Miracle-dz/UniFAR-FTUs huggingface dataset:
Synchronization of inhibitory coupled Hindmarsh-Rose neurons via adaptive sliding mode control

This paper presents an adaptive neural network (NN) sliding mode control for synchronization of inhibitory coupled Hindmarsh-Rose (HR) neurons. A single HR neuron may exhibit spike-burst chaotic behaviors. Inhibitory coupling makes two HR neurons behaves in anti-phase quasi-synchronization mode. We first derive the sliding mode controller via active control strategy. Then, a simple radial basis function (RBF) NN is designed to approximate the uncertain nonlinear part of the error dynamical system which has been assumed to be available in the active control. The weights of the NN are tuned on-line based on the sliding mode reaching law. According to the Lyapunov stability theory, the stability of the closed-loop error system is guaranteed. Synchronization is obtained by proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method.
3-D-Laser-Based Scene Measurement and Place Recognition for Mobile Robots in Dynamic Indoor Environments

Active environment perception and autonomous place recognition play a key role for mobile robots to operate within a cluttered indoor environment with dynamic changes. This paper presents a 3-D-laser-based scene measurement technique and a novel place recognition method to deal with the random disturbances caused by unexpected movements of people and other objects. The proposed approach can extract and match the Speeded-Up Robust Features (SURFs) from bearing-angle images generated by a self-built rotating 3-D laser scanner. It can cope with the irregular disturbance of moving objects and the problem of observing-location changes of the laser scanner. Both global metric information and local SURF features are extracted from 3-D laser point clouds and 2-D bearing-angle images, respectively. A large-scale indoor environment with over 1600 m2 and 30 offices is selected as a testing site, and a mobile robot, i.e., SmartROB2, is deployed for conducting experiments. Experimental results show that the proposed 3-D-laser-based scene measurement technique and place recognition approach are effective and provide robust performance of place recognition in a dynamic indoor environment.
A Cluster-Based Power-Efficient MAC Scheme for Sensor Networks

In developing an architecture for wireless sensor networks (WSNs) that is extensible to hundreds of thousands of heterogeneous nodes, fundamental advances in energy efficient communication protocols must occur. In this paper, we first propose an energy-efficient and robust intra-cluster communication bit-map assisted (BMA) MAC protocol for large-scale cluster-based WSNs and then derive energy models for BMA, conventional TDMA, and energy efficient TDMA (E-TDMA) using two different approaches. We use simulation to validate these analytical models. BMA is intended for event-driven sensing applications, that is, sensor nodes forward data to the cluster head only if significant events are observed. It has low complexity and utilizes a dynamic scheduling scheme. Clustering is a promising distributing technique used in large-scale WSNs, and when combined with an appropriate MAC scheme, high energy efficiency can be achieved. The results indicate that BMA can improve the performance of wireless sensor networks by reducing energy expenditure and packet latency. The performance of BMA as an intra-cluster MAC scheme relative to E-TDMA depends on the sensor node traffic offer load and several other key system parameters. For most sensor-based applications, the values of these parameters can be constrained such that BMA provides enhanced performance.
DATA HIDING ON IMAGE BY USING PARTICLE SWARM OPTIMIZATION AND HISTOGRAM MODIFICATION

Steganography is used to hide a secret message within a cover image, thereby yielding a stegoimage such that even the trace of the presence of secret information is wiped out. This project proposes a novel prediction based reversible steganographic scheme based on image Inpainting. First, reference pixels are chosen by using Particle Swarm Optimization technique. Then, In order to generate a prediction image, the image Inpainting technique based on partial differential equations is introduced, which has similar structural and geometric information as the cover image. Finally, by using the two selected groups of peak points and zero points, the histogram of the prediction error is shifted to embed the secret bits reversibly. Since the same reference pixels can be exploited in the extraction procedure, the embedded secret bits can be extracted from the stego image correctly, and the cover image can be restored lossless. Through the use of the PSO algorithm and the inpainting predictor, the prediction accuracy is high, and more embeddable pixels are acquired. Thus, this project provides a greater embedding rate and better visual quality compared with recently reported methods.
A novel phase current reconstruction method using a single DC-link current sensor

Instantaneous phase currents are required for successful operation of three-phase current controllers. The technique for reconstructing motor phase currents using information from a single current sensor in the dc-link of an inverter has been applied to reduce the cost, weight and volume of system. However, the duration of an active switching state may be so short that the dc-link current cannot be measured reliably. This paper presents an improved PWM modulation strategy named switching state axial translation (SSAT) to overcome this problem. The new SSAT method modifies the PWM voltage vectors by moving the switching state waveforms along the axis when the phase currents cannot be measured. A combination of analytical and experimental results shows that this method offers an attractive phase current reconstruction performance.
HRM and KM As Catalysts Of Environmental Performance: A Conceptual Framework

The purpose of this paper is to develop a theoretical framework to investigate the relationships among human resource management (HRM), knowledge management (KM) and environmental performance (EP). Environmental friendly business activities demand high level of human resource competency in terms of knowledge, skills and capabilities in order to better firm performance. This paper serves to establish the link among HRM, KM, and EP to fill the gap in the current literature. The research model is proposed based on reviews of the literature on HR and KM perspectives on environmental management and performance. Both theoretical and practical implications are discussed in this paper. With new knowledge gained on the associations among HRM, KM and EP, manufacturing firm managers can focus their effort and resources on HRM and KM to deliver better environmental management effectiveness. It is anticipated that this paper will contribute towards sustainability development for a better living environment.
The Redesign of the Matching Market for American Physicians: Some Engineering Aspects of Economic Design

We report on the design of the new clearinghouse adopted by the National Resident Matching Program, which annually fills approximately 20,000 jobs for new physicians. Because the market has complementarities between applicants and between positions, the theory of simple matching markets does not apply directly. However, computational experiments show the theory provides good approximations. Furthermore, the set of stable matchings, and the opportunities for strategic manipulation, are surprisingly small. A new kind of "core convergence" result explains this; that each applicant interviews only a small fraction of available positions is important. We also describe engineering aspects of the design process.
A model predictive Cooperative Adaptive Cruise Control approach

Reduction of fuel consumption is one of the primary goals of modern automotive engineering. While in the past the focus was on more efficient engine design and control there is an upcoming interest on economic context aware control of the complete vehicle. Technical progress will enable future vehicles to interact with other traffic participants and the surrounding infrastructure, collecting information which allow for reduction of fuel consumption by predictive vehicle control strategies. The principle of Model Predictive Control allows a straightforward integration of e.g. navigation systems, on-board radar sensors, V2V- and V2I-communication whilst regarding constraints and dynamic of the system. This paper presents a Linear Model Predictive Control approach to Cooperative Adaptive Cruise Control, directly minimizing the fuel consumption rather than the acceleration of the vehicle. To this end the nonlinear static fuel consumption map of the internal combustion engine is included into the control design by a piecewise quadratic approximation. Inclusion of a linear spacing policy prevents rear end collisions. Simulation results demonstrate the fuel and road capacity benefits, for a single vehicle and for a string of vehicles, equipped with the proposed control, in comparison to vehicles operated by a non-cooperative adaptive cruise control. Full information on the speed prediction of the predecessor is assumed, hence the purpose of this paper is twofold. On the one hand, best achievable benefits, of the proposed control, due to perfect prediction are demonstrated. On the other hand, the paper studies the behavior of the considered control and the influence of the prediction horizon.
On approximating the depth and related problems

In this paper, we study the problem of finding a disk covering the largest number of red points, while avoiding all the blue points. We reduce it to the question of finding a deepest point in an arrangement of pseudodisks and provide a near-linear expected-time randomized approximation algorithm for this problem. As an application of our techniques, we show how to solve linear programming with violations approximately. We also prove that approximate range counting has roughly the same time and space complexity as answering emptiness range queries.
Diverse Secreted Effectors Are Required for Salmonella Persistence in a Mouse Infection Model

Salmonella enterica serovar Typhimurium causes typhoid-like disease in mice and is a model of typhoid fever in humans. One of the hallmarks of typhoid is persistence, the ability of the bacteria to survive in the host weeks after infection. Virulence factors called effectors facilitate this process by direct transfer to the cytoplasm of infected cells thereby subverting cellular processes. Secretion of effectors to the cell cytoplasm takes place through multiple routes, including two separate type III secretion (T3SS) apparati as well as outer membrane vesicles. The two T3SS are encoded on separate pathogenicity islands, SPI-1 and -2, with SPI-1 more strongly associated with the intestinal phase of infection, and SPI-2 with the systemic phase. Both T3SS are required for persistence, but the effectors required have not been systematically evaluated. In this study, mutations in 48 described effectors were tested for persistence. We replaced each effector with a specific DNA barcode sequence by allelic exchange and co-infected with a wild-type reference to calculate the ratio of wild-type parent to mutant at different times after infection. The competitive index (CI) was determined by quantitative PCR in which primers that correspond to the barcode were used for amplification. Mutations in all but seven effectors reduced persistence demonstrating that most effectors were required. One exception was CigR, a recently discovered effector that is widely conserved throughout enteric bacteria. Deletion of cigR increased lethality, suggesting that it may be an anti-virulence factor. The fact that almost all Salmonella effectors are required for persistence argues against redundant functions. This is different from effector repertoires in other intracellular pathogens such as Legionella.
A 1.1-Gbit/s 10-GHz Outphasing Modulator With 23-dBm Output Power and 60-dB Dynamic Range in 45-nm CMOS SOI

A 10-GHz outphasing modulator is implemented in a 45-nm CMOS silicon-on-insulator process. The modulator is designed to provide high linearity and can operate at high data rates by using 256-QAM while maintaining low error vector magnitude (EVM). Four high-speed 10-bit digital-to-analog converters (DACs) are integrated with dual in-phase and quadrature upconverters. To deliver high output power to an off-chip power amplifier, stacked field-effect transistor current buffers are used to isolate the modulator from the load and mitigate device breakdown. As a result, this modulator delivers 23 dBm to a differential 100- Ω load. The high-resolution DACs provide a fine control of the phase between the outphased signals and support more than 60 dB of dynamic range and power steps smaller than 1 dB over the entire output power range. The outphasing modulator demonstrates an EVM of 2.2% at 80 Mbit/s and an EVM of 3.4% at 1.1 Gbit/s for 256-QAM. To our knowledge, this is the first demonstration of an outphasing modulator operating above 1 Gb/s.
Retweet prediction considering user's difference as an author and retweeter

Social network is a hot topic of interest for the researchers in the field of computer science in recent years. The vast amount of data generated by these social networks play a very important role in information diffusion. Social network data are generated by its users. So, user's behavior and activities are being investigated by the researchers to get a logical view of social network platform. This research proposed a novel retweet prediction model which considers difference in user's behavior as an author (as reflected in the tweets) and a retweeter (as reflected in the retweets) and do the prediction accordingly. The proposed retweet prediction strategy taking this difference into consideration, gave better prediction accuracy than the conventional strategy. The findings of this research explains that in social networks, some users show different behavior indifferent roles and these differences may have impact on future research.
A Hierarchic Architecture for Conceptual Information Retrieval

Conceptual retrieval returns information related to a speciic topic but not restricted to a query term. A common approach is to compare the query with all the documents in the database. When the number of documents is large, the searching time becomes signiicant. In this paper, we propose a hierarchic architecture which integrates latent semantic indexing (LSI) and hierarchic agglomerative clustering to reduce the searching time. We employ three clustering algorithms (single link, complete link, and group average) and conduct experiments on four standard document collections (CACM, CISI, CRAN, and MED). The experimental results show our method requires less searching time while maintaining comparable retrieval eeectiveness as non-clustered search.
Bayesian source separation and localization

The problem of mixed signals occurs in many different contexts; one of the most familiar being acoustics. The forward problem in acoustics consists of finding the sound pressure levels at various detectors resulting from sound signals emanating from the active acoustic sources. The inverse problem consists of using the sound recorded by the detectors to separate the signals and recover the original source waveforms. In general, the inverse problem is unsolvable without additional information. This general problem is called source separation, and several techniques have been developed that utilize maximum entropy, minimum mutual information, and maximum likelihood. In previous work it has been demonstrated that these techniques can be recast in a Bayesian framework. This paper demonstrates the power of the Bayesian approach, which provides a natural means for incorporating prior information into a source model. An algorithm is developed that utilizes information regarding both the statistics of the amplitudes of the signals meted by the sources and the relative locations of the detectors. Using this prior information,the algorithm finds the most probable source behavior and configuration. Thus, the inverse problem can be solved by simultaneously performing source separation and localization. It should be noted that this algorithm is not designed to account for delay times that are often important in acoustic source separation. However, a possible application of this algorithm is in the separation of electrophysiological signals obtained using electroencephalography and magnetoencephalography.
Implementation of CG Method on GPU Cluster with Proprietary Interconnect TCA for GPU Direct Communication

We have been developing a proprietary interconnect technology called Tightly Coupled Accelerators (TCA) architecture to improve communication latency and bandwidth between compute nodes on a GPU cluster. This paper describes the Conjugate Gradient (CG) method implementation using TCA and results of performance evaluation on the HA-PACS/TCA system, which is a proof-of-concept GPU cluster based on the TCA concept. The implementation uses the TCA for all gather and all reduce collective communications. Comparison results between the implementation using TCA and an implementation using MPI show that the TCA contributes to reduce latency for relatively small data gathering on the all gather and demonstrate about twice faster speed on the all reduce. As a result, the CG method implementation using TCA outperforms the implementation using MPI for sparse matrices whose matrix size is thousands to tens of thousands.
Perturbation analysis of mel-frequency cepstrum coefficients

Mel-frequency cepstrum coefficient (MFCC) is a widely used feature vector in speech signal precessing. Its feature extraction procedure can be seen as a mapping function which transfers the input speech signals to output MFCC feature vectors. However, this function is too complex to analyze and even a simple approximation is not easy to obtain. This paper studies the effects of each MFCC feature extraction step and obtains the relation between the input signal-to-noise ratio (SNR) and the output perturbation bound of MFCC feature vectors. Experimental results show that the obtained bounds are ”tight” and nearly full covered. This analysis method may help us to find new clue of MFCC and may has potential applications in speech recognition.
Fielded Sequential Dependence Model for Ad-Hoc Entity Retrieval in the Web of Data

Previously proposed approaches to ad-hoc entity retrieval in the Web of Data (ERWD) used multi-fielded representation of entities and relied on standard unigram bag-of-words retrieval models. Although retrieval models incorporating term dependencies have been shown to be significantly more effective than the unigram bag-of-words ones for ad hoc document retrieval, it is not known whether accounting for term dependencies can improve retrieval from the Web of Data. In this work, we propose a novel retrieval model that incorporates term dependencies into structured document retrieval and apply it to the task of ERWD. In the proposed model, the document field weights and the relative importance of unigrams and bigrams are optimized with respect to the target retrieval metric using a learning-to-rank method. Experiments on a publicly available benchmark indicate significant improvement of the accuracy of retrieval results by the proposed model over state-of-the-art retrieval models for ERWD.
On the Use of Correspondence Analysis to Learn Seed Ontologies from Text

In the present work we show our approach to generate hierarchies of concepts in the form of ontologies starting from free text. This approach relies on the statistical model of Correspondence Analysis to analyze term occurrences in text, identify the main concepts it refers to, and retrieve semantic relationships between them. We present a tool which is able to apply different methods for the generation of ontologies from text, namely hierarchy generation from hierarchical clustering representation, search for Hearst Patterns on the Web, and bootstrapping. Our evaluation shows that the precision in the generation of hierarchies of the tool is attested to be around 60% for the best automatic approach and around 90% for the best human-assisted
Proximity2-aware ranking for textual, temporal, and geographic queries

Temporal and geographic information needs are frequent and important but not well served by standard IR systems. Recent approaches address such needs by extracting and normalizing temporal and geographic expressions from documents. They calculate specific scores for the temporal and/ or geographic parts of a query. However, all approaches assume independence between the different query parts. In this paper, we present a new model to rank documents according to combined textual, temporal, and geographic queries. The independence assumption between the query parts is eliminated by calculating proximity scores. Thus, documents are regarded to be more relevant if terms and expressions satisfying the different query parts occur close to each other in a document. As our evaluations based on the NTCIR-GeoTime data show, our proposed model outperforms baseline models that do not use proximity information.
Laparoscopic Suturing and Knot Tying: A Comparison of Standard Techniques to a Mechanical Assist Device

Background: Suturing and knot tying are basic skills for surgeons. Performing these tasks laparoscopically can be a tedious, time-consuming endeavor associated with much frustration. We evaluated a mechanically assisted suture and pretied knot device (Quik-Stitch) for performing the basic tasks of suturing and knot tying. Methods: We performed a time study using 1) intracorporeal suturing and knot tying, 2) intracorporeal suturing and extracorporeal knot tying, and 3) a mechanically assisted suture and pretied knot device (Quik-Stitch). From September 2000 through March 2001, time trials were conducted using each of the different techniques. Three attending surgeons, one with much experience and 2 with less experience, and 2 chief residents, with the least experience, participated in the study. Results: For the experienced surgeon, the average times for intracorporeal knot tying, extracorporeal knot tying, and knot tying with Quik-Stitch were 97.3, 103.9, and 67.7 seconds, respectively. For the less experienced surgeons, the times were 237.2, 224.3, and 92.5 seconds, respectively. For the least experienced group, the times were 265.3, 263.0, and 128.7 seconds, respectively. Conclusions: The mechanically assisted suture device and pretied knot (Quik-Stitch by PARE Surgical, Inc, Englewood, CO, USA) provides significant time-saving to surgeons regardless of experience and thus reduces operating room costs. Less experienced surgeons and surgeons in training benefited the most by the use of this device.