Miracle-dz/UniFAR-FTUs huggingface dataset:
On the (Non-)Applicability of a Small Model Theorem to Model Checking STMs

Software Transactional Memory (STM) algorithms provide programmers with a synchronisation mechanism for concurrent access to shared variables. Basically, programmers can specify transactions (reading from and writing to shared state) which execute"seemingly"atomic. This property is captured in a correctness criterion called opacity. For model checking opacity of an STM algorithm, we -- in principle -- need to check opacity for all possible combinations of transactions writing to and reading from potentially unboundedly many variables. To still apply automatic model checking techniques to opacity checking, a so called small model theorem has been proven which states that model checking on two variables and two transactions is sufficient for correctness verification of STMs. In this paper, we take a fresh look at this small model theorem and investigate its applicability to opacity checking of STM algorithms.
Toward efficient interactions between Python and native libraries

Python has become a popular programming language because of its excellent programmability. Many modern software packages utilize Python for high-level algorithm design and depend on native libraries written in C/C++/Fortran for efficient computation kernels. Interaction between Python code and native libraries introduces performance losses because of the abstraction lying on the boundary of Python and native libraries. On the one side, Python code, typically run with interpretation, is disjoint from its execution behavior. On the other side, native libraries do not include program semantics to understand algorithm defects. To understand the interaction inefficiencies, we extensively study a large collection of Python software packages and categorize them according to the root causes of inefficiencies. We extract two inefficiency patterns that are common in interaction inefficiencies. Based on these patterns, we develop PieProf, a lightweight profiler, to pinpoint interaction inefficiencies in Python applications. The principle of PieProf is to measure the inefficiencies in the native execution and associate inefficiencies with high-level Python code to provide a holistic view. Guided by PieProf, we optimize 17 real-world applications, yielding speedups up to 6.3× on application level.
Root and community inference on the latent growth process of a network

Many existing statistical models for networks overlook the fact that many real world networks are formed through a growth process. To address this, we introduce the PAPER (Preferential Attachment Plus Erd\H{o}s--R\'{e}nyi) model for random networks, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erd\H{o}s--R\'{e}nyi (ER) random edges. The PA tree component captures the underlying growth/recruitment process of a network where vertices and edges are added sequentially, while the ER component can be regarded as random noise. Given only a single snapshot of the final network G, we study the problem of constructing confidence sets for the early history, in particular the root node, of the unobserved growth process; the root node can be patient zero in a disease infection network or the source of fake news in a social media network. We propose an inference algorithm based on Gibbs sampling that scales to networks with millions of nodes and provide theoretical analysis showing that the expected size of the confidence set is small so long as the noise level of the ER edges is not too large. We also propose variations of the model in which multiple growth processes occur simultaneously, reflecting the growth of multiple communities, and we use these models to provide a new approach to community detection.
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S. Ota, 2, ∗ G. Christian, 3, 4, 5 W. N. Catford, G. Lotay, M. Pignatari, 8, 9, 2 U. Battino, 2 E. A. Bennett, 4 S. Dede, 4 D. T. Doherty, S. Hallam, F. Herwig, 7, 2 J. Hooker, 4 C. Hunt, 4 H. Jayatissa, 4 A. Matta, M. Mouhkaddam, E. Rao, 12 G. V. Rogachev, 4, 5 A. Saastamoinen, D. Scriven, 4 J. A. Tostevin, S. Upadhyayula, 4 and R. Wilkinson Cyclotron Institute, Texas A&M University, College Station, TX 77843, USA NuGrid Collaboration, http: // nugridstars. org Department of Astronomy & Physics, Saint Mary’s University, Halifax, NS B3H 3C3, Canada Department of Physics & Astronomy, Texas A&M University, College Station, TX 77843, USA Nuclear Solutions Institute, Texas A&M University, College Station, TX 77843, USA Department of Physics, University of Surrey, Guildford GU2 7XH, UK Joint Institute for Nuclear Astrophysics Center for the Evolution of the Elements, East Lansing, 48823, USA E. A. Milne Centre for Astrophysics, Department of Physics and Mathematics, University of Hull, Hull HU6 7RX, UK Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, Hungarian Academy of Sciences, Konkoly Thege M. t 15-17, 1121, Budapest, Hungary School of Physics and Astronomy, University of Edinburgh, EH9 3FD, UK Department of Physics and Astronomy, University of Victoria, Victoria, BC V8P5C2, Canada Department of Physics & Astronomy, Rutgers University, New Brunswick, NJ, USA (Dated: July 2, 2021)
Design, Modelling and Control of SPIROS: The Six Propellers and Intermeshing Rotors Based Omnidirectional Spherical Robot

Since the past few decades, several designs and control methods have been developed for the Spherical Robots (SRs) with thoroughly analyzed mechanics on generalized 3D terrains. But the vertical motion and the steep inclination maneuver has been an unsolved problem with the existing SR's driving mechanisms. Also, the possibilities of wind-powered or air-propelled SRs have not been fully explored. This paper introduces the new Omnidirectional Spherical Robot mechanism named SPIROS: The Six Propeller and Intermeshed Rotor based Omnidirectional Spherical Robot. The SPIROS is driven by a novel octahedral arrangement of six intermeshed rotary air thrusters placed in the Goldberg Polyhedral shaped spherical gridshell. The advantage of the proposed design lies in its air-powered propulsion, which improves on the obstacle avoidance and the slope climbing abilities. The robot's dynamic models are derived using the existing kinematical models of the Continuous Rolling Spherical Robots (CR-SR), with subcategories, triple axes rolling (3R-SR), dual axes rolling (2R-SR) and rolling and turning (RT-SR) spherical robots. The path tracking control scheme based on the pure pursuit algorithm is presented. Simulations are carried out in MATLAB and Simulink to validate the developed models and the effectiveness of the proposed control schemes.
Information Security Analysis in the Passenger-Autonomous Vehicle Interaction

Autonomous vehicles (AV) are becoming a part of humans’ everyday life. There are numerous pilot projects of driverless public buses; some car manufacturers deliver their premium-level automobiles with advanced self-driving features. Thus, assuring the security of a Passenger–Autonomous Vehicle interaction arises as an important research topic, as along with opportunities, new cybersecurity risks and challenges occur that potentially may threaten Passenger’s privacy and safety on the roads. This study proposes an approach of the security requirements elicitation based on the developed threat model. Thus, information security risk management helps to fulfil one of the principles needed to protect data privacy - information security. We demonstrate the process of security requirements elicitation to mitigate arising security risks. The findings of the paper are case-oriented and are based on the literature review. They are applicable for AV system implementation used by ride-hailing service providers that enable supervisory AV control.
Deep auxiliary learning for visual localization using colorization task

Visual localization is one of the most important components for robotics and autonomous driving. Recently, inspiring results have been shown with CNN-based methods which provide a direct formulation to end-to-end regress 6-DoF absolute pose. Additional information like geometric or semantic constraints is generally introduced to improve performance. Especially, the latter can aggregate high-level semantic information into localization task, but it usually requires enormous manual annotations. To this end, we propose a novel auxiliary learning strategy for camera localization by introducing scene-specific high-level semantics from self-supervised representation learning task. Viewed as a powerful proxy task, image colorization task is chosen as complementary task that outputs pixel-wise color version of grayscale photograph without extra annotations. In our work, feature representations from colorization network are embedded into localization network by design to produce discriminative features for pose regression. Meanwhile an attention mechanism is introduced for the benefit of localization performance. Extensive experiments show that our model significantly improve localization accuracy over state-of-the-arts on both indoor and outdoor datasets.
Applications of the Free Energy Principle to Machine Learning and Neuroscience

In this PhD thesis, we explore and apply methods inspired by the free energy principle to two important areas in machine learning and neuroscience. The free energy principle is a general mathematical theory of the necessary information-theoretic behaviours of systems that maintain a separation from their environment. A core postulate of the theory is that complex systems can be seen as performing variational Bayesian inference and minimizing an information-theoretic quantity called the variational free energy. The thesis is structured into three independent sections. Firstly, we focus on predictive coding, a neurobiologically plausible process theory derived from the free energy principle which argues that the primary function of the brain is to minimize prediction errors, showing how predictive coding can be scaled up and extended to be more biologically plausible, and elucidating its close links with other methods such as Kalman Filtering. Secondly, we study active inference, a neurobiologically grounded account of action through variational message passing, and investigate how these methods can be scaled up to match the performance of deep reinforcement learning methods. We additionally provide a detailed mathematical understanding of the nature and origin of the information-theoretic objectives that underlie exploratory behaviour. Finally, we investigate biologically plausible methods of credit assignment in the brain. We first demonstrate a close link between predictive coding and the backpropagation of error algorithm. We go on to propose novel and simpler algorithms which allow for backprop to be implemented in purely local, biologically plausible computations.
Orienting (hyper)graphs under explorable stochastic uncertainty

Given a hypergraph with uncertain node weights following known probability distributions, we study the problem of querying as few nodes as possible until the identity of a node with minimum weight can be determined for each hyperedge. Querying a node has a cost and reveals the precise weight of the node, drawn from the given probability distribution. Using competitive analysis, we compare the expected query cost of an algorithm with the expected cost of an optimal query set for the given instance. For the general case, we give a polynomial-time f(α)-competitive algorithm, where f(α) ∈ [1.618+e,2] depends on the approximation ratio α for an underlying vertex cover problem. We also show that no algorithm using a similar approach can be better than 1.5-competitive. Furthermore, we give polynomial-time 4/3-competitive algorithms for bipartite graphs with arbitrary query costs and for hypergraphs with a single hyperedge and uniform query costs, with matching lower bounds.
SSC: Semantic Scan Context for Large-Scale Place Recognition

Place recognition gives a SLAM system the ability to correct cumulative errors. Unlike images that contain rich texture features, point clouds are almost pure geometric information which makes place recognition based on point clouds challenging. Existing works usually encode low-level features such as coordinate, normal, reflection intensity, etc., as local or global descriptors to represent scenes. Besides, they often ignore the translation between point clouds when matching descriptors. Different from most existing methods, we explore the use of high-level features, namely semantics, to improve the descriptor’s representation ability. Also, when matching descriptors, we try to correct the translation between point clouds to improve accuracy. Concretely, we propose a novel global descriptor, Semantic Scan Context, which explores semantic information to represent scenes more effectively. We also present a two-step global semantic ICP to obtain the 3D pose (x, y, yaw) used to align the point cloud to improve matching performance. Our experiments on the KITTI dataset show that our approach outperforms the state-of-the- art methods with a large margin. Our code is available at: https://github.com/lilin-hitcrt/SSC.
Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction

Deep learning-based models, such as recurrent neural networks (RNNs), have been applied to various sequence learning tasks with great success. Following this, these models are increasingly replacing classic approaches in object tracking applications for motion prediction. On the one hand, these models can capture complex object dynamics with less modeling required, but on the other hand, they depend on a large amount of training data for parameter tuning. Towards this end, we present an approach for generating synthetic trajectory data of unmanned-aerial-vehicles (UAVs) in image space. Since UAVs, or rather quadrotors are dynamical systems, they can not follow arbitrary trajectories. With the prerequisite that UAV trajectories fulfill a smoothness criterion corresponding to a minimal change of higher-order motion, methods for planning aggressive quadrotors flights can be utilized to generate optimal trajectories through a sequence of 3D waypoints. By projecting these maneuver trajectories, which are suitable for controlling quadrotors, to image space, a versatile trajectory data set is realized. To demonstrate the applicability of the synthetic trajectory data, we show that an RNN-based prediction model solely trained on the generated data can outperform classic reference models on a real-world UAV tracking dataset. The evaluation is done on the publicly available ANTI-UAV dataset.
Markov Decision Process modeled with Bandits for Sequential Decision Making in Linear-flow

For marketing, we sometimes need to recommend content for multiple pages in sequence. Different from general sequential decision making process, the use cases have a simpler flow where customers per seeing recommended content on each page can only return feedback as moving forward in the process or dropping from it until a termination state. We refer to this type of problems as sequential decision making in linear--flow. We propose to formulate the problem as an MDP with Bandits where Bandits are employed to model the transition probability matrix. At recommendation time, we use Thompson sampling (TS) to sample the transition probabilities and allocate the best series of actions with analytical solution through exact dynamic programming. The way that we formulate the problem allows us to leverage TS's efficiency in balancing exploration and exploitation and Bandit's convenience in modeling actions' incompatibility. In the simulation study, we observe the proposed MDP with Bandits algorithm outperforms Q-learning with $\epsilon$-greedy and decreasing $\epsilon$, independent Bandits, and interaction Bandits. We also find the proposed algorithm's performance is the most robust to changes in the across-page interdependence strength.
Never Go Full Batch (in Stochastic Convex Optimization)

We study the generalization performance of full-batch optimization algorithms for stochastic convex optimization: these are first-order methods that only access the exact gradient of the empirical risk (rather than gradients with respect to individual data points), that include a wide range of algorithms such as gradient descent, mirror descent, and their regularized and/or accelerated variants. We provide a new separation result showing that, while algorithms such as stochastic gradient descent can generalize and optimize the population risk to within ε after $ (1/ε2) iterations, full-batch methods either need at least Ω(1/ε4) iterations or exhibit a dimension-dependent sample complexity.
Semi-Sparsity for Smoothing Filters

In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-induced minimization scheme. The model is derived from the observations that semi-sparsity prior knowledge is universally applicable in situations where sparsity is not fully admitted such as in the polynomial-smoothing surfaces. We illustrate that such priors can be identified into a generalized <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula>-norm minimization problem in higher-order gradient domains, giving rise to a new “feature-aware” filter with a powerful simultaneous-fitting ability in both sparse singularities (corners and salient edges) and polynomial-smoothing surfaces. Notice that a direct solver to the proposed model is not available due to the non-convexity and combinatorial nature of <inline-formula> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula>-norm minimization. Instead, we propose to solve it approximately based on an efficient half-quadratic splitting technique. We demonstrate its versatility and many benefits to a series of signal/image processing and computer vision applications.
Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images

Deep neural networks (DNNs) have been widely used for medical image analysis. However, the lack of access a to large-scale annotated dataset poses a great challenge, especially in the case of rare diseases, or new domains for the research society. Transfer of pre-trained features, from the relatively large dataset is a considerable solution. In this paper, we have explored supervised segmentation using domain adaptation for optic nerve and orbital tumor, when only small sampled CT images are given. Even the lung image database consortium image collection (LIDC-IDRI) is a cross-domain to orbital CT, but the proposed domain adaptation method improved the performance of attention U-Net for the segmentation in public optic nerve dataset and our clinical orbital tumor dataset. The code and dataset are available at https://github.com/cmcbigdata.
General solutions toN -field cosmology with exponential potentials

We construct the general analytical solution for the N -field product-exponential potential in an expanding FLRW background. We demonstrate the relevance of this analytical solution in more general contexts for the derivation of estimates for the transitional time between an arbitrary initial state and the slow-roll solutions. In certain cases, these estimates can also be used to demonstrate the non-linear convergence towards the slow-roll solutions. In addition, we extend the solution to include stiff matter as well.
Perftoran (Vidaphor)—Introduction to Western Medicine

Perftoran, which has been rebranded as Vidaphor for marketing in North America, is an emulsion of perfluorocarbons in a surfactant and electrolyte mixture. It was developed in Russia as an oxygen-carrying intravenous plasma additive for hemorrhagic anemia and ischemic conditions from various etiologies. It was approved for clinical use in Russia in 1996 and used by the Russian Armed Forces and in civilian medical care. It was also approved in Mexico from 2005 to 2010. It has been reportedly administered to over 35,000 patients with significant evidence of benefit and relatively mild and manageable adverse effects. It may have significant potential for use in hemorrhagic shock if human red blood cells are not available, and for several other applications including treatment of vascular gas embolism, cerebral or spinal trauma, and regional ischemia. It is different from other perfluorocarbon preparations under development in the United States in that it uses a different primary perfluorocarbon (perfluorodecalin) and a surfactant (Proxanol 268) instead of egg-yok phospholipid as the emulsion vehicle. Perftoran has a much smaller particle size resulting in milder adverse effects. It has been safely administered to more patients than any oxygen carrier currently under development. A newly formed United States Corporation (FluorO2 Therapeutics, Inc.) intends to manufacture the product in the United States under GMP standards and make it available for clinical use in Mexico and Latin America and pursue research to support eventual approval in the United States for human and veterinary use. This article will briefly review key information about this product and provide references for the interested reader. KEYWORDS—Artificial blood, blood substitute, oxygen carrier, perfluorocarbon, perfluorodecalin
TransNet: Shift Invariant Transformer Network for Power Attack

Masking and desynchronization of power traces are two widely used countermeasures against power attacks. Higher-order power attacks are used to break cryptographic implementations protected by masking countermeasures. However, they require to capture long-distance dependency, the dependencies among distant Points-of-Interest (PoIs) along the time axis, which together contribute to the information leakage. Desynchronization of power traces provides resistance against power attacks by randomly shifting the individual traces, thus, making the PoIs misaligned for diﬀerent traces. Consequently, a successful attack against desynchronized traces requires to be invariant to the random shifts of the power traces. A successful attack against cryptographic implementations protected by both masking and desynchronization countermeasures requires to be both shift-invariant and capable of capturing long-distance dependency. Recently, Transformer Network (TN) has been introduced in natural language processing literature. TN is better than both Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) at capturing long-distance dependency, and thus, a natural choice against masking countermeasures. Furthermore, a TN can be made shift-invariant making it robust to desynchronization of traces as well. In this work, we introduce a TN-based model, namely TransNet, for power attacks. Our experiments show that the proposed TransNet model successfully attacks implementation protected by both masking and desynchronization even when it is trained on only synchronized traces. Particularly, it can bring down the mean key rank below 1 using only 400 power traces if evaluated on highly desynchronized ASCAD_desync100 dataset even when it is trained on ASCAD dataset which has no trace desynchronization. Moreover, if compared to other state-of-the-art deep learning models, our proposed model performs signiﬁcantly better when the attack traces are highly desynchronized.