Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupled Spin-Valley, Rashba Effect, and Hidden Spin Polarization in WSi2N4 Family.

Using first-principles calculations, we report the electronic properties with a special focus on the band splitting in the WSi2N4 class of materials. Due to the broken inversion symmetry and strong spin-orbit coupling, we detect coupled spin-valley effects at the corners of the first Brillouin zone (BZ). Additionally, we observe cubically and linearly split bands around the Γ and M points, respectively. The in-plane mirror symmetry (σh) and reduced symmetry of the arbitrary k-point, enforce the persistent spin textures (PST) to occur in full BZ. We induce the Rashba splitting by breaking the σh through an out-of-plane external electric field (EEF). The inversion asymmetric site point group of the W atom introduces the hidden spin polarization in centrosymmetric layered bulk counterparts. Low energy k.p models demonstrate that the PST along the M-K line is robust to EEF and layer thickness, making them suitable for applications in spintronics and valleytronics.
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network

3D reconstruction of novel categories based on few-shot learning is appealing in real-world applications and attracts increasing research interests. Previous approaches mainly focus on how to design shape prior models for different categories. Their performance on unseen categories is not very competitive. In this paper, we present a Memory Prior Contrastive Network (MPCN) that can store shape prior knowledge in a few-shot learning based 3D reconstruction framework. With the shape memory, a multi-head attention module is proposed to capture different parts of a candidate shape prior and fuse these parts together to guide 3D reconstruction of novel categories. Besides, we introduce a 3D-aware contrastive learning method, which can not only complement the retrieval accuracy of memory network, but also better organize image features for downstream tasks. Compared with previous few-shot 3D reconstruction methods, MPCN can handle the inter-class variabil-ity without category annotations. Experimental results on a benchmark synthetic dataset and the Pascal3D+ real-world dataset show that our model outperforms the current state-of-the-art methods significantly.
Information-Aware Guidance for Magnetic Anomaly based Navigation

In the absence of an absolute positioning system, such as GPS, autonomous vehicles are subject to accumu-lation of positional error which can interfere with reliable performance. Improved navigational accuracy without GPS enables vehicles to achieve a higher degree of autonomy and reliability, both in terms of decision making and safety. This paper details the use of two navigation systems for autonomous agents using magnetic field anomalies to localize themselves within a map; both techniques use the information content in the environment in distinct ways and are aimed at reducing the localization uncertainty. The first method is based on a nonlinear observability metric of the vehicle model, while the second is an information theory based technique which minimizes the expected entropy of the system. These conditions are used to design guidance laws that minimize the localization uncertainty and are verified both in simulation and hardware experiments are presented for the observability approach.
Simplex Clustering via sBeta with Applications to Online Adjustment of Black-Box Predictions

—We explore clustering the softmax predictions of deep neural networks and introduce a novel probabilistic clustering method, referred to as K - S B ETAS . In the general context of clustering discrete distributions, the existing methods focused on exploring distortion measures tailored to simplex data, such as the KL divergence, as alternatives to the standard Euclidean distance. We provide a general maximum a posteriori (MAP) perspective of clustering distributions, which emphasizes that the statistical models underlying the existing distortion-based methods may not be descriptive enough. Instead, we optimize a mixed-variable objective measuring the conformity of data within each cluster to the introduced sBeta density function, whose parameters are constrained and estimated jointly with binary assignment variables. Our versatile formulation approximates a variety of parametric densities for modeling simplex data, and enables to control the cluster-balance bias. This yields highly competitive performances for unsupervised adjustments of black-box model predictions in a variety of scenarios. Our code and comparisons with the existing simplex-clustering approaches along with our introduced softmax-prediction benchmarks are publicly available: https://github.com/fchiaroni/Clustering Softmax Predictions.
The Effects of Dekap Foliar Application on Some Morphological and Physiological Traits of Cucurbita pepo L.

In order to study the effects of Dekap (0, 1, 2 and 3 L ha -1 ) foliar applications on some physiological and morphological treats of Cucurbita pepo an experiment was conducted based on Randomized Complete Block Design at Azerbaijan Shahid Madani University Research Farm. The result showed that the foliar application had significant effects on plant length, number of auxiliary branches, leaf number, number of female flower, total number of fruit, fruit diameter, fruit fresh weight, intact fruit number, fruit yield, number of intact and immature seeds in fruits and oil percent. The highest number of auxiliary branches, plant height, leaf number per plant and the highest data for oil percent, seed length, fruit fresh weight and fruit diameter were recorded at 1 and 2 L ha - 1 foliar application. The highest data for intact seed number was recorded at 1 L ha -1 foliar application but, the highest number of immature seeds were recorded at 2 and 3 L ha -1 foliar application. The highest data for female flower number and total number of fruits per plant were recorded at 1 L ha -1 treatment. The treatment used in this experiment had not significant effects on protein content, seed dry weight, leaf dry weight, potassium and phosphor concentrations. Considering the fact that, in the present experiment there was no significant difference between 1 and 2 L ha -1 of Dekap foliar applications in some of the evaluated traits, so, we can claim that 1 L ha -1 Dekap foliar application can be recommended for the extension section.
Radio fossils, relics, and haloes in Abell 3266: cluster archaeology with ASKAP-EMU and the ATCA

Abell 3266 is a massive and complex merging galaxy cluster that exhibits significant substructure. We present new, highly sensitive radio continuum observations of Abell 3266 performed with the Australian Square Kilometre Array Pathfinder (0.8–1.1 GHz) and the Australia Telescope Compact Array (1.1–3.1 GHz). These deep observations provide new insights into recently reported diffuse non-thermal phenomena associated with the intracluster medium, including a ‘wrong-way’ relic, a fossil plasma source, and an as-yet unclassified central diffuse ridge, which we reveal comprises the brightest part of a large-scale radio halo detected here for the first time. The ‘wrong-way’ relic is highly atypical of its kind: it exhibits many classical signatures of a shock-related radio relic, while at the same time exhibiting strong spectral steepening. While radio relics are generally consistent with a quasi-stationary shock scenario, the ‘wrong-way’ relic is not. We study the spectral properties of the fossil plasma source; it exhibits an ultrasteep and highly curved radio spectrum, indicating an extremely aged electron population. The larger scale radio halo fills much of the cluster centre, and presents a strong connection between the thermal and non-thermal components of the intracluster medium, along with evidence of substructure. Whether the central diffuse ridge is simply a brighter component of the halo, or a mini-halo, remains an open question. Finally, we study the morphological and spectral properties of the multiple complex radio galaxies in this cluster in unprecedented detail, tracing their evolutionary history.
AdaWCT: Adaptive Whitening and Coloring Style Injection

Adaptive instance normalization (AdaIN) has become the standard method for style injection: by re-normalizing features through scale-and-shift operations, it has found widespread use in style transfer, image generation, and image-to-image translation. In this work, we present a generalization of AdaIN which relies on the whitening and coloring transformation (WCT) which we dub AdaWCT, that we apply for style injection in large GANs. We show, through experiments on the StarGANv2 architecture, that this generalization, albeit con-ceptually simple, results in signiﬁcant improvements in the quality of the generated images.
On the uniqueness of supersymmetric AdS(5) black holes with toric symmetry

We consider the classification of supersymmetric AdS5 black hole solutions to minimal gauged supergravity that admit a torus symmetry. This problem reduces to finding a class of toric Kähler metrics on the base space, which in symplectic coordinates are determined by a symplectic potential. We derive the general form of the symplectic potential near any component of the horizon or axis of symmetry, which determines its singular part for any black hole solution in this class, including possible new solutions such as black lenses and multi-black holes. We find that the most general known black hole solution in this context, found by Chong, Cvetic, Lü and Pope (CCLP), is described by a remarkably simple symplectic potential. We prove that any supersymmetric and toric solution that is timelike outside a smooth horizon, with a Kähler base metric of Calabi type, must be the CCLP black hole solution or its near-horizon geometry.
Fast Two-step Blind Optical Aberration Correction

The optics of any camera degrades the sharpness of photographs, which is a key visual quality criterion. This degradation is characterized by the point-spread function (PSF), which depends on the wavelengths of light and is variable across the imaging field. In this paper, we propose a two-step scheme to correct optical aberrations in a single raw or JPEG image, i.e., without any prior information on the camera or lens. First, we estimate local Gaussian blur kernels for overlapping patches and sharpen them with a non-blind deblurring technique. Based on the measurements of the PSFs of dozens of lenses, these blur kernels are modeled as RGB Gaussians defined by seven parameters. Second, we remove the remaining lateral chromatic aberrations (not contemplated in the first step) with a convolutional neural network, trained to minimize the red/green and blue/green residual images. Experiments on both synthetic and real images show that the combination of these two stages yields a fast state-of-the-art blind optical aberration compensation technique that competes with commercial non-blind algorithms.
Accurate Polygonal Mapping of Buildings in Satellite Imagery

This paper studies the problem of polygonal mapping of buildings by tackling the issue of mask reversibility that leads to a notable performance gap between the predicted masks and polygons from the learning-based methods. We addressed such an issue by exploiting the hierarchical supervision (of bottom-level vertices, mid-level line segments and the high-level regional masks) and proposed a novel interaction mechanism of feature embedding sourced from different levels of supervision signals to obtain reversible building masks for polygonal mapping of buildings. As a result, we show that the learned reversible building masks take all the merits of the advances of deep convolutional neural networks for high-performing polygonal mapping of buildings. In the experiments, we evaluated our method on the two public benchmarks of AICrowd and Inria. On the AICrowd dataset, our proposed method obtains unanimous improvements on the metrics of AP, APboundary and PoLiS. For the Inria dataset, our proposed method also obtains very competitive results on the metrics of IoU and Accuracy. The models and source code are available at https://github.com/SarahwXU.
The ropelength conjecture of alternating knots

A long standing conjecture states that the ropelength of any alternating knot is at least proportional to its crossing number. In this paper we prove that this conjecture is true. That is, there exists a constant $b_0>0$ such that $R(K)\ge b_0Cr(K)$ for any alternating knot $K$, where $R(K)$ is the ropelength of $K$ and $Cr(K)$ is the crossing number of $K$. In this paper, we prove that this conjecture is true.
Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression

Visual-inertial localization is a key problem in computer vision and robotics applications such as virtual reality, self-driving cars, and aerial vehicles. The goal is to estimate an accurate pose of an object when either the environment or the dynamics are known. Absolute pose regression (APR) techniques directly regress the absolute pose from an image input in a known scene using convolutional and spatio-temporal networks. Odometry methods perform relative pose regression (RPR) that predicts the relative pose from a known object dynamic (visual or inertial inputs). The localization task can be improved by retrieving information from both data sources for a cross-modal setup, which is a challenging problem due to contradictory tasks. In this work, we conduct a benchmark to evaluate deep multimodal fusion based on pose graph optimization and attention networks. Auxiliary and Bayesian learning are utilized for the APR task. We show accuracy improvements for the APR-RPR task and for the RPR-RPR task for aerial vehicles and hand-held devices. We conduct experiments on the EuRoC MAV and PennCOSYVIO datasets and record and evaluate a novel industry dataset.
Terahertz Spin‐to‐Charge Current Conversion in Stacks of Ferromagnets and the Transition‐Metal Dichalcogenide NbSe2

Transition‐metal dichalcogenides (TMDCs) are an aspiring class of materials with unique electronic and optical properties and potential applications in spin‐based electronics. Here, terahertz emission spectroscopy is used to study spin‐to‐charge current conversion (S2C) in the TMDC NbSe2 in ultra‐high‐vacuum‐grown F|NbSe2 thin‐film stacks, where F is a layer of ferromagnetic Fe or Ni. Ultrafast laser excitation triggers an ultrafast spin current that is converted into an in‐plane charge current and, thus, a measurable THz electromagnetic pulse. The THz signal amplitude as a function of the NbSe2 thickness shows that the measured signals are fully consistent with an ultrafast optically driven injection of an in‐plane‐polarized spin current into NbSe2. Modeling of the spin‐current dynamics reveals that a sizable fraction of the total S2C originates from the bulk of NbSe2 with the opposite, negative sign of the spin Hall angle as compared to Pt. By a quantitative comparison of the emitted THz radiation from F|NbSe2 to F|Pt reference samples and the results of ab initio calculations, it is estimated that the spin Hall angle of NbSe2 for an in‐plane polarized spin current lies between ‐0.2% and ‐1.1%, while the THz spin‐current relaxation length is of the order of a few nanometers.
AvatarGen: a 3D Generative Model for Animatable Human Avatars

Unsupervised generation of 3D-aware clothed humans with various appearances and controllable geometries is important for creating virtual human avatars and other AR/VR applications. Existing methods are either limited to rigid object modeling, or not generative and thus unable to generate high-quality virtual humans and animate them. In this work, we propose AvatarGen, the first method that enables not only geometry-aware clothed human synthesis with high-fidelity appearances but also disentangled human animation controllability, while only requiring 2D images for training. Specifically, we decompose the generative 3D human synthesis into pose-guided mapping and canonical representation with predefined human pose and shape, such that the canonical representation can be explicitly driven to different poses and shapes with the guidance of a 3D parametric human model SMPL. AvatarGen further introduces a deformation network to learn non-rigid deformations for modeling fine-grained geometric details and pose-dependent dynamics. To improve the geometry quality of the generated human avatars, it leverages the signed distance field as geometric proxy, which allows more direct regularization from the 3D geometric priors of SMPL. Benefiting from these designs, our method can generate animatable 3D human avatars with high-quality appearance and geometry modeling, significantly outperforming previous 3D GANs. Furthermore, it is competent for many applications, e.g., single-view reconstruction, re-animation, and text-guided synthesis/editing. Code and pre-trained model will be available at http://jeff95.me/projects/avatargen.html.
Lung nodules segmentation from CT with DeepHealth toolkit

The accurate and consistent border segmentation plays an important role in the tumor volume estimation and its treatment in the field of Medical Image Segmentation. Globally, Lung cancer is one of the leading causes of death and the early detection of lung nodules is essential for the early cancer diagnosis and survival rate of patients. The goal of this study was to demonstrate the feasibility of Deephealth toolkit including PyECVL and PyEDDL libraries to precisely segment lung nodules. Experiments for lung nodules segmentation has been carried out on UniToChest using PyECVL and PyEDDL, for data pre-processing as well as neural network training. The results depict accurate segmentation of lung nodules across a wide diameter range and better accuracy over a traditional detection approach. The datasets and the code used in this paper are publicly available as a baseline reference.
Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities

The mutual information between predictions and model parameters—also referred to as expected information gain or BALD in machine learning—measures informativeness. It is a popular acquisition function in Bayesian active learning and Bayesian optimal experiment design. In data subset selection, i.e. active learning and active sampling, several recent works use Fisher information, Hessians, similarity matrices based on the gradients, or simply the gradient lengths to compute the acquisition scores that guide sample selection. Are these diﬀerent approaches connected, and if so how? In this paper, we revisit the Fisher information and use it to show how several otherwise disparate methods are connected as approximations of information-theoretic quantities.
Neural Correlates of Face Familiarity Perception

In the domain of face recognition, there exists a puzzling timing discrepancy between results from macaque neurophysiology on the one hand and human electrophysiology on the other. Single unit recordings in macaques have demonstrated face identity specific responses in extra-striate visual cortex within 100 milliseconds of stimulus onset. In EEG and MEG experiments with humans, however, a consistent distinction between neural activity corresponding to unfamiliar and familiar faces has been reported to emerge around 250 ms. This points to the possibility that there may be a hitherto undiscovered early correlate of face familiarity perception in human electrophysiological traces. We report here a successful search for such a correlate in dense MEG recordings using pattern classification techniques. Our analyses reveal markers of face familiarity as early as 85 ms after stimulus onset. Low-level attributes of the images, such as luminance and color distributions, are unable to account for this early emerging response difference. These results help reconcile human and macaque data, and provide clues regarding neural mechanisms underlying familiar face perception. The ability to quickly categorize faces as familiar or unfamiliar is a fundamental building block of social cognition in primates. Underscoring the ecological significance of face processing, past research has found evidence for cortical regions in primate brains that are sensitive to face stimuli (Bentin Desimone, Kanwisher Tsao Much of this work has demonstrated differentiation in neural activity when viewing faces versus non-face objects. Significant progress has recently been made in identifying neural correlates of the familiar versus unfamiliar face distinction (Castello
Multi-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in Scalp and Intracranial EEG

Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last several hours or even days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse morphologies and characteristics across diﬀerent patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG (sEEG) and intracranial EEG (iEEG). First, we deploy a convolutional neural network (CNN) with transformers (TRF) and belief matching (BM) loss to detect seizures in single-channel EEG segments (channel-level detection). Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments (segment-level detection). At last, we apply postprocessing ﬁlters to the segment-level outputs to determine the start and end points of seizures in multi-channel EEGs (EEG-level detection). We introduce the minimum overlap evaluation scoring (MOES) as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) sEEG dataset and evaluated it on ﬁve other independent sEEG and iEEG datasets. On the TUH-SZ dataset, the proposed patient-independent seizure detector achieves a sensitivity (SEN), precision (PRE), average and median false positive rate per hour (aFPR/h and mFPR/h), and median oﬀset of 0.772, 0.429, 4.425, 0, and -2.125s, respectively. Across all six datasets (including neonatal and paediatric datasets), we obtained SEN of 0.227-1.00, PRE of 0.377-1.00, aFPR/h of 0.253-2.037 and mFPR/h of 0-0.559. The proposed seizure detector can reliably detect seizures in adult EEGs (to less extent in neonatal EEGs) and takes less than 15s for a 30 minutes EEG. Hence, this system could potentially aid the clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment.
Robust Planning for Multi-stage Forceful Manipulation

Multi-step forceful manipulation tasks, such as opening a push-and-twist childproof bottle, require a robot to make various planning choices that are substantially impacted by the requirement to exert force during the task. The robot must reason over discrete and continuous choices relating to the sequence of actions, such as whether to pick up an object, and the parameters of each of those actions, such how to grasp the object. To enable planning and executing forceful manipulation, we augment an existing task and motion planner with constraints that explicitly consider torque and frictional limits, captured through the proposed forceful kinematic chain constraint. In three domains, opening a childproof bottle, twisting a nut and cutting a vegetable, we demonstrate how the system selects from among a combinatorial set of strategies. We also show how cost-sensitive planning can be used to find strategies and parameters that are robust to uncertainty in the physical parameters.