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Tensors are ubiquitous in science and engineering, and tensor factorization approaches have become important tools. This article explores the use of Bayesian modeling in the context of tensor factorization, presents a probabilistic extension of the so-called block term decomposition (BTD) model, and shows how it can interpolate between two common decomposition models—canonical polyadic decomposition and Tucker decomposition. This probabilistic extension is obtained by applying Bayesian inference to the BTD model, allowing for uncertainty quantification as well as robustness to corruption by noise and model misspecification. The novelty of this model is its applicability to Nth order tensors, incorporating mode-specific orthogonality within each block and priors that penalize the complexity of the core arrays. On synthetic data and two real datasets, we highlight the benefits of probabilistic tensor factorization considering BTD, demonstrating that probabilistic BTD can successfully quantify multilinear structures and is robust to noise. [ABSTRACT FROM AUTHOR]

To address the challenges of efficient hardware design for error-tolerant applications, several techniques of applied approximate computing have been proposed. Pruning algorithms aim to approximate circuits with reduced design requirements at the cost of an acceptable degradation of their quality of result. In this letter, we present the effects of resynthesis, an iterative application of logic synthesis along with pruning algorithms, into a state-of-the-art approximate design flow, AxLS. Resynthesis strategy improves the approximation, achieving up to 70% area-power savings for the same error in the output, and reducing the number of iterations, and hence the time required to explore the design space in up to $30\times $ , to obtain an approximated design. [ABSTRACT FROM AUTHOR]

Our study introduces a novel approach that combined probabilistic logic and multimodal spatial concepts to enable a robot to efficiently acquire place–object relationships in a new home environment with few learning iterations. By leveraging probabilistic logic, which employs predicate logic with probability values, we represent common-sense knowledge of the place–object relationships. The integration of logical inference and cross-modal inference to calculate conditional probabilities across different modalities enables the robot to infer object locations even when their likely locations are undefined. To evaluate the effectiveness of our method, we conducted simulation experiments and compared the results with three baselines: multimodal spatial concepts only, common-sense knowledge only, and common-sense knowledge and multimodal spatial concepts combined. By comparing the number of room visits required by the robot to locate 24 objects, we demonstrated the improved performance of our approach. For search tasks including objects whose locations were undefined, the findings demonstrate that our method reduced the learning cost by a factor of 1.6 compared to the baseline methods. Additionally, we conducted a qualitative analysis in a real-world environment to examine the impact of integrating the two inferences and identified the scenarios that influence changes in the task success rate.

Bayesian inference provides a probabilistic reasoning process for drawing conclusions based on imprecise and uncertain data that has been successful in many applications within robotics and information processing, but is most often considered in terms of data analysis rather than synthesis of behaviours. This paper presents the use of Bayesian inference as a means by which to perform Boolean operations in a logic programme while incorporating and propagating uncertainty information through logic operations by inference. Boolean logic operations are implemented in a Bayesian network of Bernoulli random variables with tensor-based discrete distributions to enable probabilistic hybrid logic programming of a robot. This enables Bayesian inference operations to coexist with Boolean logic in a unified system while retaining the ability to capture uncertainty by means of discrete probability distributions. Using a discrete Bayesian network with both Boolean and Bayesian elements, the proposed methodology is applied to navigate a mobile robot using hybrid Bayesian and Boolean operations to illustrate how this new approach improves robotic performance by inclusion of uncertainty without increasing the number of logic elements required. As any logical system could be programmed in this manner to integrate uncertainty into decision-making, this methodology can benefit a wide range of applications that use discrete or probabilistic logic. [ABSTRACT FROM AUTHOR]

Threshold signature is a fundamental cryptographic primitive used in many practical applications. As proposed by Boneh and Komlo (CRYPTO’22), TAPS is a threshold signature that is a hybrid of privacy and accountability. It enables a combiner to combine $t$ signature shares while revealing nothing about the threshold $t$ or signing quorum to the public and asks a tracer to track a signature to the quorum that generates it. However, TAPS has three disadvantages: it 1) structures upon a centralized model, 2) assumes that both combiner and tracer are honest, and 3) leaves the tracing unnotarized and static. In this work, we introduce Decentralized, Threshold, dynamically Accountable and Private Signature (DeTAPS) that provides decentralized combining and tracing, enhanced privacy against untrusted combiners (tracers), and notarized and dynamic tracing. Specifically, we adopt Dynamic Threshold Public-Key Encryption (DTPKE) to dynamically notarize the tracing process, design non-interactive zero knowledge proofs to achieve public verifiability of notaries, and utilize the Key-Aggregate Searchable Encryption to bridge TAPS and DTPKE so as to awaken the notaries securely and efficiently. In addition, we formalize the definitions and security requirements for DeTAPS. Then we present a concrete construction and formally prove its security and privacy. To evaluate the performance, we build a prototype based on SGX2 and Ethereum. [ABSTRACT FROM AUTHOR]

Recently, facial expression recognition techniques have made significant progress on high-resolution web images. However, in real-world applications, the obtained images are often with low resolution since they are mostly captured in a wide range of public spaces. As a result, the ambiguity of the expression labels hinders recognition performance due to not only subjective emotion annotations but also ambiguous images. Existing approaches tend to perform poorly when the resolution of face images decreases. In this work, we aim to model the aleatoric uncertainty induced by low-image-resolution and label ambiguity for robust facial expression recognition. We propose probabilistic data uncertainty learning to capture the ambiguity induced by poor image resolution. Additionally, we introduce the emotion wheel to learn the label-uncertainty-aware embedding. Moreover, we exploit the ambiguous nature of neutrality and propose a neutral expression constraint to learn more robust features for facial expression recognition. To the best of our knowledge, this is the first work utilizing the intrinsic nature of neutrality as a regularization to benefit model training. Extensive experimental results show the effectiveness and robustness of our approach. Under low-resolution conditions, our proposed method outperforms the state-of-the-art approaches by 3.02% and 3.16% in terms of accuracy on RAF-DB and FERPlus, respectively. [ABSTRACT FROM AUTHOR]

Many problems in Multi-Agent Systems (MASs) research are formulated in terms of the abilities of a coalition of agents. Existing approaches to reasoning about coalitional ability are usually focused on games or transition systems, which are described in terms of states and actions. Such approaches however often neglect a key feature of multi-agent systems, namely that the actions of the agents require resources. In this paper, we describe a logic for reasoning about coalitional ability under resource constraints in the probabilistic setting. We extend Resource-bounded Alternating-time Temporal Logic (RB-ATL) with probabilistic reasoning and provide a standard algorithm for the model-checking problem of the resulting logic Probabilistic resource-bounded ATL (pRB-ATL). We implement model-checking algorithms and present experimental results using simple multi-agent model-checking problems of increasing complexity. [ABSTRACT FROM AUTHOR]

Due to the mixed right-of-way and varying traffic conditions, urban bus operations are often subject to random delays and eventually bus bunching, undermining the schedule reliability. The existing studies have proposed different models to emulate bus bunching and control strategies to mitigate bus bunching, yet few of them considered the effect of the three important traffic factors: intersections with signal coordination, varying traffic volume, and passenger demand. To fill in the research gap, we first define the stop-level frequency of bus bunching events for a bus route in this study. We proceed to present a simulation-based approach to quantify the impact of the three traffic factors on bus bunching. Numerical experiments based on different scenarios are carried out to reveal the cause–effect relationship between these factors and bus bunching events. Contributors to bus bunching are evaluated, and the effect of control delays is examined through statistical measurements. Finally, a real-world case study based on bus route 51 in Singapore is performed, and some insights are provided to alleviate the bus bunching phenomenon. [ABSTRACT FROM AUTHOR]

Modern deep neural networks have made numerous breakthroughs in real-world applications, yet they remain vulnerable to some imperceptible adversarial perturbations. These tailored perturbations can severely disrupt the inference of current deep learning-based methods and may induce potential security hazards to artificial intelligence applications. So far, adversarial training methods have achieved excellent robustness against various adversarial attacks by involving adversarial examples during the training stage. However, existing methods primarily rely on optimizing injective adversarial examples correspondingly generated from natural examples, ignoring potential adversaries in the adversarial domain. This optimization bias can induce the overfitting of the suboptimal decision boundary, which heavily jeopardizes adversarial robustness. To address this issue, we propose Adversarial Probabilistic Training (APT) to bridge the distribution gap between the natural and adversarial examples via modeling the latent adversarial distribution. Instead of tedious and costly adversary sampling to form the probabilistic domain, we estimate the adversarial distribution parameters in the feature level for efficiency. Moreover, we decouple the distribution alignment based on the adversarial probability model and the original adversarial example. We then devise a novel reweighting mechanism for the distribution alignment by considering the adversarial strength and the domain uncertainty. Extensive experiments demonstrate the superiority of our adversarial probabilistic training method against various types of adversarial attacks in different datasets and scenarios. [ABSTRACT FROM AUTHOR]

Accurate reconstruction of the brain activities from electroencephalography and magnetoencephalography (E/MEG) remains a long-standing challenge for the intrinsic ill-posedness in the inverse problem. In this study, to address this issue, we propose a novel data-driven source imaging framework based on sparse Bayesian learning and deep neural network (SI-SBLNN). Within this framework, the variational inference in conventional algorithm, which is built upon sparse Bayesian learning, is compressed via constructing a straightforward mapping from measurements to latent sparseness encoding parameters using deep neural network. The network is trained with synthesized data derived from the probabilistic graphical model embedded in the conventional algorithm. We achieved a realization of this framework with the algorithm, source imaging based on spatio-temporal basis function (SI-STBF), as backbone. In numerical simulations, the proposed algorithm validated its availability for different head models and robustness against distinct intensities of the noise. Meanwhile, it acquired superior performance compared to SI-STBF and several benchmarks in a variety of source configurations. Additionally, in real data experiments, it obtained the concordant results with the prior studies. [ABSTRACT FROM AUTHOR]

Very recent work has demonstrated tremendous improvements in facial expression recognition (FER) on laboratory-controlled datasets. However, recognizing facial expressions under in-the-wild conditions still remains challenging, especially on unseen subjects due to high inter-subject variations. In this paper, we propose a novel Probabilistic Attribute Tree Convolutional Neural Network (PAT-CNN) to explicitly deal with large intra-class variations caused by identity-related attributes, e.g., age, race, and gender. Specifically, a PAT module with an associated PAT loss is proposed to learn features in a hierarchical tree structure organized according to identity-related attributes, where the final features are less affected by the attributes. Then, expression-related features are extracted from leaf nodes. Samples are probabilistically assigned to tree nodes at different levels such that expression-related features can be learned from all samples weighted by probabilities. Furthermore, the proposed PAT-CNN can be learned from limited attribute-annotated samples to make the best use of available data. Experimental results on four spontaneous facial expression datasets, i.e., RAF-DB, SFEW, ExpW, and FER-2013, have demonstrated that the proposed PAT-CNN achieves the best performance when compared to state-of-the-art methods by explicitly modeling attributes. Impressively, a single model PAT-CNN achieves the best performance on the SFEW test dataset when compared to the state-of-the-art methods using an ensemble of hundreds of CNNs. [ABSTRACT FROM AUTHOR]

How to represent the object more appropriately in oriented object detection is an essential problem to be solved, because there are many solutions for the object represented. It is a relatively novel approach to represent objects as a number of sample points useful for both localization and recognition. However, the current point-set-based representation methods do not effectively supervise all points for learning, and the internal information of the convex hull in the point set cannot be effectively learned. Therefore, this letter proposes point set distance (PSD) loss, which learns set-to-set supervision of objects to effectively represent objects. Besides, most of the current sample selection strategies are based on the Intersection over Union (IoU), but these methods cannot comprehensively measure candidate samples’ quality. To select high-quality point sets, we propose to use the probability distribution of point sets to select the positive samples. Our probabilistic point set sample selection (PPSS) scheme effectively exploits the classification information, regression information, and distribution characteristics of the point set. Experimental results on remote-sensing image datasets, including DOTA, DIOR-R, and HRSC2016, demonstrate the proposed method for arbitrary-oriented object detection achieves consistent and substantial improvements. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) dehazing is a long-standing topic, as the contrast and clarity of images are seriously reduced in the severe environment. Recent learning-based ways achieve great success in RS dehazing, while these models usually suffer from unusual artifacts, mode collapse, and size limitation. To address these problems, we propose an adaptive region-based diffusion dehazing net (ARDD-Net), which is a novel diffusion-based model toward free-form RS dehazing. To be specific, a guided diffusion process is employed to transform clear RS region parts by adding Gaussian noise repeatedly and then restore the clear ones through a reverse generative procedure. In addition, region-based model is employed to address the free-size images for diffusion model. Furthermore, a cyclic shift (CS) strategy is applied to the inference process in order to avoid the inconsistent effect of certain regions and increase the image harmony. With extensive experiments, we state that our model gets outstanding performances than the comparing methods on both synthetic and real-world RS images. [ABSTRACT FROM AUTHOR]

Previous super-resolution (SR) reconstruction works are always designed on the assumption that the degradation operation is fixed, such as bicubic downsampling. However, as for remote sensing images (RSIs), some unexpected factors can cause the blurred visual performance, such as weather factors and orbit altitude. Blind SR methods are proposed to deal with various degradations. There are two main challenges of blind SR in RSIs: 1) the accurate estimation of degradation kernels and 2) the realistic image generation in the ill-posed problem. To rise to the challenge, we propose a novel blind SR framework based on dual conditional denoising diffusion probabilistic models (DDPMs). In our work, we introduce conditional DDPMs from two aspects: kernel estimation progress and reconstruction progress, named the dual-diffusion. As for kernel estimation progress, conditioned on low-resolution (LR) images, a new DDPM-based kernel predictor is constructed by studying the invertible mapping between the kernel distribution and the latent distribution. As for reconstruction progress, regarding the predicted degradation kernels and LR images as conditional information, we construct a DDPM-based reconstructor to learn the mapping from the LR images to high-resolution (HR) images. Comprehensive experiments show the priority of our proposal compared with state-of-the-art (SOTA) blind SR methods. Source code and supplementary materials are available at https://github.com/Lincoln20030413/DDSR. [ABSTRACT FROM AUTHOR]

Speckle is a type of multiplicative noise that affects all coherent imaging modalities including synthetic aperture radar (SAR) images. The presence of speckle degrades the image quality and can adversely affect the performance of SAR image applications such as automatic target recognition and change detection. Thus, SAR despeckling is an important problem in remote sensing. In this letter, we introduce SAR-DDPM, a denoising diffusion probabilistic model for SAR despeckling. The proposed method uses a Markov chain that transforms clean images into white Gaussian noise by successively adding random noise. The despeckled image is obtained through a reverse process that predicts the added noise iteratively, using a noise predictor conditioned on the speckled image. In addition, we propose a new inference strategy based on cycle spinning to improve the despeckling performance. Our experiments on both synthetic and real SAR images demonstrate that the proposed method leads to significant improvements in both quantitative and qualitative results over the state-of-the-art despeckling methods. The code is available at: https://github.com/malshaV/SAR_DDPM [ABSTRACT FROM AUTHOR]

In this article we present a concrete design for a probabilistic (p-) computer based on a network of p-bits, robust classical entities fluctuating between -1 and +1, with probabilities that are controlled through an input constructed from the outputs of other p-bits. The architecture of this probabilistic computer is similar to a stochastic neural network with the p-bit playing the role of a binary stochastic neuron, but with one key difference: there is no sequencer used to enforce an ordering of p-bit updates, as is typically required. Instead, we explore sequencerless designs where all p-bits are allowed to flip autonomously and demonstrate that such designs can allow ultrafast operation unconstrained by available clock speeds without compromising the solution's fidelity. Based on experimental results from a hardware benchmark of the autonomous design and benchmarked device models, we project that a nanomagnetic implementation can scale to achieve petaflips per second with millions of neurons. A key contribution of this article is the focus on a hardware metric - flips per second - as a problem and substrate-independent figure-of-merit for an emerging class of hardware annealers known as Ising Machines. Much like the shrinking feature sizes of transistors that have continually driven Moore's Law, we believe that flips per second can be continually improved in later technology generations of a wide class of probabilistic, domain specific hardware.

In this article we present a concrete design for a probabilistic (p-) computer based on a network of p-bits, robust classical entities fluctuating between -1 and +1, with probabilities that are controlled through an input constructed from the outputs of other p-bits. The architecture of this probabilistic computer is similar to a stochastic neural network with the p-bit playing the role of a binary stochastic neuron, but with one key difference: there is no sequencer used to enforce an ordering of p-bit updates, as is typically required. Instead, we explore sequencerless designs where all p-bits are allowed to flip autonomously and demonstrate that such designs can allow ultrafast operation unconstrained by available clock speeds without compromising the solution's fidelity. Based on experimental results from a hardware benchmark of the autonomous design and benchmarked device models, we project that a nanomagnetic implementation can scale to achieve petaflips per second with millions of neurons. A key contribution of this article is the focus on a hardware metric - flips per second - as a problem and substrate-independent figure-of-merit for an emerging class of hardware annealers known as Ising Machines. Much like the shrinking feature sizes of transistors that have continually driven Moore's Law, we believe that flips per second can be continually improved in later technology generations of a wide class of probabilistic, domain specific hardware.

Probabilistic spin logic is a recently proposed computing paradigm based on unstable stochastic units called probabilistic bits ( p -bits) that can be correlated to form probabilistic circuits (p-circuits). These p-circuits can be used to solve the problems of optimization, inference, and implement precise Boolean functions in an "inverted" mode, where a given Boolean circuit can operate in reverse to find the input combinations that are consistent with a given output. In this brief, we present a scalable field-programmable gate array implementation of such invertible p-circuits. We implement a "weighted" p -bit that combines stochastic units with localized memory structures. We also present a generalized tile of weighted p -bits to which a large class of problems beyond invertible Boolean logic can be mapped and how invertibility can be applied to interesting problems such as the NP-complete subset sum problem by solving a small instance of this problem in hardware.

Probabilistic spin logic is a recently proposed computing paradigm based on unstable stochastic units called probabilistic bits ( p -bits) that can be correlated to form probabilistic circuits (p-circuits). These p-circuits can be used to solve the problems of optimization, inference, and implement precise Boolean functions in an "inverted" mode, where a given Boolean circuit can operate in reverse to find the input combinations that are consistent with a given output. In this brief, we present a scalable field-programmable gate array implementation of such invertible p-circuits. We implement a "weighted" p -bit that combines stochastic units with localized memory structures. We also present a generalized tile of weighted p -bits to which a large class of problems beyond invertible Boolean logic can be mapped and how invertibility can be applied to interesting problems such as the NP-complete subset sum problem by solving a small instance of this problem in hardware.

Cyber risk affects all organizations. Cyber risk management has generally been based on the heuristics and the availability of protective tools, such as firewalls and passwords. Only recently have there been quantitative analyses of these tools’ costs and benefits. This article presents a probabilistic method based on the existing data in an organization and on their extension to assess the probabilities of new attack scenarios. The objective is to set priorities among risk management measures and to optimize the allocation of limited resources. The model is illustrated first by a statistical analysis of 60 000 incidents, such as lost or stolen laptops, over six years in a specific organization. This analysis is then expanded to the probabilistic domain to cover threats that have not occurred yet. This requires a systematic construction of new attack scenarios and an assessment both of their probability of success and of subsequent losses. The conjunction of statistics and probabilities of more extreme scenarios yields full risk curves. These curves represent the overall cyber risk for the organization and its insurers and unable assessment of the benefits of a spectrum of protective options. [ABSTRACT FROM AUTHOR]

Distributed computing supports large scale and data-intensive computations with the cooperation of a multitude of parties, each responsible for a portion of the workload. Such parties are often not fully reliable and may return incorrect results. In this article, we address the problem of assessing the integrity of the computation results. We provide a comprehensive characterization of two techniques, sentinels and twins, evaluating their effectiveness and synergy. Sentinels are pre-computed tasks whose result is known apriori, and enable checking returned results against a ground truth. Twins are replicated tasks assigned to different workers, and enable cross-checking returned results for a same task. The analysis considers many questions that arise in the design of a concrete integrity assessment strategy and identifies the parameters that have a critical impact on the overall protection. Our model enables to tune the integrity controls so to achieve best effectiveness. The model can be applied to a variety of scenarios and offers guidelines that can find extensive application. [ABSTRACT FROM AUTHOR]

Although most experts in the field believe that the study of probability would equip people in making good decisions, they have overlooked the need to examine professionals' decision-making ability in their daily activities after many years of probability studies. This study explored four non-mathematics teachers who studied probability at High and Primary schools. It appears no one has written about this topic. Despite this scholarly neglect, this study explains the need for research in the area and offers an initial interpretation of this new area. The study contends that understanding people who studied probability at least 13-years ago could provide us with insight on how people use probability in making decisions on uncertain situations and to inform the teaching of probability. Content analysis was done on the recorded data. The participants' response to tasks and questions revealed their inability to apply probabilistic skills in uncertain situations. The participants appear to be either overconfident or making choices out of habit, without a critical assessment of the various possibilities and their consequences. This study calls for a 'cross-examination approach' in the development of probabilistic thinking skills among students. [ABSTRACT FROM AUTHOR]

Inferential modeling has been of significance for modern manufacturing in estimating the quality-related process variables. As an effective inferential model, probabilistic slow feature analysis (PSFA) has gained attention in regression tasks to interpret dynamic properties with a slowness preference. However, PSFA is often challenged by the nonlinear sequential data due to its linear state-space structure. In this article, a new nonlinear extension of PSFA is proposed under the deep learning framework to enhance the dynamic feature extraction with limited labels, incorporating variational inference and Monte Carlo inference to derive the objective function. The proposed model considers the relevance of inputs with outputs as the input weights to upgrade prediction performance. The proposed model is verified through an industrial hydrocracking process to predict diesel yield with missing labels ranged from 0% to 50%, and the root mean squared error is reduced by at least 8.78% compared to PSFA. [ABSTRACT FROM AUTHOR]

The distributed, real-time algorithms for multiple pursuers cooperating to capture an evader are developed in an obstacle-free and an obstacle-cluttered environment, respectively. The developed algorithm is based on the idea of planning the control action within its safe, collision-free region for each robot. We initially present a greedy capturing strategy for an obstacle-free environment based on the Buffered Voronoi Cell (BVC). For an environment with obstacles, the obstacle-aware BVC (OABVC) is defined as the safe region, which considers the physical radius of each robot, and dynamically weights the Voronoi boundary between robot and obstacle to make it tangent to the obstacle. Each robot continually computes its safe cells and plans its control actions in a recursion fashion. In both cases, the pursuers successfully capture the evader with only relative positions of neighboring robots. A rigorous proof is provided to ensure the collision and obstacle avoidance during the pursuit-evasion games. Simulation results are presented to demonstrate the efficiency of the developed algorithms. [ABSTRACT FROM AUTHOR]

How can we assess a network's ability to maintain its functionality under attacks? Network robustness has been studied extensively in the case of deterministic networks. However, applications such as online information diffusion and the behavior of networked public raise a question of robustness in probabilistic networks. We propose three novel robustness measures for networks hosting a diffusion under the Independent Cascade or Linear Threshold model, susceptible to attacks by an adversarial attacker who disables nodes. The outcome of such a process depends on the selection of its initiators, or seeds, by the seeder, as well as on two factors outside the seeder's discretion: the attacker's strategy and the probabilistic diffusion outcome. We consider three levels of seeder awareness regarding these two uncontrolled factors, and evaluate the network's viability aggregated over all possible extents of an attack. We introduce novel algorithms from building blocks found in previous works to evaluate the proposed measures. A thorough experimental study with synthetic and real, scale-free and homogeneous networks establishes that these algorithms are effective and efficient, while the proposed measures highlight differences among networks in terms of robustness and the surprise they furnish when attacked. Last, we devise a new measure of diffusion entropy, and devise ways to enhance the robustness of probabilistic networks. [ABSTRACT FROM AUTHOR]

Good responsiveness of autonomous vehicle computing systems is crucial to safety and performance of the vehicles. For example, an autonomous vehicle (AV) may cause an accident if the end-to-end latency from sensing a pedestrian to emergency stop is too high. However, the AV software stacks are too complex to probabilistically analye the end-to-end latency on a multi-core system. They consist of a graph of tasks with different periods, and have a large variability in the task execution times, which may lead to the maximum core utilization $U^{\max }$ U max greater than 1.0 on some cores. This paper proposes a novel stochastic analysis of the end-to-end latency over the AV stacks that allows $U^{\max }$ U max to exceed 1.0 on each core. The proposed analysis models the entire stack as a graph of task graphs under a multi-core partitioned scheduling and provides a probabilistic guarantee that the analyzed latency distribution upper-bounds the one observed from a real system under the assumption of independent task execution times. Using the Autoware stack with inter-task dependent execution times, it is shown that our analysis, combined with a task grouping to mitigate the inter-task correlations, can give a latency distribution for each task path that almost upper-bounds the observed one. [ABSTRACT FROM AUTHOR]

We establish the undecidability of conditional affine information inequalities, the undecidability of the conditional independence implication problem with a constraint that one random variable is binary, and the undecidability of the problem of deciding whether the intersection of the entropic region and a given affine subspace is empty. This is a step towards the conjecture on the undecidability of conditional independence implication. The undecidability is proved via a reduction from the periodic tiling problem (a variant of the domino problem). Hence, one can construct examples of the aforementioned problems that are independent of ZFC (assuming ZFC is consistent). [ABSTRACT FROM AUTHOR]

Free-form surface reconstruction using point clouds is a common issue in manufacturing. In this article, a robust joint registration approach for multiview point clouds is proposed to address the problems brought by coarse initialization, outliers, and noise. The basic idea is that minimizing the L2 distance between probability distributions of integrated and standard models, such that a robust initialization is provided for fine registration to avoid local minima. The fine registration is formulated as a joint closet point problem, which is implicitly constrained by closed-loop consistency. In addition, a Lie algebra solution is derived to enforce rigid transformations. The robust initialization is judged by the simulated annealing algorithm. Finally, a probabilistic distance is defined and a maximum likelihood estimation of multiview transformations is designed to resist noise. The experiment on simulated and real data illustrate better robustness of our method to initial errors, outliers, and noise. [ABSTRACT FROM AUTHOR]

Although the GPS-based positioning is ubiquitous for its high precision, the high power consumption brought by the high sampling frequency and the poor GPS signal penetration limits its availability in locating low-power mobile devices (especially mobile phones). As a promising complement, the cellular-based positioning has attracted great attention since it consumes much less power as well as its higher availability. However, the sparsity of cellular-based data (due to lower sampling rate) and large localization errors make the measurement accuracy becomes the main challenge of the cellular-based positioning. hidden Markov model can well solve the problem of positioning error of GPS data, but it is less accurate when applied to map matching of cellular-base data. Therefore, to improve accuracy, in this article, we propose a novel algorithm called the tailored hidden Markov model (THMM) that is optimized for the cellular-based data. Specifically, the geometric, the topological, and the probabilistic characteristics have been considered and fully exploited in the THMM design. Our proposed schemes are evaluated using real-world motor vehicle movement trajectories collected in Tianjin and the experimental results are encouraging compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

We propose a novel semi-supervised learning (SSL) assumption—the high separation probability (HSP) assumption—that is complementary to the common low density separation (LDS) assumption, and a highly accurate and efficient SSL algorithm, the transductive minimax probability machine (TMPM). Under the HSP assumption, a preferred classifier should ensure that with high probability the samples are far away from the decision boundary. Similarly, the LDS assumption states that the decision boundary should lie in a low-density region. A remarkable property of HSP is that it provides an elegant theoretical framework for analyzing the generalization performance of SSL, while it remains difficult for the LDS assumption to have a quantitative criterion on how it fits the data. Moreover, it has the same paradigm as the minimax probability machine (MPM) and yields an intractable mixed-integer optimization for solving the transductive label assignment problem. Consequently, we propose TMPM with a label-switching strategy and iterative manner to formalize our approximate solution. Theoretically, we analyze the computational complexity, convergence property, and generalization error bound of TMPM. Empirically, we show that TMPM achieves competitive performance on a wide range of datasets, while being almost one order of magnitude faster than existing SSL approaches. [ABSTRACT FROM AUTHOR]

Robot motion planning methods based on trajectory optimization can efficiently generate feasible and optimal trajectories by minimizing a suitable cost function, even in high-dimensional spaces. However, the main drawback of these methods lies in their proneness to infeasible local minima, especially in complex environments. To mitigate this issue, we propose a novel motion planning method that represents trajectories as samples from a mixture of continuous-time Gaussian processes (MGP) and employs stochastic optimization in order to update the MGP parameters in a cost-minimizing manner. The contributions of the proposed trajectory optimization method arise from the introduced mixture representation and stochastic gradient estimation, dominantly enabling better exploration of the trajectory space and including nondifferentiable optimizing costs. We evaluated the proposed method in multiple simulation benchmarks featuring 7 degree-of-freedom (DOF) robot arms and a 10DOF mobile manipulator. We also conducted a real-world experiment with a 14DOF dual-arm robot. The experimental results demonstrated that the proposed method achieves higher success rate than several state-of-the-art methods, while the advantages stemming from MGPs and stochastic optimization, like trajectory smoothness, support of nondifferentiable cost functions, multiple trajectory solutions, and the ability to tackle high-dimensional planning problems, are inherently kept. [ABSTRACT FROM AUTHOR]

Information about the interruption probability of sensitive industrial processes is important for devising mitigation strategies against voltage sags. However, the existing method for assessing interruption probability is less practical, owing to the difficulty in collecting technical information and data from industrial users. This study proposes a low-data-dependence assessment method for the interruption probability of an industrial process. First, it establishes a general assessment model for the tripping probability of sensitive equipment. Second, it proposes a parameter estimation method to determine the probability density function of the voltage tolerance curve of sensitive equipment in the uncertainty region, according to the equipment trip information recorded by the user. This improves the accuracy of the assessment. Third, this study establishes the logic function expression form of the industrial process structure to describe the relationship between the process interruption probability and the equipment tripping probability. Finally, it proposes a solution method for the process interruption probability based on a heuristic search algorithm, which overcomes the problem of technological process information acquisition, such as the logical relationship among various equipment in the industrial process. The rationality and applicability of the proposed method are verified through a case study. [ABSTRACT FROM AUTHOR]

Identifying drug-target interactions has been a key step in drug discovery. Many computational methods have been proposed to directly determine whether drugs and targets can interact or not. Drug-target binding affinity is another type of data which could show the strength of the binding interaction between a drug and a target. However, it is more challenging to predict drug-target binding affinity, and thus a very few studies follow this line. In our work, we propose a novel co-regularized variational autoencoders (Co-VAE) to predict drug-target binding affinity based on drug structures and target sequences. The Co-VAE model consists of two VAEs for generating drug SMILES strings and target sequences, respectively, and a co-regularization part for generating the binding affinities. We theoretically prove that the Co-VAE model is to maximize the lower bound of the joint likelihood of drug, protein and their affinity. The Co-VAE could predict drug-target affinity and generate new drugs which share similar targets with the input drugs. The experimental results on two datasets show that the Co-VAE could predict drug-target affinity better than existing affinity prediction methods such as DeepDTA and DeepAffinity, and could generate more new valid drugs than existing methods such as GAN and VAE. [ABSTRACT FROM AUTHOR]

Deep-learning-based soft sensors have been extensively developed for predicting key quality or performance variables in industrial processes. However, most approaches assume that data are uniformly sampled while the multiple variables are often acquired at different rates in practical processes. This article designed a progressive transfer strategy, based on which a variational progressive-transfer network (VPTN) method is proposed for the soft sensor development of industrial multirate processes. In VPTN, the multirate data are first separated into multiple data chunks where the variables within each chunk are acquired at a uniform rate. Then, a variational multichunk data modeling framework is developed to model the multiple chunks in a unified fashion through deep variational structures. The base models, including the unsupervised ones with only partial process variables and the supervised soft sensor model share a similar network structure, such that the subsequent transfer strategy can be readily implemented. Finally, a progressive transfer learning strategy is designed to transfer the model parameters from the fastest sampled data chunk to the slowest one in a progressive manner. Thus, the knowledge from various data chunks can be sequentially explored and transferred to enhance the performance of the terminal soft sensor model. Case studies on both a debutanizer column dataset and a real coal mill dataset in a thermal power plant validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This article aims to stabilize probabilistic Boolean networks (PBNs) via a novel pinning control strategy. In a PBN, the state evolution of each gene switches among a collection of candidate Boolean functions with preassigned probability distributions, which govern the activation frequency of each Boolean function. Due to the existence of stochasticity, the mode-independent pinning controller might be disabled. Thus, both mode-independent and mode-dependent pinning controller are required here. Moreover, a criterion is derived to determine whether mode-independent controllers are applicable while the pinned nodes are given. It is worth pointing out that this pinning control is based on the $n\times n$ network structure rather than $2^{n} \times 2^{n}$ state transition matrix. Therefore, compared with the existing results, this pinning control strategy is more practicable and has the ability to handle large-scale networks, especially sparsely connected networks. To demonstrate the effectiveness of the designed control scheme, a PBN that describes the mammalian cell-cycle encountering a mutated phenotype is discussed as a simulation. [ABSTRACT FROM AUTHOR]

We introduce the probabilistic two-agent justification logic |$\textsf {IPJ}$|⁠ , a logic in which we can reason about agents that perform interactive proofs. In order to study the growth rate of the probabilities in |$\textsf {IPJ}$|⁠ , we present a new method of parametrizing |$\textsf {IPJ}$| over certain negligible functions. Further, our approach leads to a new notion of zero-knowledge proofs. [ABSTRACT FROM AUTHOR]

Subjective evaluation is a commonly used method in the real recognition process. Generally, two fuzziness can be found in evaluation information, namely what values should be given to fully describe the information and how to distinguish different values. The recently developed probabilistic hesitant fuzzy set could perfectly address these issues. In this article, we propose a dual-fuzzy convolutional neural network (DF-CNN) by fusing the hot neural network algorithm into the probabilistic hesitant fuzzy environment and then using it in a practical handwritten image recognition process. For this new DF-CNN, we provide the whole calculation process including the forward propagation, backward propagation, and parameter updating calculations. Also, the optimization algorithm of the DF-CNN is given to derive its optimal results. Finally, we apply the DF-CNN and its optimization algorithm to deal with a real issue, namely the handwritten numeral image recognition. The calculation process and the comparison fully demonstrate the feasibility and effectiveness of the proposed new model and algorithm. [ABSTRACT FROM AUTHOR]

Energy disaggregation is the process of disaggregating a household's total energy consumption into its appliance-level components. One of the limitations of energy disaggregation is its generalization capacity, which can be defined as the ability of the model to analyze new households. In this article, a new energy disaggregation approach based on adversarial autoencoder (AAE) is proposed to create a generative model and enhance the generalization capacity. The proposed method has a probabilistic structure to handle uncertainties in the unseen data. By transforming the latent space from a deterministic structure to a Gaussian prior distribution, AAEs decoder transforms into a generative model. The proposed approach is validated through experimental tests using two different datasets. The experimental results exhibit a 55% MAE performance increase compared to deterministic models and 7% compared to probabilistic models. In addition, considering the predictions made when the appliances are on, the AAE improves the performance by 16% for UKDALE and 36% for REDD dataset compared to the state-of-art models. Moreover, the online analysis performance of AAE is examined in detail, and the disadvantages of instant predictions and the possible solutions are extensively discussed. [ABSTRACT FROM AUTHOR]