Miracle-dz/UniFAR-FTUs huggingface dataset:
Information-Theoretic Foundations of Mismatched Decoding

Shannon's channel coding theorem characterizes the maximal rate of information that can be reliably transmitted over a communication channel when optimal encoding and decoding strategies are used. In many scenarios, however, practical considerations such as channel uncertainty and implementation constraints rule out the use of an optimal decoder. The mismatched decoding problem addresses such scenarios by considering the case that the decoder cannot be optimized, but is instead fixed as part of the problem statement. This problem is not only of direct interest in its own right, but also has close connections with other long-standing theoretical problems in information theory. In this monograph, we survey both classical literature and recent developments on the mismatched decoding problem, with an emphasis on achievable random-coding rates for memoryless channels. We present two widely-considered achievable rates known as the generalized mutual information (GMI) and the LM rate, and overview their derivations and properties. In addition, we survey several improved rates via multi-user coding techniques, as well as recent developments and challenges in establishing upper bounds on the mismatch capacity, and an analogous mismatched encoding problem in rate-distortion theory. Throughout the monograph, we highlight a variety of applications and connections with other prominent information theory problems.
Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features

On basis of functional magnetic resonance imaging (fMRI), researchers are devoted to designing visual encoding models to predict the neuron activity of human in response to presented image stimuli and analyze inner mechanism of human visual cortices. Deep network structure composed of hierarchical processing layers forms deep network models by learning features of data on specific task through big dataset. Deep network models have powerful and hierarchical representation of data, and have brought about breakthroughs for visual encoding, while revealing hierarchical structural similarity with the manner of information processing in human visual cortices. However, previous studies almost used image features of those deep network models pre-trained on classification task to construct visual encoding models. Except for deep network structure, the task or corresponding big dataset is also important for deep network models, but neglected by previous studies. Because image classification is a relatively fundamental task, it is difficult to guide deep network models to master high-level semantic representations of data, which causes into that encoding performance for high-level visual cortices is limited. In this study, we introduced one higher-level vision task: image caption (IC) task and proposed the visual encoding model based on IC features (ICFVEM) to encode voxels of high-level visual cortices. Experiment demonstrated that ICFVEM obtained better encoding performance than previous deep network models pre-trained on classification task. In addition, the interpretation of voxels was realized to explore the detailed characteristics of voxels based on the visualization of semantic words, and comparative analysis implied that high-level visual cortices behaved the correlative representation of image content.
Description of the asymmetric to symmetric fission transition in the neutron-deficient thorium isotopes: Role of the tensor force

In the present study, we have investigated the impact of the tensor force on fission paths, in particular the symmetric and asymmetric barriers in 230 Th, 226 Th, 222 Th and 216 Th isotopes which display an asymmetric to symmetric fission transition. This analysis has been performed within the HFB approach with (Q 20 ,Q 30 ,Q 40 ) as collective variable constraints, using the D1ST2a Gogny+tensor term interaction and comparing to the standard D1S Gogny interaction results. The effects from the tensor term on the potential energy surface landscape, and especially on barrier heights and its topology by opening a new valley in agreement with experimental data, are found to be crucial in the description of exotic actinide fission. We conclude that a tensor term should be integrated to the long range part of the effective interaction for a better description of the fission.
Giving Up Control: Neurons as Reinforcement Learning Agents

Artificial Intelligence has historically relied on planning, heuristics, and handcrafted approaches designed by experts. All the while claiming to pursue the creation of Intelligence. This approach fails to acknowledge that intelligence emerges from the dynamics within a complex system. Neurons in the brain are governed by local rules, where no single neuron, or group of neurons, coordinates or controls the others. This local structure gives rise to the appropriate dynamics in which intelligence can emerge. Populations of neurons must compete with their neighbors for resources, inhibition, and activity representation. At the same time, they must cooperate, so the population and organism can perform high-level functions. To this end, we introduce modeling neurons as reinforcement learning agents. Where each neuron may be viewed as an independent actor, trying to maximize its own self-interest. By framing learning in this way, we open the door to an entirely new approach to building intelligent systems.
Contractivity for Smoluchowski's coagulation equation with solvable kernels

We show that the Smoluchowski coagulation equation with the solvable kernels K(x,y) equal to 2, x+y or xy is contractive in suitable Laplace norms. In particular, this proves exponential convergence to a self‐similar profile in these norms. These results are parallel to similar properties of Maxwell models for Boltzmann‐type equations, and extend already existing results on exponential convergence to self‐similarity for Smoluchowski's coagulation equation.
R-FORCE: Robust Learning for Random Recurrent Neural Networks

Random Recurrent Neural Networks (RRNN) are the simplest recurrent networks to model and extract features from sequential data. The simplicity however comes with a price; RRNN are known to be susceptible to diminishing/exploding gradient problem when trained with gradient-descent based optimization. To enhance robustness of RRNN, alternative training approaches have been proposed. Specifically, FORCE learning approach proposed a recursive least squares alternative to train RRNN and was shown to be applicable even for the challenging task of target-learning, where the network is tasked with generating dynamic patterns with no guiding input. While FORCE training indicates that solving target-learning is possible, it appears to be effective only in a specific regime of network dynamics (edge-of-chaos). We thereby investigate whether initialization of RRNN connectivity according to a tailored distribution can guarantee robust FORCE learning. We are able to generate such distribution by inference of four generating principles constraining the spectrum of the network Jacobian to remain in stability region. This initialization along with FORCE learning provides a robust training method, i.e., Robust-FORCE (R-FORCE). We validate R-FORCE performance on various target functions for a wide range of network configurations and compare with alternative methods. Our experiments indicate that R-FORCE facilitates significantly more stable and accurate target-learning for a wide class of RRNN. Such stability becomes critical in modeling multi-dimensional sequences as we demonstrate on modeling time-series of human body joints during physical movements.
Coronary Artery Segmentation in Angiographic Videos Using A 3D-2D CE-Net

Coronary angiography is an indispensable assistive technique for cardiac interventional surgery. Segmentation and extraction of blood vessels from coronary angiography videos are very essential prerequisites for physicians to locate, assess and diagnose the plaques and stenosis in blood vessels. This article proposes a new video segmentation framework that can extract the clearest and most comprehensive coronary angiography images from a video sequence, thereby helping physicians to better observe the condition of blood vessels. This framework combines a 3D convolutional layer to extract spatial--temporal information from a video sequence and a 2D CE--Net to accomplish the segmentation task of an image sequence. The input is a few continuous frames of angiographic video, and the output is a mask of segmentation result. From the results of segmentation and extraction, we can get good segmentation results despite the poor quality of coronary angiography video sequences.
Current-Assisted Single Photon Avalanche Diode (CASPAD) Fabricated in 350 nm Conventional CMOS

A current-assisted single-photon avalanche diode (CASPAD) is presented with a large and deep absorption volume combined with a small p-n junction in its middle to perform avalanche trigger detection. The absorption volume has a drift field that serves as a guiding mechanism to the photo-generated minority carriers by directing them toward the avalanche breakdown region of the p-n junction. This drift field is created by a majority current distribution in the thick (highly-resistive) epi-layer that is present because of an applied voltage bias between the p-anode of the avalanching region and the perimeter of the detector. A first CASPAD device fabricated in 350-nm CMOS shows functional operation for NIR (785-nm) photons; absorbed in a volume of 40 × 40 × 14 μm3. The CASPAD is characterized for its photon-detection probability (PDP), timing jitter, dark-count rate (DCR), and after pulsing.
ANNALES DE L’INSTITUT FOURIER

— The main goal of this article is to compute the class of the divisor of M 3 obtained by taking the closure of the image of Ω M 3 (6; − 2) by the forgetful map. This is done using Porteous formula and the theory of test curves. For this purpose, we study the locus of meromorphic diﬀerentials of the second kind, computing the dimension of the map of these loci to M g and solving some enumerative problems involving such diﬀerentials in low genus. A key tool of the proof is the compactiﬁcation of strata recently introduced by Bainbridge–Chen– Résumé. — Le but principal de cet article est de calculer la classe du diviseur de M 3 qu’on obtient en prenant la fermeture de l’image de la strate Ω M 3 (6; − 2) par la fonction qui oublie la diﬀérentielle. Ceci est réalisé via la formule de Porteous et la théorie des courbes test. À cette ﬁn, nous étudions certaines propriétés du lieu des diﬀérentielles méromorphes de seconde espèce, i.e. dont tous les résidus sont nuls. Nous calculons la dimension de l’image de ce lieu dans M g par l’application d’oubli pour tout g et pour toute partition. De plus, nous résolvons certains problèmes énumératifs impliquant ces diﬀérentielles en petit genre. L’outil clef de la preuve est la compactiﬁcation des strates de diﬀérentielles abéliennes récemment introduite par Bainbridge–Chen–Gendron–Grushevsky–Möller.
Latency Minimization for Task Offloading in Hierarchical Fog-Computing C-RAN Networks

Fog-computing network combines the cloud computing and fog access points (FAPs) equipped with mobile edge computing (MEC) servers together to support computation-intensive tasks for mobile users. In this paper, we investigate the delay minimization problem for task offloading in a hierarchical fog-computing C-RAN network, which consists of three tiers of computational services: MEC server in radio units, MEC server in distributed units, and the cloud computing in central units. The receive beamforming vectors, task allocation, computing speed for offloaded tasks in each server and the transmission bandwidth split of fronthaul links are optimized by solving the formulated mixed integer programming problem. The simulation results validate the superiority of the proposed hierarchical fog-computing C-RAN network in terms of the delay performance.
On-the-Fly Adaptation of Source Code Models using Meta-Learning

The ability to adapt to unseen, local contexts is an important challenge that successful models of source code must overcome. One of the most popular approaches for the adaptation of such models is dynamic evaluation. With dynamic evaluation, when running a model on an unseen file, the model is updated immediately after having observed each token in that file. In this work, we propose instead to frame the problem of context adaptation as a meta-learning problem. We aim to train a base source code model that is best able to learn from information in a file to deliver improved predictions of missing tokens. Unlike dynamic evaluation, this formulation allows us to select more targeted information (support tokens) for adaptation, that is both before and after a target hole in a file. We consider an evaluation setting that we call line-level maintenance, designed to reflect the downstream task of code auto-completion in an IDE. Leveraging recent developments in meta-learning such as first-order MAML and Reptile, we demonstrate improved performance in experiments on a large scale Java GitHub corpus, compared to other adaptation baselines including dynamic evaluation. Moreover, our analysis shows that, compared to a non-adaptive baseline, our approach improves performance on identifiers and literals by 44\% and 15\%, respectively.
Solving Raven’s Progressive Matrices with Multi-Layer Relation Networks

Raven’s Progressive Matrices are a benchmark originally designed to test the cognitive abilities of humans. It has recently been adapted to test relational reasoning in machine learning systems. For this purpose the so-called Procedurally Generated Matrices dataset was set up, which is so far one of the most difficult relational reasoning benchmarks. Here we show that deep neural networks are capable of solving this benchmark, reaching an accuracy of 98.0 percent over the previous state-of-the-art of 62.6 percent by combining Wild Relation Networks with Multi-Layer Relation Networks and introducing Magnitude Encoding, an encoding scheme designed for late fusion architectures.
Fashion Landmark Detection and Category Classification for Robotics

Research on automated, image based identification of clothing categories and fashion landmarks has recently gained significant interest due to its potential impact on areas such as robotic clothing manipulation, automated clothes sorting and recycling, and online shopping. Several public and annotated fashion datasets have been created to facilitate research advances in this direction. In this work, we make the first step towards leveraging the data and techniques developed for fashion image analysis in vision-based robotic clothing manipulation tasks. We focus on techniques that can generalize from large-scale fashion datasets to less structured, small datasets collected in a robotic lab. Specifically, we propose training data augmentation methods such as elastic warping, and model adjustments such as rotation invariant convolutions to make the model generalize better. Our experiments demonstrate that our approach outperforms stateof-the art models with respect to clothing category classification and fashion landmark detection when tested on previously unseen datasets. Furthermore, we present experimental results on a new dataset of images where a robot holds different garments, collected in our lab.
Quantum Semantic Learning by Reverse Annealing an Adiabatic Quantum Computer

Boltzmann Machines constitute a class of neural networks with applications to image reconstruction, pattern classification and unsupervised learning in general. Their most common variants, called Restricted Boltzmann Machines (RBMs) exhibit a good trade-off between computability on existing silicon-based hardware and generality of possible applications. Still, the diffusion of RBMs is quite limited, since their training process proves to be hard. The advent of commercial Adiabatic Quantum Computers (AQCs) raised the expectation that the implementations of RBMs on such quantum devices could increase the training speed with respect to conventional hardware. To date, however, the implementation of RBM networks on AQCs has been limited by the low qubit connectivity when each qubit acts as a node of the neural network. Here we demonstrate the feasibility of a complete RBM on AQCs, thanks to an embedding that associates its nodes to virtual qubits, thus outperforming previous implementations based on incomplete graphs. Moreover, to accelerate the learning, we implement a semantic quantum search which, contrary to previous proposals, takes the input data as initial boundary conditions to start each learning step of the RBM, thanks to a reverse annealing schedule. Such an approach, unlike the more conventional forward annealing schedule, allows sampling configurations in a meaningful neighborhood of the training data, mimicking the behavior of the classical Gibbs sampling algorithm. We show that the learning based on reverse annealing quickly raises the sampling probability of a meaningful subset of the set of the configurations. Even without a proper optimization of the annealing schedule, the RBM semantically trained by reverse annealing achieves better scores on reconstruction tasks.
Real-time 3D Deep Multi-Camera Tracking

Tracking a crowd in 3D using multiple RGB cameras is a challenging task. Most previous multi-camera tracking algorithms are designed for offline setting and have high computational complexity. Robust real-time multi-camera 3D tracking is still an unsolved problem. In this work, we propose a novel end-to-end tracking pipeline, Deep Multi-Camera Tracking (DMCT), which achieves reliable real-time multi-camera people tracking. Our DMCT consists of 1) a fast and novel perspective-aware Deep GroudPoint Network, 2) a fusion procedure for ground-plane occupancy heatmap estimation, 3) a novel Deep Glimpse Network for person detection and 4) a fast and accurate online tracker. Our design fully unleashes the power of deep neural network to estimate the "ground point" of each person in each color image, which can be optimized to run efficiently and robustly. Our fusion procedure, glimpse network and tracker merge the results from different views, find people candidates using multiple video frames and then track people on the fused heatmap. Our system achieves the state-of-the-art tracking results while maintaining real-time performance. Apart from evaluation on the challenging WILDTRACK dataset, we also collect two more tracking datasets with high-quality labels from two different environments and camera settings. Our experimental results confirm that our proposed real-time pipeline gives superior results to previous approaches.
A critical realist interpretation of evolutionary growth theorising

The article discusses a critical realist interpretation of evolutionary growth theorising by focusing on some of its basic characteristics. The evolutionary ontology is complex, differentiated, structured, systemic, open, ever-changing, and radically uncertain. Its methodology tends to be increasingly based on appreciative theorising, retroductive explanations and interdisciplinary analysis. After discussing these features, the article suggests that critical realism may indeed constitute an important philosophical and methodological foundation for the future development of evolutionary theories of economic growth.
Choice functions based on sets of strict partial orders: an axiomatic characterisation

Methods for choosing from a set of options are often based on a strict partial order on these options, or on a set of such partial orders. I here provide a very general axiomatic characterisation for choice functions of this form. It includes as special cases axiomatic characterisations for choice functions based on (sets of) total orders, (sets of) weak orders, (sets of) coherent lower previsions and (sets of) probability measures.
Design, Implementation and Control of an Underactuated Hand Exoskeleton

I present the design, implementation and control of a novel, linkage-based, underactuated hand exoskeleton aimed to assist patients with hand disabilities during grasping tasks for robot assisted physical rehabilitation or robot assisted activities of daily living. Even though the proposed exoskeleton was designed with the purpose of assisting users during interactions with real objects around them, its use can be extended for haptic applications as well. In this thesis, I will deeply mention the exoskeletons in the literature, detail the design decisions and implement various control strategies to use this exoskeleton for different applications in the future.
Unsupervised domain adaptation with exploring more statistics and discriminative information

Unsupervised domain adaptation aims at transferring knowledge from the labeled source domain to the unlabeled target domain. Previous methods mainly learn a domain-invariant feature transformation, where the cross-domain discrepancy can be reduced. Maximum Mean Discrepancy(MMD) is the most popular statistic to measure domain discrepancy. However, these methods may suffer from two challenges. 1) MMD-based methods only measure the first-order statistic information across domains, while other useful information such as second-order statistic information has been ignored. 2) The classifier trained on the source domain may confuse to distinguish the correct class from a similar class, and the phenomenon is called class confusion. In this paper, we propose a method called \emph{Unsupervised domain adaptation with exploring more statistics and discriminative information}(MSDI), which tackle these two problems in the principle of structural risk minimization. We adopt the recently proposed statistic called MMCD to measure domain discrepancy which can capture both first-order and second-order statistics simultaneously in RKHS. Besides, we proposed to learn more discriminative features to avoid class confusion, where the inner of the classifier predictions with their transposes are used to reflect the confusion relationship between different classes. Moreover, we minimizing source empirical risk and adopt manifold regularization to explore geometry information in the target domain. MSDI learns a domain-invariant classifier in a unified learning framework incorporating the above objectives. We conduct comprehensive experiments on five real-world datasets and the results verify the effectiveness of the proposed method.
Superdeterministic hidden-variables models I: non-equilibrium and signalling

This is the first of two papers that attempt to comprehensively analyse superdeterministic hidden-variables models of Bell correlations. We first give an overview of superdeterminism and discuss various criticisms of it raised in the literature. We argue that the most common criticism, the violation of ‘free-will’, is incorrect. We take up Bell’s intuitive criticism that these models are ‘conspiratorial’. To develop this further, we introduce non-equilibrium extensions of superdeterministic models. We show that the measurement statistics of these extended models depend on the physical system used to determine the measurement settings. This suggests a fine-tuning in order to eliminate this dependence from experimental observation. We also study the signalling properties of these extended models. We show that although they generally violate the formal no-signalling constraints, this violation cannot be equated to an actual signal. We therefore suggest that the so-called no-signalling constraints be more appropriately named the marginal-independence constraints. We discuss the mechanism by which marginal-independence is violated in superdeterministic models. Lastly, we consider a hypothetical scenario where two experimenters use the apparent-signalling of a superdeterministic model to communicate with each other. This scenario suggests another conspiratorial feature peculiar to superdeterminism. These suggestions are quantitatively developed in the second paper.