Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Gene Clustering and Subset Selection for Sample Classification Via MDL

MOTIVATION The microarray technology allows for the simultaneous monitoring of thousands of genes for each sample. The high-dimensional gene expression data can be used to study similarities of gene expression profiles across different samples to form a gene clustering. The clusters may be indicative of genetic pathways. Parallel to gene clustering is the important application of sample classification based on all or selected gene expressions. The gene clustering and sample classification are often undertaken separately, or in a directional manner (one as an aid for the other). However, such separation of these two tasks may occlude informative structure in the data. Here we present an algorithm for the simultaneous clustering of genes and subset selection of gene clusters for sample classification. We develop a new model selection criterion based on Rissanen's MDL (minimum description length) principle. For the first time, an MDL code length is given for both explanatory variables (genes) and response variables (sample class labels). The final output of the proposed algorithm is a sparse and interpretable classification rule based on cluster centroids or the closest genes to the centroids. RESULTS Our algorithm for simultaneous gene clustering and subset selection for classification is applied to three publicly available data sets. For all three data sets, we obtain sparse and interpretable classification models based on centroids of clusters. At the same time, these models give competitive test error rates as the best reported methods. Compared with classification models based on single gene selections, our rules are stable in the sense that the number of clusters has a small variability and the centroids of the clusters are well correlated (or consistent) across different cross validation samples. We also discuss models where the centroids of clusters are replaced with the genes closest to the centroids. These models show comparable test error rates to models based on single gene selection, but are more sparse as well as more stable. Moreover, we comment on how the inclusion of a classification criterion affects the gene clustering, bringing out class informative structure in the data. AVAILABILITY The methods presented in this paper have been implemented in the R language. The source code is available from the first author.
Observing facial expressions and gaze positions for personalized webpage recommendation

We propose a new method for personalized webpage recommendation. The method is capable of inferring a user's personal reading interest distribution according to implicit user feedbacks coming from the user's past online reading activities. With the inferred user reading interest distribution, we can recommend webpages in a search result set to a user in a personalized way. Our method is featured by its novel approach to observe the facial expressions and gaze positions of a user during the user's online reading activities as two types of implicit user feedbacks for estimating the user's reading interest distribution. To capture these implicit user feedbacks, we use an ordinary web camera and a customized web browser in the setup. The setup allows us to measure the distribution of the reading time a user spends in his or her reading activities over materials of different contents. With all the captured information, our method then estimates a user's reading interest distribution by finding correlations between the implicit feedbacks of a user with the contents of the read materials. Given the estimated user reading interest distribution, our algorithm can further predict the user's potential reading interest in any new webpage. Consequently, our algorithm can produce a personalized webpage recommendation for all the result webpages in an online search session. We compared the performance of our method with that of several mainstream commercial search engines as well as a recent personalized webpage ranking algorithm. The comparison results clearly show the superiority of our new method for personalized webpage recommendation.
A General Semiconductor Process Modeling Framework (cid:3)

A general semiconductor process modeling framework is presented. The framework encompasses (cid:12)rst, a methodology for the description of any manufacturing process using state and state transformation information, and second, a conceptual process model that distinguishes between, and de(cid:12)ne terms for, the state information and state transformations involved in integrated circuit fabrication. This modeling methodology and conceptual semiconductor process model aid in the understanding of fabrication processes, provide a formalism for the description of processes, help to guide the development of process (cid:13)ow languages and representations, and support a number of process-related activities including process design and process control
A user supporting personal video recorder by implementing a generic Bayesian classifier based recommendation system

The number of receivable TV channels has highly increased in recent years. Handling the enormous offer of TV content could be a challenge for the users in case of selecting the most interesting program. Furthermore, most users only focus on their favorite TV channels. That's the reason why content on other channels won't be recognized. For this problem assistive systems and tools are desirable to support in selecting the most appealing content with respect to the user's interests. The research group Next Generation PVR (NG-PVR) extends a personal video recorder (PVR) with a generic recommendation system based on a Bayesian classifier and adapted it for the use in the application area of television. The system analyzes the user's TV watching behavior to present new choices of content. So the system is able to generate personalized TV program recommendations. The content is stored on an internal hard disc drive where it is recorded for the user to watch. This paper presents the current state of development by introducing the system's architecture and implemented recommendation mechanisms.
Nonlinear modeling of hysteresis in piezoelectric actuators

In this paper, a nonlinear modeling scheme of hysteresis in piezoelectric actuators is presented. In this modeling scheme, the extreme learning machine based model for hysteresis in piezoelectric actuators is proposed. In this method, a modified hysteresis operator extracting the main movement features of the hysteresis is introduced to construct an expanded space. Then, the multi-valued mapping of hysteresis can be transformed into a one-to-one mapping on this expanded input space. Thus, an extreme learning machine (ELM) based neural network model can be obtained to describe the behavior of hysteresis existing in piezoelectric actuators. Finally, the corresponding experimental results on a piezoelectric actuator are demonstrated.
Serial versus parallel search: A model comparison approach based on reaction time distributions

ABSTRACT For 50 years or so, visual search experiments have been used to examine how humans find behaviourally relevant objects in complex visual scenes. For the same length of time, there has been a dispute over whether this search is performed in a serial or parallel fashion. In this paper, we approach this dispute by numerically fitting a serial search model and a parallel search model to reaction time (RT) distributions from three visual search experiments (feature search, conjunction search, spatial configuration search). In order to do so, we used a free-likelihood method based on a novel kernel density estimator (KDE). The serial search model was the Competitive Guided Search (CGS) model by Moran et al. [(2013). Competitive guided search: Meeting the challenge of benchmark RT distributions. Journal of Vision, 13(8), 24–24. ]. We were able to replicate the ability of CGS to model RT distributions from visual search experiments, and demonstrated that CGS generalizes well to new data. The parallel model was based on the biased-competition theory and utilized a very simple biologically-plausible winner-take-all (WTA) mechanism from Heinke and Humphreys’s [(2003). Attention, spatial representation and visual neglect: Simulating emergent attention and spatial memory in the Selective Attention for Identification Model (SAIM). Psychological Review, 110(1), 29–87. ]. With this mechanism, SAIM has been able to explain a broad range of attentional phenomena but it was not specifically designed to model RT distributions in visual search. Nevertheless, the WTA was able to reproduce these distributions. However, a direct comparison of the two models suggested that the serial CGS is slightly better equipped to explain the RT distributions than the WTA mechanism. The CGS’s success was mainly down the usage of the Wald distribution which was specifically designed to model visual search. Future WTA versions will have to find a biologically plausible mechanism to reproduce such a RT distribution. Finally, both models suffered from a failure to generalize across all display sizes. From these comparisons, we developed suggestions for improving the models and motivated empirical studies to devise a stronger test for the two types of searches.
Site-Directed Perturbation of Protein Kinase C- Integrin Interaction Blocks Carcinoma Cell Chemotaxis

ABSTRACT Polarized cell movement is an essential requisite for cancer metastasis; thus, interference with the tumor cell motility machinery would significantly modify its metastatic behavior. Protein kinase Cα (PKCα) has been implicated in the promotion of a migratory cell phenotype. We report that the phorbol ester-induced cell polarization and directional motility in breast carcinoma cells is determined by a 12-amino-acid motif (amino acids 313 to 325) within the PKCα V3 hinge domain. This motif is also required for a direct association between PKCα and β1 integrin. Efficient binding of β1 integrin to PKCα requires the presence of both NPXY motifs (Cyto-2 and Cyto-3) in the integrin distal cytoplasmic domains. A cell-permeant inhibitor based on the PKC-binding sequence of β1 integrin was shown to block both PKCα-driven and epidermal growth factor (EGF)-induced chemotaxis. When introduced as a minigene by retroviral transduction into human breast carcinoma cells, this inhibitor caused a striking reduction in chemotaxis towards an EGF gradient. Taken together, these findings identify a direct link between PKCα and β1 integrin that is critical for directed tumor cell migration. Importantly, our findings outline a new concept as to how carcinoma cell chemotaxis is enhanced and provide a conceptual basis for interfering with tumor cell dissemination.
A three-dimensional characterization space of software components for rapidly developing multimodal interfaces

In this paper we address the problem of the development of multimodal interfaces. We describe a three-dimensional characterization space for software components along with its implementation in a component-based platform for rapidly developing multimodal interfaces. By graphically assembling components, the designer/developer describes the transformation chain from physical devices to tasks and vice-versa. In this context, the key point is to identify generic components that can be reused for different multimodal applications. Nevertheless for flexibility purposes, a mixed approach that enables the designer to use both generic components and tailored components is required. As a consequence, our characterization space includes one axis dedicated to the reusability aspect of a component. The two other axes of our characterization space, respectively depict the role of the component in the data-flow from devices to tasks and the level of specification of the component. We illustrate our three dimensional characterization space as well as the implemented tool based on it using a multimodal map navigator.
An autonomous robotic system for load transportation

This paper presents an overview of an autonomous robotic system for material handling. The system is being developed by extending the functionalities of traditional AGVs to be able to operate reliably and safely in highly dynamic environments. Traditionally, the reliable functioning of AGVs relies on the availability of adequate infrastructure to support navigation. In the target environments of our system, such infrastructure is difficult to setup in an efficient way. Additionally, the location of objects to handle are unknown, which requires runtime object detection and tracking. Another requirement to be fulfilled by the system is the ability to generate trajectories dynamically, which is uncommon in industrial AGV systems.
Single-Layered Complex-Valued Neural Network with SMOTE for Imbalanced Data Classification

One of the most popular problems in the field of classification is how to treat imbalanced data. Improper treatment causes lowering of the learning accuracy and sensitivity of the classifier. In this research, we use the single-layered complexvalued neural network (CVNN) to classify imbalanced dataset. And also, in order to overcome the imbalanced data problem, we use the well-known over-sampling algorithm called the synthetic minority over-sampling technique (SMOTE) for the CVNN. The SMOTE is the technique which over-samples the minority class data. We use the five imbalanced datasets from the UCI depository and compared with real-valued neural networks (RVNN) to verify the efficiency of the CVNN with SMOTE for imbalanced data classification problems. As a result, the CVNN with SMOTE achieved better sensitivity and accuracy with most of the tested datasets than the counterpart.
Heavy-Tailed Symmetric Stochastic Neighbor Embedding

Stochastic Neighbor Embedding (SNE) has shown to be quite promising for data visualization. Currently, the most popular implementation, t-SNE, is restricted to a particular Student t-distribution as its embedding distribution. Moreover, it uses a gradient descent algorithm that may require users to tune parameters such as the learning step size, momentum, etc., in finding its optimum. In this paper, we propose the Heavy-tailed Symmetric Stochastic Neighbor Embedding (HSSNE) method, which is a generalization of the t-SNE to accommodate various heavy-tailed embedding similarity functions. With this generalization, we are presented with two difficulties. The first is how to select the best embedding similarity among all heavy-tailed functions and the second is how to optimize the objective function once the heavy-tailed function has been selected. Our contributions then are: (1) we point out that various heavy-tailed embedding similarities can be characterized by their negative score functions. Based on this finding, we present a parameterized subset of similarity functions for choosing the best tail-heaviness for HSSNE; (2) we present a fixed-point optimization algorithm that can be applied to all heavy-tailed functions and does not require the user to set any parameters; and (3) we present two empirical studies, one for unsupervised visualization showing that our optimization algorithm runs as fast and as good as the best known t-SNE implementation and the other for semi-supervised visualization showing quantitative superiority using the homogeneity measure as well as qualitative advantage in cluster separation over t-SNE.
Free Gaits for Quadruped Robots over Irregular Terrain

Although walking machines exhibit many advantages over wheeled or tracked vehicles, legged vehicles have yet to be introduced in real applications because of the primitive development of specific techniques such as gait generation. This article addresses the design, implementation and experimentation of gaits to negotiate uneven terrain with a real machine. The gaits presented are a mixture of free and discontinuous gaits. Discontinuous gaits were selected because of their ground adaptability features and ease of implementation, while free gaits were chosen because they facilitate path tracking. The fusion of these two main gaits plus the addition of extra constraints to avoid leg-transfer deadlocking produced a new free-crab gait, a free-spinning gait and a free-turning gait. Some experiments have been conducted to illustrate the features of these gaits on a real machine.
Constraint Solving on Terms : Syntactic Methods Preliminary lecture notes

As this has made been clear from the introductory lecture on constraints, constraints on terms (also called symbolic constraints) play a fundamental role in many if not all domains of computer science. In this lecture we are presenting the main results on symbolic constraint solving for equational unification, dis-unification and some ordering constraints. This document is in a preliminary from and all comments you may have on its form as well as on its background are very welcome.
Formation of Cavities, Filaments, and Clumps by the Nonlinear Development of Thermal and Gravitational Instabilities in the Interstellar Medium under Stellar Feedback

Based on our high-resolution two-dimensional hydrodynamical simulations, we propose that large cavities may be formed by the nonlinear development of the combined thermal and gravitational instabilities, without need for stellar energy injection in a galaxy modeling the Large Magellanic Cloud (LMC). Our numerical model of star formation allows us to follow the evolution of the blast waves due to supernovae in the inhomogenous, multiphase, and turbulent-like media self-consistently. Formation of kiloparsec-scale inhomogeneity, such as cavities as seen in the observed H I map of the LMC, is suppressed by frequent supernovae (the average supernova rate for the whole disk is ~0.001 yr-1). However, the supernova explosions are necessary for the hot component (Tg > 106-107 K). Position-velocity maps show that kiloparsec-scale shells/arcs formed through nonlinear evolution in a model without stellar energy feedback have kinematics similar to explosive phenomena, such as supernovae. We also find that dense clumps and filamentary structure are formed as a natural consequence of the nonlinear evolution of the multiphase interstellar medium (ISM). Although the ISM on a small scale looks turbulent-like and transient, the global structure of the ISM is quasi-stable. In the quasi-stable phase, the volume filling factor of the hot, warm, and cold components are ~0.2, ~0.6, and ~0.2, respectively. We compare observations of H I and molecular gas of the LMC with the numerically obtained H I and CO brightness temperature distributions. The morphology and statistical properties of the numerical H I and CO maps are discussed. We find that the cloud mass spectra of our models represent a power-law shape, but their slopes change between models with and without the stellar energy injection. We also find that the slope depends on the threshold brightness temperature of CO.
Stable, Globally Non-Iterative, Non-Overlapping Domain Decomposition Parallel Solvers for Parabolic Problems

In this paper, we report a class of stabilized explicit-implicit domain decomposition (SEIDD) methods for the parallel solution of parabolic problems, based on the explicit-implicit domain decomposition (EIDD) methods. EIDD methods are globally non-iterative, non-overlapping domain decomposition methods which, when compared with Schwarz alternating algorithm based parabolic solvers, are computationally and communicationally efficient for each simulation time step but suffer from time step size restrictions due to conditional stability or conditional consistency. By adding a stabilization step to the EIDD methods, the SEIDD methods are freed from time step size restrictions while retaining EIDD’s computational and communicational efficiency for each time step, rendering themselves excellent candidates for large-scale parallel simulations. Three algorithms of the SEIDD type are implemented, which are experimentally tested to show excellent stability, computation and communication efficiencies, and high parallel speedup and scalability.
Integrated fault-tolerant scheme for a DC speed drive

In this paper, an active fault-tolerant control (FTC) scheme is presented with disturbance compensation. Fault-detection and compensation are merged together to propose a robust algorithm against model uncertainties. The GIMC control architecture is used as a feedback configuration for the active fault-tolerant scheme. The synthesis procedure for the parameters of the fault-tolerant scheme is carried out by using tools of robust control theory. A detection filter is designed for fault isolation taking into account uncertainties and disturbances in the mathematical model. Finally, the fault compensation strategy incorporates an estimate of the disturbances into the system to improve the performance of the closed-loop systems after the fault is detected. In order to illustrate these ideas, the speed regulation of a dc motor is selected as a case study, and experimental results are reported.
A New “Community” Organization Approach for Elderly Persons with Multiple Mental Disorders

 This article presents a case study for the “community” approach to social work. It is the case of an elderly client whose social adaptation level had multiple problems associated with weakened networks at a higher rate area of seniors in Japan. Specifically, the “community” approach adopted here was the Refined Short-term Reconstructing Meaningful Life-worlds (RSRM), which defines “community” as a circular system of speech acts and meaning construction. This social constructionist approach is shown to improve the client’s adaptation level in the “community” where a differentiation force is activated using circular questions. This force allows the client to make beneficial life-worlds. The RSRM also has a concrete intervention procedure. First, the client, who was in her eighties with depression, delusional disorder, slight dementia, and urination neurosis, was encouraged to tell her problematic story. To differentiate speech acts in her “community”, circular questions, positive reframing and solution-focused skills were used. Then she was encouraged to practice her newly acquired speech acts in her “community”. She regained the ability to communicate with other persons with confidence, and she could extend the “community” networks to practice her newly acquired communication skills. Through her practices in her extended “community”, her multivariate problems were
Design and implementation of an automatic on-line diagnosis with TiDiaM and TiDE

The paper deals with the diagnostics of industrial automation devices and in particular with the real-time fault isolation. A diagnostic model-based method is proposed with the aim to isolate the occurrences of faults in automatic machinery simplifying the synthesis of the diagnostic models and the application of the developed technique to real cases. The theoretical foundation lays on the diagnoser automaton, which is suitably extended with time and with special modeling techniques that make it suitable for real-time execution rather than off-line test of the diagnosability. The model here adopted is called TiDiaM (Timed Diagnostic Model) for which automatic synthesis algorithms and a Java real-time executor (TiDE, Timed Diagnosis Executor) are proposed. The approach has been applied to a test case (the subsystem of an automatic machining centre called Pallet changer) in order to demonstrate its real-time applicability to an industrial example.