Miracle-dz/UniFAR-FTUs huggingface dataset:
Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences

We study the problem of $K$-armed dueling bandit for both stochastic and adversarial environments, where the goal of the learner is to aggregate information through relative preferences of pair of decisions points queried in an online sequential manner. We first propose a novel reduction from any (general) dueling bandits to multi-armed bandits and despite the simplicity, it allows us to improve many existing results in dueling bandits. In particular, \emph{we give the first best-of-both world result for the dueling bandits regret minimization problem} -- a unified framework that is guaranteed to perform optimally for both stochastic and adversarial preferences simultaneously. Moreover, our algorithm is also the first to achieve an optimal $O(\sum_{i = 1}^K \frac{\log T}{\Delta_i})$ regret bound against the Condorcet-winner benchmark, which scales optimally both in terms of the arm-size $K$ and the instance-specific suboptimality gaps $\{\Delta_i\}_{i = 1}^K$. This resolves the long-standing problem of designing an instancewise gap-dependent order optimal regret algorithm for dueling bandits (with matching lower bounds up to small constant factors). We further justify the robustness of our proposed algorithm by proving its optimal regret rate under adversarially corrupted preferences -- this outperforms the existing state-of-the-art corrupted dueling results by a large margin. In summary, we believe our reduction idea will find a broader scope in solving a diverse class of dueling bandits setting, which are otherwise studied separately from multi-armed bandits with often more complex solutions and worse guarantees. The efficacy of our proposed algorithms is empirically corroborated against the existing dueling bandit methods.
Supporting Serendipitous Discovery and Balanced Analysis of Online Product Reviews with Interaction-Driven Metrics and Bias-Mitigating Suggestions

In this study, we investigate how supporting serendipitous discovery and analysis of online product reviews can encourage readers to explore reviews more comprehensively prior to making purchase decisions. We propose two interventions — Exploration Metrics that can help readers understand and track their exploration patterns through visual indicators and a Bias Mitigation Model that intends to maximize knowledge discovery by suggesting sentiment and semantically diverse reviews. We designed, developed, and evaluated a text analytics system called Serendyze, where we integrated these interventions. We asked 100 crowd workers to use Serendyze to make purchase decisions based on product reviews. Our evaluation suggests that exploration metrics enabled readers to efficiently cover more reviews in a balanced way, and suggestions from the bias mitigation model influenced readers to make confident data-driven decisions. We discuss the role of user agency and trust in text-level analysis systems and their applicability in domains beyond review exploration.
Consistent Bayesian community recovery in multilayer networks

Revealing underlying relations between nodes in a network is one of the most important tasks in network analysis. Using tools and techniques from a variety of disciplines, many community recovery methods have been developed for different scenarios. Despite the recent interest on community recovery in multilayer networks, theoretical results on the accuracy of the estimates are few and far between. Given a multilayer, e.g. temporal, network and a multilayer stochastic block model, we derive bounds for sufficient separation between intra- and inter-block connectivity parameters to achieve posterior exact and almost exact community recovery. These conditions are comparable to a well known threshold for community recovery by a single-layer stochastic block model. A simulation study shows that the derived bounds translate to classification accuracy that improves as the number of observed layers increases.
Factored World Models for Zero-Shot Generalization in Robotic Manipulation

World models for environments with many objects face a combinatorial explosion of states: as the number of objects increases, the number of possible arrangements grows exponentially. In this paper, we learn to generalize over robotic pick-and-place tasks using object-factored world models, which combat the combinatorial explosion by ensuring that predictions are equivariant to permutations of objects. Previous object-factored models were limited either by their inability to model actions, or by their inability to plan for complex manipulation tasks. We build on recent contrastive methods for training object-factored world models, which we extend to model continuous robot actions and to accurately predict the physics of robotic pick-and-place. To do so, we use a residual stack of graph neural networks that receive action information at multiple levels in both their node and edge neural networks. Crucially, our learned model can make predictions about tasks not represented in the training data. That is, we demonstrate successful zero-shot generalization to novel tasks, with only a minor decrease in model performance. Moreover, we show that an ensemble of our models can be used to plan for tasks involving up to 12 pick and place actions using heuristic search. We also demonstrate transfer to a physical robot.
Reliability function for streaming over a DMC with feedback

Conventionally, posterior matching is investigated in channel coding and block encoding contexts – the source symbols are equiprobably distributed and are entirely known by the encoder before the transmission. In this paper, we consider a streaming source, whose symbols progressively arrive at the encoder at a sequence of deterministic times. We derive the joint source-channel coding (JSCC) reliability function for streaming over a discrete memoryless channel (DMC) with feedback under regularity conditions. We propose a novel instantaneous encoding phase that operates during the symbol arriving period and that achieves the JSCC reliability function for streaming when followed by a block encoding scheme that achieves the JSCC reliability function for a classical source whose symbols are fully accessible before the transmission. The instantaneous encoding phase partitions the evolving message alphabet into groups whose priors are close to the capacity-achieving distribution, and randomizes the group indices to ensure that the transmitted group index has the capacity-achieving distribution. Surprisingly, the JSCC reliability function for streaming is equal to that for a fully accessible source, implying that the knowledge of the entire symbol sequence before the transmission offers no advantage in terms of the reliability function.
Faster hyperspectral image classification based on selective kernel mechanism using deep convolutional networks

Hyperspectral imagery is rich in spatial and spectral information. Using 3D-CNN can simultaneously acquire features of spatial and spectral dimensions to facilitate classification of features, but hyperspectral image information spectral dimensional information redundancy. The use of continuous 3D-CNN will result in a high amount of parameters, and the computational power requirements of the device are high, and the training takes too long. This letter designed the Faster selective kernel mechanism network (FSKNet), FSKNet can balance this problem. It designs 3D-CNN and 2D-CNN conversion modules, using 3D-CNN to complete feature extraction while reducing the dimensionality of spatial and spectrum. However, such a model is not lightweight enough. In the converted 2D-CNN, a selective kernel mechanism is proposed, which allows each neuron to adjust the receptive field size based on the two-way input information scale. Under the Selective kernel mechanism, it mainly includes two components, se module and variable convolution. Se acquires channel dimensional attention and variable convolution to obtain spatial dimension deformation information of ground objects. The model is more accurate, faster, and less computationally intensive. FSKNet achieves high accuracy on the IN, UP, Salinas, and Botswana data sets with very small parameters.
CLIPasso

Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings. Abstraction entails identifying the essential visual properties of an object or scene, which requires semantic understanding and prior knowledge of high-level concepts. Abstract depictions are therefore challenging for artists, and even more so for machines. We present CLIPasso, an object sketching method that can achieve different levels of abstraction, guided by geometric and semantic simplifications. While sketch generation methods often rely on explicit sketch datasets for training, we utilize the remarkable ability of CLIP (Contrastive-Language-Image-Pretraining) to distill semantic concepts from sketches and images alike. We define a sketch as a set of Bézier curves and use a differentiable rasterizer to optimize the parameters of the curves directly with respect to a CLIP-based perceptual loss. The abstraction degree is controlled by varying the number of strokes. The generated sketches demonstrate multiple levels of abstraction while maintaining recognizability, underlying structure, and essential visual components of the subject drawn.
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network

People undergoing neuromuscular dysfunctions and amputated limbs require automatic prosthetic appliances. In developing such prostheses, the precise detection of brain motor actions is imperative for the Grasp-and-Lift (GAL) tasks. Because of the low-cost and non-invasive essence of Electroencephalogra-phy (EEG), it is widely preferred for detecting motor actions while controlling prosthetic tools. This article has automated the hand movement activity viz GAL detection method from the 32-channel EEG signals. The proposed pipeline essentially combines preprocessing and end-to-end detection steps, eliminating the requirement of hand-crafted feature engineering. Preprocessing action consists of raw signal denoising, using either Discrete Wavelet Transform (DWT) or highpass or bandpass filtering and data standardization. The detection step consists of Convolutional Neural Network (CNN)- or Long Short Term Memory (LSTM)-based model. All the investigations utilize the publicly available WAY-EEG-GAL dataset, having six different GAL events. The best experiment reveals that the proposed framework achieves an average area under the ROC curve of 0.944, employing the DWT-based denoising filter, data standardization, and CNN-based detection model. The obtained outcome designates an excellent achievement of the introduced method in detecting GAL events from the EEG signals, turning it applicable to prosthetic appliances, brain-computer interfaces, robotic arms, etc.
Text and Image Guided 3D Avatar Generation and Manipulation

The manipulation of latent space has recently become an interesting topic in the field of generative models. Recent research shows that latent directions can be used to manipulate images towards certain attributes. However, controlling the generation process of 3D generative models remains a challenge. In this work, we propose a novel 3D manipulation method that can manipulate both the shape and texture of the model using text or image-based prompts such as ’a young face’ or ’a surprised face’. We leverage the power of Contrastive Language-Image Pre-training (CLIP) model and a pre-trained 3D GAN model designed to generate face avatars and create a fully differentiable rendering pipeline to manipulate meshes. More specifically, our method takes an input latent code and modifies it such that the target attribute specified by a text or image prompt is present or enhanced while leaving other attributes largely unaffected. Our method requires only 5 minutes per manipulation, and we demonstrate the effectiveness of our approach with extensive results and comparisons.
Adaptive Bandit Convex Optimization with Heterogeneous Curvature

We consider the problem of adversarial bandit convex optimization, that is, online learning over a sequence of arbitrary convex loss functions with only one function evaluation for each of them. While all previous works assume known and homogeneous curvature on these loss functions, we study a heterogeneous setting where each function has its own curvature that is only revealed after the learner makes a decision. We develop an efficient algorithm that is able to adapt to the curvature on the fly. Specifically, our algorithm not only recovers or \emph{even improves} existing results for several homogeneous settings, but also leads to surprising results for some heterogeneous settings -- for example, while Hazan and Levy (2014) showed that $\widetilde{O}(d^{3/2}\sqrt{T})$ regret is achievable for a sequence of $T$ smooth and strongly convex $d$-dimensional functions, our algorithm reveals that the same is achievable even if $T^{3/4}$ of them are not strongly convex, and sometimes even if a constant fraction of them are not strongly convex. Our approach is inspired by the framework of Bartlett et al. (2007) who studied a similar heterogeneous setting but with stronger gradient feedback. Extending their framework to the bandit feedback setting requires novel ideas such as lifting the feasible domain and using a logarithmically homogeneous self-concordant barrier regularizer.
Observational Learning with Competitive Prices

Will people eventually learn the value of an asset through observable information? This paper studies observational learning in a market with competitive prices. Comparing a market with public signals and a market with private signals in a sequential trading model, we find that Pairwise Informativeness (PI) is the sufficient and necessary learning condition for a market with public signals; and Avery and Zemsky Condition (AZC) is the sufficient and necessary learning condition for a market with private signals. Moreover, when the number of states is 2 or 3, PI and AZC are equivalent. And when the number of states is greater than 3, PI and Monotonic Likelihood Ratio Property (MLRP) together imply asymptotic learning in the private signal case.
Evolution of the long-period pulsar GLEAM-X J162759.5–523504.3

The long-period (P = 1091 s) of the recently discovered pulsar GLEAM-X J162759.5–523504.3 can be attained by neutron stars evolving with fallback discs and magnetic dipole moments of a few 1030 G cm3 at ages greater than ∼2 × 105 yr consistently with the observational upper limits to the period derivative, $\dot{P}$, and the X-ray luminosity, LX, of the source. The current upper limits for $\dot{P}$ allow two alternative present states: (1) The disc is still active with ongoing accretion at a low rate such that the accretion luminosity is much less than the neutron star’s cooling luminosity, which in turn is below the upper limit for LX. In this scenario the spin-down will continue at $\dot{P}\sim 10^{-10}$ s s−1 until the disc becomes inactive; the final period will be P ∼ a few 103 s. (2) The disc is already inactive, there is no accretion. In this case the period evolution has leveled off to the observed value in the final period range. The remaining, very weak, dipole torque sustaining asymptotic spin-down at $\dot{P}\sim 4 \times 10^{-18}$ s s−1. Long periods P ∼ a few 103 s were predicted for the final states of soft gamma repeaters and anomalous X-ray pulsars with relatively strong dipole fields in earlier work with the fallback disc model.
OctAttention: Octree-based Large-scale Contexts Model for Point Cloud Compression

In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution. However, the contexts gathered by the previous voxel-based methods decrease when handling sparse point clouds. To address this problem, we propose a multiple-contexts deep learning framework called OctAttention employing the octree structure, a memory-efficient representation for point clouds. Our approach encodes octree symbol sequences in a lossless way by gathering the information of sibling and ancestor nodes. Expressly, we first represent point clouds with octree to reduce spatial redundancy, which is robust for point clouds with different resolutions. We then design a conditional entropy model with a large receptive field that models the sibling and ancestor contexts to exploit the strong dependency among the neighboring nodes and employ an attention mechanism to emphasize the correlated nodes in the context. Furthermore, we introduce a mask operation during training and testing to make a trade-off between encoding time and performance. Compared to the previous state-of-the-art works, our approach obtains a 10%-35% BD-Rate gain on the LiDAR benchmark (e.g. SemanticKITTI) and object point cloud dataset (e.g. MPEG 8i, MVUB), and saves 95% coding time compared to the voxel-based baseline. The code is available at https://github.com/zb12138/OctAttention.
HAKE: A Knowledge Engine Foundation for Human Activity Understanding

Human activity understanding is of widespread interest in artificial intelligence and spans diverse applications like health care and behavior analysis. Although there have been advances with deep learning, it remains challenging. The object recognition-like solutions usually try to map pixels to semantics directly, but activity patterns are much different from object patterns, thus hindering another success. In this article, we propose a novel paradigm to reformulate this task in two-stage: first mapping pixels to an intermediate space spanned by atomic activity primitives, then programming detected primitives with interpretable logic rules to infer semantics. To afford a representative primitive space, we build a knowledge base including 26+ M primitive labels and logic rules from human priors or automatic discovering. Our framework, <italic/><bold>H</bold><italic>uman</italic> <bold>A</bold><italic>ctivity</italic> <bold>K</bold><italic>nowledge</italic> <bold>E</bold><italic>ngine</italic> (<italic>HAKE</italic>), exhibits superior generalization ability and performance upon canonical methods on challenging benchmarks. Code and data are available at <uri>http://hake-mvig.cn/</uri>.
The Puzzling Story of Flare Inactive Ultra Fast Rotating M dwarfs. II. Searching for radial velocity variations

Observations made using TESS revealed a sample of low-mass stars which show a periodic modulation on a period < 0.2 d. Surprisingly, many of these ultra fast rotating (UFR) stars showed no evidence of ﬂare activity which would be expected from such rapidly rotating stars. We present results from a spectroscopic surv e y of UFRs using the Nordic Optical Telescope to search for radial velocity variations which could reveal evidence for binarity. Our sample of 29 sources have a photometric period between 0.1–0.2 d, co v er spectral classes of M0-4V, and sho w no e vidence for ﬂares. We detect only one source with clear radial velocity shifts, with another two having Gaia RUWE values which suggests they are binaries. Further observations reveal the former star possibly contains a brown dwarf companion with a mass of M 2 > 58 M Jup and probability P( M 2 < 90 M Jup ) = 50 per cent. There is no evidence for the companion in our spectra, strengthening the case for a brown dwarf companion. We also examine the folded TESS light curves of all our targets, ﬁnding at least two are eclipsing binaries and one which has been contaminated by a spatially nearby δ Sct star. We estimate that around 1/4 of our targets may have been contaminated by short period v ariable stars. Ho we ver, the majority of our targets are consistent with being single, low mass stars whose variability is due to starspots. We outline the possible reasons why they are not ﬂare active despite being such rapid rotators.
Convolutional Neural Network with Convolutional Block Attention Module for Finger Vein Recognition

Convolutional neural networks have become a popular research in the field of finger vein recognition because of their powerful image feature representation. However, most researchers focus on improving the performance of the network by increasing the CNN depth and width, which often requires high computational effort. Moreover, we can notice that not only the importance of pixels in different channels is different, but also the importance of pixels in different positions of the same channel is different. To reduce the computational effort and to take into account the different importance of pixels, we propose a lightweight convolutional neural network with a convolutional block attention module (CBAM) for finger vein recognition, which can achieve a more accurate capture of visual structures through an attention mechanism. First, image sequences are fed into a lightweight convolutional neural network we designed to improve visual features. Afterwards, it learns to assign feature weights in an adaptive manner with the help of a convolutional block attention module. The experiments are carried out on two publicly available databases and the results demonstrate that the proposed method achieves a stable, highly accurate, and robust performance in multimodal finger recognition.
A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron

The Coronavirus (COVID-19) outbreak in December 2019 has drastically affected humans worldwide, creating a health crisis that has infected millions of lives and devastated the global economy. COVID-19 is ongoing, with the emergence of many new strains. Deep learning (DL) techniques have proven helpful in efficiently analysing and delineating infectious regions in radiological images. This survey paper draws a taxonomy of deep learning techniques for detecting COVID-19 infection in radiographic imaging modalities Chest X-Ray, and Computer Tomography. DL techniques are broadly categorised into classification, segmentation, and multi-stage approaches for COVID-19 diagnosis at the image and region-level analysis. These techniques are further classified as pre-trained and custom-made Convolutional Neural Network architectures. Furthermore, a discussion is drawn on radiographic datasets, evaluation metrics, and commercial platforms provided for detection. In the end, a brief look is paid to emerging ideas, gaps in existing research, and challenges in developing diagnostic techniques. This survey provides insight into the promising areas of research in DL and is likely to guide the research community on the upcoming development of deep learning techniques for COVID-19. This will pave the way to accelerate the research in designing customised DL-based diagnostic tools for effectively dealing with new variants of COVID-19 and emerging challenges. © 2023 Informa UK Limited, trading as Taylor & Francis Group.