Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective training and efficient decoding for statistical machine translation

Statistical machine translation, the task of translating text from one natural language into another using statistical models, can be divided into three main problems: modeling, search and training. This thesis gives a detailed description of the most popular approach to statistical machine translation, the phrase-based paradigm, and presents several improvements to the state of the art in all three of the aspects mentioned above. Regarding the search problem, we propose three novel language model look-ahead techniques which can considerably increase time efficiency of the algorithm with different quality tradeoffs. They are evaluated in detail with respect to their effect on translation quality, translation speed, number of language model queries and number of generated nodes within the search graph. We can show that our final system outperforms the popular Moses toolkit in terms of translation speed. With regard to the modeling problem we extend the state of the art with novel smoothing models based on word classes. Data sparsity is a common pitfall for statistical models. We leverage word classes that can be learned in an unsupervised fashion in order to re-parameterize the standard phrase-based models, resulting in a smoother probability distribution and reduced sparsity. The largest part of this work is dedicated to the training problem. We investigate both generative and discriminative training methods, two fundamentally different approaches to learning statistical models. Our generative procedure is inspired by the expectation-maximization algorithm and based on force-aligning the training data with the application of the leave-one-out technique to avoid overfitting. Its advantage over the standard heuristic model extraction is that it provides a framework which uses the same consistent models in training and search. The initial technique is further developed into a length-incremental procedure which does not require initialization with a Viterbi word alignment and is thus not biased by its inconsistencies. Both the learning procedure and the resulting models are analyzed in detail. As a discriminative training procedure, we employ a gradient-based method to optimize an expected Bleu objective function. Our novel contribution is the application of the resilient backpropagation algorithm, which is experimentally shown to be superior to several previously proposed techniques. It is also significantly more time and memory efficient than previous work, so that we can run training on the largest data set reported in the literature to date. Our novel techniques are experimentally evaluated against internal and external results on large-scale translation tasks and within public evaluation campaigns. Especially the word class language model and discriminative training procedure prove to be valuable for state-of-the-art large scale translation systems.
Conformal prediction in manifold learning

The paper presents a geometrically motivated view on conformal prediction applied to nonlinear multi-output regression tasks for obtaining valid measure of accuracy of Manifold Learning Regression algorithms. A considered regression task is to estimate an unknown smooth mapping f from q-dimensional inputs x ∈ X to m-dimensional outputs y = f(x) based on training dataset Z(n) consisting of “input-output” pairs {Zi = (xi,yi = f(xi)) , i = 1, 2, . , n}. Manifold Learning Regression (MLR) algorithm solves this task using Manifold learning technique. At first, unknown q-dimensional Regression manifold M(f) = {(x, f(x)) ∈ R : x ∈ X ⊂ R}, embedded in ambient (q + m)-dimensional space, is estimated from the training data Z(n), sampled from this manifold. The constructed estimator MMLR, which is also q-dimensional manifold embedded in ambient space R, is close to M in terms of Hausdorff distance. After that, an estimator fMLR of the unknown function f , mapping arbitrary input x ∈ X to output fMLR(x), is constructed as the solution to the equation M(fMLR) = MMLR. Conformal prediction allows constructing a prediction region for an unknown output y = f(x) at Out-of-Sample input point x for a given confidence level using given nonconformity measure, characterizing to which extent an example Z = (x,y) is different from examples in the known dataset Z(n). The paper proposes a new nonconformity measure based on MLR estimators using an analog of Bregman distance.
Effects of Anti-Integrin Treatment With Vedolizumab on Immune Pathways and Cytokines in Inflammatory Bowel Diseases

Background and aims Despite proven clinical efficacy of vedolizumab (VDZ) for inducing and maintaining remission in patients with Crohn’s disease (CD) and ulcerative colitis (UC), subgroups of patients have no therapeutic benefit from anti-α4β7 integrin therapy with VDZ. Within this study, we aimed to identify genetic, cellular, and immunological mechanisms that define response and failure to VDZ treatment. Methods Intestinal RNA sequencing was performed in UC and CD patients before and at week 14 of VDZ therapy. α4β7 expression on peripheral and mucosal immune cells was assessed by flow cytometry and immunohistochemistry. Cellular modes of VDZ-mediated action were analyzed ex vivo and in VDZ-treated inflammatory bowel disease patients. Results Transcriptome analysis showed an impairment of signaling cascades associated with adhesion, diapedesis, and migration of granulocytes and agranulocytes upon VDZ therapy. In non-remitters to VDZ therapy, a tissue destructive and leukocyte-mediated inflammatory activity with activation of TNF-dependent pathways was present, all of which were inhibited in remitters to VDZ. Clinical remission was associated with a significant reduction of α4β7 expression on Th2 and Th17 polarized mucosal CD4+ T cells at week 14 of VDZ therapy and with significantly higher numbers of α4β7-expressing mucosal cells prior to the initiation of VDZ therapy compared with non-remitters. Conclusion Intestinal α4β7 expression prior to VDZ therapy might represent a biomarker that predicts therapeutic response to subsequent VDZ treatment. Due to high activation of TNF signaling in VDZ non-remitters, anti-TNF treatment might represent a promising therapeutic strategy in VDZ refractory patients.
Graphene: Semantically-Linked Propositions in Open Information Extraction

We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification. In that way, we convert sentences that present a complex linguistic structure into simplified, syntactically sound sentences, from which we can extract propositions that are represented in a two-layered hierarchy in the form of core relational tuples and accompanying contextual information which are semantically linked via rhetorical relations. In a comparative evaluation, we demonstrate that our reference implementation Graphene outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. Moreover, we show that existing Open IE approaches can benefit from the transformation process of our framework.
Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations

Air pollution has become an extremely serious problem, with particulate matter having a significantly greater impact on human health than other contaminants. The small diameter of fine particulate matter (PM2.5) allows it to penetrate deep into the alveoli as far as the bronchioles, interfering with a gas exchange within the lungs. Long-term exposure to particulate matter has been shown to cause the cardiovascular disease, respiratory disease, and increase the risk of lung cancers. Therefore, forecasting air quality has also become important to help guide individual actions. This paper aims to forecast air quality for up to 48 h using a combination of multiple neural networks, including an artificial neural network, a convolutional neural network, and a long-short-term memory to extract spatial-temporal relations. The proposed predictive model considers various meteorology data from the previous few hours as well as information related to the elevation space to extract terrain impact on air quality. The model includes trends from multiple locations, extracted from correlations between adjacent locations, and among similar locations in the temporal domain. Experiments employing Taiwan and Beijing data sets show that the proposed model achieves excellent performance and outperforms current state-of-the-art methods.
OMCO: Online Multiple Coflow Scheduling in Optical Circuit Switch

Coflow is gradually prevalent as a new traffic structure in data centers, which allows applications to convey their application-level semantics into the network. Meanwhile, optical circuit switches (OCS) are increasingly deployed in data centers due to the superiority in hardware, such as high bandwidth and low energy consumption. However, few works in the literature considered both coflow scheduling with OCS. In this paper, we study the coflow scheduling problem in an OCS-based data center network, with an aim to minimize the coflow completion time (CCT). We derive an online algorithm called OMCO to solve this problem. \texttt{OMCO} can not only optimize the circuit utilization but also minimize the number of circuit reconfigurations. Extensive simulations with real-world data traces show that OMCO outperforms other existing solutions dramatically. Compared with a FIFO-based scheme and the shortest- coflow-first heuristic, OMCO reduces the average coflow completion time by up to 61% and 62%, respectively.
Energy-Aware Smart Connectivity for IoT Networks: Enabling Smart Ports

The Internet of Things (IoT) is spreading much faster than the speed at which the supporting technology is maturing. Today, there are tens of wireless technologies competing for IoT and a myriad of IoT devices with disparate capabilities and constraints. Moreover, each of many verticals employing IoT networks dictates distinctive and differential network qualities. In this work, we present a context-aware framework that jointly optimises the connectivity and computational speed of the IoT network to deliver the qualities required by each vertical. Based on a smart port application, we identify energy efficiency, security, and response time as essential quality features and consider a wireless realisation of IoT connectivity using short range and long-range technologies. We propose a reinforcement learning technique and demonstrate significant reduction in energy consumption while meeting the quality requirements of all related applications.
DHMC: A Connection-Oriented Clustering Protocol for Mobility-Centric Sensor Networks

Preserving connectivity in Mobile Wireless Sensor Networks (MWSN) having mobile nodes with heterogeneous mobility patterns is a great challenge and thus, is significantly important to ensure the success of mobility centric critical applications, like military, battlefield surveillance. This paper introduces a Dynamic, Heterogeneous Mobility-centric Clustering (DHMC) algorithm in which spatial and temporal densities are used. This protocol integrates the temporal aspect of a Minimum Period of Connectivity (MPC). Border nodes are used to reestablish connectivity among disconnected sub-clusters by increasing the communication range of the best suitable border nodes. Simulation was performed for three different mobility scenarios and showed that the proposed DHMC algorithm outperforms two well-known clustering algorithms LEACH and HEED in term of throughput and packets delivery ratio.
Real-time Video Enhancement Using Graphical Processing Units

In this paper, we propose a real-time video enhancing algorithm which uses the Graphical Processing Units(GPUs) for faster parallel computations. Also, the paper will discuss the performance gains with GPU respect to a Central Processing Units(CPUs). The video enhancing algorithm is written to address the real-time video enhancement scenario, hence this paper will discuss more on the speed and the quality of the video enhancement. The paper contains the discussion about the trade-off between the quality and speed of the real-time-video enhancement. The latter part of the paper will discuss the optimizations to have real-time performance that is needed to enhance and play videos up to 4k videos. This paper proposes a generic implementation that can be used to have high-end video quality using low-end video recording devices like mass scale CCTV cameras, etc. This implementation will use NVIDIA CUDA enabled graphics cards.
Deterministic (1/2 + ε)-Approximation for Submodular Maximization over a Matroid

We study the problem of maximizing a monotone submodular function subject to a matroid constraint and present a deterministic algorithm that achieves (1/2 + e)-approximation for the problem. This algorithm is the first deterministic algorithm known to improve over the 1/2-approximation ratio of the classical greedy algorithm proved by Nemhauser, Wolsely and Fisher in 1978.
Learning Registered Point Processes from Idiosyncratic Observations

A parametric point process model is developed, with modeling based on the assumption that sequential observations often share latent phenomena, while also possessing idiosyncratic effects. An alternating optimization method is proposed to learn a "registered" point process that accounts for shared structure, as well as "warping" functions that characterize idiosyncratic aspects of each observed sequence. Under reasonable constraints, in each iteration we update the sample-specific warping functions by solving a set of constrained nonlinear programming problems in parallel, and update the model by maximum likelihood estimation. The justifiability, complexity and robustness of the proposed method are investigated in detail, and the influence of sequence stitching on the learning results is examined empirically. Experiments on both synthetic and real-world data demonstrate that the method yields explainable point process models, achieving encouraging results compared to state-of-the-art methods.
A Tutorial on Nonorthogonal Multiple Access for 5G and Beyond

Today’s wireless networks allocate radio resources to users based on the orthogonal multiple access (OMA) principle. However, as the number of users increases, OMA based approaches may not meet the stringent emerging requirements including very high spectral efficiency, very low latency, and massive device connectivity. Nonorthogonal multiple access (NOMA) principle emerges as a solution to improve the spectral efficiency while allowing some degree of multiple access interference at receivers. In this tutorial style paper, we target providing a unified model for NOMA, including uplink and downlink transmissions, along with the extensions to multiple input multiple output and cooperative communication scenarios. Through numerical examples, we compare the performances of OMA and NOMA networks. Implementation aspects and open issues are also detailed.
Identifying Key Sentences for Precision Oncology Using Semi-Supervised Learning

We present a machine learning pipeline that identifies key sentences in abstracts of oncological articles to aid evidence-based medicine. This problem is characterized by the lack of gold standard datasets, data imbalance and thematic differences between available silver standard corpora. Additionally, available training and target data differs with regard to their domain (professional summaries vs. sentences in abstracts). This makes supervised machine learning inapplicable. We propose the use of two semi-supervised machine learning approaches: To mitigate difficulties arising from heterogeneous data sources, overcome data imbalance and create reliable training data we propose using transductive learning from positive and unlabelled data (PU Learning). For obtaining a realistic classification model, we propose the use of abstracts summarised in relevant sentences as unlabelled examples through Self-Training. The best model achieves 84% accuracy and 0.84 F1 score on our dataset
Flexible memristor based neuromorphic system for implementing multi-layer neural network algorithms

Abstract This paper describes a memristor-based neuromorphic system that can be used for ex situ training of various multi-layer neural network algorithms. This system is based on an analogue neuron circuit that is capable of performing an accurate dot product calculation. The presented ex situ programming technique can be used to map many key neural algorithms directly onto the grid of resistances in a memristor crossbar. Using this weight-to-crossbar mapping approach along with the memristor based circuit architecture, complex neural algorithms can be easily implemented using this system. Some existing memristor based circuits provide an approximated dot product based on conductance summation, but neuron outputs are not directly correlated to the numerical values obtained in a traditional software approach. To show the effectiveness and versatility of this circuit, two different powerful neural networks were simulated. These include a Restricted Boltzmann Machine for character recognition and a Multilayer Perceptron trained to perform Sobel edge detection. Following these simulations, an analysis was presented that shows how both memristor accuracy and neuron circuit gain relates to output error. This work presents a novel memristor based architecture that that is capable of implementing multiple different learning algorithms using the same hardware, which is based on crossbar structures such as the one displayed. The example presented shows the result of the memristor architecture when implementing Sobel edge detection using a multilayer perceptron.
The value of values for HCI: an informed discussion beyond philosophy

Academic literature has indicated a new moment for the HCI field that requires it to revisit methods and practices to consider aspects that are difficult to deal with, such as values and culture. This paper presents an informed discussion situated in the context of the Human Values Grand Challenge for HCI in Brazil, exploring possible understanding for values in HCI, the importance of the topic, and existing approaches. We draw on the literature and on our own research experiences in the topic to develop critical discussions and suggest possible directions for advancing the research and practice in the context of this challenge.
Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning

How consumers use review content has remained opaque due to the unstructured nature of text and the lack of review-reading behavior data. The authors overcome this challenge by applying deep learning–based natural language processing on data that tracks individual-level review reading, searching, and purchasing behaviors on an e-commerce site to investigate how consumers use review content. They extract quality and price content from 500,000 reviews of 600 product categories and achieve two objectives. First, the authors describe consumers’ review-content-reading behaviors. Although consumers do not read review content all the time, they do rely on it for products that are expensive or of uncertain quality. Second, the authors quantify the causal impact of read-review content on sales by using supervised deep learning to tag six theory-driven content dimensions and applying a regression discontinuity in time design. They find that aesthetics and price content significantly increase conversion across almost all product categories. Review content has a higher impact on sales when the average rating is higher, ratings variance is lower, the market is more competitive or immature, or brand information is not accessible. A counterfactual simulation suggests that reordering reviews based on content can have the same effect as a 1.6% price cut for boosting conversion.
Reservation Based Electric Vehicle Charging Using Battery Switch

With the growing popularization of Electric Vehicles (EVs), charging management has become an increasingly important research problem in smart cities. Different from plug-in charging technology, we alternatively enable the battery switch technology to provide fast EV charging (reduce the service waiting time from tens of minutes to a few minutes), by facilitating the switchable (fully-recharged) batteries maintained at CSs and also the batteries cycling procure to refresh their availability. Nevertheless, potential hot spot may still happen at CSs, due to running out of switchable batteries as well as long batteries charging queue. With this concern, we next propose a reservation based EV charging management scheme to alleviate such situation, considering EVs' anticipated charging reservations (including arrival time, expected charging time) to coordinate EVs' charging plans. Results under the Helsinki city scenario with realistic EV and CS characteristics show the advantage of our enabling technology, in terms of minimized waiting time for the battery switch as the benefit of EV drivers, and higher number of batteries switched as the benefit of CSs.
Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer

We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation for a good and seeks to maximize his cumulative discounted surplus. We propose a novel algorithm that never decreases offered prices and has a tight strategic regret bound of Θ(log log T ). This result closes the open research question on the existence of a no-regret horizon-independent weakly consistent pricing. We also show that the property of nondecreasing prices is nearly necessary for a weakly consistent algorithm to be a no-regret one.
Cross-conformal predictive distributions

Conformal predictive systems are a recent modification of conformal predictors that output, in regression problems, probability distributions for labels of test observations rather than set predictions. The extra information provided by conformal predictive systems may be useful, e.g., in decision making problems. Conformal predictive systems inherit the relative computational inefficiency of conformal predictors. In this paper we discuss two computationally efficient versions of conformal predictive systems, which we call split conformal predictive systems and cross-conformal predictive systems, and discuss their advantages and limitations.
Cavs: An Efficient Runtime System for Dynamic Neural Networks

Recent deep learning (DL) models are moving more and more to dynamic neural network (NN) architectures, where the NN structure changes for every data sample. However, existing DL programming models are inefﬁcient in handling dynamic network architectures because of: (1) substantial overhead caused by repeating dataﬂow graph construction and processing every example; (2) difﬁculties in batched execution of multiple samples; (3) inability to incorporate graph optimization techniques such as those used in static graphs. In this paper, we present “Cavs”, a runtime system that overcomes these bottlenecks and achieves efﬁcient training and inference of dynamic NNs. Cavs represents a dynamic NN as a static vertex function F and a dynamic instance-speciﬁc graph G . It avoids the overhead of repeated graph construction by only declaring and constructing F once, and allows for the use of static graph optimization techniques on pre-deﬁned operations in F . Cavs performs training and inference by scheduling the execution of F following the dependencies in G , hence naturally exposing batched execution opportunities over different samples. Experiments comparing Cavs to state-of-the-art frameworks for dynamic NNs (TensorFlow Fold, PyTorch and DyNet) demonstrate the efﬁcacy of our approach: Cavs achieves a near one order of magnitude speedup on training of dynamic NN architectures, and ablations verify the effectiveness of our proposed design and optimizations.