Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi Robot Exploration through Pruning Frontiers

In this paper, an approach to multi robot exploration is presented. One of the key issues in multi robot exploration is how to assign target locations to the individual robots and how to better distribute the robots over the environment. The proposed technique applies a well-known unsupervised clustering algorithm (k-means) in order to fairly divide the space into as many disjoint regions as available robots. Hungarian Method is used for the assignment of robots to the individual regions with the task to explore the corresponding area. To drive the robots around the environment, a frontier ‘regions on the boundary between open space and unexplored space’ based navigation strategy is used to decide where to move next, according to the data collected so far. Furthermore, we discuss improvements to the frontier based exploration strategy, by pruning the frontier cells that further reduces the computational time. The numbers of candidate locations are evaluated based on three criteria: number of unknown cells, number of known cells and real path travelling cost. Simulations are presented to show the performance of the proposed technique. This method can best be applied in search and rescue operations, partitioning helps to explore different regions of the workspace parallely by different robots instead of concentrating efforts in particular spot, pruning helps to make movement decisions much faster, the result is that the potential victims in a region will not have to wait much longer.
Evaluation of Computer Vision Algorithms for Autonomous Navigation in Polar Terrains

In this paper we address the issue of Computer Vision in Antarctica for robot navigation by analysing images collected at Patriot Hills, Antarctica in the Fall of 1998. Conditions produced by polar weather and terrain are unique and challenging for perception equipment and computer vision algorithms. The later aspect will be studied here through the evaluation of a colour segmentation algorithm, a stereovision algorithm and a feature points detector and tracker algorithm.
Enhanced wireless mesh networking for ns-2 simulator

The ns-2 simulator has limited support for simulating 802.11 based wireless mesh networks. We have added the following new features at the MAC and PHY layer of ns-2: (i) cumulative interference in SINR (Signal to Interference and Noise Ratio) computation, (ii) an accurate and combined shadow-fading module, (iii) multi-SINR and multi-rate link support, (iv) auto rate fallback (ARF) for rate adaptation, and (v) a framework for link probing and link quality estimation as required by most mesh routing protocols. We have made these modules publicly available. In this paper, we present an overview of these new features.
Molecular cloning and characterization of a human eotaxin receptor expressed selectively on eosinophils

The chemokine eotaxin is unusual in that it appears to be a highly specific chemoattractant for eosinophils. Ligand-binding studies with radiolabeled eotaxin demonstrated a receptor on eosinophils distinct from the known chemokine receptors CKR-1 and -2. The distinct eotaxin binding site on human eosinophils also bound RANTES (regulated on activation T expressed and secreted) and monocyte chemotactic protein (MCP)3. We have now isolated a cDNA from eosinophils, termed CKR-3, with significant sequence similarity to other well characterized chemokine receptors. Cells transfected with CKR-3 cDNA bound radiolabeled eotaxin specifically and with high affinity, comparable to the binding affinity observed with eosinophils. This receptor also bound RANTES and MCP-3 with high affinity, but not other CC or CXC chemokines. Furthermore, receptor transfectants generated in a murine B cell lymphoma cell line migrated in transwell chemotaxis assays to eotaxin, RANTES, and MCP-3, but not to any other chemokines. A monoclonal antibody recognizing CKR-3 was used to show that eosinophils, but not other leukocyte types, expressed this receptor. This pattern of expression was confirmed by Northern blot with RNA from highly purified leukocyte subsets. The restricted expression of CKR-3 on eosinophils and the fidelity of eotaxin binding to CKR-3, provides a potential mechanism for the selective recruitment and migration of eosinophils within tissues.
Social and nonsocial cognition in bipolar disorder and schizophrenia: relative levels of impairment.

OBJECTIVE This study aimed to determine the relative extent of impairment in social and nonsocial cognitive domains in patients with bipolar disorder compared with schizophrenia patients and healthy comparison subjects. METHODS Sixty-eight clinically stable outpatients with bipolar disorder, 38 clinically stable outpatients with schizophrenia, and 36 healthy comparison subjects completed a range of social (facial affect perception, emotional regulation, empathic accuracy, mental state attribution, and self-referential memory) and nonsocial (speed of processing, attention/vigilance, working memory, verbal memory, visual memory, and reasoning/problem solving) cognitive tasks. RESULTS For each social cognitive task, patients with bipolar disorder did not differ significantly from comparison subjects, and both groups performed better than schizophrenia patients. Within the bipolar group, clinical features and medication status were not related to social cognitive performance. Bipolar patients showed performance patterns across tasks (i.e., profiles) that were similar to those of comparison subjects on both social and nonsocial cognitive domains, whereas both groups differed from schizophrenia patients for both domains. Regarding relative impairment across the two cognitive domains, results revealed a significant group-by-domain interaction in which bipolar patients showed less impaired social than nonsocial cognition, while schizophrenia patients showed the opposite pattern. CONCLUSIONS Bipolar patients showed less impairment on social relative to nonsocial cognitive performance, whereas schizophrenia patients showed more impairment on social relative to nonsocial cognitive performance. These results suggest that these two cognitive domains play different roles in bipolar disorder compared with in schizophrenia.
A novel interleaved boost converter with voltage multiplier cell

The input supply available from renewable sources like solar and fuel cells will be at low level. This must be stepped up considerably for practical utilization. In this paper, a boost converter derived topology consisting of two interleaved stages and five voltage multiplier cells (VMC) is presented. The proposed topology uses two switches with two inductors for the interleaved stage, 12 diodes and capacitors including one voltage lift capacitor. In general, (2N+2) diodes and capacitors are required to obtain a voltage gain which is (N+2) times that of a conventional boost converter. The voltage lift capacitor facilitates addition of outputs obtained from two interleaved stages. The main advantage of this topology is that the voltage stress across the main switches is equal to that of a conventional boost converter. Other advantages are continuous input current, large gain without high duty cycle or transformer and modularity. Experimental results obtained from the 24V/230V, 100W prototype demonstrate the voltage gain capability of the converter and validates the converter design.
Reducing testing affine spaces to testing linearity

We consider the task of testing whether a Boolean function f : {0, 1} → {0, 1} is the indicator function of an (l − k)-dimensional affine space. An optimal tester for this property was presented by Parnas, Ron, and Samorodnitsky (SIDMA, 2002), by mimicking the celebrated linearity tester (of Blum, Luby and Rubinfeld, JCSS, 1993) and its analysis. We show that the former task (i.e., testing (l−k)-dimensional affine spaces) can be reduced to testing the linearity of a related function g : {0, 1} → {0, 1}, yielding an almost optimal tester. In addition, we show that testing monomials can be performed by using the foregoing reduction and reducing part of the analysis to the analysis of the dictatorship test.
Object tracking and classification using a multiple hypothesis approach

In this paper, an algorithm for tracking objects in traffic scenes using a multiple hypothesis approach is presented. The sensor used to get information about the environment is a laser range finder. This sensor has the advantage of obtaining accurate distance and geometric information of the objects in front of the car. In order to achieve a robust classification, geometric information is not always sufficient enough, if objects are separated into multiple parts, due to occlusions or bad segmentation. Therefore the presented method allows several classification results and keeps track of all feasible combinations of disintegrated objects. Thus, it is possible, to process several hypotheses of objects and no objects can be missed.
Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models

We present an efficient algorithm for the inference of stochastic block models in large networks. The algorithm can be used as an optimized Markov chain Monte Carlo (MCMC) method, with a fast mixing time and a much reduced susceptibility to getting trapped in metastable states, or as a greedy agglomerative heuristic, with an almost linear O(Nln2N) complexity, where N is the number of nodes in the network, independent of the number of blocks being inferred. We show that the heuristic is capable of delivering results which are indistinguishable from the more exact and numerically expensive MCMC method in many artificial and empirical networks, despite being much faster. The method is entirely unbiased towards any specific mixing pattern, and in particular it does not favor assortative community structures.
Plasmon Excitation of Atomic Clusters and Their Assembly by Electrons and Photons

The structures and properties of the atomic clusters are neither like those of the corresponding atoms and molecules nor bulk solid. It is quite natural that the materials composed of those clusters exhibit some new properties. Moreover, cluster assemblies have shown even large variety of optical, electric and magnetic responses due to localized coupling between the clusters, leading to the enhancement of certain unusual properties. In this review we present some examples such as the enhanced Raman scattering of ordered lattice of metal clusters, multi-phase plasmon excitation of silver cluster dimer and cluster chains by electrons and photons. We demonstrate that the novel properties of these cluster assemblies could have potential applications in manufacturing new-type nanomaterials and quantum devices, such as plasmonics and nano-optical devices.
Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection

We address the problem of estimating the ratio of two probability density functions (a.k.a. the importance). The importance values can be used for various succeeding tasks such as non-stationarity adaptation or outlier detection. In this paper, we propose a new importance estimation method that has a closed-form solution; the leave-one-out cross-validation score can also be computed analytically. Therefore, the proposed method is computationally very efficient and numerically stable. We also elucidate theoretical properties of the proposed method such as the convergence rate and approximation error bound. Numerical experiments show that the proposed method is comparable to the best existing method in accuracy, while it is computationally more efficient than competing approaches.
KESeDa: Knowledge Extraction from Heterogeneous Semi-Structured Data Sources

A large part of the free knowledge existing on the Web is available as heterogeneous, semi-structured data, which is only weakly interlinked and in general does not include any semantic classification. Due to the enormous amount of information the necessary preparation of this data for integrating it in the Web of Data requires automated processes. The extraction of knowledge from structured as well as unstructured data has already been the topic of research. But especially for the semi-structured data format JSON, which is widely used as a data exchange format e.g., in social networks, extraction solutions are missing. Based on the findings we made by analyzing existing extraction methods, we present our KESeDa approach for extracting knowledge from heterogeneous, semi-structured data sources. We show how knowledge can be extracted by describing different analysis and processing steps. With the resulting semantically enriched data the potential of Linked Data can be utilized.
Optimization of Resistance Spot Welding on Aluminum Magnesium 5052 Grade with 23 Factorial Designs

Aluminum magnesium alloys 5052 grade are also attractive in these fields due to their lower density and high specific strength. Therefore, it can be expected that the joining of aluminum alloys and magnesium alloys needs to be solved in industrial application. This research was study the effect of resistance spot welding (RSW) parameter on mechanical properties and macro structure. The specimen was aluminum magnesium 5052 grade sheet of 1 mm thickness. This experimental study aims at 23 factorial design optimizing various RSW parameters including welding current at 14,500 and 15,500 amp, welding time at 50 and 60 cycle and electrode force were set at 1.2 and 2.2 kN. The welded specimens were tested by tensile shear testing according to JIS Z 3136: 1999 and macro structure testing according to JIS Z 3139: 1978. The result showed that both of welding current, welding time and electrode force had interaction on tensile shear and nugget size at 95% confidential (P value < 0.05). Factors affecting the optimum were welding current of 14,500 amp. , welding time of 60 cycle and electrode force of 1.2 kN. This research can bring information to the foundation in choosing the appropriate parameters to RSW process.
Bootstrapping a spoken language identification system using unsupervised integrated sensing and processing decision trees

In many inference and learning tasks, collecting large amounts of labeled training data is time consuming and expensive, and oftentimes impractical. Thus, being able to efficiently use small amounts of labeled data with an abundance of unlabeled data—the topic of semi-supervised learning (SSL) [1]—has garnered much attention. In this paper, we look at the problem of choosing these small amounts of labeled data, the first step in a bootstrapping paradigm. Contrary to traditional active learning where an initial trained model is employed to select the unlabeled data points which would be most informative if labeled, our selection has to be done in an unsupervised way, as we do not even have labeled data to train an initial model. We propose using unsupervised clustering algorithms, in particular integrated sensing and processing decision trees (ISPDTs) [2], to select small amounts of data to label and subsequently use in SSL (e.g. transductive SVMs). In a language identification task on the CallFriend1 and 2003 NIST Language Recognition Evaluation corpora [3], we demonstrate that the proposed method results in significantly improved performance over random selection of equivalently sized training data.
An experimentally validated panel of subfamily‐specific oligonucleotide primers (Vα1‐w29/Vβ1‐w24) for the study of human T cell receptor variable V gene segment usage by polymerase chain reaction

We report here the characterization of a series of T cell receptor (TcR) Vα or Vβ subfamily‐specific oligonucleotide primers. Criteria that have guided the design of each oligonucleotide include appropriate thermodynamic parameters as well as differential base‐pairing scores with related and unrelated target sequences. The specificity of the oligonucleotides for each Vα or Vβ subfamily was tested by polymerase chain reaction (PCR) on both a series of TcR encoding plasmid DNA and clonal T cell populations. Unexpected cross‐reactivities were observed with plasmid cDNA sequences corresponding to unrelated subfamily gene segments. This led to the synthesis of additional series of oligonucleotides to obtain a relevant panel. A series of Vα1‐w29/Vβ1‐w24 TcR subfamily‐specific oligonucleotides was eventually selected which generates little, if any, cross‐reactivity. The use of Cα or Cβ primers for the amplification of internal positive control templates (i.e. Cβ for the Vα series and Cα for the Vβ series) has been tested in PCR performed with cDNA derived from peripheral blood lymphocytes; it was shown not to alter the amplification of the V subfamily‐specific DNA fragments. This panel of oligonucleotides will be helpful in the study of TcR V gene segment usage and, thus, may lead to a better characterization of T cell responses in physiological and pathological situations.
End-User Mapping: Next Generation Request Routing for Content Delivery

Content Delivery Networks (CDNs) deliver much of the world's web, video, and application content on the Internet today. A key component of a CDN is the mapping system that uses the DNS protocol to route each client's request to a ``proximal'' server that serves the requested content. While traditional mapping systems identify a client using the IP of its name server, we describe our experience in building and rolling-out a novel system called end-user mapping that identifies the client directly by using a prefix of the client's IP address. Using measurements from Akamai's production network during the roll-out, we show that end-user mapping provides significant performance benefits for clients who use public resolvers, including an eight-fold decrease in mapping distance, a two-fold decrease in RTT and content download time, and a 30% improvement in the time-to-first byte. We also quantify the scaling challenges in implementing end-user mapping such as the 8-fold increase in DNS queries. Finally, we show that a CDN with a larger number of deployment locations is likely to benefit more from end-user mapping than a CDN with a smaller number of deployments.
Medical Color Image Enhancement using Wavelet Transform and Contrast Stretching Technique

Low contrast and poor quality images are the main problems in the medical field. Wavelet transform based techniques are of greater interest because of their performance over Fourier and other spatial domain techniques. By using the wavelet transforms using Haar wavelet followed by the Laplacian operator to obtain the sharpened image gives a novel method for medical image enhancement. First, a medical image is decomposed with wavelet transform. Secondly, all highfrequency sub-images were decomposed with Haar transform. The contrast of the image is adjusted by linear contrast enhancement approaches. Filters are applied to identify the edges. Finally, the enhanced image was obtained by subtracting resulting image from the original image. Experiments showed that this method can not only enhance an image’s details but can also preserve its edge features effectively.
Familial hypercholesterolaemia in patients with ischaemic stroke or transient ischaemic attack

Identification of patients with familial hypercholesterolaemia (FH) is a prerequisite for the appropriate management of their excess cardiovascular risk. It is currently unknown how many patients with acute ischaemic stroke or transient ischaemic attack (TIA) are affected by FH and whether systematic screening for FH is warranted in these patients.
Performance and Scalability of Client-Server Database Architectures

Recent developments in software and hardware changed the way database systems are built and operate. In this paper we present database architectures based on the Client-Server paradigm and study their performance and scalability under different query/update workloads. The architectures are: Standard Client-Server, ClientServer with Multiple Disks, and Enhanced Client-Server. Data replication and client query result caching are used as the main mechanisms to improve the query throughput. The role of the server is to maintain system-wide data consistency and in the case of Enhanced ClientServer to selectively propagate updates on demand. Our study shows that except for the case of mostly update workloads, the Standard Client-Server architecture is outperformed by the other two architectures by one or more orders of magnitude. The Client-Server with Multiple Disks architecture offers performance comparable to that achieved by the Enhanced ClientServer for up to 100 clients, but the latter scales up a lot better for higher number of clients.
European Conference on Information Systems ( ECIS ) 5-2-2012 THE KNOWLEDGE CUBE : SCAFFOLDING FOR A BODY OF KNOWLEDGE ABOUT INFORMATION SYSTEMS

This paper answers calls for a body of knowledge (BoK) for the IS field. Its new approach for thinking about a BoK provides a path for addressing a longstanding problem in the IS discipline. The paper proposes organizing knowledge as a three dimensional "knowledge cube" that goes beyond compilations of categories of knowledge, sets of generalizations, and sets of documents. The structure of the knowledge cube is based on recognition that "work system in general" is a general case whose subordinate cases include information system in general and project in general. In turn, the special cases have their own special cases. The knowledge cube starts with a 10 X 10 layer for compiling concepts, principles and generalizations, and empirical findings related to the nine elements of the work system framework and "work system as a whole. " Those properties are tentatively inherited by special cases, with the caveat that BoK developers may remove properties that are not relevant to special cases and that special cases may have additional properties that are not relevant to more general cases. The paper presents examples that illustrate these points. It also discusses next steps and important issues revealed by the knowledge cube's structure.