Miracle-dz/UniFAR-FTUs huggingface dataset:
Sending a Bivariate Gaussian Over a Gaussian MAC

We study the power-versus-distortion tradeoff for the distributed transmission of a memoryless bivariate Gaussian source over a two-to-one average-power limited Gaussian multiple-access channel. In this problem, each of two separate transmitters observes a different component of a memoryless bivariate Gaussian source. The two transmitters then describe their source component to a common receiver via an average-power constrained Gaussian multiple-access channel. From the output of the multiple-access channel, the receiver wishes to reconstruct each source component with the least possible expected squared-error distortion. Our interest is in characterizing the distortion pairs that are simultaneously achievable on the two source components. We focus on the ¿equal bandwidth¿ case, where the source rate in source-symbols per second is equal to the channel rate in channel-uses per second. We present sufficient conditions and necessary conditions for the achievability of a distortion pair. These conditions are expressed as a function of the channel signal-to-noise ratio (SNR) and of the source correlation. In several cases, the necessary conditions and sufficient conditions are shown to agree. In particular, we show that if the channel SNR is below a certain threshold, then an uncoded transmission scheme is optimal. Moreover, we introduce a ¿source-channel vector-quantizer¿ scheme which is asymptotically optimal as the SNR tends to infinity.
Computationally E cient Nonparametric Importance Sampling

The variance reduction established by importance sampling strongly depends on the choice of the importance sampling distribution. A good choice is often hard to achieve especially for high-dimensional integration problems. Nonparametric estimation of the optimal importance sampling distribution (known as nonparametric importance sampling) is a reasonable alternative to parametric approaches. A common critique on nonparametric importance sampling is the increased computational burden compared to parametric methods. We solve this problem to a large degree by utilizing a multivariate frequency polygon instead of a kernel estimator. Mean square error convergence properties are investigated leading to recommendations for the e cient application of nonparametric importance sampling. Empirical evidence for the usefulness of the suggested algorithms is obtained by means of three benchmark integration problems including an option pricing example.
Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning

Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.
Convolutional Experts Constrained Local Model for Facial Landmark Detection

Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. However, they have recently fallen out of favor to cascaded regression-based approaches. This is in part due to the inability of existing CLM local detectors to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories. In our work, we present a novel local detector -- Convolutional Experts Network (CEN) -- that brings together the advantages of neural architectures and mixtures of experts in an end-to-end framework. We further propose a Convolutional Experts Constrained Local Model (CE-CLM) algorithm that uses CEN as local detectors. We demonstrate that our proposed CE-CLM algorithm outperforms competitive state-of-the-art baselines for facial landmark detection by a large margin on four publicly-available datasets. Our approach is especially accurate and robust on challenging profile images.
On continuous timed automata with input-determined guards

We consider a general class of timed automata parameterized by a set of “input-determined” operators, in a continuous time setting. We show that for any such set of operators, we have a monadic second order logic characterization of the class of timed languages accepted by the corresponding class of automata. Further, we consider natural timed temporal logics based on these operators, and show that they are expressively equivalent to the first-order fragment of the corresponding MSO logics. As a corollary of these general results we obtain an expressive completeness result for the continuous version of MTL.
Deep Reinforcement Learning based Path Planning for Mobile Robot in Unknown Environment

It is a trend for robots to replace human in industrial fields with the increment of labor cost. Mobile robots are widely used for executing tasks in harsh industrial environment. It is an important problem for mobile robots to plan their path in unknown environment. The ordinary deep Q-network (DQN) which is an efficient method of reinforcement learning has been used for mobile robot path planning in unknown environment, but the DQN generally has low convergence speed. This paper presents a method based on Double DQN (DDQN) with prioritized experience replay (PER) for mobile robot path planning in unknown environment. With sensing its surrounding local information, the mobile robot plans its path with this method in unknown environment. The experiment results show that the proposed method has higher convergence speed and success rate than the normal DQN method at the same experimental environment.
Conferencing in Wyner's asymmetric interference network: Effect of number of rounds

In this paper, we study how the number of conferencing rounds effects the capacity of large interference networks. We take Wyner's asymmetric linear (soft-handoff) model, include conferencing links between closely located transmitters and receivers, and we consider the per-user asymptotic multiplexing gain. Our results show, for example, that when the capacities of the conferencing links scale at most (1/4) log P with the power P and when one can choose which transmitters and receivers cooperate, then there is no loss in terms of asymptotic multiplexing gain in having only one round of conferencing. In contrast, when the capacities of the conferencing links grow faster than (1/4) log P, then the asymptotic multiplexing gain with one round of conferencing is strictly smaller than that achieved with multiple rounds.
P REDICTING INTO UNKNOWN SPACE ? E STIMATING THE AREA OF APPLICABILITY OF SPATIAL PREDICTION MODELS

while the cross-validation error does not apply outside the AOA. This applies to models being trained with randomly distributed training data, as well as when training data are clustered in space and where spatial cross-validation is applied. Restricting predictions to the AOA is important especially when predictions are used for decision making or subsequent environmental modelling. We suggest to report the AOA alongside predictions, complementary to validation measures.
F eb 2 00 6 Hierarchical Characterization of Complex Networks

While the majority of approaches to the characterization of complex networks has relied on measurements considering only the immediate neighborhood of each network node, valuable information about the network topological properties can be obtained by considering further neighborhoods. The current work discusses on how the concepts of hierarchical node degree and hierarchical clustering coefficient (introduced in cond-mat/0408076), complemented by new hierarchical measurements, can be used in order to obtain a powerful set of topological features of complex networks. The interpretation of such measurements is discussed, including an analytical study of the hierarchical node degree for random networks, and the potential of the suggested measurements for the characterization of complex networks is illustrated with respect to simulations of random, scale-free and regular network models as well as real data (airports, proteins and word associations). The enhanced characterization of the connectivity provided by the set of hierarchical measurements also allows the use of agglomerative clustering methods in order to obtain taxonomies of relationships between nodes in a network, a possibility which is also illustrated in the current article.
A Bayesian approach to reconstructing genetic regulatory networks with hidden factors

MOTIVATION We have used state-space models (SSMs) to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T cell activation. SSMs are a class of dynamic Bayesian networks in which the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be directly measured in a gene expression profiling experiment, for example: genes that have not been included in the microarray, levels of regulatory proteins, the effects of mRNA and protein degradation, etc. RESULTS We have approached the problem of inferring the model structure of these state-space models using both classical and Bayesian methods. In our previous work, a bootstrap procedure was used to derive classical confidence intervals for parameters representing 'gene-gene' interactions over time. In this article, variational approximations are used to perform the analogous model selection task in the Bayesian context. Certain interactions are present in both the classical and the Bayesian analyses of these regulatory networks. The resulting models place JunB and JunD at the centre of the mechanisms that control apoptosis and proliferation. These mechanisms are key for clonal expansion and for controlling the long term behavior (e.g. programmed cell death) of these cells. AVAILABILITY Supplementary data is available at http://public.kgi. edu/wild/index.htm and Matlab source code for variational Bayesian learning of SSMs is available at http://www.cse. ebuffalo.edu/faculty/mbeal/software.html.
D ec 2 00 7 ALGORITHMICALLY RANDOM POINTS IN MEASURE PRESERVING SYSTEMS , STATISTICAL BEHAVIOUR , COMPLEXITY AND ENTROPY

We consider the dynamical behavior of Martin-Löf random points in dynamical systems over metric spaces with a computable dynamics and a computable invariant measure. We use computable partitions to define a sort of effective symbolic model for the dynamics. Trough this construction we prove that such points have typical statistical behavior (the behavior which is typical in the Birkhoff ergodic theorem) and are recurrent. We introduce and compare some notion of complexity for orbits in dynamical systems and we prove that the complexity of the orbits of random points equals the entropy of the system.
Multi-mode Core Tensor Factorization based Low-Rankness and Its Applications to Tensor Completion

Low-rank tensor completion has been widely used in computer vision and machine learning. This paper develops a tensor low-rank decomposition method together with a tensor low-rankness measure (MCTF) and a better nonconvex relaxation form of it (NonMCTF). This is the first method that can accurately restore the clean data of intrinsic low-rank structure based on few known inputs. This metric encodes low-rank insights for general tensors provided by Tucker and T-SVD. Furthermore, we studied the MCTF and NonMCTF regularization minimization problem, and designed an effective BSUM algorithm to solve the problem. This efficient solver can extend MCTF to various tasks, such as tensor completion and tensor robust principal component analysis. A series of experiments, including hyperspectral image (HSI) denoising, video completion and MRI restoration, confirmed the superior performance of the proposed method
Type-driven Neural Programming by Example

Programming by example (PBE) has traditionally seen a split between formal versus neural approaches, meaning programming types had yet to be used in neural program synthesis. We propose a way to incorporate programming types into a neural program synthesis approach for programming by example. We propose the Typed Neuro-Symbolic Program Synthesis (TNSPS) method based on this idea, and test it in the functional programming context to empirically verify type information may help improve generalization in neural synthesizers on limited-size datasets. Finally we discuss several topics of interest that may help take these ideas further. For reproducibility, we release our code publicly. [32]
A PRINCIPLED APPROACH TO BRIDGING THE GAP BETWEEN RDF DATA AND THEIR SCHEMAS

ix Resumen . x Chapter
Differentially private filtering

Emerging systems such as smart grids or intelligent transportation systems often require end-user applications to continuously send information to external data aggregators performing monitoring or control tasks. This can result in an undesirable loss of privacy for the users in exchange of the benefits provided by the application. Motivated by this trend, we introduce privacy concerns in a system theoretic context, and address here the problem of releasing filtered signals that respect the privacy of the input data stream.We rely on a formal notion of privacy introduced in the database literature, called differential privacy, which provides strong privacy guarantees against adversaries with arbitrary side information, and extend this notion to dynamic systems. We then describe methods to approximate a given filter by a differentially private version, so that the distortion introduced by the privacy mechanism is minimized. Two specific scenarios are considered, where users either provide independent input signals or contribute events to a single integer-valued stream.
Pythia: Scheduling of Concurrent Network packet Processing Applications on Heterogeneous Devices [EXTENDED VERSION]

Modern commodity computing systems are composed by a number of different heterogeneous processing units, each of which has its own unique performance and energy characteristics. However, the majority of current network packet processing frameworks targets only a specific processing unit (either the CPU or accelerator), leaving the remaining computational resources under-utilized or even idle. In this paper, we propose an adaptive scheduling approach for network packet processing applications, that supports any heterogeneous and asymmetric architectures that can be found in a commodity high-end hardware setup. Our scheduler not only distributes the workloads to the appropriate devices in the system to achieve the desired performance results, but also enables the multiplexing of diverse network packet processing applications that execute concurrently, eliminating the interference effects introduced at runtime. The evaluation results show that our scheduler is able to tackle interferences in the shared hardware resources as well to respond quickly to dynamic fluctuations (e.g., application overloads, traffic bursts, infrastructural changes, etc.) that may occur at real time.
Energy beamforming with one-bit feedback

Wireless energy transfer (WET) via far-field radio signal has emerged as a new solution for powering wireless networks. To overcome the significant path loss in wireless channels, multi-antenna or multiple-input multiple-output (MIMO) techniques have been proposed to enhance the transmission efficiency and distance for WET. However, in order to reap the large energy beamforming gain in WET, acquiring channel state information (CSI) at the energy transmitter (ET) is an essential task. This task is particularly challenging for WET systems, since existing channel training and feedback methods used for communication receivers cannot be implemented at the energy receiver (ER) due to the hardware limitation. To tackle this problem, in this paper we consider a point-to-point MIMO WET system with transmit energy beamforming, and propose a new channel learning method that requires only one feedback bit from the ER to ET per feedback interval. Each feedback bit indicates the increase or decrease of the harvested energy by the ER between the present and previous intervals, which can be measured without changing the existing hardware at the ER. Based on such feedback information, the ET adjusts transmit energy beamforming in different intervals and at the same time obtains an improved estimate of the MIMO channel by applying the analytic center cutting plane method (AC-CPM). By numerical examples, we show the performance of our proposed new channel learning algorithm for MIMO WET systems in terms of convergence speed and energy transfer efficiency, as compared to existing algorithms.
D ec 2 02 1 THE RIEMANN-LIOUVILLE FRACTIONAL INTEGRAL IN BOCHNER-LEBESGUE SPACES II

In this work we study the Riemann-Liouville fractional integral of order α ∈ (0, 1/p) as an operator from L(I;X) into L(I;X), with 1 ≤ q ≤ p/(1 − pα), whether I = [t0, t1] or I = [t0,∞) and X is a Banach space. When I = [t0, t1], we prove an important property on the uniform continuity of this operator, regarding the order of integration, that allowed us to deduce necessary and sufficient conditions to ensure the compactness of the Riemann-Liouville fractional integral, which was an open problem.
Benchmarking AutoML algorithms on a collection of binary problems

Automated machine learning (AutoML) algorithms have grown in popularity due to their high performance and ﬂexibility to adapt to diﬀerent problems and data sets. With the increasing number of AutoML algorithms, deciding which would best suit a given problem becomes increasingly more work. Therefore, it is essential to use complex and challenging benchmarks which would be able to diﬀerentiate the AutoML algorithms from each other. This paper compares the performance of four diﬀerent AutoML algorithms: Tree-based Pipeline Optimization Tool (TPOT), Auto-Sklearn, Auto-Sklearn 2, and H2O AutoML. We use the Diverse and Generative ML benchmark (DIGEN), a diverse set of synthetic datasets derived from generative functions designed to highlight the strengths and weaknesses of the performance of common machine learning algorithms. We conﬁrm that AutoML can identify pipelines that perform well on all included datasets. Most AutoML algorithms performed similarly without much room for improvement; however, some were more consistent than others at ﬁnding high-performing solutions for some datasets.
Gesture-based Continuous Authentication for Wearable Devices : the Google Glass Case

With over 250 million devices to be sold, wearable devices revenue is forecasted to hit $2 billion by 2018. Wearables are expected to play a key role in achieving ubiquitous computing in the future. The ease of use and comfort in accessing online content comes however at the expense of potential privacy and security issues. This represents today a challenging and growing concern. In their current design, wearables are prone to several risks ranging from the possible exposure of data when communicating with tethering devices to the ease of access to sensitive data in case of compromised devices. In this paper, we study the feasibility of finger movement pattern based behavioural biometrics for implicit authentication of users on Google Glass. Using data collected from 20 volunteers, we employ two different classification methods: statistical inference (white box) and machine learning (black box). Our experimental study shows that such authentication is both effective, in terms of classification accuracy, and feasible, in terms of computational load, on wearable devices. Our results show classification accuracy of up to 99% with only 75 training samples using behavioural biometric data for four different gestures types. Our implemented classifier on Glass takes between 25 to 110 milliseconds execution time for prediction depending on the gesture type, once an adjustable threshold number of gestures have been performed.
The Case for Claim Difficulty Assessment in Automatic Fact Checking

Fact-checking is the process (human, automated, or hybrid) by which claims (i.e., purported facts) are evaluated for veracity. In this article, we raise an issue that has received little attention in prior work – that some claims are far more difﬁcult to fact-check than others. We discuss the implications this has for both practical fact-checking and research on automated fact-checking, including task formulation and dataset design. We report a manual analysis undertaken to explore factors underlying varying claim difﬁculty and categorize several distinct types of difﬁculty. We argue that prediction of claim difﬁculty is a missing component of today’s automated fact checking architectures, and we describe how this difﬁculty prediction task might be split into a set of distinct subtasks.
Retrospective Approximation for Smooth Stochastic Optimization

We consider stochastic optimization problems where a smooth (and potentially nonconvex) ob-jective is to be minimized using a stochastic ﬁrst-order oracle. These type of problems arise in many settings from simulation optimization to deep learning. We present Retrospective Approximation (RA) as a universal sequential sample-average approximation (SAA) paradigm where during each iteration k , a sample-path approximation problem is implicitly generated using an adapted sample size M k , and solved (with prior solutions as “warm start”) to an adapted error tolerance (cid:15) k , using a “deterministic method” such as the line search quasi-Newton method. The principal advantage of RA is that decouples optimization from stochastic approximation, allowing the direct adoption of existing deterministic algorithms without modiﬁcation, thus mitigating the need to redesign algorithms for the stochastic context. A second advantage is the obvious manner in which RA lends itself to parallelization. We identify conditions on { M k , k ≥ 1 } and { (cid:15) k , k ≥ 1 } that ensure almost sure convergence and convergence in L 1 -norm, along with optimal iteration and work complexity rates. We illustrate the performance of RA with line-search quasi-Newton on an ill-conditioned least squares problem, as well as an image classiﬁcation problem using a deep convolutional neural net.
2 1 Ja n 20 07 Certifying controls and systems software

Software system certification presents itself with many challenges, including the necessity to certify the system at the level of functional requirements, code and binary levels, the need to chase down run-time errors, and the need for proving timing properties of the eventual, compiled system. This paper illustrates possible approaches for certifying code that arises from control systems requirements as far as stability properties are concerned. The relative simplicity of the certification process should encourage the development of systematic procedures for certifying control system codes for more complex environments.