Miracle-dz/UniFAR-FTUs huggingface dataset:
Redundant baseline calibration in CHIME : a first implementation & application as beam probe

The nature of dark energy is one of the most intriguing scientific questions of the twenty-first century. There are many ways to probe dark energy, but one method involves detecting baryon acoustic oscillations (BAO) throughout the universe’s history. BAO have a characteristic size scale and therefore act as a “standard ruler,” an advantageous property for a method of tracking the universe’s expansion history. While baryon acoustic oscillations can be probed in many ways, one of the most intriguing and promising methods is through twenty-one centimeter hydrogen intensity mapping. Several experiments devoted to twenty-one centimeter hydrogen mapping will be coming on line in coming years, and these experiments have stringent calibration requirements due to the need to remove bright foreground signals. These calibration requirements necessitate new and improved methods for calibration. One proposed method is redundant baseline calibration, a selfcalibration method which takes advantage of the massively redundant designs of many hydrogen intensity mapping experiments. With the Canadian Hydrogen Intensity Mapping Experiment as a test case, we demonstrate that the redundant baseline method is effective in even its simplest implementation for an idealized version of a real telescope. We then show that redundant baseline calibration fails in real CHIME Pathfinder data in a way that is consistent with deviations from redundancy observed in processed CHIME Pathfinder data. These deviations from redundancy are themselves consistent with the effects of feed-to-feed beam pattern variations, a possibility not considered in the conventional redundant baseline calibration algorithm. We simulate the CHIME Pathfinder including beam width perturbations and verify that similar failures in the redundant baseline calibration can be generated
Handheld Devices for Control

With present and future wireless technologies, such as IEEE 802.11, BlueTooth, RF-Lite, and G3, handheld devices will frequently be in close, interactive communication. Many environments, including offices, meeting rooms, automobiles and classrooms already contain many computers and computerized appliances, and the smart homes of the future will have ubiquitous embedded computation. When the user enters one of these en- vironments carrying a handheld device, how will that device interact with the environment? We are exploring, as part of the Pebbles research project, the many ways that handheld devices such as PalmOS Organizers, PocketPC and Windows CE devices, and smart cell phones can serve as useful adjuncts to the "fixed" computers and com- puterized appliances in the user's vicinity. This brings up many interesting research questions, such as: How can the handheld device improve the user interfaces of everything else in the user's environment, rather than being just be another complex gadget that must be learned? What is the best way to provide a user interface that spans mul- tiple devices that are in use at the same time? How will users and systems decide which functions should be pre- sented and in what manner on what device? How can the user's handheld device be effectively used as a "Personal Universal Controller" to provide an easy-to-use and familiar interface to all of the complex appliances available to a user? How can communicating handheld devices enhance the effectiveness of meetings and classroom lectures? We present some preliminary observations on these issues, and discuss some of the systems that we have built to investigate them.
Harnessing online social media to deal with information overload

In online social media, users become information creators and disseminators through the active interplay between information items and other users, instead of just being information consumers of a decade ago. This kind of information production and dissemination in collaborative and active manner further aggravates the problem of information overload on the World Wide Web (WWW). The existing approaches for information retrieval (IR) and natural language processing (NLP) tasks often offer an intolerable response time for Web users. Moreover, given the numerous interactions between users and information items, new kinds of information needs are emerging, such as opinion mining, event detection and summarization, etc. However, the existing IR technologies (based on bag-of-word model), and NLP technologies (based on the linguistical features), often fail to satisfy the web users in these emerging information needs. On the other hand, people participate in online social media to share stories, photos with their friends, vote and leave opinions, or tag web pages, and so on. The digital footprints of these behaviors make online social media semantic resources which we can exploit to better understand and organize the astronomical information. In this dissertation, we first analyze online social media as multi-dimensional social network by taking Wikipedia as a case study. We find that given the multiple relations exposed from different perspectives in the network, focusing on only one specific relation could lead to biased or even wrong conclusion. Traditional information retrieval approaches are mainly bag-of-word model and keyword based, which ignore the word
Revue de la boucle a remodulation pour la synchronisation de phase en modulation lineaire multi-voie avec trajets multiples. Application en CDMA et OFDM

Cet article traite de la synchronisation de phase pour un recepteur radio-mobile en voie descendante, lorsque les trajets multiples du canal de propagation sont soumis a des variations de phases, mais avec des delais quasi-constants sur la duree d'un bloc de donnees (mode “ burst ”). On suppose un systeme assez general de modulation lineaire multi-voie, englobant notamment les cas des modulations CDMA et OFDM. Nous proposons une discussion generale du probleme s'appuyant sur un formalisme par formes d'ondes. Pour cela, nous partons de la boucle a re-modulation (“Decision-Directed-Loop'') standard utilisee en contexte mono-trajet et mono-utilisateur, et discutons de son utilisation en contexte a trajets et voies. L'extension de l'algorithme et les difficultes associees apparaissent ainsi de nature completement differentes en CDMA et en OFDM, malgre un formalisme de modulation identique. Ceci est mis en evidence a l'aide d'illustrations s'appuyant sur les formes d'ondes specifiques a chaque modulation (large-bande en CDMA, bande-etroite en OFDM mais avec une absence d'interference preservee en presence de trajets multiples). Pour finir, nous presentons, pour le cas CDMA, une amelioration de la boucle standard, qui permet de fonctionner en presence de trajets non resolus, et de suivre de rapides variations de phases. L'amelioration consiste a inserer un pre-filtre dans la boucle de phase de chaque trajet, afin de corriger les formes d'ondes et rendre l'estimation independante (a retards constants) des autres trajets.
A Tabu Search, Augment-Merge Heuristic to Solve the Stochastic Location Arc Routing Problem

The location arc routing problem (LARP) is a network optimization problem combining strategic facility location decisions and tactical or operational vehicle routing decisions for customer demand located on arcs of a network. The LARP seeks to locate facilities, or depots, and create vehicle delivery routes to minimize costs. The total cost is comprised of three components: fixed facility locations costs, fixed route creation (or vehicle acquisition) costs, and variable arc traversal costs. The applications of the LARP are varied and often include public services such as mail delivery, garbage collection, and street sweeping. In all of these applications, the magnitude of customer demand may be unknown at the outset of the problem and realized uncertainty can greatly affect the final solution. To the author’s knowledge, there is currently no discussion of formulating or solving a LARP with uncertainty. This paper presents an iterative tabu search, augment-merge heuristic to solve the LARP with stochastic customer demand. Each realization of customer demand for a particular network, represented by an individual scenario, was generated using the deterministic mval instances (with 24-50 nodes and 44-138 arcs) created by Hashemi Doulabi and Seifi (2013) and a truncated normal probability distribution. The tabu search phase handles the depot location decisions and chooses a set of depots to be used across all scenarios. The augment-merge phase creates a set of vehicle routes for each scenario. One-third of the initial experiments resulted in stochastic solution costs less than their deterministic counterparts indicating the promising value of considering customer demand uncertainty using the proposed stochastic LARP algorithm. TABLE OF CONTENTS
Edge distance graph kernel and its application to small molecule classification

Graph classification is an important problem in graph mining with various applications in different fields. Kernel methods have been successfully applied to this problem, recently producing promising results. A graph kernel that mostly specifies classification performance has to be defined in order to apply kernel methods to a graph classification problem. Although there are various previously proposed graph kernels, the problem is still worth investigating, as the available kernels are far from perfect. In this paper, we propose a new graph kernel based on a recently proposed concept called edge distance-k graphs. These new graphs are derived from the original graph and have the potential to be used as novel graph descriptors. We propose a method to convert these graphs into a multiset of strings that is further used to compute a kernel for graphs. The proposed graph kernel is then evaluated on various data sets in comparison to a recently proposed group of graph kernels. The results are promising, both in terms of performance and computational requirements.
Confidence Measures for Large Vocabulary Continuous Speech Recognition

Large vocabulary speech recognition with multiple speakers and variable channel conditions is a challenging task. State-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) systems are nevertheless able to generate high quality transcriptions of speech utterances under such conditions. These transcriptions are however not entirely accurate for a number of reasons. This fact has given rise to the field of Confidence Estimation (CE) for speech recognition systems, in which the aim is to investigate methods through which accurate measures of confidence in recognition results may be obtained. Such measures have become increasingly useful within many areas of speech recognition research, particularly those in which recogniser output is used as input for downstream applications. This report aims to address the problem of CE in LVCSR within the domain of Conversational Telephone Speech (CTS). The fundamental premise of this work being that improved confidence measures may be obtained through the application of alternative modelling techniques and algorithms, which are able to leverage the available sources of information to greater effect. The current state-of-the-art is therefore assessed in some detail, before new approaches that may prove useful in yielding improved confidence measures are presented and preliminarily investigated.
IMPACT ASSESSMENT OF IMAGE FEATURE EXTRACTORS ON THE PERFORMANCE OF SLAM SYSTEMS

This work evaluates an impact of image feature extractors on the performance of a visual SLAM method in terms of pose accuracy and computational requirements. In particular, the S-PTAM (Stereo Parallel Tracking and Mapping) method is considered as the visual SLAM framework for which both the feature detector and feature descriptor are parametrized. The evaluation was performed with a standard dataset with ground-truth information and six feature detectors and four descriptors. The presented results indicate that the combination of the GFTT detector and the BRIEF descriptor provides the best trade-off between the localization precision and computational requirements among the evaluated combinations of the detectors and descriptors.
Design and Modeling of High Speed Global On-Chip Interconnects

Interconnect has become a dominant factor in deep submicrometer (DSM) integrated circuits (ICs). With increasing levels of on-chip integration, more functional units are integrated onto a single die, such as a multi-core microprocessor and a system-on-chip. Global interconnect, which acts as a communication media among these functional units, plays an increasingly important role and can significantly limit the performance of advanced systems. With decreasing on-chip clock periods, the timing characteristics of on-chip signals need to be determined and controlled more precisely. Accurate interconnect models are therefore critical to the IC design process. In this dissertation, two global interconnect models are presented. Closed-form expressions of the signal waveform are developed, which achieve good agreement with Spectre simulations. During the interconnect design process, multiple design criteria are considered, such as delay, power, bandwidth, and noise. Repeaters are widely used in digital ICs to reduce interconnect delay and signal transition time with the penalty of additional
Pengembangan Rules-Driven Workflow Management System (RDWfMS) dengan Menggunakan Teknik Data Mining untuk Sistem Informasi Research Center

The tight competition of business and technology today makes the organisation must be able to adapt to changes. The changes could affect not only business process model of the organisation but also their information system, which lead to high cost and time-consuming. Therefore, the design and implementation of an information system should handle with care. Furthermore, the organisation also needs to process data into information and knowledge to support their business. Hence, this research is developed to undertake those problems by developing the information system using hybrid approach of the workflow system and data mining technique with research centre organisation as a case study. The system named as SIMPLER which applying rules-driven workflow and data mining to process data into information and knowledge from research data. SIMPLER tested by measuring its ability as a workflow management system as well as measuring the quality based on end-user satisfaction and perception by using WebQual. Based on the test results indicate that the SIMPLER been able to satisfy the need for business process execution XYZ research centre, able to adapt to changes without reconstructing the source code and gained end users satisfaction of 81.81% and 85% of accuracy.
Rapid Adaptation of Brain Reading Interfaces based on Threshold Adjustment

Brain Reading Interfaces (BRIs) can be used, e.g., to detect whether a user has recognized or missed an infrequent but task-relevant warning. Machine learning allows to train an electroencephalography-based BRI such that it can dis- tinguish between the two corresponding brain patterns. Unfortunately, acquiring a sucient number of training examples is time-consuming since infrequent warnings cannot be displayed often and its not under the BRI's control how often a user misses a warning. Because of that, we propose to train the BRI instead on data as- sociated with the recognition of an important warning and data associated with the perception of an irrelevant stimulus. Since irrelevant stimuli can be displayed with a higher frequency, large amounts of training data can be acquired more easily. We show that a BRI trained for this dierent but related task (the \source" task) can surprisingly well distinguish between recognized and missed warnings (the \target" task). This may indicate that similar brain patterns are evoked by missed warnings and irrelevant stimuli. To improve performance further, we propose to adjust the threshold which maps the scalar classier output onto the two class labels in order to adapt the BRI from the source to the target task. A close-to-optimal threshold can be chosen based on a comparatively small training set from the target task. We show empirically on data acquired in the Labyrinth Oddball testbed (Kirchner et al. (2010)) that the proposed procedure is well-suited for rapid adaptation of the BRI to the target task based on a small amount of training data.
A Pose Estimation Algorithm for Polyhedral Objects in X-Ray Image (特集 実環境におけるマシンビジョン適用技術)

In an x-ray vision, identifying the pose, i. e. the position and orientation of objects from x-ray projection images is extremely important to monitor in real time and analyze mechanical parts which are invisible from outside. It is assumed here that the x-ray imaging conditions that include the relative coordinates of the x-ray source and the image plane are predetermined and the object geometry is known. In this situation, an x-ray image of an object at a given pose can be estimated computationally by using a priori known x-ray projection image model. It is based on the assumption that a pose of an object can be determined uniquely to a given x-ray projection image. Thus, once we have the numerical model of x-ray imaging process, x-ray image of the known object at any pose could be estimated. Then, among these estimated images, the best matched image could be searched and found. When adequate features in the images are available instead of the image itself, the problem becomes easier and simpler. In this work, we propose an efficient pose estimation algorithm for polyhedral objects whose image features consist of corner points and edge lines in their projection images. Based on the corner points and lines found in the images, the best-matched pose of a polyhedral object can be determined. To achieve this, we proposed an adequate and efficient image processing algorithm to extract the features of objects in x-ray images. The performance of the algorithm is discussed in detail including the limitations of the method. To evaluate the performance of the proposed method a series of simulation studies is carried out for various imaging conditions.
Effect of Brevibacillus brevis OZF6 on Reduction of Chromium (VI) and Pea Growth

Aim: Hexavalent chromium (Cr (VI)) is toxic due to its high solubility in water and permeability through biological membranes and Cr (VI) interacts with proteins and nucleic acids which makes it more toxic and carcinogenic than trivalent. Microbes converting toxic chromium Cr (VI) to stable and less soluble Cr (III) can be used for detoxification of Cr (VI) from contaminated environment. In this study the authors wanted to evaluate the effect of chromium (VI) reducing Brevibacillus brevis OZF6 on the growth, photosynthestic pigments, nodulation and metal accumulation in pea crop. Place and Duration of Study: This study was carried out at the Department of Biological Sciences, Original Research Article Wani et al.; AIR, 9(3): 1-10, 2017; Article no.AIR. 31986 2 Crescent University, Abeokuta, Ogun State, Nigeria in 2015. Methodology: Cr (VI) reduction in both free and immobilized cells was evaluated by 1, 5-Diphenyl Carbazide method. Pigments, plant growth, and metal accumulation were determined as per the standard methods as described in materials and methods. Results: Brevibacillus brevis OZF6 significantly reduced Cr (VI) when bacteria were immobilized by sodium alginate as compared to the free cells. Seed germination, pea growth, nodulation, photosynthetic pigments, and protein increased in pea plants after inoculation with Brevibacillus brevis OZF6 compared to un-inoculated pea plants amended with metal. OZF6 significantly decreased accumulation of chromium in roots and shoots compared to only metal-amended plants. Conclusions: Due to above properties, B. brevis OZF6 could therefore be used as bioremediator of Cr (VI) in chromium contaminated environment and thus will protect the environment.
Enriching low resource Statistical Machine Translation using induced bilingual lexicons

In this work we present an experiment for enriching a Statistical Machine Translation (SMT) phrase table with automatically created bilingual word pairs. The bilingual lexicon is induced with a supervised classifier trained using a joint representation of word embeddings (WE) and Brown clusters (BC) of translation equivalent word pairs as features. The classifier reaches a 0.94 F-score and the MT experiment results show an improvement of up to +0.70 BLEU over a low resource Chinese-Spanish phrase-based SMT baseline, demonstrating that bad entries delivered by the classifier are well handled.
RYNSORD: a novel, decentralized algorithm for railway networks with "soft reservation"

This paper presents RYNSORD (a novel decentralized algorithm with soft reservation for efficient scheduling and congestion mitigation in railway networks), wherein every train utilizes lookahead to dynamically re-plan its route. Thus, a train, currently at position X and headed for the ultimate destination position Y, first requests and reserves N tracks, ahead of its current position, for use at appropriate future times. The N tracks are chosen either along the shortest path or an alternate shortest path where the tracks of the two paths are mutually exclusive. The train issues two sets of requests, concurrently, for the tracks on the two paths. After it has been granted reservations, the train selects the path that promises the quickest arrival time at the final destination. The train moves through the N tracks and, upon completion, it again requests and reserves N subsequent tracks ahead of itself. The process continues until the train reaches its destination, Y. The reservation process may be characterized by less abruptness and greater flexibility in negotiation between the trains and stations. This characteristic is termed "soft" reservations in this paper. Under "soft reservations", a train negotiating for N tracks will, in general, always succeeds. This paper also reports on an implementation of RYNSORD and a distributed approach that is similar to RYNSORD except that it employs hard reservations, on a network of SUN spare workstations, configured as a loosely-coupled parallel processor. Experimental results are presented.
Assessing h- and g-Indices of Scientific Papers using k-Means Clustering

K-means clustering technique works as a greedy algorithm for partition the n-samples into k-clusters so as to reduce the sum of the squared distances to the centroids. A very familiar task in data analysis is that of grouping a set of objects into subsets such that all elements within a group are more related among them than they are to the others. Kmeans clustering is a method of grouping items into k groups. In this work, an attempt has been made to study the importance of clustering techniques on hand g-indices, which are prominent markers of scientific excellence in the fields of publishing papers in various national and international journals. From the analysis, it is evidenced that k-means clustering algorithm has successfully partitioned the set of 18 observations into 3 clusters.