Miracle-dz/UniFAR-FTUs huggingface dataset:
Inter and Intra Topic Structure Learning with Word Embeddings

One important task of topic modeling for text analysis is interpretability. By discovering structured topics one is able to yield improved interpretability as well as modeling accuracy. In this paper, we propose a novel topic model with a deep structure that explores both inter-topic and intra-topic structures informed by word embeddings. Specifically, our model discovers inter topic structures in the form of topic hierarchies and discovers intra topic structures in the form of sub-topics, each of which is informed by word embeddings and captures a fine-grained thematic aspect of a normal topic. Extensive experiments demonstrate that our model achieves the state-of-the-art performance in terms of perplexity, document classification, and topic quality. Moreover, with topic hierarchies and sub-topics, the topics discovered in our model are more interpretable, providing an illuminating means to understand text data.
Simple Features for Strong Performance on Named Entity Recognition in Code-Switched Twitter Data

In this work, we address the problem of Named Entity Recognition (NER) in code-switched tweets as a part of the Workshop on Computational Approaches to Linguistic Code-switching (CALCS) at ACL’18. Code-switching is the phenomenon where a speaker switches between two languages or variants of the same language within or across utterances, known as intra-sentential or inter-sentential code-switching, respectively. Processing such data is challenging using state of the art methods since such technology is generally geared towards processing monolingual text. In this paper we explored ways to use language identification and translation to recognize named entities in such data, however, utilizing simple features (sans multi-lingual features) with Conditional Random Field (CRF) classifier achieved the best results. Our experiments were mainly aimed at the (ENG-SPA) English-Spanish dataset but we submitted a language-independent version of our system to the (MSA-EGY) Arabic-Egyptian dataset as well and achieved good results.
Mutation Landscape of Base Substitutions, Duplications, and Deletions in the Representative Current Cholera Pandemic Strain

Abstract Pandemic cholera is a major concern for public health because of its high mortality and morbidity. Mutation accumulation (MA) experiments were performed on a representative strain of the current cholera pandemic. Although the base-pair substitution mutation rates in Vibrio cholerae (1.24 × 10−10 per site per generation for wild-type lines and 3.29 × 10−8 for mismatch repair deficient lines) are lower than that previously reported in other bacteria using MA analysis, we discovered specific high rates (8.31 × 10−8 site/generation for wild-type lines and 1.82 × 10−6 for mismatch repair deficient lines) of base duplication or deletion driven by large-scale copy number variations (CNVs). These duplication–deletions are located in two pathogenic islands, IMEX and the large integron island. Each element of these islands has discrepant rate in rapid integration and excision, which provides clues to the pandemicity evolution of V. cholerae. These results also suggest that large-scale structural variants such as CNVs can accumulate rapidly during short-term evolution. Mismatch repair deficient lines exhibit a significantly increased mutation rate in the larger chromosome (Chr1) at specific regions, and this pattern is not observed in wild-type lines. We propose that the high frequency of GATC sites in Chr1 improves the efficiency of MMR, resulting in similar rates of mutation in the wild-type condition. In addition, different mutation rates and spectra were observed in the MA lines under distinct growth conditions, including minimal media, rich media and antibiotic treatments.
Gut Microbiota of Great Spotted Cuckoo Nestlings is a Mixture of Those of Their Foster Magpie Siblings and of Cuckoo Adults

Diet and host genetic or evolutionary history are considered the two main factors determining gut microbiota of animals, although studies are scarce in natural populations. The system of great spotted cuckoos (Clamator glandarius) parasitizing magpies (Pica pica) is ideal to study both effects since magpie adults feed cuckoo and magpie nestlings with the same diet and, consequently, differences in gut microbiota of nestlings of these two species will mainly reflect the importance of genetic components. Moreover, the diet of adults and of nestling cuckoos drastically differ from each other and, thus, differences and similarities in their microbiotas would respectively reflect the effect of environmental and genetic factors. We used next-generation sequencing technologies to analyze the gut microbiota of cuckoo adults and nestlings and of magpie nestlings. The highest α-diversity estimates appeared in nestling cuckoos and the lowest in nestling magpies. Moreover, despite the greatest differences in the microbiome composition of magpies and cuckoos of both ages, cuckoo nestlings harbored a mixture of the Operational Taxonomic Units (OTUs) present in adult cuckoos and nestling magpies. We identified the bacterial taxa responsible for such results. These results suggest important phylogenetic components determining gut microbiome of nestlings, and that diet might be responsible for similarities between gut microbiome of cuckoo and magpie nestlings that allow cuckoos to digest food provided by magpie adults.
On the consistency of the spacings test for multivariate uniformity, including on manifolds

Abstract We give a simple conceptual proof of the consistency of a test for multivariate uniformity in a bounded set K ⊂ ℝd that is based on the maximal spacing generated by independent and identically distributed points X1, . , Xn in K, i.e. the volume of the largest convex set of a given shape that is contained in K and avoids each of these points. Since asymptotic results for the d > 1 case are only availabe under uniformity, a key element of the proof is a suitable coupling. The proof is general enough to cover the case of testing for uniformity on compact Riemannian manifolds with spacings defined by geodesic balls.
LASA: Lightweight, Auditable and Secure Access Control in ICN with Limitation of Access Times

Information Centric Networking (ICN), a future network architecture candidate, aims to alleviate the problem of insufficient bandwidth in traditional IP network. In ICN, contents are distributed in the whole network, so access control becomes more intractable. As we know, almost all of existing solutions consider it as a "Yes or No" problem, where a user either has the permission to access the corresponding content or not. However, in many practical situations, a content provider doesn't expect a single authorized user has the ability to access its repertory without times limitation when taking copyright protection into account. In this paper, we propose LASA, a lightweight, auditable and secure solution where legitimate users are limited to access a content provider's data within pre-designate times. In LASA, each content provider sets maximum access times for each legitimate user and edge routers perform authentication and audit based on users' signatures attached to interest packets. Once a legitimate user attempts to exceed his/her limited access times, his/her secret key will be leaked and the dishonest behavior will be detected. Our security analysis shows that LASA can provide signature unforgeability, data confidentiality and other security features. Experiment results show that our scheme LASA brings a little computational cost.
QUENN: QUantization engine for low-power neural networks

Deep Learning is moving to edge devices, ushering in a new age of distributed Artificial Intelligence (AI). The high demand of computational resources required by deep neural networks may be alleviated by approximate computing techniques, and most notably reduced-precision arithmetic with coarsely quantized numerical representations. In this context, Bonseyes comes in as an initiative to enable stakeholders to bring AI to low-power and autonomous environments such as: Automotive, Medical Healthcare and Consumer Electronics. To achieve this, we introduce LPDNN, a framework for optimized deployment of Deep Neural Networks on heterogeneous embedded devices. In this work, we detail the quantization engine that is integrated in LPDNN. The engine depends on a fine-grained workflow which enables a Neural Network Design Exploration and a sensitivity analysis of each layer for quantization. We demonstrate the engine with a case study on Alexnet and VGG16 for three different techniques for direct quantization: standard fixed-point, dynamic fixed-point and k-means clustering, and demonstrate the potential of the latter. We argue that using a Gaussian quantizer with k-means clustering can achieve better performance than linear quantizers. Without retraining, we achieve over 55.64% saving for weights' storage and 69.17% for run-time memory accesses with less than 1% drop in top5 accuracy in Imagenet.
Neural Dynamics during Resting State: A Functional Magnetic Resonance Imaging Exploration with Reduction and Visualization

The brain is a complex high-order system. Body movements or mental activities are both dependent on the transmission of information among billions of neurons. However, potential patterns are hardly discoverable due to the high dimensionality in neural signals. Previous studies have identified rotary trajectories in rhythm and nonrhythm movements when projecting the neural electrical signals into a two-dimensional space. However, it is unclear how well this analogy holds at the resting state. Given the low-frequency fluctuations noted during spontaneous neural activities using functional magnetic resonance imaging (fMRI), it is natural to hypothesize that the neural response at resting state also shows a periodic trajectory. In this study, we explored the potential patterns in resting state fMRI data at four frequency bands (slow 2–slow 5) on two cohorts, one of which consisted of young and elderly adults and the other of patients with Alzheimer’s disease and normal controls (NC). The jPCA algorithm was applied to reduce the high-dimensional BOLD signal into a two-dimensional space for visualization of the trajectory. The results indicated that the “resting state” is a basic state showing an inherent dynamic pattern with a low frequency and long period during normal aging, with changes appearing in the rotary period at the slow 4 frequency band (0.027–0.073 Hz) during the pathological process of Alzheimer’s disease (AD). These findings expand the original understanding that neural signals can rotate themselves and that motor executive signals consist of neural signals. Meanwhile, the rotary period at band slow 4 may be a physiological marker for AD, and studies of this frequency band may be useful for understanding the potential pathophysiology of AD and ultimately facilitate characterization and auxiliary diagnosis of AD.
Multiactivation Pooling Method in Convolutional Neural Networks for Image Recognition

Convolutional neural networks (CNNs) are becoming more and more popular today. CNNs now have become a popular feature extractor applying to image processing, big data processing, fog computing, etc. CNNs usually consist of several basic units like convolutional unit, pooling unit, activation unit, and so on. In CNNs, conventional pooling methods refer to 2×2 max-pooling and average-pooling, which are applied after the convolutional or ReLU layers. In this paper, we propose a Multiactivation Pooling (MAP) Method to make the CNNs more accurate on classification tasks without increasing depth and trainable parameters. We add more convolutional layers before one pooling layer and expand the pooling region to 4×4, 8×8, 16×16, and even larger. When doing large-scale subsampling, we pick top-k activation, sum up them, and constrain them by a hyperparameter σ. We pick VGG, ALL-CNN, and DenseNets as our baseline models and evaluate our proposed MAP method on benchmark datasets: CIFAR-10, CIFAR-100, SVHN, and ImageNet. The classification results are competitive.
Multipath QUIC: A Deployable Multipath Transport Protocol

QUIC is the emerging transport layer protocol, providing encrypted, stream-multiplexed, low-latency data transfer. In this paper, we propose multipath-enabled QUIC (MPQUIC) to leverage multiple network interfaces, such as WiFi and LTE on today's mobile devices. We show how our MPQUIC design conceptually evolves beyond existing multipathing protocols, such as MPTCP, as it provides fine-grained stream-to-path scheduling, reduced head-of-line blocking, and faster subflow establishment. We present an userland implementation of MPQUIC that is deployable without operating system changes. Our evaluation results show that MPQUIC increases throughput in comparison to traditional QUIC, TCP and even the currently de facto multipath transport protocol MPTCP. First real world measurements confirm that MPQUIC is deployable in the Internet to reduce download times. Moreover, we show that MPQUIC's conceptual advantages over MPTCP efficiently reduce head-of-line blocking in heterogeneous environments. With multipathing support, QUIC is ready to become the universal stream transport protocol in today's Internet.
Bio-inspired optimization algorithms for multi-objective problems

Multi-Objective Problems (MOPs) are characterized by having two or more objective functions to be simultaneously optimized. In these problems, the goal is to find a set of non-dominated solutions usually called Pareto optimal set whose image in the objective space is called Pareto front. MOPs presenting more than three objective functions to be optimized are known as Many-Objective Problems (MaOPs) and several studies indicate that the search ability of Pareto-based algorithms is severely deteriorated in such problems. The development of bio-inspired optimizers to tackle MOPs and MaOPs is a field that has been gaining attention in the community, however there are many opportunities to innovate. Multi-objective Particle Swarm Optimization (MOPSO) is one of the bio-inspired algorithms suitable to be modified and improved, mostly due to its simplicity, flexibility and good results. To enhance the search ability of MOPSOs, we followed two different research lines: The first focus on leader and archiving methods. Previous works have pointed that these components can influence the algorithm performance, however the selection of these components can be problem-dependent. An alternative to dynamically select them is by employing hyper-heuristics. By combining hyper-heuristics and MOPSO, we developed a new framework called H-MOPSO. The second research line, is also based on previous works of the group that focus on multi-swarm. This is done by selecting as base framework the iterated multi swarm (I-Multi) algorithm, whose search procedure can be divided into diversity and multi-swarm searches, and the latter employs clustering to split a swarm into several sub-swarms. In order to improve the performance of I-Multi, we explored two possibilities: the first was to further investigate the effect of different characteristics of the clustering mechanism of I-Multi. The second was to investigate alternatives to improve the convergence of each sub-swarm, like hybridizing it to an Estimation of Distribution Algorithm (EDA). This work on EDA increased our interest in this approach, hence we followed another research line by investigating alternatives to create multi-objective versions of one of the most powerful EDAs from the literature, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). In order to validate our work, several empirical studies were conducted to investigate the search ability of the approaches proposed. In all studies, our investigated algorithms have reached competitive or better results than well established algorithms from the literature.
In vivo analysis of cardiomyocyte proliferation during trabeculation

ABSTRACT Cardiomyocyte proliferation is crucial for cardiac growth, patterning and regeneration; however, few studies have investigated the behavior of dividing cardiomyocytes in vivo. Here, we use time-lapse imaging of beating hearts in combination with the FUCCI system to monitor the behavior of proliferating cardiomyocytes in developing zebrafish. Confirming in vitro observations, sarcomere disassembly, as well as changes in cell shape and volume, precede cardiomyocyte cytokinesis. Notably, cardiomyocytes in zebrafish embryos and young larvae mostly divide parallel to the myocardial wall in both the compact and trabecular layers, and cardiomyocyte proliferation is more frequent in the trabecular layer. While analyzing known regulators of cardiomyocyte proliferation, we observed that the Nrg/ErbB2 and TGFβ signaling pathways differentially affect compact and trabecular layer cardiomyocytes, indicating that distinct mechanisms drive proliferation in these two layers. In summary, our data indicate that, in zebrafish, cardiomyocyte proliferation is essential for trabecular growth, but not initiation, and set the stage to further investigate the cellular and molecular mechanisms driving cardiomyocyte proliferation in vivo. Summary: Live imaging in zebrafish shows that cardiomyocyte division is preceded by sarcomere disassembly and changes in cell shape and volume, with distinct signaling pathways controlling proliferation of compact and trabecular cardiomyocytes.
Variational Inference and Deep Generative Models

NLP has seen a surge in neural network models in recent years. These models provide state-of-the-art performance on many supervised tasks. Unsupervised and semi-supervised learning has only been addressed scarcely, however. Deep generative models (DGMs) make it possible to integrate neural networks with probabilistic graphical models. Using DGMs one can easily design latent variable models that account for missing observations and thereby enable unsupervised and semi-supervised learning with neural networks. The method of choice for training these models is variational inference. This tutorial offers a general introduction to variational inference followed by a thorough and example-driven discussion of how to use variational methods for training DGMs. It provides both the mathematical background necessary for deriving the learning algorithms as well as practical implementation guidelines. Importantly, the tutorial will cover models with continuous and discrete variables. We provide practical coding exercises implemented in IPython notebooks as well as short notes on the more intricate mathematical details that the audience can use as a reference after the tutorial. We expect that with these additional materials the tutorial will have a long-lasting impact on the community.
Pointer Provenance in a Capability Architecture

We design and implement a framework for tracking pointer provenance, using our CHERI fat-pointer capability architecture to facilitate analysis of security implications of program pointer flows in both user and privileged code, with minimal instrumentation. CHERI enforces pointer provenance validity at the architectural level, in the presence of complex pointer arithmetic and type casting. CHERI present new opportunities for provenance research: we discuss use cases and highlight lessons and open questions from our work.
A Novel Physical Layer Encryption Algorithm Based on Statistical Characteristics of Time-Selective Channels

Currently, the security of information in the physical layer has attracted much attentions. However, such a physical layer security of conventional study depends on the assumption of reciprocity between the uplink and downlink channels. This assumption cannot be ensured in the time-selective channel of mobile communications. Hence, this paper proposes an encryption algorithm in mobile communications. First, the statistics of time-selective channel are extracted to act as the seeds for multiple chaos generator. Second, the produced chaos sequences are combined to produce the secret code. Finally, the physical signal is encrypted by the simple scrambling operation. We verify the proposed algorithm by computer simulations, and the results demonstrate that the encrypted data can be successfully recovered by the legal receiver, while the sniffer produces poor and flat bit-error-rate performance in a wide range of signal-to-noise ratios.
NDBench: Benchmarking Microservices at Scale

Software vendors often report performance numbers for the sweet spot or running on specialized hardware with specific workload parameters and without realistic failures. Accurate benchmarks at the persistence layer are crucial, as failures may cause unrecoverable errors such as data loss, inconsistency or corruption. To accurately evaluate data stores and other microservices at Netflix, we developed Netflix Data Benchmark (NDBench), a Cloud benchmark tool. It can be deployed in a loosely-coupled fashion with the ability to dynamically change the benchmark parameters at runtime so we can rapidly iterate on different tests and failure modes. NDBench offers pluggable patterns and loads, support for pluggable client APIs, and was designed to run continually. This design enabled us to test long-running maintenance jobs that may affect the performance, test numerous different systems under adverse conditions, and uncover long-term issues like memory leaks or heap pressure.
Exploring System Challenges of Ultra-Low Latency Solid State Drives

We quantitatively characterize performance behaviors of a real ultra-low latency (ULL) SSD archive by using a real 800GB Z-SSD prototype, and analyze systemlevel challenges that the current storage stack exhibits. Specifically, our comprehensive empirical evaluations and studies demonstrate i) diverse performance analyses of the ULL SSD, including a wide range of latency and queue examinations, ii) I/O interference characteristics, which are considered as one of the great performance bottlenecks of modern SSDs, and iii) efficiency and challenge analyses of a polling-based I/O service (newly added into Linux 4.4 kernel) by comparing it with conventional interrupt-based I/O services. In addition to these performance characterizations, we discuss several system implications, which are required to take a full benefit of ULL SSD in the future.
Gated Fusion Network for Joint Image Deblurring and Super-Resolution

Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution. If the input image contains degraded pixels, the artifacts caused by the degradation could be amplified by super-resolution methods. Image blur is a common degradation source. Images captured by moving or still cameras are inevitably affected by motion blur due to relative movements between sensors and objects. In this work, we focus on the super-resolution task with the presence of motion blur. We propose a deep gated fusion convolution neural network to generate a clear high-resolution frame from a single natural image with severe blur. By decomposing the feature extraction step into two task-independent streams, the dual-branch design can facilitate the training process by avoiding learning the mixed degradation all-in-one and thus enhance the final high-resolution prediction results. Extensive experiments demonstrate that our method generates sharper super-resolved images from low-resolution inputs with high computational efficiency.