Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Blind Compressed Sensing Under Signal-Dependent Noise

In this paper, we consider the problem of nonlinear blind compressed sensing, i.e. jointly estimating the sparse codes and sparsity-promoting basis, under signal-dependent noise. We focus our efforts on the Poisson noise model, though other signal-dependent noise models can be considered. By employing a well-known variance stabilizing transform such as the Anscombe transform, we formulate our task as a nonlinear least squares problem with the ℓ1 penalty imposed for promoting sparsity. We solve this objective function under non-negativity constraints imposed on both the sparse codes and the basis. To this end, we propose a multiplicative update rule, similar to that used in non-negative matrix factorization (NMF), for our alternating minimization algorithm. To the best of our knowledge, this is the first attempt at a formulation for nonlinear blind compressed sensing, with and without the Poisson noise model. Further, we also provide some theoretical bounds on the performance of our algorithm.
A Functional Language for Specifying Business Reports

We describe our work on developing a functional domain speciﬁc language for specifying business reports. The report speciﬁcation language is part of a novel enterprise resource planing system based on the idea of a providing a lean core system that is highly customisable via a variety of domain spe-ciﬁc languages.
Localization of RFID Tags by a Moving Robot, via Phase Unwrapping and Non-Linear Optimization

In this paper, we propose a new method for the localization of RFID tags, by deploying off-the-shelf RFID equipment on a robotic platform. The constructed robot is capable to perform Simultaneous Localization (of its own position) and Mapping (SLAM) of the environment and then locate the RFID tags around its path. The proposed method is based on properly treating the measured phase of the backscattered signal by each tag at the reader’s antenna, located on top of the robot. More specifically, the measured phase samples are reconstructed, such that the $2\pi $ discontinuities are eliminated (phase-unwrapping). This allows for the formation of an optimization problem, which can be solved rapidly by standard methods. The proposed method is experimentally compared against the SAR/imaging methods, which represent the accuracy benchmark in prior-art, deploying off-the-shelf equipment. It is shown that the proposed method solves exactly the same problem as holographic-imaging methods, overcoming the grid-density constraints of the latter. Furthermore, the problem, being calculations-grid-independent, is solved orders of magnitude faster, allowing for the applicability of the method in real-time inventorying and localization. It is also shown that the state-of-the-art SLAM method, which is used for the estimation of the trace of the robot, also suffers from errors, which directly affect the accuracy of the RFID localization method. Deployment of reference RFID tags at known positions, seems to significantly reduce such errors.
Command Shaped Robust Control of a Flexible Spacecraft

In this paper a model of a hypothetical flexible spacecraft that resembles French multi mission satellite SPOT is selected. This model represents a multivariable system that is difficult to control due to strong cross couplings. In order to get a good insight, initially analysis of the model is carried out from the perspective of control design challenge peculiar to this system. Later a systematic multivariable robust controller is designed based on  H Loop Shaping Design Procedure. The designed controller is able to stabilize the overall system but is unable to minimize oscillations in structure that cause fine pointing issues. To remove the problem of small amplitude oscillations for this design, input command shaping technique is applied that is able to effectively minimize oscillations. Finally, robustness and performance of the designed controller is demonstrated through numerical simulations. 
A Fuzzy-Based Approach and Adaptive Genetic Algorithm in Multi-Criteria Recommender Systems

A R T I C L E I N F O A B S T R A C T Article history: Received: 25 April, 2019 Accepted: 29 July, 2019 Online: 16 August, 2019 Recommender Systems (RSs) are termed as web-based applications that make use of filtering methods and several machine learning algorithms to suggest relevant user objects. It can be said that some techniques are usually adopted or trained to develop these systems that generate lists of suitable recommendations. Conventionally, RS uses a single rating approach to preference user recommendation over an item. Recently, multi-criteria technique has been identified as a new approach of recommending user items based on several attributes or features of user items. This new technique of item recommendation has been adopted to solve several recommendation problems compared to the single rating approach. Furthermore, the predictive performance of the multi-criteria technique when tested proves to be further efficient as compared to the traditional single ratings approach. This paper gives a comparative study between two models that are based on the features and architecture of fuzzy sets system and adaptive genetic algorithm. Genetic Algorithms (GAs) are robust and stochastic search techniques centered on natural selection and evaluation that are often applied when encountering optimization problems. Fuzzy logic (FL) on the other hand, is known for its wide application in diverse fields in science. This study aims to evaluate, analyze, and compare the predictive performance of both methods and present their results. The study has been accomplished using Yahoo! Movies dataset, and the results of the performance of each model have been presented in this paper. The results proved that both techniques have significantly enhanced the system’s accuracy.
Functional Decoding using Convolutional Networks on Brain Graphs

A key goal in neuroscience is to understand brain mechanisms of cognitive functions. An emerging approach is the study of brain states dynamics using functional magnetic resonance imaging (fMRI). In this project, we applied graph convolutional networks (GCN) to decode brain activity over short time windows in a task fMRI dataset, i.e. associate a given window of fMRI time series with the task used. We investigated the performance of this GCN ”cognitive state annotation” in the Human Connectome Project (HCP) database, which features 21 different experimental conditions spanning seven major cognitive domains, and high temporal resolution in task fMRI data. Using a 10-second window, the 21 cognitive states were identified with an excellent average test accuracy of 92% (chance level 4.8%). Performance remained good (60%) even at a temporal resolution of one volume (720 ms of duration). As the HCP task battery was designed to selectively activate a wide range of specialized functional networks, we anticipate the GCN annotation to be applicable over a broad range of paradigms, including resting-state.
Orientation representations in convolutional neural networks are more discriminable around the cardinal axes

Convolutional neural networks (CNNs) share some similarity in representational structure to the primate ventral visual stream, however less is known about whether low-level visual features are represented in the same way by CNNs and the brain. Here, we focus on orientation perception, a well-understood aspect of the primate visual system. We asked whether convolutional neural networks trained to perform object recognition on a natural image database would exhibit an “oblique effect” such that cardinal (vertical and horizontal) orientations are represented with higher precision than oblique (diagonal) orientations, as has been measured in the primate brain. We obtained activation patterns from two networks (NASnet and Inception-V3) presented with oriented grating stimuli, and used a Euclidean distance metric to measure the discriminability between patterns corresponding to different pairs of orientations. In agreement with human perception, we find that the discriminability of representations generally peaks around the cardinal axes. This finding suggests that cardinality effects in human visual perception are not dependent on a hard-wired anatomical bias, but can instead emerge through experience with the statistics of natural images.
An Energy-Aware Bit-Serial Streaming Deep Convolutional Neural Network Accelerator

Over the past decade, deep convolutional neural networks (CNN) have been widely embraced in various visual recognition applications owing to their extraordinary accuracy. However, their high computational complexity and excessive data storage present two challenges when designing CNN hardware. We propose an energy-aware bit-serial streaming deep CNN accelerator to tackle these challenges. With several optimization methods and the proposed ring streaming dataflow, the computational performance is improved and the external memory access is reduced. We evaluate our design with the roofline model by exploring the design space to find the best performance and computation and communication (CTC) ratio solution. We can reach 1.36x speed up and reduce energy consumption by 41% for external memory access compared with the design in [1]. The hardware implementation for our PE Array architecture design can reach the operating frequency of 119 MHz and consumes 68 k gates with a power consumption of 10.08mW using TSMC 90-nm technology.
Revising ISO-Space for the Semantic Annotation of Dynamic Spatial Information in Language

ISO-Space refers to the ISO-Space (2014b) annotation standard for spatial information in language. Soon after its publication, Lee (2016) and Pustejovsky and Lee (2017) noted its failure to conform to the principles of semantic annotation laid down in the principles of semantic annotation (ISO 2016) and also its inadequacy to capture the dynamic aspect of spatial information involving trajectories created by motions. This paper thus proposes to revise ISO-Space to focus on the dynamic aspect of spatial information, while making the revised version, tentatively named ”I-Space”, conform to the ISO (2016) principles of semantic annotation. For this revision, four modifications are introduced. First, event-paths are formally defined as sequences of spatio- temporal locations associated with motional trajectories. Second, these trajectories are treated as genuine spatial entities of the event-path type in their annotation structures. Third, the link, tagged , in ISO-Space is re-formulated as a relation between a moving object and an event-path traversed by that object. Fourth, the spatial measure link, tagged , in ISO-Space is generalized to cover spatiotemporal measures involving both space and time, and motions.
Design Adaptive Fuzzy Sliding Mode Controller for Pantograph Mechanism Apply to Massage Therapy Robot for Healthcare

This paper proposes an adaptive fuzzy sliding mode controller (AFSMC) for pantograph haptic device. It has form mechanism with a 2-DoF redundancy actuated parallel robotic manipulator. An adaptive single input single output (SISO) fuzzy system is applied to calculate each element of the control gain vector in a sliding mode controller for purport to reject phenomenon chattering by the constant value of K and discontinuous function. The adaptive laws are designed based on the Lyapunov stability theory method. The adaptive laws are practiced online by fuzzy controller combine with the sliding mode control (SMC) to control stability the robot manipulator despite external forces disturbance. Many operation situations such as set point control also the trajectory control are simulated also experiment to demonstrate the operation controllers are good working.
Computing Sound Space: World-centered Sound Localization in Ferrets

The ability to localize sounds is central to healthy hearing. We can perceive sound location in multiple coordinate systems including those defined by the observer (e.g. “the phone is on my right”) or by the environment (e.g. “the phone is in the office”). Although we can describe sound locations in multiple spaces, the coordinate frames in which non-human animals can perceive sounds remains unclear. Here, we designed a task that required subjects (ferrets) to report the location of sounds in the world across changes in head pose. We developed simulations of the task using world-centered (allocentric) or head-centered (egocentric) models of spatial processing, and compared model predictions to animal behavior. We found that observed behavior most closely matched performance of allocentric models, indicating that subjects solved the task using a world-centered strategy. Our findings indicate that ferrets, like humans, can perceive allocentric sound space and thus abstract sound location beyond momentary head-centered acoustic cues.
Semi-Supervised Semantic Role Labeling with Cross-View Training

The successful application of neural networks to a variety of NLP tasks has provided strong impetus to develop end-to-end models for semantic role labeling which forego the need for extensive feature engineering. Recent approaches rely on high-quality annotations which are costly to obtain, and mostly unavailable in low resource scenarios (e.g., rare languages or domains). Our work aims to reduce the annotation effort involved via semi-supervised learning. We propose an end-to-end SRL model and demonstrate it can effectively leverage unlabeled data under the cross-view training modeling paradigm. Our LSTM-based semantic role labeler is jointly trained with a sentence learner, which performs POS tagging, dependency parsing, and predicate identification which we argue are critical to learning directly from unlabeled data without recourse to external pre-processing tools. Experimental results on the CoNLL-2009 benchmark dataset show that our model outperforms the state of the art in English, and consistently improves performance in other languages, including Chinese, German, and Spanish.
Fitness Based Layer Rank Selection Algorithm for Accelerating Cnns by Candecomp/Parafac (CP) Decompositions

We present the Fitness Based Layer Rank Selection (FLRS) Algorithm for Accelerating Convolutional Neural Networks by CANDECOMP/PARAFAC (CP) Decompositions. FLRS selects the layers and corresponding ranks based on a parameter fitness factor. The advantage of the proposed FLRS algorithm is that it does not require retraining iteratively during rank selection. The experimental results show that VGG-16 Network can be replaced by an approximate network where the convolutional layers are replaced by a sequence of four convolutional layers with smaller kernels. The approximated network has less than one-fifth of the original model parameters and performs less than one-fifth of the total number of computations as compared to the original model with an accuracy drop of less than 1% across SVHN, CIFAR-10 and CALTECH-101 datasets.
Sigara Alışkanlığının HbA1c Üzerine Etkisinin Değerlendirilmesi

Amac: Sigara aliskanliginin, glikolize hemoglobin degeri olan HbA1c uzerine etkisinin degerlendirilmesi amaclanmistir. Materyal ve Metot: Calismamiz, Aile Hekimligi Poliklinigi’ne basvuran ve HbA1c degeri olculmus olan 204 kisiye; sigara icme durumu ve kisisel bilgiler iceren anket uygulanarak yapilmistir. Hastalarin vucut kitle indeksleri hesaplanmis ve sigara icenlere Fagerstrom Nikotin Bagimlilik Testi uygulanmistir. Bulgular: Calismamiza 101(%49,5) erkek, 103(%50,5) kadin hasta olmak uzere 204 kisi dahil edildi. Katilimcilardan 69(%33,8) kisi sigarayi birakmis, 63(%30,9) kisi sigara icmekte, 72(%35,3) kisi ise hic sigara icmemisti. Hastalarin ortalama yasi sigara birakanlarda 34,84(±12,53) yas, sigara icenlerde 35,83(±14,70) yas, sigara hic icmemisler icin 41,75(±13,84) yas olarak bulundu. Cinsiyet ile HbA1c iliskisine baktigimizda, erkeklerin HbA1c degerlerinin kadinlardan daha yuksek oldugu (p<0,05); yasa gore degerlendirildiginde, yas arttikca HbA1c degerlerinin de arttigi saptandi (p<0,05). Sigara icenlerde ve birakanlarda, hic icmemis olanlara gore HbA1c degerlerinin anlamli derecede yuksek oldugu bulundu (p<0,001). Sonuc: Calismamiza gore; sigara aliskanligi, HbA1c degerini arttirmakta dolayisiyla basta diabetes mellitus hastalari olmak uzere hastalarda degistirilebilir risk faktoru olarak onem arz etmektedir. Diabetes mellitus gelisimini onlemek ve kan glukoz regulasyonunu saglamak icin hastalarin sigara icmeyi birakmasi hedeflenmelidir.
Identification of Persons by Fingerprint using Huffman Coding Algorithm

Fingerprint system is one of the most common biometric features used for personal identification and verification. There are many techniques used for finding the features of fingerprint when it matches the other images of fingerprint. This system must input the fingerprint for the corresponding profile. It converts the binary image. This image will convert the Huffman code approach and to generate codes for input fingerprint. The system has the profile information along with their associated fingerprints. When the system identified the fingerprint, the system will display the associated profile information for the corresponding image. Huffman compression technique was the most accurate technique for compression of fingerprint image.
Fast Partitioning Decision Strategies for The Upcoming Versatile Video Coding (VVC) Standard

One of the key technologies in the upcoming Versatile Video Coding (VVC) standard is the flexible partitioning. Compared to HEVC, partitioning alone accounts for about 8.5% bitrate savings. The added flexibility comes at a cost of large search space expansion.This paper gives an overview of fast encoder strategies for selecting the block partitioning in the Test Model VTM-3.0 for VVC. In total, 13 approaches for speeding up the block partitioning selection in an encoder are described; 8 of these techniques were developed by the authors. 10 of the 13 methods are used in the common test conditions of the Joint Video Experts Team (JVET); these 10 methods achieve an overall speed-up factor of 7 at a bit rate increase of only 1.1%. By enabling 12 approaches, a speed up factor of over 8.5 is obtained at a bit rate increase of about 1.5%.
A Fully-Connected Ising Model Embedding Method and Its Evaluation for CMOS Annealing Machines

SUMMARY Combinatorial optimization problems with a large solu-tion space are di ﬃ cult to solve just using von Neumann computers. Ising machines or annealing machines have been developed to tackle these problems as a promising Non-von Neumann computer. In order to use these annealing machines, every combinatorial optimization problem is mapped onto the physical Ising model , which consists of spins, interactions between them, and their external magnetic ﬁelds. Then the annealing machines operate so as to search the ground state of the physical Ising model, which corresponds to the optimal solution of the original combinatorial optimization problem. A combinatorial optimization problem can be ﬁrstly described by an ideal fully-connected Ising model but it is very hard to embed it onto the physical Ising model topology of a particular annealing machine, which causes one of the largest issues in annealing machines. In this paper, we propose a fully-connected Ising model embedding method targeting for CMOS annealing machine. The key idea is that the proposed method replicates every logical spin in a fully-connected Ising model and embeds each logical spin onto the physical spins with the same chain length. Experimental results through an actual combinatorial problem show that the proposed method obtains spin embeddings superior to the conventional de facto standard method, in terms of the embedding time and the probability of obtaining a feasible solution.
Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model

Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages, which may benefit many knowledge-driven applications. It is challenging due to the heterogeneity of KGs and limited seed alignments. In this paper, we propose a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model (KECG). It can make better use of seed alignments to propagate over the entire graphs with KG-based constraints. Specifically, as for the knowledge embedding model, we utilize TransE to implicitly complete two KGs towards consistency and learn relational constraints between entities. As for the cross-graph model, we extend Graph Attention Network (GAT) with projection constraint to robustly encode graphs, and two KGs share the same GAT to transfer structural knowledge as well as to ignore unimportant neighbors for alignment via attention mechanism. Results on publicly available datasets as well as further analysis demonstrate the effectiveness of KECG. Our codes can be found in https: //github.com/THU-KEG/KECG.
Extending LBP and Convolution-Like Operations on the Mesh

Extending the concept of texture to the geometry of a mesh manifold surface is an emerging topic in image processing. This concept is different from gluing images to the surface, but rather indicates the presence of relief patterns that locally change the surface geometry, showing some regular and repetitive pattern. In this paper, we propose an efficient and effective framework to address this novel task, which encompasses the convolution operation and the casting of a variety of Local Binary Pattern on the mesh manifold. Results show that our technique outperforms the existing state-of-the-art methods in the challenging task of relief patterns classification.
What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons

Memory neurons of long short-term memory (LSTM) networks encode and process information in powerful yet mysterious ways. While there has been work to analyze their behavior in carrying low-level information such as linguistic properties, how they directly contribute to label prediction remains unclear. We find inspiration from biologists and study the affinity between individual neurons and labels, propose a novel metric to quantify the sensitivity of neurons to each label, and conduct experiments to show the validity of our proposed metric. We discover that some neurons are trained to specialize on a subset of labels, and while dropping an arbitrary neuron has little effect on the overall accuracy of the model, dropping label-specialized neurons predictably and significantly degrades prediction accuracy on the associated label. We further examine the consistency of neuron-label affinity across different models. These observations provide insight into the inner mechanisms of LSTMs.
Simplex Method for Solving Maximum Problems in Linear Programming

In this paper, the simplex method in linear programming is discussed for solving maximum problems with constraints. The simplex method is a general mathematical solution technique for solving linear programming problems. In the simplex method, the model is put into the form of a table, and then a number of mathematical steps are performed on the table. This simplex method is an algebraic procedure in which a series of repetitive operations are used to reach the optimal solution.