Miracle-dz/UniFAR-FTUs huggingface dataset:
The large space of information structures

We revisit the question of modeling incomplete information among 2 Bayesian players, following an ex-ante approach based on values of zero-sum games. K being the finite set of possible parameters, an information structure is defined as a probability distribution u with finite support over K × N × N with the interpretation that: u is publicly known by the players, (k, c, d) is selected according to u, then c (resp. d) is announced to player 1 (resp. player 2). Given a payoff structure g, composed of matrix games indexed by the state, the value of the incomplete information game defined by u and g is denoted val(u, g). We evaluate the pseudo-distance d(u, v) between 2 information structures u and v by the supremum of |val(u, g) − val(v, g)| for all g with payoffs in [−1, 1], and study the metric space Z * of equivalent information structures. We first provide a tractable characterization of d(u, v), as the minimal distance between 2 polytopes, and recover the characterization of Peski (2008) for u v, generalizing to 2 players Blackwell's comparison of experiments via garblings. We then show that Z * , endowed with a weak distance d W , is homeomorphic to the set of consistent probabilities with finite support over the universal belief space of Mertens and Zamir. Finally we show the existence of a sequence of information structures, where players acquire more and more information, and of e > 0 such that any two elements of the sequence have distance at least e : having more and more information may lead nowhere. As a consequence, the completion of (Z * , d) is not compact, hence not homeomorphic to the set of consistent probabilities over the states of the worldaworlda la Mertens and Zamir. This example answers by the negative the second (and last unsolved) of the three problems posed by J.F. Mertens in his paper "Repeated Games", ICM 1986.
Training Object Detectors on Synthetic Images Containing Reflecting Materials

One of the grand challenges of deep learning is the requirement to obtain large labeled training data sets. While synthesized data sets can be used to overcome this challenge, it is important that these data sets close the reality gap, i.e., a model trained on synthetic image data is able to generalize to real images. Whereas, the reality gap can be considered bridged in several application scenarios, training on synthesized images containing reflecting materials requires further research. Since the appearance of objects with reflecting materials is dominated by the surrounding environment, this interaction needs to be considered during training data generation. Therefore, within this paper we examine the effect of reflecting materials in the context of synthetic image generation for training object detectors. We investigate the influence of rendering approach used for image synthesis, the effect of domain randomization, as well as the amount of used training data. To be able to compare our results to the state-of-the-art, we focus on indoor scenes as they have been investigated extensively. Within this scenario, bathroom furniture is a natural choice for objects with reflecting materials, for which we report our findings on real and synthetic testing data.
Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches

We propose a Bayesian framework for the received-signal-strength-based cooperative localization problem with unknown path loss exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path loss exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. For numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. An important ingredient of the proposed auxiliary importance sampler is the ability to sample from a normalized likelihood function. To this end, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The proposed message passing algorithms are analyzed systematically in terms of computational complexity, demonstrating the computational efficiency of the proposed auxiliary importance sampler. Various simulations are conducted to validate the overall good performance of the proposed algorithms.
Harvesting Visual Objects from Internet Images via Deep-Learning-Based Objectness Assessment

The collection of internet images has been growing in an astonishing speed. It is undoubted that these images contain rich visual information that can be useful in many applications, such as visual media creation and data-driven image synthesis. In this article, we focus on the methodologies for building a visual object database from a collection of internet images. Such database is built to contain a large number of high-quality visual objects that can help with various data-driven image applications. Our method is based on dense proposal generation and objectness-based re-ranking. A novel deep convolutional neural network is designed for the inference of proposal objectness, the probability of a proposal containing optimally located foreground object. In our work, the objectness is quantitatively measured in regard of completeness and fullness, reflecting two complementary features of an optimal proposal: a complete foreground and relatively small background. Our experiments indicate that object proposals re-ranked according to the output of our network generally achieve higher performance than those produced by other state-of-the-art methods. As a concrete example, a database of over 1.2 million visual objects has been built using the proposed method, and has been successfully used in various data-driven image applications.
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds With Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields

Deep learning techniques have become the to-go models for most vision-related tasks on 2D images. However, their power has not been fully realised on several tasks in 3D space, e.g., 3D scene understanding. In this work, we jointly address the problems of semantic and instance segmentation of 3D point clouds. Specifically, we develop a multi-task pointwise network that simultaneously performs two tasks: predicting the semantic classes of 3D points and embedding the points into high-dimensional vectors so that points of the same object instance are represented by similar embeddings. We then propose a multi-value conditional random field model to incorporate the semantic and instance labels and formulate the problem of semantic and instance segmentation as jointly optimising labels in the field model. The proposed method is thoroughly evaluated and compared with existing methods on different indoor scene datasets including S3DIS and SceneNN. Experimental results showed the robustness of the proposed joint semantic-instance segmentation scheme over its single components. Our method also achieved state-of-the-art performance on semantic segmentation.
Fully Learnable Group Convolution for Acceleration of Deep Neural Networks

Benefitted from its great success on many tasks, deep learning is increasingly used on low-computational-cost devices, e.g. smartphone, embedded devices, etc. To reduce the high computational and memory cost, in this work, we propose a fully learnable group convolution module (FLGC for short) which is quite efficient and can be embedded into any deep neural networks for acceleration. Specifically, our proposed method automatically learns the group structure in the training stage in a fully end-to-end manner, leading to a better structure than the existing pre-defined, two-steps, or iterative strategies. Moreover, our method can be further combined with depthwise separable convolution, resulting in 5 times acceleration than the vanilla Resnet50 on single CPU. An additional advantage is that in our FLGC the number of groups can be set as any value, but not necessarily 2^k as in most existing methods, meaning better tradeoff between accuracy and speed. As evaluated in our experiments, our method achieves better performance than existing learnable group convolution and standard group convolution when using the same number of groups.
Exponentially stable solution of mathematical model based on graph theory of agents dynamics on time scales

In this paper an emergence of leader-following model based on graph theory on the arbitrary time scales is investigated. It means that the step size is not necessarily constant but it is a function of time. We propose and prove conditions ensuring a leader-following consensus for any time scales using Gronwall inequality. The presented results are illustrated by examples.
Dynamic control of an Euler–Bernoulli equation with time-delay and disturbance in the boundary control

ABSTRACT The boundary control problem of a cantilever Euler– Bernoulli is considered in this paper. If the control at the right end of the beam is of the form w xxx (1, t) = u(t − τ) + r(t), where τ > 0 is the input time-delay and r(t) is an unknown external disturbance, a dynamic feedback control strategy based on the methods of partial state predictor and active disturbance rejection control is used to stabilise the system. Under some assumptions on r(t), it is proven that the state of the system exponentially converges to and stays in the compact set . The radius ϵ is determined by the time-delay τ and the properties of r(t). The simulations are provided to compare the influence of τ and r(t) on the radius ϵ.
Exploiting SIFT Descriptor for Rotation Invariant Convolutional Neural Network

This paper presents a novel approach to exploit the distinctive invariant features in convolutional neural network. The proposed CNN model uses Scale Invariant Feature Transform (SIFT) descriptor instead of the max-pooling layer. Max-pooling layer discards the pose, i.e., translational and rotational relationship between the low-level features, and hence unable to capture the spatial hierarchies between low and high level features. The SIFT descriptor layer captures the orientation and the spatial relationship of the features extracted by convolutional layer. The proposed SIFT Descriptor CNN, therefore, combines the feature extraction capabilities of CNN model and rotation invariance of SIFT descriptor. Experimental results on the MNIST and fashionMNIST datasets indicate reasonable improvements over conventional methods available in literature.
Fuzzy Logic based Improved P&O MPPT Technique for Partial Shading Conditions

This paper presents a hybrid method of maximum power point tracking (MPPT) under partial shading conditions for photovoltaic (PV) power generators. The perturb and observe (P&O) algorithm is one of extensively used scheme for MPPT, but in case of partial shading conditions, the P-V curve have multiple peaks and this conventional method may not converge to the global maximum power point (GMPP). Here, whole search space of MPP is divided into three regions and a fuzzy logic controller has been proposed to identify the region of GMPP. After identification P&O algorithm is used to track the GMPP. Detailed analysis of the proposed scheme is done using MATLAB/Simulink software.
A Neural Network Approach to Score Fusion for Emotion Recognition

Automatic facial emotion recognition has been one of the interesting research topics in the recent decades. There have been recent advances in convolutional neural networks (CNNs) which have become the state-of-the-art approaches in pattern recognition. This paper presents an effective facial emotion recognition system that uses convolutional neural networks (GoogleNet-CNN) for eyeglass detection and feature extraction followed by a novel score fusion model. The proposed system has three key components for improving recognition performance: 1) a highly accurate glasses detector to differentiate between images in which the human subject is wearing glasses and images in which the human subject is not wearing glasses, 2) convolutional neural networks to extract nine different sets of emotional features from faces with and without glasses, which are then classified by support vector machines (SVMs), 3) a multiple classifier system (MCS) to accomplish decision fusion by using a neural network. The USTC-NVIE (NVIE) database is used to evaluate the performance of the proposed system. Experimental results show that the eyeglass detector obtained a best overall accuracy of 99.7% and the proposed facial emotion recognition system can achieve 7-15% higher classification rates when using the eyeglass detector. By applying the neural network approach in the multiple classifier system for score fusion, the classification rates of the system increase by about 10%.
On the SPRification of Linear Descriptor Systems via Output Feedback

We consider input–output systems in descriptor form and ask when such systems can be rendered strictly positive real (SPR) via output feedback. Time- and frequency-domain conditions are given to determine when and how this is possible. In addition, a synthesis procedure for controller design is also derived. Together, the results provide a complete answer to when a linear descriptor system can be made SPR via output feedback, and provide a recipe for design of a feedback controller when one exists.
Equilibrium-Independent Dissipativity With Quadratic Supply Rates

Equilibrium-independent dissipativity (EID) is a recently introduced system property that requires a system to be dissipative with respect to any forced equilibrium configuration. This paper is a detailed examination of EID with quadratic supply rates for a common class of nonlinear control-affine systems. We provide an algebraic characterization of EID for such systems in the spirit of the Hill–Moylan lemma, where the usual stability condition is replaced by an incremental stability condition. Based on this characterization, we state results concerning internal stability, feedback stability, and absolute stability of EID systems. Finally, we study EID for discrete-time systems, providing the relevant definitions and an analogous Hill–Moylan-type characterization. Results for both continuous-time and discrete-time systems are illustrated through examples on physical systems and convex optimization algorithms.
Omega-K Algorithm for Near-Field 3-D Image Reconstruction Based on Planar SIMO/MIMO Array

The characteristics of the range cell migration (RCM) under single-input-multiple-output (SIMO) bi-static geometry are studied and an omega-K algorithm for near-field 3-D imaging based on planar SIMO or multiple-input-multiple-output (MIMO) array is proposed. The RCM and the linear part of the range offset (RO) within SIMO data are corrected by employing a 3-D Stolt transformation. The residual range error is further compensated by phase multiplication and an image-domain interpolation. Reconstruction for a MIMO aperture can be achieved by adding together all the focusing results from its SIMO subarrays. The implementation details of the algorithm are described. The imaging resolution and the sampling requirements for a MIMO aperture are discussed. Since the RO correction is achieved by an approximate way, the spatial limitation for accurate reconstruction is also given. The imaging accuracy and the high efficiency of the algorithm are demonstrated both by simulations and experiments with various distributed targets based on planar MIMO arrays. Real-time 3-D imaging for planar SIMO/MIMO aperture is expected to be achieved by using the algorithm.
Bit Error Rate and Packet Error Rate Analysis of Coded Co-operative Communication with Multiple Relays Under Nakagami-m fading Scenario

Cooperative communication is a relay based model in which multiple communication users share their resources to achieve better overall performance. Bit error rate and Packet error rate of a system give a clear insight of its performance. These are important Quality of Service parameters for a wireless network system.In this paper system performance of coded cooperative communication with multiple relays has been analysed with an approach of improving the Bit Error Rate (BER) and Packet Error Rate (PER). These parameters for coded cooperation under Nakagami-m fading scenario has been evaluated and critically analysed. The Effects of different parameters like co-operational ratio, number of relays, and data rate on BER and PER have been evaluated.
A Nonlinear Guided Filter for Polarimetric SAR Image Despeckling

Despeckling is a fundamental preprocessing step for applications using polarimetric synthetic aperture radar data in most cases. In this paper, a guided filter with nonlinear weight kernels and adaptive filtering windows is presented for PolSAR image despeckling, in which the guidance image is constructed by a weighted average using the statistical traits of the speckled image. The output result is then estimated by another weighted average, with the aid of the fully polarimetric information from both the guidance image and the speckled image. In the experimental part, the filtering results obtained with both simulated and real PolSAR images reveal the positive performance of the proposed method in both reducing speckle and retaining details, when compared with some of the state-of-the-art algorithms. Furthermore, the relatively low computational complexity is another strength of the proposed method.
RCDR: Reliability Control Framework for Data Rate Prediction in Wireless Sensor Networks

Machine learning or popularly known as prediction technique is an application of Artificial Intelligence. It provides network the ability to learn from past experience and make them more efficient. This technique of prediction is being used in diverse fields due to its adaptive nature. In particular, the prediction technique has been proven very useful in the design of reliable Wireless Sensor Networks. This paper proposes a reliability control framework with date rate prediction in Wireless Sensor Network, and provides the community a general view of this vibrant research area. Various, simulations are carried out to generate a primary networking dataset with many performance parameters including the data rate. The data rate is predicted using different prediction techniques. Lastly, to check the robustness of best predictive model, N-fold cross-validation technique is used.
A 1-mW Class-AB Amplifier With −101 dB THD+N for High-Fidelity 16  $\Omega$  Headphones in 65-nm CMOS

A low-power class-AB amplifier for high-fidelity headphones is presented. The linearity of a class-AB amplifier strongly depends on the loop gain over the audio band and voltage swing. In this paper, a cascode-driven mesh technique limits the number of high-swing nodes in the output stage, thereby preserving the loop gain of the preceding gain stages. A frequency compensation scheme is proposed that extends the unity-gain bandwidth (UGB) and increases the loop gain at 20 kHz. Combining these two techniques, the amplifier achieves −101.4-dB total harmonic distortion plus noise (THD+N) over the full audio band, the lowest ever reported nonlinearity among sub-milliwatt CMOS class-AB amplifiers. The amplifier delivers 51.2-mW peak power to a <inline-formula> <tex-math notation="LaTeX">$16\Omega \|0.33$ </tex-math></inline-formula>-nF load while consuming 0.97 mW of total static power. It has UGB of 12.3 MHz and SNR of 108 dB. These results are consistent when measured over eight test chips. Compared to the recent prior art, the amplifier exhibits >12 dB better linearity and >14-dB dynamic range. This results in 2.5<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> improvement in figure of merit (FOM = Peak Load Power/(Quiescent Power <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> THD+N[%])). It occupies 0.16 mm <sup>2</sup> in a standard 65-nm CMOS.
Ultra-Compact Terabit Plasmonic Modulator Array

A new plasmonic transmitter solution offering 0.8 Tbit/s on an ultra-compact 90 µm × 5.5 µm footprint is introduced. It comprises a densely arranged four-channel plasmonic phase modulator array that directly interconnects an optical fiber array. Each plasmonic modulator features high-index grating couplers—for direct and efficient conversion from a fiber mode to a plasmonic slot mode and vice versa—and a plasmonic waveguide—for efficient high-speed modulation. The individual devices achieve data rates of 200 Gbit/s with a symbol rate of 100 GBd. Electrical and optical crosstalk between neighboring modulators were found to have no significant influence on the data modulation experiment. The modulator array has been tested in a 100 GBd experiment with signals at a single wavelength (mimicking a space division multiplexing scheme) and at different wavelengths (mimicking a wavelength division multiplexing experiment).
Improving the Calibration of Suomi NPP VIIRS Thermal Emissive Bands During Blackbody Warm-Up/Cool-Down

The Suomi National Polar-orbiting Partnership Program Visible Infrared Imaging Radiometer Suite (VIIRS) thermal emissive bands (TEB) have been performing well during nominal operations since launch. However, small but persistent calibration anomalies are observed in all TEBs during the quarterly blackbody (BB) warm-up/cool-down (WUCD) events. As a result, the time series of daytime sea surface temperature (SST) (derived from bands M15–M16) show warm spikes on the order of 0.25 K. This paper suggests that VIIRS TEB WUCD biases are band dependent, with daily-averaged biases about −0.04 and 0.05 K for I4 and I5, and −0.05, −0.05, 0.11, 0.09, and 0.05 K for M12–M16, respectively. Two correction methods—Ltrace and WUCD-C—have been implemented and evaluated using colocated observations from the Cross-track Infrared Sounder (CrIS), radiative transfer simulations, and SST retrievals. Also an error in the National Oceanic and Atmospheric Administration operational processing was identified and fixed. Both correction methods effectively minimize WUCD-induced SST anomalies. The Ltrace method works well for I5, M12, and M14–M16, with residual biases about 0.01 K. The WUCD-C method, on the other hand, performs well to correct WUCD biases in all TEBs, with residual biases also about 0.01 K. However, it introduces warm biases relative to CrIS at cold scene temperatures, which requires further study. Applying nonequal BB thermistor weights improves calibration at BB temperature set points, but its impact on daily-averaged WUCD biases is small. The proposed methodologies may also be applied to the VIIRS onboard the follow-on Joint Polar Satellite System satellites.
SLDPC: Towards Second Order Learning for Detecting Persistent Clusters in Data Streams

The main attention of research on data stream clustering algorithms so far has been focused on the adaptation of the algorithms for static datasets to the data streams and improvements of the existing adapted algorithms. Such algorithms fulfil the purpose of the first-order learning from data to clusters. This paper prompts a new question on second-order learning of cluster models from data streams and presents a learning algorithm that detects persistent clusters from consecutive clustering snapshots in data streams. In this work, we first collect a sequence of cluster snapshots as the output clusters at selected query points and then identify the persistent clusters within a given timeframe. The algorithm is evaluated on collections of synthetic datasets. The experimental results have demonstrated the effectiveness of the algorithm in detecting such persistent clusters.