Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonvanishing sub-gap photocurrent as a probe of correlation effects

For semiconductors and insulators, it is commonly believed that in-gap transitions into non-localized states are smoothly suppressed in the clean limit, i.e. at zero temperature they vanish even without accounting for localization effects. We show in a counterexample that this intuition does not generalize beyond the linear response. Namely, we find that the dc current due to the bulk photovoltaic effect can be finite and mostly temperature independent in an allowed window of sub-gap transitions. As the reason we identify a nontrivial phase accumulation which is related to the fact that the electron motion due to the applied electric field is, within limits, coherent. We expect that a moderate range of excitation energies, where this effect is observable, lies between the bulk energy gap and the mobility edge. We find that the sub-gap dc-current is temperature-independent for non-interacting systems but temperature-dependent for strongly interacting systems. Thus, the sub-gap response may be used to distinguish whether a state is single-particle localized or many-body localized.
Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling

We consider the problem of minimizing a high-dimensional objective function, which may include a regularization term, using (possibly noisy) evaluations of the function. Such optimization is also called derivative-free, zeroth-order, or black-box optimization. We propose a new $\textbf{Z}$eroth-$\textbf{O}$rder $\textbf{R}$egularized $\textbf{O}$ptimization method, dubbed ZORO. When the underlying gradient is approximately sparse at an iterate, ZORO needs very few objective function evaluations to obtain a new iterate that decreases the objective function. We achieve this with an adaptive, randomized gradient estimator, followed by an inexact proximal-gradient scheme. Under a novel approximately sparse gradient assumption and various different convex settings, we show the (theoretical and empirical) convergence rate of ZORO is only logarithmically dependent on the problem dimension. Numerical experiments show that ZORO outperforms the existing methods with similar assumptions, on both synthetic and real datasets.
Designing Color Filters That Make Cameras More Colorimetric

When we place a colored filter in front of a camera the effective camera response functions are equal to the given camera spectral sensitivities multiplied by the filter spectral transmittance. In this article, we solve for the filter which returns the modified sensitivities as close to being a linear transformation from the color matching functions of the human visual system as possible. When this linearity condition - sometimes called the Luther condition- is approximately met, the ‘camera+filter’ system can be used for accurate color measurement. Then, we reformulate our filter design optimisation for making the sensor responses as close to the CIEXYZ tristimulus values as possible given the knowledge of real measured surfaces and illuminants spectra data. This data-driven method in turn is extended to incorporate constraints on the filter (smoothness and bounded transmission). Also, because how the optimisation is initialised is shown to impact on the performance of the solved-for filters, a multi-initialisation optimisation is developed. Experiments demonstrate that, by taking pictures through our optimised color filters, we can make cameras significantly more colorimetric.
A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles

Electric vehicles (EVs) are critical to the transition to a low-carbon transportation system. The successful adoption of EVs heavily depends on energy consumption models that can accurately and reliably estimate electricity consumption. This paper reviews the state-of-the-art of EV energy consumption models, aiming to provide guidance for future development of EV applications. We summarize influential variables of EV energy consumption into four categories: vehicle component, vehicle dynamics, traffic and environment related factors. We classify and discuss EV energy consumption models in terms of modeling scale (microscopic vs. macroscopic) and methodology (data-driven vs. rule-based). Our review shows trends of increasing macroscopic models that can be used to estimate trip-level EV energy consumption and increasing data-driven models that utilized machine learning technologies to estimate EV energy consumption based on large volume real-world data. We identify research gaps for EV energy consumption models, including the development of energy estimation models for modes other than personal vehicles (e.g., electric buses, electric trucks, and electric non-road vehicles); the development of energy estimation models that are suitable for applications related to vehicle-to-grid integration; and the development of multi-scale energy estimation models as a holistic modeling approach.
BRAIN CONTROLLED ROBOTS FOR SEVERELY DISABLED PEOPLE AND VICTIMS OF PARALYSIS

: A Brain Controlled Interface is such an arrangement by means of which a user can be in control of an external device with regard to the brain signals. A non-invasive technique, Electroencephalography is a means to measure the activity of the brain and has proven to be compelling and efficacious prospect for the buildout of BCI. The past two decades have encountered soul stirring breakthrough in the calibre to model the brain signals that are jot down by EEG. In this paper we provide an inclusive review of how the EEG based Brain Controlled Robots can suffice as powerful abet for helping the severely disabled people and victims of paralysis to have dealings with the outside world by just their thoughts. The aerobicized users can utilise the robots by either the input devices like keyboard, mouse, joystick, touch screen or can easily operate the speech or gesture controlled robots. However, the use of assistive robots could be much mentally strenuous to use for disabled people and crippled or locked in patients of paralysis. These individuals have lost most of their muscle control making it arduous for them to exude their intensions to robots with these interfaces. But by means of these brain controlled robots natural cognitive signals can be used to control these robots means that by using this approach the severely disabled people need not to be trained in a suggested way that robots can recognise. A slick perspective like that would ameliorate the calibre of users to pilot factory robots, driverless cars, wheel chairs and many other technologies. These brain controlled robots can prove to be an efficient conveyance method for those who are not able to use verbal or gesture means such as crippled or disabled victims of paralysis and they would be able to use prosthetic devices like artificial arm, artificial leg, an automatic wheel chair just by means of their thoughts.
Adaptive Personalized Federated Learning

Investigation of the degree of personalization in federated learning algorithms has shown that only maximizing the performance of the global model will confine the capacity of the local models to personalize. In this paper, we advocate an adaptive personalized federated learning (APFL) algorithm, where each client will train their local models while contributing to the global model. Information theoretically, we prove that the mixture of local and global models can reduce the generalization error. We also propose a communication-reduced bilevel optimization method, which reduces the communication rounds to $O(\sqrt{T})$ and show that under strong convexity and smoothness assumptions, the proposed algorithm can achieve a convergence rate of $O(1/T)$ with some residual error. The residual error is related to the gradient diversity among local models, and the gap between optimal local and global models. The extensive experiments demonstrate the effectiveness of our personalization, as well as the correctness of our theory.
Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions

Convolutional neural networks (CNNs) have a large number of variables and hence suffer from a complexity problem for their implementation. Different methods and techniques have developed to alleviate the problem of CNN's complexity, such as quantization, pruning, etc. Among the different simplification methods, computation in the Fourier domain is regarded as a new paradigm for the acceleration of CNNs. Recent studies on Fast Fourier Transform (FFT) based CNN aiming at simplifying the computations required for FFT. However, there is a lot of space for working on the reduction of the computational complexity of FFT. In this paper, a new method for CNN processing in the FFT domain is proposed, which is based on input splitting. There are problems in the computation of FFT using small kernels in situations such as CNN. Splitting can be considered as an effective solution for such issues aroused by small kernels. Using splitting redundancy, such as overlap-and-add, is reduced and, efficiency is increased. Hardware implementation of the proposed FFT method, as well as different analyses of the complexity, are performed to demonstrate the proper performance of the proposed method.
How the Degeneracy Helps for Triangle Counting in Graph Streams

We revisit the well-studied problem of triangle count estimation in graph streams. Given a graph represented as a stream of m edges, our aim is to compute a (1+-ε)-approximation to the triangle count T, using a small space algorithm. For arbitrary order and a constant number of passes, the space complexity is known to be essentially Θ(min(m3/2 /T, m/√T)) (McGregor et al., PODS 2016, Bera et al., STACS 2017). We give a (constant pass, arbitrary order) streaming algorithm that can circumvent this lower bound for low degeneracy graphs. The degeneracy, K, is a nuanced measure of density, and the class of constant degeneracy graphs is immensely rich (containing planar graphs, minor-closed families, and preferential attachment graphs). We design a streaming algorithm with space complexity ~O(mK/T). For constant degeneracy graphs, this bound is ~O(m/T), which is significantly smaller than both m3/2 /T and m/√T. We complement our algorithmic result with a nearly matching lower bound of Ω(mK/T).
The covariance shift (C-SHIFT) algorithm for normalizing biological data

Omics technologies are powerful tools for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in experiments, the raw gene expression data is often obfuscated by undesirable technical noises. Various normalization techniques were designed in an attempt to remove these non-biological errors prior to any statistical analysis. One of the reasons for normalizing data is the need for recovering the covariance matrix used in gene network analysis. In this paper, we introduce a novel normalization technique, called the covariance shift (C-SHIFT) method. This normalization algorithm uses optimization techniques together with the blessing of dimensionality philosophy and energy minimization hypothesis for covariance matrix recovery under additive noise (in biology, known as the bias). Thus, it is perfectly suited for the analysis of logarithmic gene expression data. Numerical experiments on synthetic data demonstrate the method’s advantage over the classical normalization techniques. Namely, the comparison is made with rank, quantile, cyclic LOESS (locally estimated scatterplot smoothing), and MAD (median absolute deviation) normalization methods.
Distance Surface for Event-Based Optical Flow

We propose DistSurf-OF, a novel optical flow method for neuromorphic cameras. Neuromorphic cameras (or event detection cameras) are an emerging sensor modality that makes use of dynamic vision sensors (DVS) to report asynchronously the log-intensity changes (called “events”) exceeding a predefined threshold at each pixel. In absence of the intensity value at each pixel location, we introduce a notion of “distance surface”—the distance transform computed from the detected events—as a proxy for object texture. The distance surface is then used as an input to the intensity-based optical flow methods to recover the two dimensional pixel motion. Real sensor experiments verify that the proposed DistSurf-OF accurately estimates the angle and speed of each events.
A Comparison of Data Augmentation Techniques in Training Deep Neural Networks for Satellite Image Classification

Satellite imagery allows a plethora of applications ranging from weather forecasting to land surveying. The rapid development of computer vision systems could open new horizons to the utilization of satellite data due to the abundance of large volumes of data. However, current state-of-the-art computer vision systems mainly cater to applications that mainly involve natural images. While useful, those images exhibit a different distribution from satellite images in addition to having more spectral channels. This allows the use of pretrained deep learning models only in a subset of spectral channels that are equivalent to natural images thus discarding valuable information from other spectral channels. This calls for research effort to optimize deep learning models for satellite imagery to enable the assessment of their utility in the domain of remote sensing. This study focuses on the topic of image augmentation in training of deep neural network classifiers. I tested different techniques for image augmentation to train a standard deep neural network on satellite images from EuroSAT. Results show that while some image augmentation techniques commonly used in natural image training can readily be transferred to satellite images, some others could actually lead to a decrease in performance. Additionally, some novel image augmentation techniques that take into account the nature of satellite images could be useful to incorporate in training.
Elektive Behandlung intrakranieller Stenosen mit dem ballonexpandierbaren Pharos Vitesse Stent

\(\bf Problem:\) Fur Patienten mit symptomatischen intrakraniellen Stenosen ist die Behandlung mittels Stent eine Behandlungsoption. Fruhzeitige Komplikationen reduzieren den Nutzen fur den Patienten: die Senkung des Schlaganfallrisikos. \(\bf Methode:\)In die retrospektive Auswertung wurden 92 Patienten eingeschlossen, welche mit dem ballonexpandierbaren Pharos Vitesse Stent behandelt wurden. Die Komplikationsraten prozedural und im 30-Tagesverlauf wurden ermittelt. \(\bf Ergebnis:\) Im 30-Tages Intervall lag insgesamt die Schlaganfallrate bei 6,5% (95% CI: 3,0 – 13,5%), die Schlaganfall- und Todesrate bei 7,6% (95% CI: 3,7 – 14,9%). \(\bf Diskussion:\) Bezogen auf die in dieser Studie ausgewerteten Daten, mit moderaten Komplikationsraten, bleibt die Behandlung hochgradiger intrakranieller Stenosen mit dem Pharos Vitesse Stent eine individuelle Option fur Patienten, bei denen die bestmogliche medikamentose Therapie nicht ausreichend wirkt.
Revisiting "Over-smoothing" in Deep GCNs

Oversmoothing has been assumed to be the major cause of performance drop in deep graph convolutional networks (GCNs). In this paper, we propose a new view that deep GCNs can actually learn to anti-oversmooth during training. This work interprets a standard GCN architecture as layerwise integration of a Multi-layer Perceptron (MLP) and graph regularization. We analyze and conclude that before training, the final representation of a deep GCN does over-smooth, however, it learns anti-oversmoothing during training. Based on the conclusion, the paper further designs a cheap but effective trick to improve GCN training. We verify our conclusions and evaluate the trick on three citation networks and further provide insights on neighborhood aggregation in GCNs.
A note on Onicescu's informational energy and correlation coefficient in exponential families

The informational energy of Onicescu is a positive quantity that measures the amount of uncertainty of a random variable. However, contrary to Shannon’s entropy, the informational energy is strictly convex and increases when randomness decreases. We report a closed-form formula for Onicescu’s informational energy and its associated correlation coefficient when the probability distributions belong to an exponential family. We show how to instantiate the generic formula for several common exponential families. Finally, we discuss the characterization of valid thermodynamic process trajectories on a statistical manifold by enforcing that the entropy and the informational energy shall vary in opposite directions.
Inferring Semantic Information with 3D Neural Scene Representations

Biological vision infers multi-modal 3D representations that support reasoning about scene properties such as materials, appearance, affordance, and semantics in 3D. These rich representations enable us humans, for example, to acquire new skills, such as the learning of a new semantic class, with extremely limited supervision. Motivated by this ability of biological vision, we demonstrate that 3D-structure-aware representation learning leads to multi-modal representations that enable 3D semantic segmentation with extremely limited, 2D-only supervision. Building on emerging neural scene representations, which have been developed for modeling the shape and appearance of 3D scenes supervised exclusively by posed 2D images, we are first to demonstrate a representation that jointly encodes shape, appearance, and semantics in a 3D-structure-aware manner. Surprisingly, we find that only a few tens of labeled 2D segmentation masks are required to achieve dense 3D semantic segmentation using a semi-supervised learning strategy. We explore two novel applications for our semantically aware neural scene representation: 3D novel view and semantic label synthesis given only a single input RGB image or 2D label mask, as well as 3D interpolation of appearance and semantics.
Decentralized Learning for Channel Allocation in IoT Networks over Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game

We study a decentralized channel allocation problem in an ad-hoc Internet of Things network underlaying on the spectrum licensed to a primary cellular network. In the considered network, the impoverished channel sensing/probing capability and computational resource on the IoT devices make them difficult to acquire the detailed Channel State Information (CSI) for the shared multiple channels. In practice, the unknown patterns of the primary users’ transmission activities and the time-varying CSI (e.g., due to small-scale fading or device mobility) also cause stochastic changes in the channel quality. Decentralized IoT links are thus expected to learn channel conditions online based on partial observations, while acquiring no information about the channels that they are not operating on. They also have to reach an efficient, collision-free solution of channel allocation with limited coordination. Our study maps this problem into a contextual multi-player, multi-armed bandit game, and proposes a purely decentralized, three-stage policy learning algorithm through trial-and-error. Theoretical analyses shows that the proposed scheme guarantees the IoT links to jointly converge to the socially optimal channel allocation with a sub-linear (i.e., polylogarithmic) regret with respect to the operational time. Simulations demonstrate that it strikes a good balance between efficiency and network scalability when compared with the other state-of-the-art decentralized bandit algorithms.
Environmental Adaptation of Robot Morphology and Control Through Real-World Evolution

Abstract Robots operating in the real world will experience a range of different environments and tasks. It is essential for the robot to have the ability to adapt to its surroundings to work efficiently in changing conditions. Evolutionary robotics aims to solve this by optimizing both the control and body (morphology) of a robot, allowing adaptation to internal, as well as external factors. Most work in this field has been done in physics simulators, which are relatively simple and not able to replicate the richness of interactions found in the real world. Solutions that rely on the complex interplay among control, body, and environment are therefore rarely found. In this article, we rely solely on real-world evaluations and apply evolutionary search to yield combinations of morphology and control for our mechanically self-reconfiguring quadruped robot. We evolve solutions on two distinct physical surfaces and analyze the results in terms of both control and morphology. We then transition to two previously unseen surfaces to demonstrate the generality of our method. We find that the evolutionary search finds high-performing and diverse morphology-controller configurations by adapting both control and body to the different properties of the physical environments. We additionally find that morphology and control vary with statistical significance between the environments. Moreover, we observe that our method allows for morphology and control parameters to transfer to previously unseen terrains, demonstrating the generality of our approach.
Distributed Collision-Free Motion Coordination on a Sphere: A Conic Control Barrier Function Approach

This letter studies a distributed collision avoidance control problem for a group of rigid bodies on a sphere. A rigid body network, consisting of multiple rigid bodies constrained to a spherical surface and an interconnection topology, is first formulated. In this formulation, it is shown that motion coordination on a sphere is equivalent to attitude coordination on the 3-dimensional Special Orthogonal group. Then, an angle-based control barrier function that can handle a geodesic distance constraint on a spherical surface is presented. The proposed control barrier function is then extended to a relative motion case and applied to a collision avoidance problem for a rigid body network operating on a sphere. Each rigid body chooses its control input by solving a distributed optimization problem to achieve a nominal distributed motion coordination strategy while satisfying constraints for collision avoidance. The proposed collision-free motion coordination law is validated via simulation.
Entropy and Energy of Static Spherically Symmetric Black Hole in f(R) Theory

We consider the new horizon first law in f ( R ) theory. We derive the general formulas to computed the entropy and energy for static spherically symmetric black hole. For applications, some nontrivial solutions in some popular f ( R ) theories are investigated, the entropies and the energies of static spherically symmetric black holes in these models are first calculated.