Miracle-dz/UniFAR-FTUs huggingface dataset:
Scale-free Unconstrained Online Learning for Curved Losses

A sequence of works in unconstrained online convex optimisation have investigated the possibility of adapting simultaneously to the norm U of the comparator and the maximum norm G of the gradients. In full generality, matching upper and lower bounds are known which show that this comes at the unavoidable cost of an additive GU 3 , which is not needed when either G or U is known in advance. Surprisingly, recent results by Kempka et al. (2019) show that no such price for adaptivity is needed in the speciﬁc case of 1 -Lipschitz losses like the hinge loss. We follow up on this observation by showing that there is in fact never a price to pay for adaptivity if we specialise to any of the other common supervised online learning losses: our results cover log loss, (linear and non-parametric) logistic regression, square loss prediction, and (linear and non-parametric) least-squares regression. We also ﬁll in several gaps in the literature by providing matching lower bounds with an explicit dependence on U . In all cases we obtain scale-free algorithms, which are suitably invariant under rescaling of the data. Our general goal is to establish achievable rates without concern for computational efﬁciency, but for linear logistic regression we also provide an adaptive method that is as efﬁcient as the recent non-adaptive algorithm by Agarwal et al. (2021).
“Forbidden” Polarisation and Extraordinary Piezoelectric Effect in Organometallic Lead Halide Perovskites

Organometallic lead halide perovskites are highly efficient materials for solar cells and other optoelectronic applications due to their high quantum efficiency and exceptional semiconducting properties. A peculiarity of these perovskites is the substantial ionic motion under external forces. Here, it is revealed that electric field‐and light‐induced ionic motion in MAPbX3 crystals (X = Cl, Br, I and MA = CH3NH3) leads to an unexpectedly high piezoelectric‐like response that is at low frequencies an order of magnitude larger than in ferroelectric perovskite oxides. The nominal macroscopic symmetry of the crystals is broken by redistribution of ionic species, which can be controlled deterministically by light and electric field. The revealed piezoelectric response is possibly present in other materials with significant ionic activity, but the unique feature of organometallic perovskites is the strong effect on the piezoelectric‐like response of interplay of ionic motion (MA+1 and X–1) and photoelectrons generated with illumination.
Achieving Minimax Rates in Pool-Based Batch Active Learning

We consider a batch active learning scenario where the learner adaptively issues batches of points to a labeling oracle. Sampling labels in batches is highly desirable in practice due to the smaller number of interactive rounds with the labeling oracle (often human beings). However, batch active learning typically pays the price of a reduced adaptivity, leading to suboptimal results. In this paper we propose a solution which requires a careful trade off between the informativeness of the queried points and their diversity. We theoretically investigate batch active learning in the practically relevant scenario where the unlabeled pool of data is available beforehand ( pool-based active learning). We analyze a novel stage-wise greedy algorithm and show that, as a function of the label complexity, the excess risk of this algorithm matches the known minimax rates in standard statistical learning settings. Our results also exhibit a mild dependence on the batch size. These are the ﬁrst theoretical results that employ careful trade offs between informativeness and diversity to rigorously quantify the statistical performance of batch active learning in the pool-based scenario.
Artemis

We, humans, are entering into a virtual era and indeed want to bring animals to the virtual world as well for companion. Yet, computer-generated (CGI) furry animals are limited by tedious off-line rendering, let alone interactive motion control. In this paper, we present ARTEMIS, a novel neural modeling and rendering pipeline for generating ARTiculated neural pets with appEarance and Motion synthesIS. Our ARTEMIS enables interactive motion control, real-time animation, and photo-realistic rendering of furry animals. The core of our ARTEMIS is a neural-generated (NGI) animal engine, which adopts an efficient octree-based representation for animal animation and fur rendering. The animation then becomes equivalent to voxel-level deformation based on explicit skeletal warping. We further use a fast octree indexing and efficient volumetric rendering scheme to generate appearance and density features maps. Finally, we propose a novel shading network to generate high-fidelity details of appearance and opacity under novel poses from appearance and density feature maps. For the motion control module in ARTEMIS, we combine state-of-the-art animal motion capture approach with recent neural character control scheme. We introduce an effective optimization scheme to reconstruct the skeletal motion of real animals captured by a multi-view RGB and Vicon camera array. We feed all the captured motion into a neural character control scheme to generate abstract control signals with motion styles. We further integrate ARTEMIS into existing engines that support VR headsets, providing an unprecedented immersive experience where a user can intimately interact with a variety of virtual animals with vivid movements and photo-realistic appearance. Extensive experiments and showcases demonstrate the effectiveness of our ARTEMIS system in achieving highly realistic rendering of NGI animals in real-time, providing daily immersive and interactive experiences with digital animals unseen before. We make available our ARTEMIS model and dynamic furry animal dataset at https://haiminluo.github. io/publication/artemis/.
Strategy Synthesis for Zero-sum Neuro-symbolic Concurrent Stochastic Games

Neuro-symbolic approaches to artificial intelligence, which combine neural networks with classical symbolic techniques, are growing in prominence, necessitating formal approaches to reason about their correctness. We propose a novel modelling formalism called neuro-symbolic concurrent stochastic games (NS-CSGs), which comprise two probabilistic finite-state agents interacting in a shared continuous-state environment. Each agent observes the environment using a neural perception mechanism, which converts inputs such as images into symbolic percepts, and makes decisions symbolically. We focus on the class of NS-CSGs with Borel state spaces and prove the existence and measurability of the value function for zero-sum discounted cumulative rewards under piecewise-constant restrictions on the components of this class of models. To compute values and synthesise strategies, we present, for the first time, practical value iteration (VI) and policy iteration (PI) algorithms to solve this new subclass of continuous-state CSGs. These require a finite decomposition of the environment induced by the neural perception mechanisms of the agents and rely on finite abstract representations of value functions and strategies closed under VI or PI. First, we introduce a Borel measurable piecewise-constant (B-PWC) representation of value functions, extend minimax backups to this representation and propose a value iteration algorithm called B-PWC VI. Second, we introduce two novel representations for the value functions and strategies, constant-piecewise-linear (CON-PWL) and constant-piecewise-constant (CON-PWC) respectively, and propose Minimax-action-free PI by extending a recent PI method based on alternating player choices for finite state spaces to Borel state spaces, which does not require normal-form games to be solved.
Enumeration of Dyck paths with air pockets

We introduce and study the new combinatorial class of Dyck paths with air pockets. We exhibit a bijection with the peakless Motzkin paths which transports several pattern statistics and give bivariate generating functions for the distribution of patterns as peaks, returns and pyramids. Then, we deduce the popularities and asymptotic expectations of these patterns and point out a link between the popularity of pyramids and a special kind of closed smooth self-overlapping curves, a subset of Fibonacci meanders. A similar study is conducted for non-decreasing Dyck paths with air pockets.
Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment

Low-light image enhancement (LIE) has attracted tremendous research interests in recent years. Retinex theory-based deep learning methods, following a decomposition-adjustment pipeline, have achieved promising performance due to their physical interpretability. However, existing Retinex-based deep learning methods are still suboptimal, failing to leverage useful insights from traditional approaches. Meanwhile, the adjustment step is either oversimplified or overcomplicated, resulting in unsatisfactory performance in practice. To address these issues, we propose a novel deep-learning framework for LIE. The framework consists of a decomposition network (DecNet) inspired by algorithm unrolling and adjustment networks considering both global and local brightness. The algorithm unrolling allows the integration of both implicit priors learned from data and explicit priors inherited from traditional methods, facilitating better decomposition. Meanwhile, considering global and local brightness guides the design of effective yet lightweight adjustment networks. Moreover, we introduce a self-supervised fine-tuning strategy that achieves promising performance without manual hyperparameter tuning. Extensive experiments on benchmark LIE datasets demonstrate the superiority of our approach over existing state-of-the-art methods both quantitatively and qualitatively. Code is available at https://github.com/Xinyil256/RAUNA2023.
Multi-Task Deep Residual Echo Suppression with Echo-Aware Loss

This paper introduces the NWPU Team’s entry to the ICASSP 2022 AEC Challenge. We take a hybrid approach that cascades a linear AEC with a neural post-filter. The former is used to deal with the linear echo components while the latter suppresses the residual non-linear echo components. We use gated convolutional F-T-LSTM neural network (GFTNN) as the backbone and shape the post-filter by a multi-task learning (MTL) framework, where a voice activity detection (VAD) module is adopted as an auxiliary task along with echo suppression, with the aim to avoid over suppression that may cause speech distortion. Moreover, we adopt an echo-aware loss function, where the mean square error (MSE) loss can be optimized particularly for every time-frequency bin (TF-bin) according to the signal-to-echo ratio (SER), leading to further suppression on the echo. Extensive ablation study shows that the time delay estimation (TDE) module in neural post-filter leads to better perceptual quality, and an adaptive filter with better convergence will bring consistent performance gain for the post-filter. Besides, we find that using the linear echo as the input of our neural post-filter is a better choice than using the reference signal directly. In the ICASSP 2022 AEC-Challenge, our approach has ranked the 1st place on word accuracy (WAcc) (0.817) and the 3rd place on both mean opinion score (MOS) (4.502) and the final score (0.864).
A novel silicon photomultiplier readout architecture for low-light applications

In this article we describe the photon detection readout electronics for the nEDM@SNS experiment. The chosen “photon counting” architecture, which utilizes high-efficiency silicon photomultipliers (SiPMs) and is appropriate for low-light applications, allows the use of a relatively high SiPM operating voltage. This maximizes photon detection efficiency and minimizes gain/efficiency voltage-dependence while eliminating direct optical cross-talk.
Product-Coproduct Prographs and Triangulations of the Sphere

In this paper, we explain how the classical Catalan families of objects involving paths, tableaux, triangulations, parentheses configurations and more generalize canonically to a three-dimensional version. In particular, we present product-coproduct prographs as central objects explaining the combinatorics of the triangulations of the sphere. Then we expose a natural way to extend the Tamari lattice to the product-coproduct prographs.
Kevin: de Bruijn-based topology with demand-aware links and greedy routing

We propose Kevin, a novel demand-aware reconfigurable rack-to-rack datacenter network realized with a simple and efficient control plane. In particular, Kevin makes effective use of the network capacity by supporting integrated and multi-hop routing as well as work-conserving scheduling. To this end, Kevin relies on local greedy routing with small forwarding tables which require local updates only during topological reconfigurations, making this approach ideal for dynamic networks. Specifically, Kevin is based on a de Bruijn topology (using a small number of optical circuit switches) in which static links are enhanced with opportunistic links.
Towards Power Efficient DNN Accelerator Design on Reconfigurable Platform

The exponential emergence of Field Programmable Gate Array (FPGA) has accelerated the research of hardware implementation of Deep Neural Network (DNN). Among all DNN processors, domain specific architectures, such as, Google's Tensor Processor Unit (TPU) have outperformed conventional GPUs. However, implementation of TPUs in reconfigurable hardware should emphasize energy savings to serve the green computing requirement. Voltage scaling, a popular approach towards energy savings, can be a bit critical in FPGA as it may cause timing failure if not done in an appropriate way. In this work, we present an ultra low power FPGA implementation of a TPU for edge applications. We divide the systolic-array of a TPU into different FPGA partitions, where each partition uses different near threshold (NTC) biasing voltages to run its FPGA cores. The biasing voltage for each partition is roughly calculated by the proposed static schemes. However, further calibration of biasing voltage is done by the proposed runtime scheme. Four clustering algorithms based on the minimum slack value of different design paths of Multiply Accumulates (MACs) study the partitioning of FPGA. To overcome the timing failure caused by NTC, the MACs which have higher minimum slack are placed in lower voltage partitions and the MACs have lower minimum slack path are placed in higher voltage partitions. The proposed architecture is simulated in a commercial platform : Vivado with Xilinx Artix-7 FPGA and academic platform VTR with 22nm, 45nm, 130nm FPGAs. The simulation results substantiate the implementation of voltage scaled TPU in FPGAs and also justifies its power efficiency.
Industrial multi-arm handling system for automated manufacture of composite broadgoods

Presents the design and implementation of an industrial four-arm robotic device for the gripping, handling and manipulation of flexible sheet material. The system has been developed as part of a collaborative research project in the automated manufacture of composite broadgoods from prepregs, i.e. structures made from layers of woven or unidirectional glass and carbon fibre pre-impregnated with a resin system. The paper considers the gripping, handling, peeling, lay-up and consolidation aspects in terms of the actual equipment implemented, giving reasons for the designs and results of the completed trials.
SANCTIONING IN THE WILD: RATIONAL CALCULUS AND RETRIBUTIVE INSTINCTS IN GOURMET CUISINE

Why do we sanction norm violations? Despite near universal agreement on the role of sanctions for maintaining norms of cooperation, scholars hotly dispute whether individuals sanction based on a rational calculus or because of strong retributive instincts. In this paper we report on a mixed-method field study examining sanctioning behavior. Our goal is to extend theories of sanctioning by evaluating the conditions under which individuals are more likely to administer a sanction in response to a norm violation. To guide the development of our hypotheses, we engage in a qualitative examination of sanctioning decisions in the context of gourmet cuisine. We then test our predictions in a field experiment involving more than 500 gourmet chefs in Italy. Our results suggest that individuals follow retributive instincts, but they also engage in cost–benefit calculations. Indeed, we find that the two logics of sanctioning jointly influence participation in social exchange. Recognizing their own tendency to sanction at a cost, individuals avoid circumstances that could trigger the need for costly sanctions.
Recurrence-in-Recurrence Networks for Video Deblurring

State-of-the-art video deblurring methods often adopt recurrent neural networks to model the temporal dependency between the frames. While the hidden states play key role in delivering information to the next frame, abrupt motion blur tend to weaken the relevance in the neighbor frames. In this paper, we propose recurrence-in-recurrence network architecture to cope with the limitations of short-ranged memory. We employ additional recurrent units inside the RNN cell. First, we employ inner-recurrence module (IRM) to manage the long-ranged dependency in a sequence. IRM learns to keep track of the cell memory and provides complementary information to find the deblurred frames. Second, we adopt an attention-based temporal blending strategy to extract the necessary part of the information in the local neighborhood. The adpative temporal blending (ATB) can either attenuate or amplify the features by the spatial attention. Our extensive experimental results and analysis validate the effectiveness of IRM and ATB on various RNN architectures.
Diﬀerentiable Neural Planners with Temporally Extended Actions

Deep Reinforcement Learning has brought about various advances in game or puzzle-like benchmarks that other traditional AI agents long had diﬃculty with. However, the policies learned by the Deep Neural Networks have usually been reactive policies using primitive actions of the environment at hand. While certain components that aid in generalization have been in recent use, like external memory, other have largely been missing like explicit long-term planning computations. Additionally, a general (robust and “well-generalizing”) artiﬁcial intelligence is likely to need temporally extended actions with a planner that can decide when, which and for how long to execute them. In this paper, Value Iteration Networks, deep neural networks with explicit planning components, are replicated and analyzed. Then, hierarchies of planning computations are considered through temporally extended options and simple experiments to demonstrate the viability of such Diﬀerentiable Neural Planners with Temporally Extended Actions are described with interpretable results. Finally, some possible directions for future work are noted.
Long Short-Term Memory with Slower Information Decay

Learning to process long-range dependencies has been a challenge for recurrent neural networks. Despite improvements achieved by long short-term memory (LSTMs), its gating mechanism re-sults in exponential decay of information, limiting their capacity of capturing long-range dependencies. In this work, we present a power law forget gate , which instead has a slower rate of information decay. We propose a power law-based LSTM (pLSTM) based on the LSTM but with a power law forget gate. We test empirically the pLSTM on the copy task, sentiment classiﬁcation, and sequential MNIST, all with long-range dependency tasks. The pLSTM solves these tasks outperforming an LSTM, specially for long-range dependencies. Further, the pLSTM learns sparser and more robust representations.
Exploiting Hardware Transactional Memory for Efﬁcient In-Memory Transaction Processing

The commercial availability of Intel’s Haswell processor suggests that hardware transactional memory (HTM), a technique inspired by database transactions, is likely to be widely exploited for in-memory databases in the near future. Its features such as hardware-maintained read/write sets and automatic conﬂict detection naturally put forward a challenge on how HTM can successfully support concurrency control for fast in-memory transaction processing. To answer this challenge, we ﬁrst conduct a detailed analysis on solely using HTM to protect a database transaction without other concurrency controls such as OCC or 2PL. Our analysis reveals that such an approach may lead to scalability issues due to the high probability of aborting transactions. To reduce HTM-protected transaction regions, we propose to leverage the theory of transaction chopping with several workload-inspired optimizations, including recovering commutativity with deferred execution and a database cache for database operations, to reduce HTM aborts while preserving serializability. While transaction chopping effectively reduces HTM aborts due to memory access conﬂicts, it also creates a new challenge for read-only transactions, which can no longer observe a consistent database snapshot. To address this problem, we propose a lazy snapshot mechanism that constructs a database snapshot during execution which eventually becomes consistent. We implement the proposed techniques in DBX-TC on an Intel Haswell processor and conduct an extensive performance study. Performance evaluation shows that DBX-TC with our mechanisms outperforms a naively constructed database using HTM by 4.77x and 36% over DBX on a 4-core/8-threads machine. The speedup is especially signiﬁcant under high contention.
BATTERIES: CLASSIFICATION AND REVIEW OF ELECTRIC CIRCUIT MODELS FOR ELECTRIC VEHICLE

Traditional Transportation system is used fossil fuels which created Green House Gas Emission (GHE) and exploited the environmental conditions. Now days the initiative is reflected in many countries of transforming from traditional gasoline based transportation system towards Electric Transportation. There are two types of Electric Transportation system such as Retrofitting based Electric vehicle system and Battery operated Electrical Vehicle (EV) system. Mostly Battery operated Electric Vehicles are attracting to users. Utilisations of EV are day by day increasing and hence there is need to enhance the entire performance of EV by designing the optimistic internal parameters of Battery used in sub systems of EV. The entire performance of battery is depending upon the various factors of battery such as specific energy, specific power, life cycle, safety, charge time, internal resistances, charge and discharge time, temperature, cost, and toxicity. Hence there is need to deals with the above mentioned factors and internal parameters of battery for improvement in performance of Battery. The main objective of proposed research paper is to study and analyse the electric circuit model (ECM) of Battery that used in EV to find the internal parameter by using Algorithm. The various models of ECM such as Simple Model, Enhanced Simple Model, Dynamic Model, Thevenin based model, Modified generic model, Tremblay model are critically reviewed and all are mentioned the elaboration of Internal parameters of Battery. During the study of Thevenin model, it has been highlighted that third order is responsible for creating the huge transient response in ECM and hence to reduce the transient response there is need to design and developed the optimistic R, C parameters of ECM. Detailed studies of comparative analysis of various models are highlighted by considering the uncertainty analysis.