Miracle-dz/UniFAR-FTUs huggingface dataset:
Norm-Agnostic Linear Bandits

Linear bandits have a wide variety of applications including recommendation systems yet they make one strong assumption: the algorithms must know an upper bound $S$ on the norm of the unknown parameter $\theta^*$ that governs the reward generation. Such an assumption forces the practitioner to guess $S$ involved in the confidence bound, leaving no choice but to wish that $\|\theta^*\|\le S$ is true to guarantee that the regret will be low. In this paper, we propose novel algorithms that do not require such knowledge for the first time. Specifically, we propose two algorithms and analyze their regret bounds: one for the changing arm set setting and the other for the fixed arm set setting. Our regret bound for the former shows that the price of not knowing $S$ does not affect the leading term in the regret bound and inflates only the lower order term. For the latter, we do not pay any price in the regret for now knowing $S$. Our numerical experiments show standard algorithms assuming knowledge of $S$ can fail catastrophically when $\|\theta^*\|\le S$ is not true whereas our algorithms enjoy low regret.
The Line Speed Effect in Roll-to-Roll Dry Transfer of Chemical Vapor Deposition Graphene

A roll-to-roll (R2R) technique is especially desirable for transfer of chemical vapor deposition (CVD) graphene towards high-speed, low-cost, renewable, and environmentally friendly manufacturing of graphenebased electronic devices, such as flexible touchscreens, field effect transistors and organic solar cells. A R2R graphene dry transfer system is recently developed. Monolayer graphene is transferred from a copper growth substrate to a polymer backing layer by mechanical peeling. In this work, we present an experimental study to examine the effects of line speed of the mechanical peeling process on the transferred graphene quality. It is shown that the effect of line speed is not monotonic, and an optimal speed exists to yield the highest and most consistent electrical conductivity of transferred graphene among the process conditions studied. This study provides understanding of process parameter effects and demonstrates the potential of the R2R dry transfer process for large-scale CVD graphene toward industrial
A Novel Deep Learning Approach for Short Term Photovoltaic Power Forecasting Based on GRU-CNN Model

The integration of photovoltaic power brings the key to clean energy. However, the increasing proportion of photovoltaic (PV) energy in power systems due to the random and intermittent nature of solar energy resources is causing difficulties for system operators to dispatch PV power stations. To reduce the negative influence of the use of PV power, it is great significant to predict PV power accurately. In this paper, we propose a high-precision hybrid neural network model that employs Gated Recurrent Units (GRU) and Convolution Neural Network (CNN) to build a GRU-CNN model to forecast PV system output power. The proposed framework has two major phases. Firstly, the sample data is divided into training set and test set. For this, the temporal characteristics of the data set are extracted using a GRU model and the spatial characteristics are obtained using the CNN model. Secondly, the final predicted PV power is obtained through the output layer. The forecasting accuracy of GRU-CNN is determined by the mean absolute error (MAE), mean square error (MSE), determination coefficient (R2) and root mean square error (RMSE) values. The findings of the comparison experiments show that the GRU-CNN model has better accuracy than some deep learning methods, including, GRU, CNN and long-short term memory model (LSTM).
Altered brain reward response to monetary incentives in fibromyalgia: A replication study

Dysregulated brain reward systems have been observed in chronic pain. Using functional magnetic resonance imaging (fMRI) and a monetary incentive delay (MID) task, Martucci et al. (2018) showed that neural responses to reward anticipation and outcome are altered in patients with fibromyalgia. The current study aimed to replicate these results in a separate cohort of patients with fibromyalgia recruited at a new location using a similar study design. Twenty patients with fibromyalgia and 20 healthy controls were included in the replication study. Group fMRI analyses revealed a solid and consistent trend of main findings similar to the previous results. Specifically, in the replication cohort of patients with fibromyalgia, medial prefrontal cortex (MPFC) activity was reduced during gain anticipation and increased during no-loss (non-punishment) outcomes, as compared to controls. Similar to the Martucci et al. results, again in the replication cohort, nucleus accumbens activity during gain anticipation did not differ in patients compared to controls. The same behavioral, correlational, and exploratory analyses that were conducted in the Martucci et al. study were conducted in the present replication study, with prior results largely replicated here. Thus, the present replication study results solidify observations of altered cortico-striatal processing to monetary rewards in chronic pain, which underscore relevance of altered brain reward circuits, particularly as related to the MPFC in patients with fibromyalgia.
Towards Effective Resource Procurement in MEC: A Resource Re-Selling Framework

On-demand and resource reservation pricing models, widely used in cloud computing, are currently used in Multi-Access Edge Computing (MEC). Nevertheless the edge's resources are distributed and each server has lower capacity. If too much resources were reserved in advance, on-demand users may not get their jobs served on time, jeopardizing MEC's latency benefits. Concurrently, reservation plan users may possess un-used quota. Therefore, we propose a sharing platform where reservation plan users can re-sell unused resource quota to on-demand users. To investigate the mobile network operator's (MNO‘s) incentive of allowing re-selling, we formulate a 3-stage non-cooperative Stackelberg Game and characterize the optimal strategies of buyers and re-sellers. We show that users’ actions give rise to 4 different outcomes at equilibrium, dependent on the prices and supply levels of the sharing and on-demand pools. Based on the 4 possible outcomes, we characterise the MNO's optimal prices for on-demand users. Numerical results show that having both pools gives the MNO an optimal revenue when the on-demand pool's supply is low, and unexpectedly, when the MNO's commission is low. We develop an interactive prototype, and show that users’ decision distributions in studies on our prototype are similar to that of our decision model.
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue

Selecting an appropriate response from many candidates given the utterances in a multi-turn dialogue is the key problem for a retrieval-based dialogue system. Existing work formalizes the task as matching between the utterances and a candidate and uses the cross-entropy loss in learning of the model. This paper applies contrastive learning to the problem by using the supervised contrastive loss. In this way, the learned representations of positive examples and representations of negative examples can be more distantly separated in the embedding space, and the performance of matching can be enhanced. We further develop a new method for supervised contrastive learning, referred to as two-level supervised contrastive learning, and employ the method in response selection in multi-turn dialogue. Our method exploits two techniques: sentence token shuffling (STS) and sentence re-ordering (SR) for supervised contrastive learning. Experimental results on three benchmark datasets demonstrate that the proposed method significantly outperforms the contrastive learning baseline and the state-of-the-art methods for the task.
Bi- and tetracritical phase diagrams in three dimensions

The critical behavior of many physical systems involves two competing [Formula: see text]- and n2-component order-parameters, S1 and S2, respectively, with n = n1 + n2. Varying an external control parameter g, one encounters ordering of S1 below a critical (second-order) line for g < 0 and of S2 below another critical line for g > 0. These two ordered phases are separated by a first-order line, which meets the above critical lines at a bicritical point, or by an intermediate (mixed) phase, bounded by two critical lines, which meet the above critical lines at a tetracritical point. For n = 1 + 2 = 3, the critical behavior around the (bi- or tetra-) multicritical point either belongs to the universality class of a non-rotationally invariant (cubic or biconical) fixed point, or it has a fluctuation driven first-order transition. These asymptotic behaviors arise only very close to the transitions. We present accurate renormalization-group flow trajectories yielding the effective crossover exponents near multicriticality.
Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models

Simulations of high energy density physics are expensive, largely in part for the need to produce nonlocal thermodynamic equilibrium opacities. High-fidelity spectra may reveal new physics in the simulations not seen with low-fidelity spectra, but the cost of these simulations also scales with the level of fidelity of the opacities being used. Neural networks are capable of reproducing these spectra, but neural networks need data to train them, which limits the level of fidelity of the training data. This article demonstrates that it is possible to reproduce high-fidelity spectra with median errors in the realm of 3%–4% using as few as 50 samples of high-fidelity Krypton data by performing transfer learning on a neural network trained on many times more low-fidelity data.
Computerization of Clinical Pathways: A Literature Review and Directions for Future Research

Clinical Pathways (CP) are medical management plans developed to standardize patient treatment activities, optimize resource usage, reduce expenses, and improve the quality of healthcare services. Most CPs currently in use are paper-based documents (i.e., not computerized). CP computerization has been an active research topic since the inception of CP use in hospitals. This literature review research aims to examine studies that focused on CP computerization and offers recommendations for future research in this important research area. Some critical research suggestions include centralizing computerized CPs in Healthcare Information Systems (HIS), CP term standardization using international medical terminology systems, developing a global CP-specific digital coding system, creating a unified CP metaontology, developing independent Clinical Pathway Management Systems (CPMS), and supporting CPMSs with machine learning sub-systems.
Task-grasping from human demonstration

A challenge in robot grasping is to achieve task-grasping which is to select a grasp that is advantageous to the success of tasks before and after grasps. One of the frameworks to address this difficulty is Learning-from-Observation (LfO), which obtains various hints from human demonstrations. This paper solves three issues in the grasping skills in the LfO framework: 1) how to functionally mimic human-demonstrated grasps to robots with limited grasp capability, 2) how to coordinate grasp skills with reaching body mimicking, 3) how to robustly perform grasps under object pose and shape uncertainty. A deep reinforcement learning using contact-web based rewards and domain randomization of approach directions is proposed to achieve such robust mimicked grasping skills. Experiment results show that the trained grasping skills can be applied in an LfO system and executed on a real robot. In addition, it is shown that the trained skill is robust to errors in the object pose and to the uncertainty of the object shape and can be combined with various reach-coordination.
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input

We address the problem of devising the means for a robot to rapidly and safely learn insertion skills with just a few human interventions and without hand-crafted rewards or demonstrations. Our InsertionNet version 2.0 provides an improved technique to robustly cope with a wide range of use-cases featuring different shapes, colors, initial poses, etc. In particular, we present a regression-based method based on multimodal input from stereo perception and force, augmented with contrastive learning for the efficient learning of valuable features. In addition, we introduce a one-shot learning technique for insertion, which relies on a relation network scheme to better exploit the collected data and to support multi-step insertion tasks. Our method improves on the results obtained with the original InsertionNet, achieving an almost perfect score (above 97.5% on 200 trials) in 16 real-life insertion tasks while minimizing the execution time and contact during insertion. We further demonstrate our method's ability to tackle a real-life 3-step insertion task and perfectly solve an unseen insertion task without learning.
Rings and arcs around evolved stars -- III. Physical conditions of the ring-like structures in the planetary nebula IC 4406 revealed by MUSE

We present the analysis of Very Large Telescope (VLT)Multi Unit Spectroscopic Explorer (MUSE) observations of the planetary nebula (PN) IC 4406. MUSE images in key emission lines are used to unveil the presence of at least 5 ring-like structures North and South of the main nebula of IC 4406. MUSE spectra are extracted from the rings to unambiguously assess for the first time in a PN their physical conditions, electron density (ne) and temperature (Te). The rings are found to have similar Te than the rim of the main nebula, but smaller ne. Ratios between different ionic species suggest that the rings of IC 4406 have a lower ionization state than the main cavity, in contrast to what was suggested for the rings in NGC6543, the Cat’s Eye Nebula.
Follow-Up of Extended Shells around B[e] Stars

B[e] stars are massive B type emission line stars in different evolutionary stages ranging from pre-main sequence to post-main sequence. Due to their mass loss and ejection events these objects deposit huge amounts of mass and energy into their environment and enrich it with chemically processed material, contributing significantly to the chemical and dynamical evolution of their host galaxies. However, the large-scale environments of these enigmatic objects have not attracted much attention. The first and so far only catalog reporting the detection of extended shells around a sample of B[e] stars was an Hα imaging survey carried out in the year 2001, and was limited to bright targets in the northern hemisphere. We have recently started a follow-up of those targets to detect possible evolution of their nebulae in the plane of the sky over a baseline of two decades. Furthermore, we extend our survey to southern targets and fainter northern ones to complement and complete our knowledge on large-scale ejecta surrounding B[e] stars. Besides imaging in Hα and selected nebular lines, we utilize long-slit and 3D spectral observations across the nebulae to derive their physical properties. We discovered pronounced nebula structures around 15 more objects, resulting in a total of 27 B[e] stars with a large-scale nebula. Here we present our (preliminary) results for three selected objects: the two massive supergiants MWC137 and MWC 314, and the unclassified B[e] star MWC 819.
Imitation of Manipulation Skills Using Multiple Geometries

Daily manipulation tasks are characterized by geometric primitives related to actions and object shapes. Such geometric descriptors are poorly represented by only using Cartesian coordinate systems. In this paper, we propose a learning approach to extract the optimal representation from a dictionary of coordinate systems to encode an observed movement/behavior. This is achieved by using an extension of Gaussian distributions on Riemannian manifolds, which is used to analyse a set of user demonstrations statistically, by considering multiple geometries as candidate representations of the task. We formulate the reproduction problem as a general optimal control problem based on an iterative linear quadratic regulator (iLQR), where the Gaussian distribution in the extracted coordinate systems are used to define the cost function. We apply our approach to object grasping and box opening tasks in simulation and on a 7-axis Franka Emika robot. The results show that the robot can exploit several geometries to execute the manipulation task and generalize it to new situations, by maintaining the invariant characteristics of the task in the coordinate system(s) of interest.
Slow-fluor scintillator for low energy solar neutrinos and neutrinoless double beta decay

The potential for using slow-ﬂuor liquid scintillators to study low energy solar neutrinos and neutrinoless double beta decay (0 νββ ) is explored through a series of simulations. The ﬂuorescence model assumed for the primary ﬂuor has characteristics similar to acenaphthene, recently used to demonstrate Cherenkov separation at energies around 1 MeV [1]. Results here indicate notably better directional reconstruction in large-scale detectors than has previously been suggested by other approaches, allowing better identiﬁcation of low energy solar neutrinos. These studies indicate that a detector with as little as ∼ 30% coverage using currently available photomultiplier tubes could be able to make a measurement of the CNO solar neutrino ﬂux to a precision of better than 10% (enough to distinguish metallicity models) with a few kiloton-years of exposure. In terms of 0 νββ , studies here suggest that the ability to separate mechanisms based on angular distributions is weak, but that the rejection of solar neutrino backgrounds with such a technique might potentially approach a factor of 10 for endpoint energies near 2.5 MeV in the angular hemisphere deﬁned by the solar direction.
Improving Generalization of Deep Networks for Estimating Physical Properties of Containers and Fillings

We present methods to estimate the physical properties of house-hold containers and their fillings manipulated by humans. We use a lightweight, pre-trained convolutional neural network with coordinate attention as a backbone model of the pipelines to accurately locate the object of interest and estimate the physical properties in the CORSMAL Containers Manipulation (CCM) dataset. We address the filling type classification with audio data and then combine this information from audio with video modalities to address the filling level classification. For the container capacity, dimension, and mass estimation, we present a data augmentation and consistency measurement to alleviate the over-fitting issue in the CCM dataset caused by the limited number of containers. We augment the training data using an object-of-interest-based re-scaling that increases the variety of physical values of the containers. We then perform the consistency measurement to choose a model with low prediction variance in the same containers under different scenes, which ensures the generalization ability of the model. Our method improves the generalization ability of the models to estimate the property of the containers that were not previously seen in the training.
The Implementation of Intelligent Biochemical Monitoring System based on Cloud Platform

In order to grasp the developing rules of chronic illness better, give timely and accurate treatment, and reduce the risk of syndromes occuring, we need more time for patients with some of themselves long-term health indicators for monitoring, and adjust the diet structure and treatment. Aiming at the present development situation and social demand of current cloud computing and health awareness, this paper mainly designed a system of Intelligent biochemical monitor system based on cloud computing. The system uses wireless sensor technology, Bluetooth technology, network communication technology and cloud storage technology based on cloud computing. It is composed of the wireless biochemical data collection sensor, intelligent mobile terminal and the cloud platform. It can complete the measurement of biochemical data, wireless data transmission, data storage, historical data trend and intelligent risk early warning function conveniently and reliably. KEYWORD: Wireless intelligent terminal; MSP430; Bluetooth; Biochemistry; Cloud computing
Half Wavelet Attention on M-Net+ for Low-Light Image Enhancement

Low-Light Image Enhancement is a computer vision task which intensifies the dark images to appropriate brightness. It can also be seen as an illposed problem in image restoration domain. With the success of deep neural networks, the convolutional neural networks surpass the traditional algorithm-based methods and become the mainstream in the computer vision area. To advance the performance of enhancement algorithms, we propose an image enhancement network (HWMNet) based on an improved hierarchical model: M-Net+. Specifically, we use a half wavelet attention block on M-Net+ to enrich the features from wavelet domain. Furthermore, our HWMNet has competitive performance results on two image enhancement datasets in terms of quantitative metrics and visual quality. The source code and pretrained model are available at https://github.com/FanChiMao/HWMNet.
DextAIRity: Deformable Manipulation Can be a Breeze

This paper introduces DextAIRity, an approach to manipulate deformable objects using active airflow. In contrast to conventional contact-based quasi-static manipulations, DextAIRity allows the system to apply dense forces on out-of-contact surfaces, expands the system's reach range, and provides safe high-speed interactions. These properties are particularly advantageous when manipulating under-actuated deformable objects with large surface areas or volumes. We demonstrate the effectiveness of DextAIRity through two challenging deformable object manipulation tasks: cloth unfolding and bag opening. We present a self-supervised learning framework that learns to effectively perform a target task through a sequence of grasping or air-based blowing actions. By using a closed-loop formulation for blowing, the system continuously adjusts its blowing direction based on visual feedback in a way that is robust to the highly stochastic dynamics. We deploy our algorithm on a real-world three-arm system and present evidence suggesting that DextAIRity can improve system efficiency for challenging deformable manipulation tasks, such as cloth unfolding, and enable new applications that are impractical to solve with quasi-static contact-based manipulations (e.g., bag opening). Video is available at https://youtu.be/_B0TpAa5tVo
Are two H-Spaces Homotopy Equivalent? An Algorithmic View Point

This paper proposes an algorithm that decides if two simply connected spaces represented by finite simplicial sets of finite $k$-type and finite dimension $d$ are homotopy equivalent. If the spaces are homotopy equivalent, the algorithm finds a homotopy equivalence between their Postnikov stages in dimension $d$. As a consequence, we get an algorithm deciding if two spaces represented by finite simplicial sets are stably homotopy equivalent.