltonian path problem using optical fiber network

NP-Complete (Non-deterministic Polynomial Complete) problem is a famous mathematical problem, here we choose the Hamiltonian Path problem as the sample of the NP-Complete problems and put forward a method to solve it using optical fibernetwork. Instead of finding the path itself, the point of our solution is proving whether there exists such a Hamiltonian path which can travel all of the cities only once. We use optical fiberas information carriers and whether the time delay of passing through the system is equal to the sum of each node decides the existence of the Hamiltonian path. Because light has advantages of high speed of travelling and massive parallelism, utilizing this method to solve the Hamiltonian Path problem is faster than other common algorithms.
RETROSPECTIVE : Software Pipelining : An Effective Scheduling Technique for VLIW Machines

The basic idea behind software pipelining was first developed by Patel and Davidson for scheduling hardware pipe-lines. As instructionlevel parallelism made its way into general-purpose computing, it became necessary to automate scheduling. How and whether instructions can be scheduled statically have major ramifications on the design of computer architectures. Rau and Glaeser were the first to use software pipelining in a compiler for a machine with specialized hardware designed to support software pipelining. In the meantime, trace scheduling was touted to be the scheduling technique of choice for VLIW (Very Long Instruction Word) machines. The most important contribution from this paper is to show that software pipelining is effective on VLIW machines without complicated hardware support. Our understanding of software pipelining subsequently deepened with the work of many others. And today, software pipelining is used in all advanced compilers for machines with instruction-level parallelism, none of which, except the Intel Itanium, relies on any specialized support for software pipelining.
A hierarchical GMDH-based polynomial neural network for handwritten numeral recognition using topological features

We propose a multiclass hierarchical abductive learning classifier and apply it to improve the recognition rate of handwritten numerals while reduce the dimensionality of the feature space. For handwritten recognition, there are ten classes. Using 9 binary GMDH-based neural network models structured in a hierarchy has led to improving balance factor of the dataset for each classifier and improving the classification of handwritten numerals. It also has the advantage of removing the need to resolve classification ties that exist in other forms of combining a number of classifiers to solve a multiclass classification problem whether using one-versus-all or one-versus-one approaches. The proposed approach is empirically evaluated and compared with five other state-of-the-art machine learning classifiers using a publicly available dataset based on non-Gaussian topological features.
Rôle du coordinateur dans un dispositif d'apprentissage collaboratif à distance

Notre contribution est un temoignage portant sur une pratique de gestion et d’organisation des cursus de formation a distance de l’Universite Louis Pasteur (ULP) de Strasbourg I, formation organisee en partenariat avec l’Agence universitaire de la francophonie (AUF). Etaye par une analyse d’echanges de courriels, cet ecrit met en evidence le role central du coordinateur dans un dispositif instrumente d’apprentissage collaboratif.
An efficient unique state coding algorithm for signal transition graphs

Current algorithms to force the complete state coding (CSC) property for signal transition graphs work on the state graph and, therefore require exponential time and space. Polynomial algorithms have been only proposed for marked graphs. In this paper, a P-time algorithm for unique state coding (USC) is presented. Although more restrictive than CSC, it is shown that the USC property can be efficiently guaranteed for large STGs. Several experiments evidence that the obtained results are even better than those generated by exponential-time techniques. <<ETX>>
Robust distributed incremental LMS for parameter estimation using heterogeneous adaptive networks

Adaptive networks include a set of nodes with adaptation and learning abilities for modeling various types of self-organized and complex activities encountered in the real world. This paper presents the effect of heterogeneous distributed incremental LMS algorithm with ideal links on the quality of unknown parameter estimation. In heterogeneous adaptive networks, a fraction of the nodes, defined based on previously calculated SNR, is assumed to be the informed nodes that collect data and perform in-network processing, while the remaining nodes are assumed to be uninformed and only participate in the processing tasks. As our simulation results show, the proposed algorithm not only considerably improves the performance of the Distributed Incremental LMS algorithm in a same condition, but also proves a good accuracy of estimation in cases where some of the nodes make unreliable observations (noisy nodes).
The antitumor function of tumor necrosis factor (TNF) II. Analysis of the role of endogenous TNF in endotoxin-induced hemorrhagic necrosis and regression of an established sarcoma

In agreement with the results of previous studies (1), it was shown that intravenous injection of endotoxin into mice bearing 9-d SA1 sarcoma resulted in a tumor hemorrhagic reaction that rapidly caused necrosis of most of the center of the tumor, and then the complete regression of the rim of living tumor tissue that survived the hemorrhagic reaction. The tumor hemorrhagic reaction was confined to the vascular bed of the tumor, and its rate and extent of development were measured in terms of the intratumor extravasation of 51Cr-labeled syngeneic red cells. The development of the hemorrhagic reaction was associated with the presence in the tumor over a 6-h period of endogenous TNF that was measured in terms of its capacity to kill L929B cells in vitro and identified by its susceptibility to neutralization with a monospecific, polyvalent anti-rTNF antibody. The same antibody was capable in vivo of inhibiting the endotoxin-induced tumor hemorrhagic reaction by only approximately 50%, even when present in the tumor in excess. However, it was capable when given in the same quantity of inhibiting the ability of endotoxin to cause complete tumor regression. The fact that TNF was generated in the tumor during the tumor hemorrhagic reaction, and that infusion of a sufficient quantity of anti-rTNF antibody severely interfered with hemorrhagic necrosis and prevented tumor regression represents convincing evidence that TNF is an essential participant in endotoxin-induced regression of an established SA1 sarcoma. Moreover, because tumor regression, as opposed to hemorrhagic necrosis, failed to occur if the tumor was growing in immunoincompetent mice, but did so if the mice were infused with tumor- sensitized T cells, it can be concluded that an adequate level of T cell-mediated immunity is also an essential requirement for endotoxin- induced tumor regression. The participation of other endotoxin-induced mediators in tumor regression cannot be ruled out.
Concierge: a service platform for resource-constrained devices

As mobile and embedded devices become widespread, the management and configuration of the software in the devices is increasingly turning into a critical issue. OSGi is a business standard for the life cycle management of Java software components. It is based on a service oriented architecture where functional units are decoupled and components can be managed independently of each other. However, the focus continuously shifts from the originally intended area of small and embedded devices towards large-scaled enterprise systems. As a result, implementations of the OSGi framework are increasingly becoming more heavyweight and less suitable for smaller computing devices. In this paper, we describe the experience gathered during the design of Concierge, an implementation of the OSGi specification tailored to resource-constrained devices. Comprehensive benchmarks show that Concierge performs better than existing implementations and consumes less resources.