Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeted next-generation sequencing of 50 cancer-related genes in Japanese patients with oral squamous cell carcinoma

Somatic mutation analysis is a standard of practice for human cancers to identify therapeutic sensitization and resistance mutations. We performed a multigene sequencing screen to explore mutational hotspots in cancer-related genes using a semiconductor-based sequencer. DNA from oral squamous cell carcinoma samples was used as a template to amplify 207 regions from 50 cancer-related genes. Of the 80 oral squamous cell carcinoma specimens from Japanese patients, including formalin-fixed paraffin-embedded samples, 56 specimens presented at least one somatic mutation among the 50 investigated genes, and 17 of these samples showed multiple gene somatic mutations. TP53 was the most commonly mutated gene (50.0%), followed by CDKN2A (16.3%), PIK3CA (7.5%), HRAS (5.0%), MET (2.5%), and STK11 (2.5%). In total, 32 cases (40.0%) were human papillomavirus positive and they were significantly less likely to have a TP53, mutation than human papillomavirus–negative oral squamous cell carcinomas (8/32, 25.0% vs 32/48, 66.7%, p = 0.00026). We also detected copy number variations, in which segments of the genome could be duplicated or deleted from the sequencing data. We detected the tumor-specific TP53 mutation in the plasma cell-free DNA from two oral squamous cell carcinoma patients, and after surgery, the test for these mutations became negative. Our approach facilitates the simultaneous high-throughput detection of somatic mutations and copy number variations in oral squamous cell carcinoma samples.
Robust Detection of Extreme Events Using Twitter: Worldwide Earthquake Monitoring

Timely detection and accurate description of extreme events, such as natural disasters and other crisis situations, are crucial for emergency management and mitigation. Extreme-event detection is challenging, since one has to rely upon reports from human observers appointed to specific geographical areas, or on an expensive and sophisticated infrastructure. In the case of earthquakes, geographically dense sensor networks are expensive to deploy and maintain. Therefore, only some regions—or even countries—are able to acquire useful information about the effects of earthquakes in their own territory. An inexpensive and viable alternative to this problem is to detect extreme real-world events through people's reactions in online social networks. In particular, Twitter has gained popularity within the scientific community for providing access to real-time “citizen sensor” activity. Nevertheless, the massive amount of messages in the Twitter stream, along with the noise it contains, underpin a number of difficulties when it comes to Twitter-based event detection. We contribute to address these challenges by proposing an online method for detecting unusual bursts in discrete-time signals extracted from Twitter. This method only requires a one-off semisupervised initialization and can be scaled to track multiple signals in a robust manner. We also show empirically how our proposed approach, which was envisioned for generic event detection, can be adapted for worldwide earthquake detection, where we compare the proposed model to the state of the art for earthquake tracking using social media. Experimental results validate our approach as a competitive alternative in terms of precision and recall to leading solutions, with the advantage of implementation simplicity and worldwide scalability.
Laser printing of microbial systems: effect of absorbing metal film

Recently, it was shown that laser‐induced forward transfer (LIFT) technology and the laser engineering of microbial systems (LEMS) technique (based on LIFT method) are effective for isolation of micro‐organisms from different complex substrates. These techniques frequently utilize Au as an absorbing layer material. The purpose of this study was to investigate the influence of absorbing film materials (Au, Ti and Cr) on the effectiveness of laser printing of micro‐organisms to improve LEMS and LIFT techniques. It was shown that application of Ti and Cr absorbing layers activates bacterial growth after laser printing and is significantly more effective in comparison to Au films, which actually show a suppressing effect on bacterial cells. Results of this study can be applied for LEMS and LIFT protocols for improving bacterial isolation and microbial growth.
The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets

Autonomous driving is a topic in computer vision which has captured a great deal of attention in recent years. One key problem is the detection and state analysis of traffic lights. Even over time, very few datasets for research in this topic have been published and they vary widely in quantity and in quality. To address the complexity of traffic light recognition, we introduce the DriveU**driveU is a joint innovation center of the Daimler AG and the University of Ulm Traffic Light Dataset (DTLD), a large-scale dataset consisting of more than 230,000 annotations. All annotations are hand-labeled according to strict rules and show a high quality. Recordings were made in eleven different cities during different weather conditions. Our dataset exceeds previous traffic light datasets in size, variance, annotation quality and amount of additional sensor data. We prove the extent of our dataset by an extensive comparison with existing traffic light datasets. Miscellaneous dataset criteria are compared, illustrated and statistically analyzed. In the process, metrics to express the quality and variance of datasets are developed and verified. The dataset can be downloaded from http://traffic-light-data.de.
Efficient Event-Driven Forward Kinematics of Open Kinematic Chains with O(Log n) Complexity

This paper presents novel event-driven forward kinematics algorithms for open kinematic chains with O(log n) complexity. This event-driven algorithm can efficiently update forward kinematics only when new sensory data comes. This will also contribute to localization of computational resources at sensitive joints to the position of the endpoint (e.g. a fingertip), like a root joint. We constructed 3 event-driven FK algorithms. We proved that the algorithms have the complexity of O(logn) for updating 1 joint angle, and O(logn) for obtaining a homogeneous transformation matrix between links. We compared the 3 algorithms with a conventional forward kinematics algorithm in the viewpoint of complexity, computation time, time-variance and algebraic structures. The results showed that the computation time is well adequate for real-time computation. Computation time is less than 2 us per 1 query, for 40,000 kinematic chains.
Blind Bandwidth Extension Based on Convolutional and Recurrent Deep Neural Networks

A blind bandwidth extension (BBWE) expands the bandwidth of telephone speech which often is limited to 0.2 to 3.4 kHz. The advantage is an increased perceived quality as well as an increased intelligibility. This work presents a BBWE similar to state-of-the-art bandwidth extensions like Intelligent Gap Filling with the difference that all processing is done in the decoder without the need of transmitting extra bits. Parameters like spectral envelope are estimated by a regressive Convolutional Deep Neuronal Network (CNN) with long short-term memory (LSTM). The system operates on frames of 20 ms without additional algorithmic delay and can be applied in state-of-the-art speech and audio codecs.
Fore-Aft Leg Specialization Controller for a Dynamic Quadruped

Ahstract- Many running animals, unlike their robotic counterparts, have distinct morphologies and functional roles for their front and rear legs. In this paper we present a new control approach for a 5kg autonomous dynamic quadruped that explicitly encodes separate roles for each contralateral pair of legs. This controller utilizes a functional dynamic decomposition similar to Raibert's three part control law, but focuses on fore-aft leg specialization to regulate the robot's performance. The velocity of this controller, which exceeds 5 body lengths per sec, is compared with an improved trajectory-based controller and shown to be significantly more robust to changes in environment.
Processing of Signals from the Grid Receiver for Measuring of Laser Radiation Characteristics

In the paper presented are results of investigation of the interaction of laser radiation with grid bolometric receivers, subsequent processing of signals using specially developed algorithms and determination of the space-energy, polarization, and time characteristics of laser radiation. For determining of radiation intensity distribution, direction of polarization and shape of the radiation pulse it has been used the Radon transform for processing of signals.
Visual Odometry Using a Homography Formulation with Decoupled Rotation and Translation Estimation Using Minimal Solutions

In this paper we present minimal solutions for two-view relative motion estimation based on a homography formulation. By assuming a known vertical direction (e.g. from an IMU) and assuming a dominant ground plane we demonstrate that rotation and translation estimation can be decoupled. This result allows us to reduce the number of point matches needed to compute a motion hypothesis. We then derive different algorithms based on this decoupling that allow an efficient estimation. We also demonstrate how these algorithms can be used efficiently to compute an optimal inlier set using exhaustive search or histogram voting instead of a traditional RANSAC step. Our methods are evaluated on synthetic data and on the KITTI data set, demonstrating that our methods are well suited for visual odometry in road driving scenarios.
Efficient Sampling on HEALPix Grid

We propose an iso-latitude sampling scheme for the representation of band-limited signals on the sphere. The proposed scheme is designed as a variant of the widely used Hierarchical Equal Area iso-Latitude Pixelization (HEALPix) scheme on the sphere. We use HEALPix grid of resolution $N_{\mathrm{side}}$ to represent a signal of bandlimit (spherical harmonic degree) $L$. To reduce the number of samples, the proposed algorithm takes only $L$ iso-latitude rings out of $4N_{\mathrm{side}}-1$ rings of the HEALPix. This selection is carried out by ensuring that the spherical harmonic transform (SHT) of the signal is computed accurately from the samples. The number of samples required by the proposed sampling scheme is smaller than that required by HEALPix by at least a factor of 3/2. For the proposed sampling scheme, we also formulate the spherical harmonic transform and conduct numerical experiments to evaluate the number of samples required by the proposed sampling scheme and the accuracy of the associated SHT.
Classification of Human Emotions Using Multiclass Support Vector Machine

Emotion recognition plays a very important role in establishing brain computer interface. Emotion recognition can be done by analyzing speech signal or facial expressions. But these methods cannot be considered as reliable indicators of emotion, because it is possible to generate fake data in these methods. In this paper, Electroencephalography (EEG) is used for detection and classification of different emotions. EEG proves to be more reliable method as it is not possible for the subject to alter the data. The proposed method consists of four steps, viz., data acquisition, pre-processing, feature extraction and classification. Emotions are invoked by using audio visual stimuli. EEG signal is captured for four emotions viz. happy, sad, angry and neutral using power lab instrument by ADInsruments. The recorded EEG signal is then filtered using band pass filter with cutoff frequencies of 3Hz and 30Hz. Discrete Wavelet Transform is applied to the filtered data and then statistical features are extracted. Multiclass Support Vector Machine is incorporated to classify EEG signals into different emotion classes.
Distributed Diffusion Adaptation Over Graph Signals

Most works on graph signal processing assume static graph signals, which is a limitation even in comparison to traditional DSP techniques where signals are modeled as sequences that evolve over time. For broader applicability, it is necessary to develop techniques that are able to process dynamic or streaming data. Many earlier works on adaptive networks have addressed problems related to this challenge by developing effective strategies that are particularly well-suited to data streaming into graphs. We are thus faced with two paradigms: one where signals are modeled as static and sitting on the graph nodes, and another where signals are modeled as dynamic and streaming into the graph nodes. The objective of this work is to blend these concepts and propose diffusion strategies for adaptively learning from streaming graph signals.
Deformation Stability of Deep Convolutional Neural Networks on Sobolev Spaces

Our work is based on a recently introduced mathematical theory of deep convolutional neural networks (DCNNs). It was shown that DCNN s are stable with respect to deformations of bandlimited input functions. In the present paper, we generalize this result: We prove deformation stability on Sobolev spaces. Further, we show a weak form of deformation stability for the whole input space L2(Rd). The basic components of DCNNs are semi-discrete frames. For practical applications, a concrete choice is necessary. Therefore, we conclude our work by suggesting a construction method for semi-discrete frames based on bounded uniform partitions of unity (BUPUs) and give a specific example that uses B-splines.
Security and Privacy in the Age of the Smart Internet of Things: An Overview from a Networking Perspective

Pervasively implemented IoT systems are introducing unprecedented threats to our society. In this article, aiming to explore the security impact from the emerging and anarchistic IoT systems, we survey the state of the art of the security and privacy issues in the smart IoT world from a networking perspective. We predict that IoT systems will be extensively used by hackers to commit malicious actions given the features of IoT devices and systems. We also discuss the privacy issue caused by IoT systems. Finally, we present our understanding of counterattacks from both the application and theoretical aspects. We hope this article will shed light on the research field for interested readers.
Area-Improved High-Speed Hybrid 1-bit Full Adder Circuit Using 3T-XNOR Gate

A 1-bit hybrid full adder circuit design is presented here using modified XNOR gate to improve the area and speed performance. The design is first implemented for 1-bit hybrid full adder and then is extended to the 32-bit adder. To enhance the performance of in terms of area, delay and power the combination of CMOS, CPL, and transmission gate logic is used. For the performance analysis of the proposed design; simulation is carried out in 90nm technology with 1.2v supply voltage. The simulation result for the proposed implementation of 1-bit hybrid full adder shows the area (13 transistors) and delay (5.1831nsec) reduction in comparison to the conventional hybrid 1-bit full adder.
Privacy-Preserving Auction-based Incentive Mechanism for Mobile Crowdsensing Systems

Many people use mobile devices as the basic sensing units since they are cheap and convenient. Several incentive mechanisms have been proposed in past literature to incentivize worker participation in such sensing units. However, existing auction-based privacy-preserving incentive mechanisms only consider asymptotically truthful property apart from exactly dominant strategy truthful property while maximizing crowdsourcers' revenue. Past research has shown that a single mechanism model is difficult to solve this problem, so we designed a novel hybrid mechanism with differential privacy to strike the balance between utility property and truthful bidding property. The hybrid mechanism, denoted as PQHM, consists of price-privacy auction-based mechanism (denoted as PPAM) and quality-privacy auction-based mechanism (denoted as QPAM). PPAM can get great social utility and QPAM can get truthful bidding property. We could randomize between running the two mechanisms according to a probability distribution (defined as $\lambda$) to guarantee the reasonable utility for the crowdsourcers, and give each bidder a strict positive incentive to report truthfully. This paper found that the hybrid incentive mechanism has the characteristics of differential private, exactly truthful, individual rationality, reasonable platform profitability, and calculation efficiency. Furthermore, the selection probability $\lambda$ of PQHM is linearly controllable.
Phoneme Based Embedded Segmental K-Means for Unsupervised Term Discovery

Identifying and grouping the frequently occurring word-like patterns from raw acoustic waveforms is an important task in the zero resource speech processing. Embedded segmental K-means (ES-KMeans) discovers both the word boundaries and the word types from raw data. Starting from an initial set of subword boundaries, the ES-Kmeans iteratively eliminates some of the boundaries to arrive at frequently occurring longer word patterns. Notice that the initial word boundaries will not be adjusted during the process. As a result, the performance of the ES-Kmeans critically depends on the initial subword boundaries. Originally, syllable boundaries were used to initialize ES-Kmeans. In this paper, we propose to use a phoneme segmentation method that produces boundaries closer to true boundaries for ES-KMeans initialization. The use of shorter units increases the number of initial boundaries which leads to a significant increment in the computational complexity. To reduce the computational cost, we extract compact lower dimensional embeddings from an auto-encoder. The proposed algorithm is benchmarked on Zero Resource 2017 challenge, which consists of 70 hours of unlabeled data across three languages, viz. English, French, and Mandarin. The proposed algorithm outperforms the baseline system without any language-specific parameter tuning.
An Efficient Heuristic for Joint User Association and RRH Clustering in Cloud Radio Access Networks

Cloud Radio Access Network (C-RAN) is a promising mobile network architecture that breaks down the conventional base station into two main parts: the Base Band Unit (BBU) and the Remote Radio Head (RRH). In this context, deciding to which RRH users connect is known as the user association problem. Moreover, RRHs may be mapped to a single BBU, achieving statistical multiplexing gain. Deciding what RRHs are grouped together is known as the RRH clustering problem. As these two problems are mutually dependent, we formulate in this paper the joint user association and RRH clustering problem. Our objective is to maximize the network throughput, while reducing the network power consumption. Since our joint problem is NP-hard, we propose to decompose it into two sub-problems: the user association (UA) sub-problem and the RRH clustering (RC) sub-problem. First, a low-complexity heuristic, based on the received SINR, is used to solve the UA sub-problem. Second, a low-complexity heuristic, based on the merge-and-split rules, is introduced to solve the RC sub-problem. These two sub-problems are sequentially and iteratively solved until convergence is reached. We further evaluate the performance of our proposed solution. Simulation results show that our proposed heuristic solution for the RC sub-problem strikes a good compromise between computational complexity and performance, in comparison with the optimal exhaustive search method. Furthermore, our proposed solution for the RC sub-problem outperforms the no-clustering method, where one BBU is exclusively dedicated to each RRH, and the grand coalition method, where all RRHs are attached to a single BBU.
Optimizing Multilingual Knowledge Transfer for Time-Delay Neural Networks with Low-Rank Factorization

When producing speech-to-text (STT) systems on a lower resource language, it is often beneficial to use knowledge obtained from a significantly larger multilingual dataset. We have seen benefits from using a multilingual TDNN as initialization for training an acoustic model on a target low resource language. In this work, we expand upon recent research that found benefits from applying sequential low-rank factorization (LRF) by extending it to a TDNN acoustic model trained on a large multilingual corpus. We also examine and optimize the knowledge transfer methodology, with the goal of avoiding the loss of useful information from the multilingual initialization during the knowledge transfer process. Our approach limits the updates to the multilingual network parameters during lattice-free maximum mutual information (LF-MMI) training on the target low resource language by fixing the multilingual network parameters and only optimizing the target output layer. The multilingual parameters and new output layer are jointly optimized using the state-level minimum Bayes risk (sMBR) objective function. By combining sequential LRF with this optimization method, we show across low resource target languages an average absolute WER reduction of 1.2%, yielding a better result than our previous best approach.
Depression Speaks: Automatic Discrimination between Depressed and Non-Depressed Speakers Based on Nonverbal Speech Features

This article proposes an automatic approach - based on nonverbal speech features - aimed at the automatic discrimination between depressed and non-depressed speakers. The experiments have been performed over one of the largest corpora collected for such a task in the literature (62 patients diagnosed with depression and 54 healthy control subjects), especially when it comes to data where the depressed speakers have been diagnosed as such by professional psychiatrists. The results show that the discrimination can be performed with an accuracy of over 75% and the error analysis shows that the chances of correct classification do not change according to gender, depression-related pathology diagnosed by the psychiatrists or length of the pharmacological treatment (if any). Furthermore, for every depressed subject, the corpus includes a control subject that matches age, education level and gender. This ensures that the approach actually discriminates between depressed and non depressed speakers and does not simply capture differences resulting from other factors.
Gene Expression Dynamics Induced by Ciprofloxacin and Loss of Lexa Function in Pseudomonas Aeruginosa PAO1 Using Data Mining and Network Analysis

Pseudomonas aeruginosa is an opportunistic pathogen that causes a variety of infections in humans and frequently develops mechanisms of resistance to antibiotics, which makes its treatment difficult. In this study we applied gene expression analysis using data mining techniques and network analysis to evaluate the temporal effects of exposure to ciprofloxacin and the changes caused by the loss of function of LexA, a regulator of the SOS response to the cellular stress. Initially, global differential expression profiles using clustering algorithms suggested that the effects of antibiotic exposure were determined primarily by time and not by loss of LexA function. This was verified by performing attribute selection and differential expression analysis among conditions, where less than 3.3% of maximum difference between strains but up to 21% of differences were observed over time. Together with network analysis, a significant increase in topological metrics was determined when evaluating temporal changes. Functional annotation showed metabolic pathways enriched over time but not when comparing strains. Overall, the results obtained revealed that the response to ciprofloxacin tends to be exacerbated over time and that it remains stable in the face of the loss of function of LexA activity.