Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech for the Disabled

Pen-based interface systems, which are used for character recognition technology, have been widely developed and used for creating a much more comfortable user interface experience than using a keyboard. But almost all systems aren't taking into consideration for speech-disabled persons. Engineers make things not only simple but also feasible. One of the important problems that the community faces is that people with disabilities are finding it hard to cope up the fast growing technology. It is highly essential that a cost effective product is made available to the society to help speech-disabled people. In this project, we propose a device system, which can be used to provide speech for the disabled persons to help people with difficulties in speaking. The words that the person wishes to convey are entered as text. This text is converted into speech by the speech-processing unit. The speech-processing unit uses the concept of phonetics, and processes each phoneme. Phonetics is focused on sounds; it isn’t necessarily concerned with how a particular letter sounds. Instead, what’s important is how certain combinations of letters affect the sound. Hence by using phonetics it is possible to produce speech that is as good as actual speech. The frequently spoken words are stored in memory and can be easily retrieved by using hot keys. The system will be made portable by housing the entire system inside a cell phone casing with charging facilities.
Subband Temporal Envelope Features and Data Augmentation for End-to-end Recognition of Distant Conversational Speech

This paper investigates the use of subband temporal envelope (STE) features and speed perturbation based data augmentation in end-to-end recognition of distant conversational speech in everyday home environments. STE features track energy peaks in perceptual frequency bands which reflect the resonant properties of the vocal tract. Data augmentation is performed by adding more training data obtained after modifying the speed of the original training data. Experiments show that using STE features and speed perturbation based data augmentation helps improving the performance of end-to-end speech recognition on a challenging corpus which was used for the CHiME 2018 speech separation and recognition challenge. STE features provide up to 2.0% relative word error rate (WER) reduction compared to the conventional log-Mel filter-bank (FBANK) features. Data augmentation is used with both features and provides up to 5.2% relative WER reduction. We propose a simple hypothesis selection method to combine the hypotheses produced by the end-to-end systems using FBANK and STE features. This method additionally provides up to 4.7% relative WER reduction.
An Efficient Approach for Generating Pencil Filter and Its Implementation on GPU

Traditional pencil drawing methods have their own drawbacks, such as modeling complexity and higher time-consuming. Thus, they are difficult to be suitable for the real-time rendering. In this paper, we present a new pencil texture generating method based on the pencil filter. This approach can conveniently generate the pencil drawing effect by convoluting the input image with the pencil filter. With the help of accelerating hardware GPU (graphics processing unit), the method accomplished real-time video stylization.
Analysis and reduction of estimation bias for an iterative frequency estimator of complex sinusoid

Frequency estimation of a complex sinusoidal signal is a fundamental problem in signal processing. In this regard, Aboutanios and Mulgrew (A&M) proposed an iterative frequency estimator which can approach the theoretical bound in two iterations, thus, made it one of the best iterative estimators. In this paper, we theoretically analyze the two versions of the A&M estimator and show that the estimation biases of the two versions are not equivalent. The results of the theoretical analysis indicate that the bias of the first iteration can be accurately predicted by a polynomial equation. We then propose to use the roots of the polynomial equation to improve the estimation and reduce the bias. Experiments show that the proposed new estimator can significantly reduce the bias.
Subcellular localization of recombinant truncated Gag precursor proteins of HIV expressed in Saccharomyces cerevisiae

ObjectiveTo determine signals contained in the HIV-1 Gag precursor implicated in protein transport. DesignTo study the localization of truncated Gag proteins expressed in Saccharomyces cerevisiae. MethodsThin-section immunoelectron microscopy studies were performed on S. cerevisiae cells producing myristoylated or non-myristoylated Pr55gag, the core protein (p24) and several truncated Gag proteins. ResultsPr55gag and the carboxy-terminal truncated Gag proteins were myristoylated and localized at the plasma membrane. p24 was localized in the nucleus or perinuclear membrane. However, addition of a myristoyl group to p24 targeted this molecule to the plasma membrane. ConclusionsThe myristoylated amino-terminal 214 amino acids are sufficient to target Pr55gag to the plasma membrane. Subcellular signals implicated in protein transport are present in the core p24 polypeptide which may become dominant or accessible in the absence of the amino-myristoyl group.
Beyond the vision paradigm: design strategies for crossmodal interaction with dynamic digital displays

The purpose of our research is to develop new interaction paradigms for dynamic digital displays (DDDs) in public space. Our lab is currently developing an ontological framework that comprises seven different interaction paradigms for DDDs. Still in its budding stages, this framework is intended to assist HCI practitioners in the conception and evaluation of architectural scale DDD installations. This paper theoretically discusses crossmodal interaction as one of these seven interaction paradigms. We used an architectural approach that draws on medium specificity --- a fine arts concept foreign to HCI --- to conduct a phenomenological analysis of DDDs that have been deployed in an actual public space in Montréal, Canada. After interviewing DDD design artists and performing our preliminary observational analyses, we found four design strategies that were used to produce crossmodal interaction on an architectural scale, helping shift the experience of DDDs beyond the vision paradigm.
Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud

Fog computing is expected to be an enabler of mobile cloud computing, which extends the cloud computing paradigm to the edge of the network. In the mobile cloud, not only central data centers but also pervasive mobile devices share their heterogeneous resources (e. g. CPUs, bandwidth, content) and support services. The mobile cloud based on such resource sharing is expected to be a powerful platform for mobile cloud applications and services. In this paper, we propose an architecture and mathematical framework for heterogeneous resource sharing based on the key idea of service-oriented utility functions. Since heterogeneous resources are often measured/quantified in disparate scales/units (e.g. power, bandwidth, latency), we present a unified framework where all these quantities are equivalently mapped to "time" resources. We formulate optimization problems for maximizing (i) the sum of the utility functions, and (ii) the product of the utility functions, and solve them via convex optimization approaches. Our numerical results show that service-oriented heterogeneous resource sharing reduces service latencies effectively and achieves high energy efficiency, making it attractive for use in the mobile cloud.
Are numbers good enough for you? - A linguistically meaningful MT evaluation method

This poster explores a way to qualitatively identify linguistic properties of a particular MT system, so that we can identify its strengths and weaknesses more readily. The paper provides preliminary results for two English-to-Japanese SMT systems. We demonstrate that comparison of n-gram frequencies between human translations and machine-translated outputs can lead us to linguistically meaningful information about a given MT system. We argue that our method has the potential to guide the research and development process in a way that numeric scores alone cannot and that it can shed new lights on how we assess MT quality.
The N Termini of Focal Adhesion Kinase Family Members Regulate Substrate Phosphorylation, Localization, and Cell Morphology*

The focal adhesion kinase (FAK) and cell adhesion kinase β (CAKβ, PYK2, CADTK, RAFTK) are highly homologous FAK family members, yet clearly have unique roles in the cell. Comparative analyses of FAK and CAKβ have revealed intriguing differences in their activities. These differences were investigated further through the characterization of a set of FAK/CAKβ chimeric kinases. CAKβ exhibited greater catalytic activity than FAK in vitro, providing a molecular basis for differential substrate phosphorylation by FAK and CAKβ in vivo. Furthermore, the N terminus may regulate catalytic activity since chimeras containing the FAK N terminus and CAKβ catalytic domain exhibited a striking high level of catalytic activity and substrate phosphorylation. Unexpectedly, a modulatory role for the N termini in subcellular localization was also revealed. Chimeras containing the FAK N terminus and CAKβ C terminus localized to focal adhesions, whereas chimeras containing the N and C termini of CAKβ did not. Finally, prominent changes in cell morphology were induced upon expression of chimeras containing the CAKβ N terminus, which were not associated with apoptotic cell death, cell cycle progression delay, or changes in Rho activity. These results demonstrate novel regulatory roles for the N terminus of FAK family kinases.
Modelling of Human Body Shadowing Based on 28 GHz Indoor Measurement Results

Having the unique feature of wide continuous bandwidth, millimeter-wave communication has attracted great attention from both industry and academics. However, due to the large path loss and diffraction loss, human body shadowing (HBS) has a huge impact on millimeter- wave communication and requires intensive research. In this paper, we conduct HBS measurements in three different cases at 28 GHz by using a pair of horn antennas. Based on our measurement results, we propose a simple HBS model, and then use the double-edge and the multiple-edge diffraction method to study the characteristics of HBS. The gain of HBS is generally 10 dB or greater for the 16 m LOS link. It grows with the distance between TX and RX, and a relative small gain will be obtained when people are close to the antennas and LOS path. Furthermore, human body's direction will have great impact on the effective height, which finally causes 4.63 dB loss.
Predictors of All-Cause Mortality in Patients with Stable COPD: Medical Co-morbid Conditions or High Depressive Symptoms

Abstract Co-morbid conditions are frequently found in patients with COPD. We evaluate the association of co-morbidities with mortality, in stable COPD. 224 patients, mean age 61.2 (±10.00), 48.2% female, mean FEV1 1.1 (±0.5) liters, median follow-up time 4.2 years, participated. Medical co-morbidities were scored according to the Charlson Co-morbidity Index (CCI). Depressive symptoms were assessed with the Hospital Anxiety and Depression Scale (HADS) and Symptom Checklist-90 (SCL-90). The Cox proportional hazard model was used for survival analyses. In our sample, 70% of all patients have a co-morbid medical condition or high depressive symptoms. During follow-up 51% of all patients died, and those with heart failure have the highest mortality rate (75%). Age, fat-free mass and exercise capacity were predictive factors, contrary to CCI-scores and high depressive symptoms. An unadjusted association between heart failure and survival was found. Although the presence of co-morbidities, using the CCI-score, is not related to survival, heart failure seems to have a detrimental effect on survival. Higher age and lower exercise capacity or fat-free mass predict mortality.
Pedestrians Detection Based on the Integration of Human Features and Kernel Density Estimation

Kernel density estimation doesn’t need the distribution hypothesis of the background characteristics, and also it does not need to estimate the parameters. Therefore, it can deal with moving object detection under complicated background, but the algorithm application is limited by the selection of kernel function bandwidth. In view of this problem, the method of integrating the human features and kernel density estimation is presented in this paper, aiming at the pedestrian detection. First of all, selecting the kernel function bandwidth through the priori information of moving object, then, based on kernel density estimation, extracting the foreground (i.e. moving object), and finally, once again using the human characteristic to detect the video pedestrians. The experiments show, compared with the traditional methods, the method introducing the priori information can greatly reduce the computing burden of kernel density estimation. Even with the changes of the light and the outside noise interference, the method presented in the paper can accurately detect pedestrians and no-pedestrians. This method can be applied to the vehicle, animal detecting, but a priori information plays an important role.
Update: the status of clinical trials with kinase inhibitors in thyroid cancer.

CONTEXT Thyroid cancer is usually cured by timely thyroidectomy; however, the treatment of patients with advanced disease is challenging because their tumors are mostly unresponsive to conventional therapies. Recently, the malignancy has attracted much interest for two reasons: the dramatic increase in its incidence over the last three decades, and the discovery of the genetic mutations or chromosomal rearrangements causing most histological types of thyroid cancer. OBJECTIVE This update reviews the molecular genetics of thyroid cancer and the clinical trials evaluating kinase inhibitors (KIs) in patients with locally advanced or metastatic disease. The update also reviews studies in other malignancies, which have identified mechanisms of efficacy, and also resistance, to specific KIs. This information has been critical both to the development of effective second-generation drugs and to the design of combinatorial therapeutic regimens. Finally, the update addresses the major challenges facing clinicians who seek to develop more effective therapy for patients with thyroid cancer. RESULTS PubMed was searched from January 2000 to November 2013 using the following terms: thyroid cancer, treatment of thyroid cancer, clinical trials in thyroid cancer, small molecule therapeutics, kinase inhibitors, and next generation sequencing. CONCLUSIONS A new era in cancer therapy has emerged based on the introduction of KIs for the treatment of patients with liquid and solid organ malignancies. Patients with thyroid cancer have benefited from this advance and will continue to do so with the development of drugs having greater specificity and with the implementation of clinical trials of combined therapeutics to overcome drug resistance.
General and Robust Communication-Efficient Algorithms for Distributed Clustering

As datasets become larger and more distributed, algorithms for distributed clustering have become more and more important. In this work, we present a general framework for designing distributed clustering algorithms that are robust to outliers. Using our framework, we give a distributed approximation algorithm for k-means, k-median, or generally any L_p objective, with z outliers and/or balance constraints, using O(m(k+z)(d+log n)) bits of communication, where m is the number of machines, n is the size of the point set, and d is the dimension. This generalizes and improves over previous work of Bateni et al. and Malkomes et al. As a special case, we achieve the first distributed algorithm for k-median with outliers, answering an open question posed by Malkomes et al. For distributed k-means clustering, we provide the first dimension-dependent communication complexity lower bound for finding the optimal clustering. This improves over the lower bound from Chen et al. which is dimension-agnostic. Furthermore, we give distributed clustering algorithms which return nearly optimal solutions, provided the data satisfies the approximation stability condition of Balcan et al. or the spectral stability condition of Kumar and Kannan.
Structure-Function Studies of Two Novel UDP-GlcNAc C6 Dehydratases/C4 Reductases

Two subfamilies of UDP-GlcNAc C6 dehydratases were recently identified. FlaA1, a short soluble protein that exhibits a typical SYK catalytic triad, characterizes one of these subfamilies, and WbpM, a large membrane protein that harbors an altered SMK triad that was not predicted to sustain activity, represents the other subfamily. This study focuses on investigating the structure and function of these C6 dehydratases and the role of the altered triad as well as additional amino acid residues involved in catalysis. The significant activity retained by the FlaA1 Y141M triad mutant and the low activity of the WbpM M438Y mutant indicated that the methionine residue was involved in catalysis. A Glu589 residue, which is conserved only within the large homologues, was shown to be essential for activity in WbpM. Introduction of this residue in FlaA1 enhanced the activity of the corresponding V266E mutant. Hence, this glutamate residue might be responsible for the retention of catalytic efficiency in the large homologues despite alteration of their catalytic triad. Mutations of residues specific for the short homologues (Asp70, Asp149-Lys150, Cys103) abolished the activity of FlaA1. Among them, C103M prevented dimerization but did not significantly affect the secondary structure. The fact that we could identify subfamily-specific residues that are essential for catalysis suggested an independent evolution for each subfamily of C6 dehydratases. Finally, the loss of activity of the FlaA1 G20A mutant provided evidence that a cofactor is involved in catalysis, and kinetic study of the FlaA1 H86A mutant revealed that this conserved histidine is involved in substrate binding. None of the mutations investigated altered the substrate, product, and function specificity of these enzymes.
Multipath Beamforming UWB Signal Design Based on Ternary Sequences

We study an adaptive transmission approach that exploits the increased phase coherence of ultra-wideband (UWB) channels with a “discrete” number of reflectors relative to narrowband wireless channels. The proposed multipath beamforming technique suggests selecting a ternary or multilevel sequence for an impulsive direct sequence UWB signal waveform based on the signs of the reflection coefficients corresponding to a few strongest paths so that the signal energy at the output of a non-adaptive correlator is enhanced. The approach to beamforming signal design is illustrated by several example designs.
Keyword extraction from emails*

Abstract Emails constitute an important genre of online communication. Many of us are often faced with the daunting task of sifting through increasingly large amounts of emails on a daily basis. Keywords extracted from emails can help us combat such information overload by allowing a systematic exploration of the topics contained in emails. Existing literature on keyword extraction has not covered the email genre, and no human-annotated gold standard datasets are currently available. In this paper, we introduce a new dataset for keyword extraction from emails, and evaluate supervised and unsupervised methods for keyword extraction from emails. The results obtained with our supervised keyword extraction system (38.99% F-score) improve over the results obtained with the best performing systems participating in the SemEval 2010 keyword extraction task.
The Effectiveness of Public Sponsored Training Revisited: The Importance of Data and Methodological Choices

As the first, substantive contribution, this paper revisits the effectiveness of two widely used public sponsored training programs, the first one focusing on intensive occupational training and the second one on short-term activation and job entry. We use an exceptionally rich administrative data set for Germany to estimate their employment and earnings effects in the early 2000s. We employ a stratified propensity score matching approach to address dynamic selection into heterogeneous programs. As a second, methodological contribution, we carefully assess to what extent various aspects of our empirical strategy such as conditioning flexibly on employment and benefit histories, the availability of rich personal information, handling of later program participations, and further methodological and specification choices affect estimation results. Our results imply pronounced negative lock-in effects in the short run in general and positive medium-run effects on employment and earnings when job-seekers enroll after having been unemployed for some time. We find that data and specification issues can have a large effect on impact estimates.
Wrist Rehabilitation Assisted by an Electromyography-Driven Neuromuscular Electrical Stimulation Robot After Stroke

Background. Augmented physical training with assistance from robot and neuromuscular electrical stimulation (NMES) may introduce intensive motor improvement in chronic stroke. Objective. To compare the rehabilitation effectiveness achieved by NMES robot–assisted wrist training and that by robot-assisted training. Methods. This study was a single-blinded randomized controlled trial with a 3-month follow-up. Twenty-six hemiplegic subjects with chronic stroke were randomly assigned to receive 20-session wrist training with an electromyography (EMG)-driven NMES robot (NMES robot group, n = 11) and with an EMG-driven robot (robot group, n = 15), completed within 7 consecutive weeks. Clinical scores, Fugl-Meyer Assessment (FMA), Modified Ashworth Score (MAS), and Action Research Arm Test (ARAT) were used to evaluate the training effects before and after the training, as well as 3 months later. An EMG parameter, muscle co-contraction index, was also applied to investigate the session-by-session variation in muscular coordination patterns during the training. Results. The improvement in FMA (shoulder/elbow, wrist/hand) obtained in the NMES robot group was more significant than the robot group (P < .05). Significant improvement in ARAT was achieved in the NMES robot group (P < .05) but absent in the robot group. NMES robot–assisted training showed better performance in releasing muscle co-contraction than the robot-assisted across the training sessions (P < .05). Conclusions. The NMES robot–assisted wrist training was more effective than the pure robot. The additional NMES application in the treatment could bring more improvements in the distal motor functions and faster rehabilitation progress.