Miracle-dz/UniFAR-FTUs huggingface dataset:
Re-Weighted $\ell_1$ Algorithms within the Lagrange Duality Framework: Bringing Interpretability to Weights

We consider an important problem in signal processing, which consists in finding the sparsest solution of a linear system $\Phi x=b$. This problem has applications in several areas, but is NP-hard in general. Usually an alternative convex problem is considered, based on minimizing the (weighted) $\ell_{1}$ norm. For this alternative to be useful, weights should be chosen as to obtain a solution of the original NP-hard problem. A well known algorithm for this is the Re-Weighted $\ell_{1}$, proposed by Candes, Wakin and Boyd. In this article we introduce a new methodology for updating the weights of a Re-Weighted $\ell_{1}$ algorithm, based on identifying these weights as Lagrange multipliers. This is then translated into an algorithm with performance comparable to the usual methodology, but allowing an interpretation of the weights as Lagrange multipliers. The methodology may also be used for a noisy linear system, obtaining in this case a Re-Weighted LASSO algorithm, with a promising performance according to the experimental results.
On the genesis of BBP formulas

We present a general procedure to generate infinitely many BBP and BBP-like formulas for the simplest transcendental numbers. This provides some insight and a better understanding into their nature. In particular, we can derive the main known BBP formulas for π. We can understand why many of these formulas are rearrangements of each other. We also understand better where some null BBP formulas representing 0 come from. We also explain what is the observed relation between some BBP formulas for log 2 and π, that are obtained by taking real and imaginary parts of a general complex BBP formula. Our methods are elementary, but motivated by transal-gebraic considerations, and offer a new way to obtain and to search many new BBP formulas and, conjecturally, to better understand transalgebraic relations between transcendental constants.
The Use of Oral Activities to Improve the Scores of Written Spelling Quizzes in a Group of Third Graders

This article presents the effects of implementing oral activities to help students improve their spelling written quizzes’ scores in a group of third graders from a private elementary school. Twenty-six students from a spelling class participated in the investigation. The purpose of this spelling class is to help students acquire vocabulary. However, almost 50% of the 26 participants were not obtaining passing grades in the quizzes. After a two-week treatment period where the only change in the class dynamics was the manner in which the target words were practiced, a post-test was administered to the group of participants. The results obtained in the post-test indicated that there was a significant improvement in the scores after the implementation of the oral activities designed to practice the vocabulary items. Therefore, it was concluded that promoting the use of vocabulary through meaningful speaking tasks may lead to more learning and retention of vocabulary than traditional ways such as repetition and rote memorization of words.
Closed-Form Delay-Optimal Computation Offloading in Mobile Edge Computing Systems

Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we study the delay-optimal computation offloading in computation-constrained MEC systems. We consider the computation task queue at the MEC server due to its constrained computation capability. In this case, the task queue at the MT and that at the MEC server are strongly coupled in a cascade manner, which creates complex interdependences and brings new technical challenges. We model the computation offloading problem as an infinite horizon average cost Markov decision process (MDP) and approximate it to a virtual continuous time system (VCTS) with reflections. Different from most of the existing works, we develop the dynamic instantaneous rate estimation for deriving the closed-form approximate priority functions in different scenarios. Based on the approximate priority functions, we propose a closed-form multi-level water-filling computation offloading solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI). Furthermore, we discuss the extension of our proposed scheme to multi-MT multi-server scenarios. Finally, the simulation results show that the proposed scheme outperforms the conventional schemes.
Graph-Based Method for Anomaly Prediction in Brain Network

Resting-state functional MRI (rs-fMRI) in functional neuroimaging techniques have improved in brain disorders, dysfunction studies via mapping the topology of the brain connections, i.e. connectopic mapping. Since, there are the slight differences between healthy and unhealthy brain regions and functions, investigation into the complex topology of functional and structural brain networks in human is a complicated task with the growth of evaluation criteria. Irregular graph deep learning applications have widely spread to understanding human cognitive functions that are linked to gene expression and related distributed spatial patterns, because the neuronal networks of the brain can hold dynamically a variety of brain solutions with different activity patterns and functional connectivity, these applications might also be involved with both node-centric and graph-centric tasks. In this paper, we performed a novel approach of individual generative model and high order graph analysis for the region of interest recognition areas of the brain which do not have a normal connection during applying certain tasks. Here, we proposed a high order framework of Graph Auto-Encoder (GAE) with a hypersphere distributer for functional data analysis in brain imaging studies that is underlying non-Euclidean structure in the learning of strong non-rigid graphs among large scale data. In addition, we distinguished the possible modes of correlations in abnormal brain connections. Our finding will show the degree of correlation between the affected regions and their simultaneous occurrence over time that can be used to diagnose brain diseases or revealing the ability of the nervous system to modify in brain topology at all angles, brain plasticity, according to input stimuli.
Investigating Biases in Textual Entailment Datasets

The ability to understand logical relationships between sentences is an important task in language understanding. To aid in progress for this task, researchers have collected datasets for machine learning and evaluation of current systems. However, like in the crowdsourced Visual Question Answering (VQA) task, some biases in the data inevitably occur. In our experiments, we find that performing classification on just the hypotheses on the SNLI dataset yields an accuracy of 64%. We analyze the bias extent in the SNLI and the MultiNLI dataset, discuss its implication, and propose a simple method to reduce the biases in the datasets.
Agents on the Web: Trust and Persistence

We rely on computers to control our power plants and water supplies, our automobiles and transportation systems, and soon our economic and political systems. Increasingly, software agents are enmeshed in these systems, serving as the glue that connects distributed components. Clearly, we need mechanisms to determine whether these agents are trustworthy. What do we need to establish trust? Agents are often characterized by features such as autonomy, sociability, proactiveness, and persistent identity. This latter feature is key in determining trust. When agents operate over an extended period, they can earn a reputation for competence, timeliness, ease of use, and trustworthiness, which is something ephemeral agents cannot do. Along with persistence, we need a reliable way to identify an agent and ensure that its true identity is not concealed. How can we assess an agent's trustworthiness? As with other aspects of agents and multiagent systems, we can take our cue from the human domain. Our reputations for trustworthiness are determined and maintained by the people we deal with. Analogously, a software agent's reputation will reside within the other agents with whom it interacts. For some agent interactions, such as those involving commerce, agents will simply inherit the reputation of their human owner, sharing, for example, their owner's credit rating and financial capability. For other types of interactions, such as those involving information gathering, an agent will determine its own reputation through its efforts at gathering and distilling information. An agent with a reputation for conducting thorough searches will be trusted by other agents wishing to use its Web search results.
Using Human Ratings for Feedback Control: A Supervised Learning Approach With Application to Rehabilitation Robotics

This article presents a method for tailoring a parametric controller based on human ratings. The method leverages supervised learning concepts in order to train a reward model from data. It is applied to a gait rehabilitation robot with the goal of teaching the robot how to walk patients physiologically. In this context, the reward model judges the physiology of the gait cycle (instead of therapists) using sensor measurements provided by the robot and the automatic feedback controller chooses the input settings of the robot to maximize the reward. The key advantage of the proposed method is that only a few input adaptations are necessary to achieve a physiological gait cycle. Experiments with nondisabled subjects show that the proposed method permits the incorporation of human expertise into a control law and to automatically walk patients physiologically.
DAL: Dual Adversarial Learning for Dialogue Generation

In open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe responses and unnatural responses. To alleviate these two problems, we propose a novel framework named Dual Adversarial Learning(DAL) for high-quality response generation. DAL innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses. Additionally, DAL uses adversarial learning to mimic human judges and guides the system to generate natural responses. Experimental results demonstrate that DAL effectively improves both diversity and overall quality of the generated responses. DAL outperforms state-of-the-art methods regarding automatic metrics and human evaluations.
Classical Music Prediction and Composition by means of Variational Autoencoders

This paper proposes a new model for music prediction based on Variational Autoencoders (VAEs). In this work, VAEs are used in a novel way to address two different issues: music representation into the latent space, and using this representation to make predictions of the future note events of the musical piece. This approach was trained with different songs of Handel. As a result, the system can represent the music in the latent space, and make accurate predictions. Therefore, the system can be used to compose new music either from an existing piece or from a random starting point. An additional feature of this system is that a small dataset was used for training. However, results show that the system is able to return accurate representations and predictions on unseen data.
Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural Networks

Driver assistance systems as well as autonomous cars have to rely on sensors to perceive their environment. A heterogeneous set of sensors is used to perform this task robustly. Among them, radar sensors are indispensable because of their range resolution and the possibility to directly measure velocity. Since more and more radar sensors are deployed on the streets, mutual interference must be dealt with. In the so far unregulated automotive radar frequency band, a sensor must be capable of detecting, or even mitigating the harmful effects of interference, which include a decreased detection sensitivity. In this paper, we address this issue with Convolutional Neural Networks (CNNs), which are state-of-the-art machine learning tools. We show that the ability of CNNs to find structured information in data while preserving local information enables superior denoising performance. To achieve this, CNN parameters are found using training with simulated data and integrated into the automotive radar signal processing chain. The presented method is compared with the state of the art, highlighting its promising performance. Hence, CNNs can be employed for interference mitigation as an alternative to conventional signal processing methods. Code and pre-trained models are available at https://github.com/johanna-rock/imRICnn.
Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models

Contextual automatic speech recognition, i.e., biasing recognition towards a given context (e.g. user's playlists, or contacts), is challenging in end-to-end (E2E) models. Such models maintain a limited number of candidates during beam-search decoding, and have been found to recognize rare named entities poorly. The problem is exacerbated when biasing towards proper nouns in foreign languages, e.g., geographic location names, which are virtually unseen in training and are thus out-of-vocabulary (OOV). While grapheme or wordpiece E2E models might have a difficult time spelling OOV words, phonemes are more acoustically salient and past work has shown that E2E phoneme models can better predict such words. In this work, we propose an E2E model containing both English wordpieces and phonemes in the modeling space, and perform contextual biasing of foreign words at the phoneme level by mapping pronunciations of foreign words into similar English phonemes. In experimental evaluations, we find that the proposed approach performs 16% better than a grapheme-only biasing model, and 8% better than a wordpiece-only biasing model on a foreign place name recognition task, with only slight degradation on regular English tasks.
Algorithms for Similarity Search and Pseudorandomness

We study the problem of approximate near neighbor (ANN) search and show the following results: - An improved framework for solving the ANN problem using locality-sensitive hashing, reducing the number of evaluations of locality-sensitive hash functions and the word-RAM complexity compared to the standard framework. - A framework for solving the ANN problem with space-time tradeoffs as well as tight upper and lower bounds for the space-time tradeoff of framework solutions to the ANN problem under cosine similarity. - A novel approach to solving the ANN problem on sets along with a matching lower bound, improving the state of the art. - A self-tuning version of the algorithm is shown through experiments to outperform existing similarity join algorithms. - Tight lower bounds for asymmetric locality-sensitive hashing which has applications to the approximate furthest neighbor problem, orthogonal vector search, and annulus queries. - A proof of the optimality of a well-known Boolean locality-sensitive hashing scheme. We study the problem of efficient algorithms for producing high-quality pseudorandom numbers and obtain the following results: - A deterministic algorithm for generating pseudorandom numbers of arbitrarily high quality in constant time using near-optimal space. - A randomized construction of a family of hash functions that outputs pseudorandom numbers of arbitrarily high quality with space usage and running time nearly matching known cell-probe lower bounds.
Learning Image and Video Compression Through Spatial-Temporal Energy Compaction

Compression has been an important research topic for many decades, to produce a significant impact on data transmission and storage. Recent advances have shown a great potential of learning based image and video compression. Inspired from related works, in this paper, we present an image compression architecture using a convolutional autoencoder, and then generalize image compression to video compression, by adding an interpolation loop into both encoder and decoder sides. Our basic idea is to realize spatial-temporal energy compaction in learning image and video compression. Thereby, we propose to add a spatial energy compaction-based penalty into loss function, to achieve higher image compression performance. Furthermore, based on temporal energy distribution, we propose to select the number of frames in one interpolation loop, adapting to the motion characteristics of video contents. Experimental results demonstrate that our proposed image compression outperforms the latest image compression standard with MS-SSIM quality metric, and provides higher performance compared with state-of-the-art learning compression methods at high bit rates, which benefits from our spatial energy compaction approach. Meanwhile, our proposed video compression approach with temporal energy compaction can significantly outperform MPEG-4, and is competitive with commonly used H.264. Both our image and video compression can produce more visually pleasant results than traditional standards.
Adversarial Computation of Optimal Transport Maps

Computing optimal transport maps between high-dimensional and continuous distributions is a challenging problem in optimal transport (OT). Generative adversarial networks (GANs) are powerful generative models which have been successfully applied to learn maps across high-dimensional domains. However, little is known about the nature of the map learned with a GAN objective. To address this problem, we propose a generative adversarial model in which the discriminator's objective is the $2$-Wasserstein metric. We show that during training, our generator follows the $W_2$-geodesic between the initial and the target distributions. As a consequence, it reproduces an optimal map at the end of training. We validate our approach empirically in both low-dimensional and high-dimensional continuous settings, and show that it outperforms prior methods on image data.
On the Secrecy Rate of Spatial Modulation-Based Indoor Visible Light Communications

In this paper, we investigate the physical-layer security for a spatial modulation (SM)-based indoor visible light communication (VLC) system, which includes multiple transmitters, a legitimate receiver, and a passive eavesdropper (Eve). At the transmitters, the SM scheme is employed, i.e., only one transmitter is active at each time instant. To choose the active transmitter, a uniform selection (US) scheme is utilized. Two scenarios are considered: one is with non-negativity and average optical intensity constraints and the other is with non-negativity, average optical intensity, and peak optical intensity constraints. Then, lower and upper bounds on the secrecy rate are derived for these two scenarios. Besides, the asymptotic behaviors for the derived secrecy rate bounds at high signal-to-noise ratio (SNR) are analyzed. To further improve the secrecy performance, a channel adaptive selection (CAS) scheme and a greedy selection (GS) scheme are proposed to select the active transmitter. Numerical results show that the lower and upper bounds of the secrecy rate are tight. At high SNR, small asymptotic performance gaps exist between the derived lower and the upper bounds. Moreover, the proposed GS scheme has the best performance, followed by the CAS scheme and the US scheme.
Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training

Abdominal magnetic resonance imaging (MRI) provides a straightforward way of characterizing tissue and locating lesions of patients as in standard diagnosis. However, abdominal MRI often suffers from respiratory motion artifacts, which leads to blurring and ghosting that significantly deteriorate the imaging quality. Conventional methods to reduce or eliminate these motion artifacts include breath holding, patient sedation, respiratory gating, and image post-processing, but these strategies inevitably involve extra scanning time and patient discomfort. In this paper, we propose a novel deep-learning-based model to recover MR images from respiratory motion artifacts. The proposed model comprises a densely connected U-net with generative adversarial network (GAN)-guided training and a perceptual loss function. We validate the model using a diverse collection of MRI data that are adversely affected by both synthetic and authentic respiration artifacts. Effective outcomes of motion removal are demonstrated. Our experimental results show the great potential of utilizing deep-learning-based methods in respiratory motion correction for abdominal MRI.
Deep Exemplar-Based Video Colorization

This paper presents the first end-to-end network for exemplar-based video colorization. The main challenge is to achieve temporal consistency while remaining faithful to the reference style. To address this issue, we introduce a recurrent framework that unifies the semantic correspondence and color propagation steps. Both steps allow a provided reference image to guide the colorization of every frame, thus reducing accumulated propagation errors. Video frames are colorized in sequence based on the colorization history, and its coherency is further enforced by the temporal consistency loss. All of these components, learned end-to-end, help produce realistic videos with good temporal stability. Experiments show our result is superior to the state-of-the-art methods both quantitatively and qualitatively.
Selection of Critical Paths for Reliable Frequency Scaling under BTI-Aging Considering Workload Uncertainty and Process Variations Effects

Conventional clock guardbanding to assure a circuit’s reliable operation under device aging due to NBTI/PBTI and process variations introduce significant performance loss in modern nanometer circuits. Dynamic Frequency Scaling (DFS) is a more efficient technique that allows us to adjust the system clock frequency according to the process condition and aging deterioration of the circuit. At the design phase, the DFS technique requires the identification of the logic paths to be monitored to introduce the required circuitry to monitor their delay. However, critical path identification is a complex problem due to three major challenges: (1) The critical paths of the circuit depend on the stress duty cycle of the devices, which are unknown in advance at design phase; (2) the critical paths of the circuit depend on the process parameters variations, whose impact on delay depend on the spatial correlation due to proximity at the circuit layout; and (3) the critical paths reordering probability may change over time due to aging. This article presents a methodology for efficient selection of the critical paths to be monitored under a DFS framework, addressing the aforementioned challenges. Experimental results on ISCAS 85/89 benchmark circuits show the feasibility of the proposed approach to select a restricted path set while providing reliable aging monitoring.