Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Optimality of Batch Policy Optimization Algorithms

Batch policy optimization considers leveraging existing data for policy construction before interacting with an environment. Although interest in this problem has grown significantly in recent years, its theoretical foundations remain under-developed. To advance the understanding of this problem, we provide three results that characterize the limits and possibilities of batch policy optimization in the finite-armed stochastic bandit setting. First, we introduce a class of confidence-adjusted index algorithms that unifies optimistic and pessimistic principles in a common framework, which enables a general analysis. For this family, we show that any confidence-adjusted index algorithm is minimax optimal, whether it be optimistic, pessimistic or neutral. Our analysis reveals that instance-dependent optimality, commonly used to establish optimality of on-line stochastic bandit algorithms, cannot be achieved by any algorithm in the batch setting. In particular, for any algorithm that performs optimally in some environment, there exists another environment where the same algorithm suffers arbitrarily larger regret. Therefore, to establish a framework for distinguishing algorithms, we introduce a new weighted-minimax criterion that considers the inherent difficulty of optimal value prediction. We demonstrate how this criterion can be used to justify commonly used pessimistic principles for batch policy optimization.
Delayed buckling of spherical shells due to viscoelastic knockdown of the critical load

We performed dynamic pressure buckling experiments on defect-seeded spherical shells made of a common silicone elastomer. Unlike in quasi-static experiments, shells buckled at ostensibly subcritical pressures, i.e. below the experimentally determined critical load at which buckling occurs elastically, often following a significant delay period from the time of load application. While emphasizing the close connections to elastic shell buckling, we rely on viscoelasticity to explain our observations. In particular, we demonstrate that the lower critical load may be determined from the material properties, which is rationalized by a simple analogy to elastic spherical shell buckling. We then introduce a model centred on empirical quantities to show that viscoelastic creep deformation lowers the critical load in the same predictable, quantifiable way that a growing defect would in an elastic shell. This allows us to capture how both the deflection at instability and the time delay depend on the applied pressure, material properties and defect geometry. These quantities are straightforward to measure in experiments. Thus, our work not only provides intuition for viscoelastic behaviour from an elastic shell buckling perspective but also offers an accessible pathway to introduce tunable, time-controlled actuation to existing mechanical actuators, e.g. pneumatic grippers.
Strategy synthesis for partially-known switched stochastic systems

We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems. The properties of the system are specified using linear temporal logic (LTL) over finite traces (LTLf), which is as expressive as LTL and enables interpretations over finite behaviors. The framework first learns the unknown dynamics via Gaussian process regression. Then, it builds a formal abstraction of the switched system in terms of an uncertain Markov model, namely an Interval Markov Decision Process (IMDP), by accounting for both the stochastic behavior of the system and the uncertainty in the learning step. Then, we synthesize a strategy on the resulting IMDP that maximizes the satisfaction probability of the LTLf specification and is robust against all the uncertainties in the abstraction. This strategy is then refined into a switching strategy for the original stochastic system. We show that this strategy is near-optimal and provide a bound on its distance (error) to the optimal strategy. We experimentally validate our framework on various case studies, including both linear and non-linear switched stochastic systems.
Nonlinear Model Based Guidance with Deep Learning Based Target Trajectory Prediction Against Aerial Agile Attack Patterns

In this work, we propose a novel missile guidance algorithm that combines deep learning based trajectory prediction with nonlinear model predictive control. Although missile guidance and threat interception is a well-studied problem, existing algorithms' performance degrade significantly when the target is pulling high acceleration attack maneuvers while rapidly changing its direction. We argue that since most threats execute similar attack maneuvers, these nonlinear trajectory patterns can be processed with modern machine learning methods to build high accuracy trajectory prediction algorithms. We train a long short-term memory network (LSTM) based on a class of simulated structured agile attack patterns, then combine this predictor with quadratic programming based nonlinear model predictive control (NMPC). Our method, named nonlinear model based predictive control with target acceleration predictions (NMPC-TAP), significantly outperforms compared approaches in terms of miss distance, for the scenarios where the target/threat is executing agile maneuvers.
Localizing Visual Sounds the Hard Way

The objective of this work is to localize sound sources that are visible in a video without using manual annotations. Our key technical contribution is to show that, by training the network to explicitly discriminate challenging image fragments, even for images that do contain the object emitting the sound, we can significantly boost the localization performance. We do so elegantly by introducing a mechanism to mine hard samples and add them to a contrastive learning formulation automatically. We show that our algorithm achieves state-of-the-art performance on the popular Flickr SoundNet dataset. Furthermore, we introduce the VGG-Sound Source (VGG-SS) benchmark, a new set of annotations for the recently-introduced VGG-Sound dataset, where the sound sources visible in each video clip are explicitly marked with bounding box annotations. This dataset is 20 times larger than analogous existing ones, contains 5K videos spanning over 200 categories, and, differently from Flickr SoundNet, is video-based. On VGG-SS, we also show that our algorithm achieves state-of-the-art performance against several baselines. Code and datasets can be found at http://www.robots. ox.ac. uk/˜vgg/research/lvs/.
IronMask: Modular Architecture for Protecting Deep Face Template

Convolutional neural networks have made remarkable progress in the face recognition field. The more the technology of face recognition advances, the greater discriminative features into a face template. However, this increases the threat to user privacy in case the template is exposed.In this paper, we present a modular architecture for face template protection, called IronMask, that can be combined with any face recognition system using angular distance metric. We circumvent the need for binarization, which is the main cause of performance degradation in most existing face template protections, by proposing a new real-valued error-correcting-code that is compatible with real-valued templates and can therefore, minimize performance degradation. We evaluate the efficacy of IronMask by extensive experiments on two face recognitions, ArcFace and Cos-Face with three datasets, CMU-Multi-PIE, FEI, and Color-FERET. According to our experimental results, IronMask achieves a true accept rate (TAR) of 99.79% at a false accept rate (FAR) of 0.0005% when combined with ArcFace, and 95.78% TAR at 0% FAR with CosFace, while providing at least 115-bit security against known attacks.
IGRINS RV: A Precision Radial Velocity Pipeline for IGRINS Using Modified Forward Modeling in the Near-infrared

Application of the radial velocity (RV) technique in the near-infrared is valuable because of the diminished impact of stellar activity at longer wavelengths, making it particularly advantageous for the study of late-type stars but also for solar-type objects. In this paper, we present the IGRINS RV open-source python pipeline for computing infrared RV measurements from reduced spectra taken with IGRINS, an R ≡ λ/Δλ ∼ 45,000 spectrograph with simultaneous coverage of the H band (1.49–1.80 μm) and K band (1.96–2.46 μm). Using a modified forward-modeling technique, we construct high-resolution telluric templates from A0 standard observations on a nightly basis to provide a source of common-path wavelength calibration while mitigating the need to mask or correct for telluric absorption. Telluric standard observations are also used to model the variations in instrumental resolution across the detector, including a yearlong period when the K band was defocused. Without any additional instrument hardware, such as a gas cell or laser frequency comb, we are able to achieve precisions of 26.8 m s−1 in the K band and 31.1 m s−1 in the H band for narrow-line hosts. These precisions are empirically determined by a monitoring campaign of two RV standard stars, as well as the successful retrieval of planet-induced RV signals for both HD 189733 and τ Boo A; furthermore, our results affirm the presence of the Rossiter–McLaughlin effect for HD 189733. The IGRINS RV pipeline extends another important science capability to IGRINS, with publicly available software designed for widespread use.
Photoisomerization reaction of unsymmetrical azobenzene disulfide self-assembled monolayers: Modification of azobenzene dyes to improve thermal endurance for photoreaction

Recently we have developed an unsymmetrical azobenzene disulfide with a short alkyl side chain, 4-hexyl-4‘-(12-(dodecyldithio)-dodecyloxy)azobenzene (C6AzSSC12), aiming of a high efficiency in photoisomerization in SAMs on planar gold surfaces (Tamada, K.; et al. Langmuir 2002, 18, 5239). In this paper, we introduce an additional modification on the molecule to improve the thermal endurance for the photoreaction by attachment of a methyl group to the azobenzene ring, thus avoiding dye aggregation sterically. A “methyl-derivatized” azobenzenethiol (C6Az(Me)SH) SAM revealed a significant improvement in their photoreactivity compared with an unmodified azobenzenethiol (C6AzSH) SAM due to the steric effect of the methyl group. A “methyl-derivatized” unsymmetrical azobenzene disulfide (C6Az(Me)SSC12) SAM exhibited quite a similar photoresponse to that of C6AzSSC12 SAM before heat treatment owing to the free volume given by the unsymmetrical structure; however, only the C6Az(Me)SSC12 SAM could retain high photo.
On Performance Loss of DOA Measurement Using Massive MIMO Receiver With Mixed-ADCs

High hardware cost and high power consumption of massive multiple-input and multiple output (MIMO) are two challenges for the future wireless communications including sixth generation (6G). Adopting the low-resolution analog-to-digital converter (ADC) is viewed as a promising solution. Additionally, the direction of arrival (DOA) estimation is an indispensable technology for beam alignment and tracking in massive MIMO systems. Thus, in this letter, the performance of DOA estimation with mixed-ADC structure is firstly investigated. The Cramér-Rao lower bound (CRLB) for this architecture is derived based on the additive quantization noise model. Eventually, a performance loss factor and the associated energy efficiency factor is defined for analysis in detail. Simulation results show that the mixed-ADC architecture can strike a good balance among performance loss, circuit cost and energy efficiency. More importantly, just a few bits (up to 4 bits) of low-resolution ADCs can achieve a satisfactory performance for DOA measurement.
GASP. XXXIII. The Ability of Spatially Resolved Data to Distinguish among the Different Physical Mechanisms Affecting Galaxies in Low-density Environments

Galaxies inhabit a wide range of environments and therefore are affected by different physical mechanisms. Spatially resolved maps combined with the knowledge of the hosting environment are very powerful for classifying galaxies by physical process. In the context of the GAs Stripping Phenomena in galaxies (GASP), we present a study of 27 non-cluster galaxies: 24 of them were selected for showing asymmetries and disturbances in the optical morphology, suggestive of gas stripping; 3 of them are passive galaxies and were included to characterize the final stages of galaxy evolution. We therefore provide a panorama of the different processes taking place in low-density environments. The analysis of VLT/MUSE data allows us to separate galaxies into the following categories: galaxy–galaxy interactions (2 galaxies), mergers (6), ram pressure stripping (4), cosmic web stripping (2), cosmic web enhancement (5), gas accretion (3), and starvation (3). In one galaxy we identify the combination of merger and ram pressure stripping. Only 6/27 of these galaxies have just a tentative classification. We then investigate where these galaxies are located on scaling relations determined for a sample of undisturbed galaxies. Our analysis shows the successes and limitations of a visual optical selection in identifying the processes that deplete galaxies of their gas content and probes the power of IFU data in pinning down the acting mechanism.
The Extragalactic Distance Database: The Color–Magnitude Diagrams/Tip of the Red Giant Branch Distance Catalog

The Extragalactic Distance Database (EDD) was created as a repository for high-quality, redshift-independent distances. A key component of EDD is the Color–Magnitude Diagrams/Tip of the Red Giant Branch (CMDs/TRGB) catalog, which provides information on the stellar content of nearby galaxies observed with the Hubble Space Telescope (HST). Here we provide a decadal update to this catalog, which has now doubled in size to over 500 galaxies. We highlight the additions to our data reduction and analysis techniques and provide examples of the science that has been made possible with this large data set. We find the TRGB to be a reliable measure for distance, and we aim to extend its distance coverage with HST to every galaxy within 10 Mpc. In the near future, the combination of the James Webb Space Telescope and the Nancy Grace Roman Space Telescope will dramatically increase the number of targets within our grasp.
Malignancy Prediction and Lesion Identification from Clinical Dermatological Images

We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture. Additionally, we do not assume that images contain single lesions, thus the framework supports both focal or wide-field images. Specifically, we propose a two-stage approach in which we first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy that can be used for high-level screening processes. Further, we consider augmenting the proposed approach with clinical covariates (from electronic health records) and publicly available data (the ISIC dataset). Comprehensive experiments validated on an independent test dataset demonstrate that i) the proposed approach outperforms alternative model architectures; ii) the model based on images outperforms a pure clinical model by a large margin, and the combination of images and clinical data does not significantly improves over the image-only model; and iii) the proposed framework offers comparable performance in terms of malignancy classification relative to three board certified dermatologists with different levels of experience.
Hyperspectral and Lidar Data Classification Based on Linear Self-Attention

In this paper, an efficient linear self-attention fusion model is proposed for the task of hyperspectral image (HSI) and LiDAR data joint classification. The proposed method is comprised of a feature extraction module, an attention module, and a fusion module. The attention module is a plug-and-play linear self-attention module that can be extensively used in any model. The proposed model has achieved the overall accuracy of 95.40% on the Houston dataset. The experimental results demonstrate the superiority of the proposed method over other state-of-the-art models.
Software Defined Edge Computing for Distributed Management and Scalable Control in IoT Multinetworks

Recently, edge computing has emerged as a promising paradigm to support mobile access in IoT multinetworks. However, coexistence of heterogeneous wireless communication schemes brings about new challenges to the mobility management and access control in such networks. To maintain its computing continuum, Software-Defined Networking (SDN) architecture can be applied to the distributed edge networks, which provides consistent frameworks for mobility management and creates secure mechanisms for access control. In this paper, we first review the state-of-the-art SDN technology and its applications in edge computing. In particular, we highlight the prominent issues related to mobility management and access control for SDN in the edge computing environment. We also introduce a number of effective solutions to these issues by presenting real-world testbed experiments, prototypes and numerical analysis of adopting SDN based edge computing paradigms for IoT multinetworks.
Improving thermal conductivity and decreasing supercooling of paraffin phase change materials by n-octadecylamine-functionalized multi-walled carbon nanotubes

Improving thermal conductivity and decreasing supercooling are essential for the utilization of paraffin phase change materials (PCMs). In this work, n-octadecylamine-functionalized multi-walled carbon nanotubes (f-MWCNTs) are obtained through a simple method of carboxylation of the MWCNTs with mixed acids of H2SO4 and HNO3 and then salt-forming reaction with n-octadecylamine. The paraffin/f-MWCNTs (paraffin/f-MWCNTs) composite PCMs are fabricated by mixing paraffin with f-MWCNTs under ultrasonication at 70 °C. It is found that the f-MWCNTs are homogenously dispersed in a toluene or paraffin matrix due to the existence of long chain alkanes in f-MWCNTs. As a result, the thermal conductivity and heat transfer of the paraffin/f-MWCNTs composite PCMs are significantly enhanced. Moreover, differential scanning calorimetry (DSC) analysis indicates that the incorporation of f-MWCNTs reduces the supercooling of paraffin, mainly due to the well-dispersed f-MWCNTs serving as nuclei to promote the heterogeneous nucleation and crystallization process of paraffin.
The disturbance leg-lift response (DLR): an undescribed behavior in bumble bees

Background Bumble bees, primarily Bombus impatiens and B. terrestris, are becoming increasingly popular organisms in behavioral ecology and comparative psychology research. Despite growing use in foraging and appetitive conditioning experiments, little attention has been given to innate antipredator responses and their ability to be altered by experience. In this paper, we discuss a primarily undescribed behavior, the disturbance leg-lift response (DLR). When exposed to a presumably threatening stimulus, bumble bees often react by lifting one or multiple legs. We investigated DLR across two experiments. Methods In our first experiment, we investigated the function of DLR as a prerequisite to later conditioning research. We recorded the occurrence and sequence of DLR, biting and stinging in response to an approaching object that was either presented inside a small, clear apparatus containing a bee, or presented directly outside of the subject’s apparatus. In our second experiment, we investigated if DLR could be altered by learning and experience in a similar manner to many other well-known bee behaviors. We specifically investigated habituation learning by repeatedly presenting a mild visual stimulus to samples of captive and wild bees. Results The results of our first experiment show that DLR and other defensive behaviors occur as a looming object approaches, and that the response is greater when proximity to the object is lower. More importantly, we found that DLR usually occurs first, rarely precedes biting, and often precedes stinging. This suggests that DLR may function as a warning signal that a sting will occur. In our second experiment, we found that DLR can be altered as a function of habituation learning in both captive and wild bees, though the captive sample initially responded more. This suggests that DLR may be a suitable response for many other conditioning experiments.
Paediatrics: how to manage viral gastroenteritis

Background Viral gastroenteritis is the most common diarrhoeal disorder seen in general practice and emergency departments. This article aims to provide a narrative updated review on the evaluation and management of viral gastroenteritis in children. Methods A PubMed search was performed with Clinical Queries using the key term ‘viral gastroenteritis’. The search strategy included clinical trials, meta-analyses, randomized controlled trials, observational studies and reviews. The search was restricted to the English literature and the paediatric population. Results Acute viral gastroenteritis is usually self-limiting. However, it can lead to dehydration and electrolyte imbalance if not properly treated. Adequate fluids containing physiological concentrations of glucose and electrolytes should be provided to compensate for gastrointestinal losses and cover maintenance needs. Oral rehydration therapy is as effective as intravenous (IV) fluid therapy for rehydration for children with mild-to-moderate dehydration. Measurements of serum electrolytes, creatinine and glucose are usually not necessary and should only be considered in a subset of children with severe dehydration who require hospitalization and IV therapy. Judicious use of ondansetron can increase the success rate of oral rehydration therapy and minimize the need for IV therapy and hospitalization. Conclusion Acute viral gastroenteritis is associated with substantial morbidity in developed countries and significant mortality in developing countries. Physicians should educate caregivers on proper personal hygiene and handwashing to prevent faecal to oral transmission of the pathogen as well as the importance of rotavirus vaccine in the prevention of rotavirus gastroenteritis. Several norovirus vaccines are currently undergoing clinical trials with promising results. It is hoped that development of an effective norovirus vaccine will further reduce the incidence of viral gastroenteritis.