Miracle-dz/UniFAR-FTUs huggingface dataset:
Mammalian TBX1 Preferentially Binds and Regulates Downstream Targets Via a Tandem T-site Repeat

Haploinsufficiency or mutation of TBX1 is largely responsible for the etiology of physical malformations in individuals with velo-cardio-facial/DiGeorge syndrome (VCFS/DGS/22q11.2 deletion syndrome). TBX1 encodes a transcription factor protein that contains an evolutionarily conserved DNA binding domain termed the T-box that is shared with other family members. All T-box proteins, examined so far, bind to similar but not identical consensus DNA sequences, indicating that they have specific binding preferences. To identify the TBX1 specific consensus sequence, Systematic Evolution of Ligands by Exponential Enrichment (SELEX) was performed. In contrast to other TBX family members recognizing palindrome sequences, we found that TBX1 preferentially binds to a tandem repeat of 5′-AGGTGTGAAGGTGTGA-3′. We also identified a second consensus sequence comprised of a tandem repeat with a degenerated downstream site. We show that three known human disease-causing TBX1 missense mutations (F148Y, H194Q and G310S) do not alter nuclear localization, or disrupt binding to the tandem repeat consensus sequences, but they reduce transcriptional activity in cell culture reporter assays. To identify Tbx1-downstream genes, we performed an in silico genome wide analysis of potential cis-acting elements in DNA and found strong enrichment of genes required for developmental processes and transcriptional regulation. We found that TBX1 binds to 19 different loci in vitro, which may correspond to putative cis-acting binding sites. In situ hybridization coupled with luciferase gene reporter assays on three gene loci, Fgf8, Bmper, Otog-MyoD, show that these motifs are directly regulated by TBX1 in vitro. Collectively, the present studies establish new insights into molecular aspects of TBX1 binding to DNA. This work lays the groundwork for future in vivo studies, including chromatin immunoprecipitation followed by next generation sequencing (ChIP-Seq) to further elucidate the molecular pathogenesis of VCFS/DGS.
Towards A Better Understanding of Workload Dynamics on Data-Intensive Clusters and Grids

This paper presents a comprehensive statistical analysis of workloads collected on data-intensive clusters and grids. The analysis is conducted at different levels, including virtual organization (VO) and user behavior. The aggregation procedure and scaling analysis are applied to job arrival processes, leading to the identification of several basic patterns, namely, pseudo-periodicity, long range dependence (LRD), and (multi)fractals. It is shown that statistical measures based on interarrivals are of limited usefulness and count based measures should be trusted instead when it comes to correlations. We also study workload characteristics like job run time, memory consumption, and cross correlations between these characteristics. A "bag-of-tasks" behavior is empirically proved, strongly indicating temporal locality. We argue that pseudo-periodicity, LRD, and "bag-of-tasks" behavior are important workload properties on data-intensive clusters and grids, which are not present in traditional parallel workloads. This study has important implications on workload modeling and performance predictions in data-intensive grid environments.
Towards Independent Control of Multiple Magnetic Mobile Microrobots†

In this paper, we have developed an approach for independent autonomous navigation of multiple microrobots under the influence of magnetic fields and validated it experimentally. We first developed a heuristics based planning algorithm for generating collision-free trajectories for the microrobots that are suitable to be executed by an available magnetic field. Second, we have modeled the dynamics of the microrobots to develop a controller for determining the forces that need to be generated for the navigation of the robots along the trajectories at a suitable control frequency. Next, an optimization routine is developed to determine the input currents to the electromagnetic coils that can generate the required forces for the navigation of the robots at the controller frequency. We then validated our approach by simulating an electromagnetic system that contains an array of sixty-four magnetic microcoils designed for generating local magnetic fields suitable for simultaneous independent actuation of multiple microrobots. Finally, we prototyped an mm-scale version of the system and present experimental results showing the validity of our approach.
Laughlin's function on a cylinder: plasma analogy and representation as a quantum polymer

We investigate Laughlin's fractional quantum Hall effect wave function in the cylinder geometry of Laughlin's integer quantum Hall effect argument, at filling factor 1/3. We show that the plasma analogy leads to a periodic density, and that the wave function admits a representation as a “quantum polymer”, reminiscent of the quantum dimer model by Rokhsar and Kivelson. We explain how the representation can be exploited to compute the normalization and one‐particle density in the limit of infinitely many particles. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Automatic detection and classification of acoustic breathing cycles

This paper focuses on respiratory phase detection and classification without the help of the airflow measurements. Instead of using the airflow measurements to identify breathing phases, the proposed work depends on advanced digital signal processing techniques to process the acoustic signal of respiration that was collected using a microphone placed in front of the subject's nose. The recorded signal is processed using the voiced-unvoiced algorithm to differentiate between the voiced period and the unvoiced (silence) period. The desired features are extracted from each voiced phase. Finally, support vector machine is used to distinguish between the inspiration and the expiration phases according to the extracted features. The signals were recorded from a number of subjects who do not have a history of pulmonary diseases. The proposed method has achieved an accuracy of 95% when tested on the subjects.
STUDENTS’ PERCEPTIONS TOWARDS THE EFFECTIVE FEEDBACK PRACTICES IN THE LARGE EFL WRITING CLASS BASED ON STUDENTS’ ENGLISH PROFICIENCY LEVEL

Motivated by my own experience in teaching and learning academic writing in a large class of more than 50 students, I aim to find out the students’ perceptions towards the effective feedback practices in a large EFL writing class in Indonesia. There were 150 students participating by answering the the questionnaire. The data was analyzed based on English proficiency level (the students’ TOEFL scores). The findings showed that based on the students’ English proficiency level, they perceived feedback from their lecturers is effective when it is given in written form, while from their peers, it should be in oral form.
Multi-Task CRF Model for Predicting Issue Resolution Status in Social Media based Customer Care

In this paper, we present a multitask learning method for predicting the resolution status of the issues expressed in social media conversations among customer-care agents and social media users, along with the nature of dialogues of those conversations. Our method extends beyond social media conversation analysis, and is naturally applicable to general multiple sequence labeling tasks where each example sequence has multiple label sequences. Our method learns multiple models, one model for each task, i.e., issue status prediction task and dialogue act prediction task. Each model computes the joint probability of both label sequences (dialogue act and issues status) given the example sequence, i.e., conversation among customers and agents. Such multiple models are learned simultaneously by facilitating the learning transfer among models through explicit parameter sharing. We experiment the proposed method on real social media conversations dataset collected from Twitter as well as on a publicly available NLP dataset, and show that our method outperforms the state-of-the-art method. In addition, we illustrate how the issue status and dialogue act prediction tasks can be an integral part of socially aware customer care engagement system.
A modified a priori SNR for speech enhancement using spectral subtraction rules

The letter addresses the problem of single-channel speech enhancement using a spectral subtraction method. The proposed approach is directed toward finding a self-adaptive averaging factor to estimate the a priori SNR. Performance of the modified averaging factor is evaluated using conventional spectral-subtraction algorithms in which the averaging factor is otherwise kept as a constant. Improved results are obtained in terms of speech quality measures for various types of noise and at different SNR levels when the proposed time-frequency varying averaging factor is adapted in the conventional subtraction rules.
Learning an EMG Controlled Game: Task-Specific Adaptations and Transfer

Video games that aim to improve myoelectric control (myogames) are gaining popularity and are often part of the rehabilitation process following an upper limb amputation. However, direct evidence for their effect on prosthetic skill is limited. This study aimed to determine whether and how myogaming improves EMG control and whether performance improvements transfer to a prosthesis-simulator task. Able-bodied right-handed participants (N = 28) were randomly assigned to 1 of 2 groups. The intervention group was trained to control a video game (Breakout-EMG) using the myosignals of wrist flexors and extensors. Controls played a regular Mario computer game. Both groups trained 20 minutes a day for 4 consecutive days. Before and after training, two tests were conducted: one level of the Breakout-EMG game, and grasping objects with a prosthesis-simulator. Results showed a larger increase of in-game accuracy for the Breakout-EMG group than for controls. The Breakout-EMG group moreover showed increased adaptation of the EMG signal to the game. No differences were found in using a prosthesis-simulator. This study demonstrated that myogames lead to task-specific myocontrol skills. Transfer to a prosthesis task is therefore far from easy. We discuss several implications for future myogame designs.
NVS: A Substrate for Virtualizing Wireless Resources in Cellular Networks

This paper describes the design and implementation of a network virtualization substrate (NVS ) for effective virtualization of wireless resources in cellular networks. Virtualization fosters the realization of several interesting deployment scenarios such as customized virtual networks, virtual services, and wide-area corporate networks, with diverse performance objectives. In virtualizing a base station's uplink and downlink resources into slices, \ssr NVS meets three key requirements-isolation, customization, and efficient resource utilization-using two novel features: 1) NVS introduces a provably optimal slice scheduler that allows existence of slices with bandwidth-based and resource-based reservations simultaneously; and 2) NVS includes a generic framework for efficiently enabling customized flow scheduling within the base station on a per-slice basis. Through a prototype implementation and detailed evaluation on a WiMAX testbed, we demonstrate the efficacy of \ssr NVS. For instance, we show for both downlink and uplink directions that \ssr NVS can run different flow schedulers in different slices, run different slices simultaneously with different types of reservations, and perform slice-specific application optimizations for providing customized services.
Comparison of Neural Networks and Fuzzy Logic Control Designed by Multi-objective Genetic aWlgorithm

In this work we consider the design of neural network and Takagi Sugeno fuzzy logic controller, TSFLC, with Multi-Obejctive Genetic Algorithm, MOGA. For the neural network, the MOGA has to minimize three objectives, the cumulated error, the number of neurons in the hidden layer and the number and type of inputs to the network. In the case of the TS FLC, the objectives are the cumulated error and the parameters of the rules consequence. Both algorithms are applied for the control of temperature system.
A Fast Algorithm for 3-D Inductance Extraction Based on Investigation of Open-Circuit Current

As a famous interconnect extraction software, FastHenry can compute frequency-dependent interconnect inductance with high accuracy. But its low speed prevents it from being applied to deal with industry examples with large scale. Up to now, almost all acceleration methods for inductance extraction, e.g. the K-element method, are based on omitting the current of victim conductors, which is shorted and far away from aggressor conductor with 1 volt bias voltage. In this paper, we investigate the filament currents distribution on conductors while setting victim conductor open or shorted. Based on observation of less current on open-circuit conductors, we propose a new acceleration method for inductance extraction. The numerical results show that this method is faster than FastHenry for hundred times and preserves the similar accuracy.
Balance Control and Knee Osteoarthritis Severity

Objective To investigate balance control according to the severity of knee osteoarthritis (OA) using clinical tests and Tetra-ataxiometric posturography (Tetrax®). Method A total 80 patients with primary knee OA classified according to American College of Rheumatology criteria, and 40 age-matched controls were enrolled in this study. Of those with OA, 39 patients had mild OA (Kellgren-Lawrence [KL] grade 1, 2) and the other 41 had moderate to severe OA (KL grade 3, 4). The postural control capabilities of the subjects were assessed using the timed up and go test (TUG), Berg balance scale (BBS), and Tetrax®, which utilizes two paired force plates to measure vertical pressure fluctuations over both heels and forefeet. The subjects were checked for their stability index (ST), Fourier index, weight distribution index (WDI), and synchronization index (SI) in eight positions using Tetrax®. Results Patients with moderate to severe OA exhibited significantly higher stability indices in all positions than patients with mild OA. The Fourier index was also higher in patients with moderate to severe OA than in patients with mild OA. However, the weight distribution index and synchronization of both heels and forefeet were not significantly different in the three groups. Conclusion These findings suggest that patients with moderate to severe OA have more deficits in balance control than those with mild disease. Therefore, evaluation of balance control and education aimed at preventing falls would be useful to patients with knee OA.
Classification of ECG signals of normal and abnormal subjects using common spatial pattern

In this paper, an ECG signal classification method is presented to classify multi-lead ECG signals into normal and abnormal classes using Common Spatial Pattern (CSP) as the feature extraction algorithm. The method consists of two main stages: CSP-based feature extraction and classification. After segmenting the signal into non-overlapping segments, each segment is projected onto a CSP projection matrix to extract the training and testing feature vectors. These vectors are used in the classification stage. In this study, three classifiers — linear discriminant analysis (LDA), naive Bayes (NB), and support vector machine (SVM)—were used. The proposed approach was evaluated using 104 subjects' recordings (52 normal and 52 abnormal) from the Physikalisch-Technische Bundesanstalt (PTB) dataset. The three classifiers achieved accuracy rates of 80.65%, 84%, and 100%, respectively.
A combined potential function and graph search approach for free gait generation of quadruped robots

This paper presents an algorithm for planning the foothold positions of quadruped robots on irregular terrain. The input to the algorithm is the robot kinematics, the terrain geometry, a required motion path, as well as initial posture. Our goal is to develop general algorithm that navigate quadruped robots quasi-statically over rough terrain, using an APF (Artificial Potential Field) and graph searching. The algorithm is planning a sequence set of footholds that navigates the robot along the required path with controllable motion characteristics. Simulations results demonstrate the algorithm in a planner environment.
Vowel Detection and Classification using Support Vector Machines (SVM)

This paper presents our work on vowel detection and classification as part of a project of Persian continuous speech recognition based on demi-syllable units. Recent work in machine learning has focused on models, such as the support vector machine (SVM), that automatically control the generalization and discrimination as part of overall optimization process. In this research we track all vowel phonemes in speech signal and in order to achieve minimum vowel losses and accurate detection, we focus on taking special care of vowel detector and classifier using support vector machines and acoustic data of speech signal. At first this technique has been tested with Persian isolated words database and good results have been achieved from this algorithm Experiments with this database result in 1.62 % of total detection and classification errors. The insertion error is 1.06 %, the deletion error is 0.56 % and the classification error is 0 %
Towards bioinspired superhydrophobic poly(L-lactic acid) surfaces using phase inversion-based methods

The water repellency and self-cleaning ability of many biological surfaces has inspired many fundamental and practical studies related to the development of synthetic superhydrophobic surfaces. However, the investigation of such substrates made of biodegradable polymers has been scarce. Simple approaches based on a single step, performed at room temperature (and pressure), were implemented to obtain superhydrophobic poly(L-lactic acid) (PLLA) surfaces via phase inversion-based methods, without addition of low-surface-energy compounds. Water contact angles above 150° were obtained using some processing conditions. In such cases scanning electronic microscopy micrographs of such surfaces revealed a clear rough texture composed by leafy clusters with micro-nano binary structures. Such materials could be used in specific environmental and biomedical applications, namely in implantable materials or in antibacterial or antithrombogenic surfaces.
Intelligent humanoids

The future of humanoid robots is to become efficient helpers for humans, both in the execution of everyday tasks and in the accomplishment of tedious and dangerous works. Driven by this vision, researchers have been challenged to design more and more complex robots, that show an increasing number of degrees of freedom and sensors; these robots should be able to cope with the unstructured environment in which humans daily live and act. In particular, it would be desirable that robot behaviors become autonomous (not requiring the supervision of a human expert) and flexible (applicable to different situations and contexts).
Spatial and temporal avoidance of risk within a large carnivore guild

Abstract Within a large carnivore guild, subordinate competitors (African wild dog, Lycaon pictus, and cheetah, Acinonyx jubatus) might reduce the limiting effects of dominant competitors (lion, Panthera leo, and spotted hyena, Crocuta crocuta) by avoiding them in space, in time, or through patterns of prey selection. Understanding how these competitors cope with one other can inform strategies for their conservation. We tested how mechanisms of niche partitioning promote coexistence by quantifying patterns of prey selection and the use of space and time by all members of the large carnivore guild within Liuwa Plain National Park in western Zambia. Lions and hyenas specialized on wildebeest, whereas wild dogs and cheetahs selected broader diets including smaller and less abundant prey. Spatially, cheetahs showed no detectable avoidance of areas heavily used by dominant competitors, but wild dogs avoided areas heavily used by lions. Temporally, the proportion of kills by lions and hyenas did not detectably differ across four time periods (day, crepuscular, early night, and late night), but wild dogs and especially cheetahs concentrated on time windows that avoided nighttime hunting by lions and hyenas. Our results provide new insight into the conditions under which partitioning may not allow for coexistence for one subordinate species, the African wild dog, while it does for cheetah. Because of differences in responses to dominant competitors, African wild dogs may be more prone to competitive exclusion (local extirpation), particularly in open, uniform ecosystems with simple (often wildebeest dominated) prey communities, where spatial avoidance is difficult.
Squashing Alternatives for Software-Based Speculative Parallelization

Speculative parallelization is a runtime technique that optimistically executes sequential code in parallel, checking that no dependence violations arise. In the case of a dependence violation, all mechanisms proposed so far either switch to sequential execution, or conservatively stop and restart the offending thread and all its successors, potentially discarding work that does not depend on this particular violation. In this work we systematically explore the design space of solutions for this problem, proposing a new mechanism that reduces the number of threads that should be restarted when a data dependence violation is found. Our new solution, called exclusive squashing, keeps track of inter-thread dependencies at runtime, selectively stopping and restarting offending threads, together with all threads that have consumed data from them. We have compared this new approach with existent solutions on a real system, executing different applications with loops that are not analyzable at compile time and present as much as 10% of inter-thread dependence violations at runtime. Our experimental results show a relative performance improvement of up to 14%, together with a reduction of one-third of the numbers of squashed threads. The speculative parallelization scheme and benchmarks described in this paper are available under request.
Xregion: A New Approach to Storing XML Data in Relational Databases

In this paper, we propose a new structure-based approach, called Xregion, to storing XML data in relational databases. Our approach ﬁrst partitions an XML document into several disjoint regions according to the cardinality of element nodes, and then maps these regions into separate relations. The experimental results demonstrate that the proposed approach dramatically improves the performance of queries on the XML data over the generic mapping approaches.