Miracle-dz/UniFAR-FTUs huggingface dataset:
A Sharp Leap from Quantified Boolean Formula to Stochastic Boolean Satisfiability Solving

Stochastic Boolean Satisfiability (SSAT) is a powerful representation for the concise encoding of quantified decision problems with uncertainty. While it shares commonalities with quantified Boolean formula (QBF) satisfiability and has the same PSPACE-complete complexity, SSAT solving tends to be more challenging as it involves expensive model counting, a.k. a. Sharp-SAT. To date, SSAT solvers, especially those imposing no restrictions on quantification levels, remain much lacking. In this paper, we present a new SSAT solver based on the framework of clause selection and cube distribution previously proposed for QBF solving. With model counting integrated and learning techniques strengthened, our solver is general and effective. Experimental results demonstrate the overall superiority of the proposed algorithm in both solving performance and memory usage compared to the state-of-the-art solvers on a number of benchmark formulas.
ASHF-Net: Adaptive Sampling and Hierarchical Folding Network for Robust Point Cloud Completion

Estimating the complete 3D point cloud from an incomplete one lies at the core of many vision and robotics applications. Existing methods typically predict the complete point cloud based on the global shape representation extracted from the incomplete input. Although they could predict the overall shape of 3D objects, they are incapable of generating structure details of objects. Moreover, the partial input point sets obtained from range scans are often sparse, noisy and non-uniform, which largely hinder shape completion. In this paper, we propose an adaptive sampling and hierarchical folding network (ASHF-Net) for robust 3D point cloud completion. Our main contributions are two-fold. First, we propose a denoising auto-encoder with an adaptive sampling module, aiming at learning robust local region features that are insensitive to noise. Second, we propose a hierarchical folding decoder with the gated skip-attention and multi-resolution completion goal to effectively exploit the local structure details of partial inputs. We also design a KL regularization term to evenly distribute the generated points. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on multiple 3D point cloud completion benchmarks.
Positions, Channels, and Layers: Fully Generalized Non-Local Network for Singer Identification

Recently, a non-local (NL) operation has been designed as the central building block for deep-net models to capture long-range dependencies (Wang et al. 2018). Despite its excellent performance, it does not consider the interaction between positions across channels and layers, which is crucial in fine-grained classification tasks. To address the limitation, we target at singer identification (SID) task and present a fully generalized non-local (FGNL) module to help identify fine-grained vocals. Specifically, we first propose a FGNL operation, which extends the NL operation to explore the correlations between positions across channels and layers. Secondly, we further apply a depth-wise convolution with Gaussian kernel in the FGNL operation to smooth feature maps for better generalization. More, we modify the squeeze-and-excitation (SE) scheme into the FGNL module to adaptively emphasize correlated feature channels to help uncover relevant feature responses and eventually the target singer. Evaluating results on the benchmark artist20 dataset shows that the FGNL module significantly improves the accuracy of the deep-net models in SID. Codes are available at https://github.com/ian-k-1217/Fully-Generalized-Non-Local-Network.
Computing an Efficient Exploration Basis for Learning with Univariate Polynomial Features

Barycentric spanners have been used as an efficient exploration basis in online linear optimization problems in a bandit framework. We characterise the barycentric spanner for decision problems in which the cost (or reward) is a polynomial in a single decision variable. Our characterisation of the barycentric spanner is two-fold: we show that the barycentric spanner under a polynomial cost function is the unique solution to a set of nonlinear algebraic equations, as well as the solution to a convex optimization problem. We provide numerical results to show that our method computes the barycentric spanner for the polynomial case significantly faster than the only other known algorithm for the purpose. As an application, we consider a dynamic pricing problem in which the revenue is an unknown polynomial function of the price. We then empirically show that the use of a barycentric spanner to initialise the prior distribution in a Thompson sampling setting leads to lower cumulative regret as compared to standard initialisations. We also illustrate the importance of barycentric spanners in adversarial settings by showing, both theoretically and empirically, that a barycentric spanner achieves the minimax value in a static adversarial linear regression problem where the learner selects the training points while an adversary selects the testing points and controls the variance of the noise corrupting the training samples
Scalable Equilibrium Computation in Multi-agent Influence Games on Networks

We provide a polynomial-time, scalable algorithm for equilibrium computation in multi-agent influence games on networks, extending work of Bindel, Kleinberg, and Oren (2015) from the single-agent to the multi-agent setting. In games of influence, agents have limited advertising budget to influence the initial predisposition of nodes in some network towards their products, but the eventual decisions of the nodes are determined by the stationary state of DeGroot opinion dynamics on the network, which takes over after the seeding (Ahmadinejad et al. 2014, 2015). In multi-agent systems, how should agents spend their budgets to seed the network to maximize their utility in anticipation of other advertising agents and the network dynamics? We show that Nash equilibria of this game are pure and (under weak assumptions) unique, and can be computed in polynomial time; we test our model by computing equilibria using mirror descent for the two-agent case on random graphs.
Hybrid-order Stochastic Block Model

Community detection is a research hotspot in machine learning and data mining. However, most of the existing community detection methods only rely on the lower-order connectivity patterns, while ignoring the higher-order connectivity patterns, and unable to capture the building blocks of the complex network. In recent years, some community detection methods based on higher-order structures have been developed, but they mainly focus on the motif network composed of higher-order structures, which violate the original lower-order topological structure and are affected by the fragmentation issue, resulting in the deviation of community detection results. Therefore, there is still a lack of community detection methods that can effectively utilize higher-order connectivity patterns and lower-order connectivity patterns. To overcome the above limitations, this paper proposes the Hybrid-order Stochastic Block Model (HSBM) from the perspective of the generative model. Based on the classical stochastic block model, the generation of lower-order structure and higher-order structure of the network is modeled uniformly, and the original topological properties of the network are maintained while using higher-order connectivity patterns. At the same time, a heuristic algorithm for community detection is proposed to optimize the objective function. Extensive experiments on six real-world datasets show that the proposed method outperforms the existing approaches.
Encoding Human Domain Knowledge to Warm Start Reinforcement Learning

Deep reinforcement learning has been successful in a variety of tasks, such as game playing and robotic manipulation. However, attempting to learn tabula rasa disregards the logical structure of many domains as well as the wealth of readily available knowledge from domain experts that could help "warm start" the learning process. We present a novel reinforcement learning technique that allows for intelligent initialization of a neural network weights and architecture. Our approach permits the encoding domain knowledge directly into a neural decision tree, and improves upon that knowledge with policy gradient updates. We empirically validate our approach on two OpenAI Gym tasks and two modified StarCraft 2 tasks, showing that our novel architecture outperforms multilayer-perceptron and recurrent architectures. Our knowledge-based framework finds superior policies compared to imitation learning-based and prior knowledge-based approaches. Importantly, we demonstrate that our approach can be used by untrained humans to initially provide >80% increase in expected reward relative to baselines prior to training (p < 0.001), which results in a >60% increase in expected reward after policy optimization (p = 0.011).
Precise Yet Efficient Semantic Calibration and Refinement in ConvNets for Real-time Polyp Segmentation from Colonoscopy Videos

We propose a novel convolutional neural network (ConvNet) equipped with two new semantic calibration and refinement approaches for automatic polyp segmentation from colonoscopy videos. While ConvNets set state-of-the-are performance for this task, it is still difficult to achieve satisfactory results in a real-time manner, which is a necessity in clinical practice. The main obstacle is the huge semantic gap between high-level features and low-level features, making it difficult to take full advantage of complementary semantic information contained in these hierarchical features. Compared with existing solutions, which either directly aggregate these features without considering the semantic gap or employ sophisticated non-local modeling techniques to refine semantic information by introduce many extra computational costs, the proposed ConvNet is able to more precisely yet efficiently calibrate and refine semantic information for better segmentation performance without increasing model complexity; we call the proposed ConvNet as SCR-Net, which has two key modules. We first propose a semantic calibration module (SCM) to effectively transmit the semantic information from high-level layers to low-level layers by learning the semantic-spatial relations during the training procedure. We then propose a semantic refinement module (SRM) to, based on the features calibrated by SCM, enhance the discrimination capability of the features for targeting objects. Extensive experiments on the Kvasir-SEG dataset demonstrate that the proposed SCR-Net is capable of achieving better segmentation accuracy than state-of-the-art approaches with a faster speed. The proposed techniques are general enough to be applied to similar applications where precise and efficient multi-level feature fusion is critical. The code is available at https://github.com/jiafuz/SCR-Net.
Crystal Growth and Design of Sapphire: Experimental and Calculation Studies of Anisotropic Crystal Growth upon Pulling Directions

The anisotropic growth of large-size sapphire single crystals along different pulling directions was studied on the basis of the chemical bonding theory of single crystal growth and practical Czochralski growth. The projection of thermodynamic morphology of sapphire single crystal respectively along [210], [110], [001], and [001] rotated 57.62° directions can be used to confirm the growth directions of surfaces that are preferred to be exposed thermodynamically in Czochralski growth. Starting from these thermodynamically preferred directions, the possible radial directions that are normal to the four typical pulling directions by kinetic controls have been identified by anisotropic chemical bonding distributions of sapphire single crystal. Chemical bonding calculations demonstrate that the lower pulling rate should be designed when Raxial/Rradial > 1, whereas the higher pulling rate should be designed when Raxial/Rradial < 1. The anisotropic chemical bonding conditions demonstrate the lowest chemical bond.
A Blind Block Term Decomposition of High Order Tensors

Tensor decompositions have found many applications in signal processing, data mining, machine learning, etc. In particular, the block term decomposition (BTD), which is a generalization of CP decomposition and Tucker decomposition/HOSVD, has been successfully used for the compression and acceleration of neural networks. However, computing BTD is NP-hard, and optimization based methods usually suffer from slow convergence or even fail to converge, which limits the applications of BTD. This paper considers a “blind” block term decomposition (BBTD) of high order tensors, in which the block diagonal structure of the core tensor is unknown. Our contributions include: 1) We establish the necessary and sufficient conditions for the existence of BTD, characterize the condition when a BTD solves the BBTD problem, and show that the BBTD is unique under a “low rank” assumption. 2) We propose an algebraic method to compute the BBTD. This method transforms the problem of determining the block diagonal structure of the core tensor into a clustering problem of complex numbers, in polynomial time. And once the clustering problem is solved, the BBTD can be obtained via computing several matrix decompositions. Numerical results show that our method is able to compute the BBTD, even in the presence of noise to some extent, whereas optimization based methods (e.g., MINF and NLS in TENSORLAB) may fail to converge.
Fabrication and Characterization of Al/Methyl Orange/n-Si Heterojunction Diode

Herein, the organic semiconductor methyl orange (MO), is investigated for the first time for its el ectronic applications. For this purpose, Al/MO/n-Si heterojunction is fabr icated through economical cheap and simple "drop casting" technique. The current- voltage (I-V) measurements of the device are made at room temperature under dark conditions. The I-V characteristics of Al/MO/n-Si junction exhibits asymmetrical and recti fying behavior that confirms the formation of diode. The diode par ameters such as rectification ratio (RR), turn on voltage (V turn on), reverse saturation current (I0), ideality factor (n), barrier height ( b φ ), series resistance (Rs) and shunt resistance ( Rsh) are determined from I-V curves using Schottky equations. These values of these parameter s are also extracted and verified by applying Cheung's functio ns. The conduction mechanisms are explained from the forward bias I-V characteristics using the power law. Keywords—Electrical properties, Organic/inorganic heterojunction diode, Methyl Orange, Cheungs Functions
Characterization of Noise Like Activity of A Spiking Neuron: A Signal Processing Perspective

Several classical models are proposed in computational neuroscienc to emulate the response of a spiking neuron. Some researchers have observed a random component in the membrane potential which is known as noise like activity (NLA) of a neuron. However, complete modeling of this noise like activity is still an important issue. Attempts have been made in this paper to characterize such NLA from experimental data provided by Allen Institute for Brain Science. Elementary signal processing techniques are used to highlight the nature and properties of the NLA associated with a spiking neuron. Both time and frequency domain properties are investigated. It is asserted that NLA is a low amplitude and low frequency (0 to 100 Hz) random signal. Interestingly, instantaneous variance of NLA seems to increase non-linearly with rise in membrane potential. It has also been observed that probability of firing a spike for a given membrane potential follows an exponential trend in presence of NLA.
A Novel Embedded Long-Range Radar and Dome Camera Linkage System

In this paper, we proposed an embedded longrange radar and dome camera linkage system, which consists of equipment calibration, accurate snapshot, global tracking and target recognition. The phased-array radar is used to guide the high-precision laser dome camera to track the target visually and snapshot when the camera gets best angle. Based on the improved YOLOv3 model, target recognition was carried out and transplanted to the embedded AI analysis box. The system can meet the needs of target discovery, tracking, capturing and identification within 1 kilometer. It is of great value to make up for the defects of existing technology such as short protection distance, poor effect at night and reduce the cost of unit distance protection.
Wearable Metasurface-Enabled Quasi-Yagi Antenna for UHF RFID Reader With End-Fire Radiation Along the Forearm

We present a quasi-Yagi antenna mounted on a periodic surface for a wearable UHF RFID reader operating in the UHF RFID frequency band centered at 915 MHz. The periodic surface was co-optimized with the antenna to enhance the launching of surface waves to enable the end-fire radiation along the forearm so that a user can identify objects by pointing her/his hand towards them. In addition to the radiation pattern modification, the ground plane of the periodic surface serves the second purpose of isolating the antenna from the human body. We optimized the antenna in a full-wave EM simulator using a simplified cylindrical model of the forearm and in the simulation, it achieved the end-fire directivity of 5.9 dBi along the forearm. In the wireless testing, the quasi-Yagi antenna provided the read range of 3.8 m for a typical UHF RFID tag having 0 dBi gain when the reader’s output power was 32 dBm that corresponds with EIRP =0.56 W and SAR =0.191 W/kg in our simulations. Considering both, the RFID emission regulations with EIRP = 3.28 W or 4 W and the SAR limit of 1.6 W/kg averaged over 1 gram of tissue, the read range could be further enhanced for reader units with higher output power.
Miniaturized Dual-Band Power Dividers Based on Conjugately Characteristic-Impedance Transmission Lines (CCITLs)

A miniaturized dual-band power divider for microwave frequencies has been developed. The present design utilizes the two-section impedance transformers implemented by the conjugately characteristic-impedance transmission lines (CCITL) to obtain size reduction with dual-band characteristic. The properly designed CCITL can achieve shorter electrical length than the normal transmission line (TL) to provide the same impedance transformation characteristic. Approximately one-fourth size reduction has been achieved by the proposed technique with reasonable results of scattering parameters, as compared with the similar design using the two-section impedance transformers but realized by the normal TLs.
Minutiae Attention Network With Reciprocal Distance Loss for Contactless to Contact-Based Fingerprint Identification

Interoperability between contactless and conventional contact-based fingerprint recognition systems is fundamental for the success of emerging contactless fingerprint technologies which are highly sought, especially due to current pandemic. However, image formation differences and acquisition distortions between these two modalities pose significant challenges for such interoperability. In order to address these challenges, this paper presents a minutiae attention network with Siamese architecture and the reciprocal distance loss function to enable more accurate contactless to contact-based fingerprint identification. The proposed network contains two branches, a global-net branch to recover global features and a minutiae attention branch that focuses on the local minutiae areas. Attention mechanism is introduced to guide the minutiae attention branch to concentrate on distorted areas and recover minutiae/features correspondence for contactless and contact-based fingerprint images from the same fingers. Meanwhile, reciprocal distance loss is specifically designed to impose strong penalty towards contactless and contact-based fingerprint images from different fingers and guide the network to learn robust features for distinguishing identities. Experimental results on two publicly available databases illustrate significant performance improvements, over state-of-art methods in the literature, and validate the effectiveness of the proposed framework for the contactless to contact-based fingerprint identification.
Answer Quality Evaluation of Social Q&A Platforms Based on Feature Filtering

At present, social Q&A platforms mainly evaluate and rank the quality of answers based on the user voting mechanism, and lack an objective answer quality evaluation mechanism. This leads to the behavior of some users can let some low-quality answers be ranked higher, which affects users' access to relevant knowledge. Based on this phenomenon, our study proposes an answer quality evaluation model based on a text feature filtering mechanism. It distinguishes high-quality answers from general answers by quantifying various text features, ranking the importance of those text features, and assigning weights to them. The experimental results from 49758 answers to the Zhihu website demonstrate that the proposed model can be more accurately filter and predict high-quality answers to users than the state-of-the-art model.
Dense Vector Embedding Based Approach to Identify Prominent Disseminators From Twitter Data Amid COVID-19 Outbreak

The unprecedented transmission of the Coronavirus COVID-19 across the globe has grown to be a matter of prime concern for researchers, authorities, and healthcare professionals alike. Owing to the unavailability of vaccination, educating people is reckoned to be of utmost importance to mitigate the risk. With a plethora of unstructured data available on social media, it becomes crucial to comprehend information and use it effectively to combat COVID-19. A fine-grained knowledge base could be advantageous in developing a reliable social network for pandemic situations. However, there has been no prior finding related to the identification of disseminators forCOVID-19 and hence, there is a need to build a computationally intelligent system that utilizes the potential of a massive amount of data to disseminate information more effectively. In this work, we gathered Twitter data of 3.2 million unique users, consisting of over 12 million tweets. We divided our work into four parts. Firstly, by employing dense vector embedding, one of the techniques of the neural network, to generate semantically similar keywords. Secondly, we classified the collected data into three awareness categories i.e., information, prevention, and action. Thereafter, we used the statistical physics of complex networks to recognize prominent disseminators w.r. t. the identified categories. Finally, we sub-categorized the prominent disseminators into media, people, and organizations based on their profile information. From the result, we concluded that data generated broadly fall into information and prevention categories, whereas the print media, politicians, and health organizations are the forerunners of the selected prominent disseminators.