Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification

Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within these datasets (a process known as re-identification). To address this issue, we propose an AI-powered medical imaging retrieval framework called DeepBrainPrint, which is designed to retrieve brain MRI scans of the same patient. Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations. First, we use a combination of self-supervised and supervised paradigms to create an effective brain fingerprint from MRI scans that can be used for real-time image retrieval. Second, we use a special weighting function to guide the training and improve model convergence. Third, we introduce new imaging transformations to improve retrieval robustness in the presence of intensity variations (i.e. different scan contrasts), and to account for age and disease progression in patients. We tested DeepBrainPrint on a large dataset of T1-weighted brain MRIs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and on a synthetic dataset designed to evaluate retrieval performance with different image modalities. Our results show that DeepBrainPrint outperforms previous methods, including simple similarity metrics and more advanced contrastive deep learning frameworks.
Leveraging Video Coding Knowledge for Deep Video Enhancement

Recent advancements in deep learning techniques have significantly improved the quality of compressed videos. However, previous approaches have not fully exploited the motion characteristics of compressed videos, such as the drastic change in motion between video contents and the hierarchical coding structure of the compressed video. This study proposes a novel framework that leverages the low-delay configuration of video compression to enhance the existing state-of-the-art method, BasicVSR++. We incorporate a context-adaptive video fusion method to enhance the final quality of compressed videos. The proposed approach has been evaluated in the NTIRE22 challenge, a benchmark for video restoration and enhancement, and achieved improvements in both quantitative metrics and visual quality compared to the previous method.
Wavelet analysis of radar echo from finite-size targets

The wavelet analysis technique is applied to analyze the frequency-domain electromagnetic backscattered signal from finite-size targets. Since the frequency-domain radar echo consists of both small-scale natural resonances and large-scale scattering center information, the multiresolution property of the wavelet transform is well suited for analyzing such multiscale signals. Wavelet analysis examples of backscattered data from an open-ended waveguide cavity and a plasma cylinder are presented. Compared with the conventional short-time Fourier transform, the wavelet transform provides a more efficient representation of both the early-time scattering center data and the late-time resonances. The different scattering mechanisms are clearly resolved in the time-frequency representation. >
Data Imputation for Sparse Radio Maps in Indoor Positioning

Indoor location-based services rely on the availability of sufficiently accurate positioning in indoor spaces. A popular approach to positioning relies on so-called radio maps that contain pairs of a vector of Wi-Fi signal strength indicator values (RSSIs), called a fingerprint, and a location label, called a reference point (RP), in which the fingerprint was observed. The positioning accuracy depends on the quality of the radio maps and their fingerprints. Radio maps are often sparse, with many pairs containing vectors missing many RSSIs as well as RPs. Aiming to improve positioning accuracy, we present a complete set of techniques to impute such missing values in radio maps. We differentiate two types of missing RSSIs: missing not at random (MNAR) and missing at random (MAR). Specifically, we design a framework encompassing a missing RSSI differentiator followed by a data imputer for missing values. The differentiator identifies MARs and MNARs via clustering-based fingerprint analysis. Missing RSSIs and RPs are then imputed jointly by means of a novel encoder-decoder architecture that leverages temporal dependencies in data collection as well as correlations among fingerprints and RPs. A time-lag mechanism is used to consider the aging of data, and a sparsity-friendly attention mechanism is used to focus attention score calculation on observed data. Extensive experiments with real data from two buildings show that our proposal outperforms the alternatives with significant advantages in terms of imputation accuracy and indoor positioning accuracy.
Equilibrium Bandits: Learning Optimal Equilibria of Unknown Dynamics

Consider a decision-maker that can pick one out of $K$ actions to control an unknown system, for $T$ turns. The actions are interpreted as different configurations or policies. Holding the same action fixed, the system asymptotically converges to a unique equilibrium, as a function of this action. The dynamics of the system are unknown to the decision-maker, which can only observe a noisy reward at the end of every turn. The decision-maker wants to maximize its accumulated reward over the $T$ turns. Learning what equilibria are better results in higher rewards, but waiting for the system to converge to equilibrium costs valuable time. Existing bandit algorithms, either stochastic or adversarial, achieve linear (trivial) regret for this problem. We present a novel algorithm, termed Upper Equilibrium Concentration Bound (UECB), that knows to switch an action quickly if it is not worth it to wait until the equilibrium is reached. This is enabled by employing convergence bounds to determine how far the system is from equilibrium. We prove that UECB achieves a regret of $\mathcal{O}(\log(T)+\tau_c\log(\tau_c)+\tau_c\log\log(T))$ for this equilibrium bandit problem where $\tau_c$ is the worst case approximate convergence time to equilibrium. We then show that both epidemic control and game control are special cases of equilibrium bandits, where $\tau_c\log \tau_c$ typically dominates the regret. We then test UECB numerically for both of these applications.
Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting

Predicting how the world can evolve in the future is crucial for motion planning in autonomous systems. Classical methods are limited because they rely on costly human annotations in the form of semantic class labels, bounding boxes, and tracks or HD maps of cities to plan their motion - and thus are difficult to scale to large unlabeled datasets. One promising self-supervised task is 3D point cloud forecasting [11, 18–20] from unannotated LiDAR sequences. We show that this task requires algorithms to implicitly capture (1) sensor extrinsics (i.e., the egomotion of the autonomous vehicle), (2) sensor intrinsics (i.e., the sampling pattern specific to the particular LiDAR sensor), and (3) the shape and motion of other objects in the scene. But autonomous systems should make predictions about the world and not their sensors! To this end, we factor out (1) and (2) by recasting the task as one of spacetime (4D) occupancy forecasting. But because it is expensive to obtain ground-truth 4D occupancy, we “render” point cloud data from 4D occupancy predictions given sensor extrinsics and intrinsics, allowing one to train and test occupancy algorithms with unannotated LiDAR sequences. This also allows one to evaluate and compare point cloud forecasting algorithms across diverse datasets, sensors, and vehicles.
Symmetric non-Hermitian skin effect with emergent nonlocal correspondence

The non-Hermitian skin effect (NHSE) refers to that an extensive number of eigenstates of a non-Hermitian system are localized in open boundaries. Here we predict a universal phenomenon that with local particle-hole(-like) symmetry (PHS) the skin modes must be equally distributed on different boundaries, manifesting a novel nonlocalization of the local PHS, which is unique to non-Hermitian systems. We develop a generic theory for the emergent nonlocal symmetry-protected NHSE by connecting the non-Hermitian system to an extended Hermitian Hamiltonian in a quadruplicate Hilbert space, which maps the skin modes to the topological zero modes and the PHS to an emergent nonlocal symmetry in the perspective of many body physics. The predicted NHSE is robust against perturbations. We propose optical Raman lattice models to observe the predicted phenomena in all physical dimensions, which are accessible with cold-atom experiments.
Language Is Not All You Need: Aligning Perception with Language Models

A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence. In this work, we introduce Kosmos-1, a Multimodal Large Language Model (MLLM) that can perceive general modalities, learn in context (i.e., few-shot), and follow instructions (i.e., zero-shot). Specifically, we train Kosmos-1 from scratch on web-scale multimodal corpora, including arbitrarily interleaved text and images, image-caption pairs, and text data. We evaluate various settings, including zero-shot, few-shot, and multimodal chain-of-thought prompting, on a wide range of tasks without any gradient updates or finetuning. Experimental results show that Kosmos-1 achieves impressive performance on (i) language understanding, generation, and even OCR-free NLP (directly fed with document images), (ii) perception-language tasks, including multimodal dialogue, image captioning, visual question answering, and (iii) vision tasks, such as image recognition with descriptions (specifying classification via text instructions). We also show that MLLMs can benefit from cross-modal transfer, i.e., transfer knowledge from language to multimodal, and from multimodal to language. In addition, we introduce a dataset of Raven IQ test, which diagnoses the nonverbal reasoning capability of MLLMs.
Magnetic field in the wake of Venus and the formation of ionospheric holes

Magnetic field structures are analyzed for both the ionospheric hole region and the magnetosheath-ionosphere interaction region of the nightside of Venus, in search of possible coupling between these two regimes. A magnetic coordinate system based on the directions of the solar wind and the interplanetary magnetic field (IMF) is found to order the data reasonably well, allowing consistent superposition of observational data from individual passes of the Pioneer Venus orbiter. The main findings are (1) ionospheric holes form in a zone of ±45° magnetic latitude covering possibly the entire width of the Venus wake in the longitudinal direction; (2) the magnetic field within the holes is strong and ordered, and its direction is determined by the IMF direction, being consistent with the field line draping concept; (3) the magnetic field direction in the holes, particularly at low altitudes near the terminator, shows evidence for the existence of strong plasma flow toward the antisolar meridian in the direction parallel to the magnetic equatorial plane; and (4) the average magnetic field distribution in the nightside magnetosheath also evidences strong plasma flow toward the antisolar meridian. Together these results strongly indicate that the magnetosheath plasma flow in the wake region plays an important role in forming the ionospheric holes, through deformation of the nightside ionopause. These results are combined in a model of the three-dimensional magnetic field structure around the ionosphere of Venus.
Underdetermined elliptic PDE on asymptotically Euclidean manifolds, and generalizations

We study underdetermined elliptic linear partial differential operators $P$ on asymptotically Euclidean manifolds, such as the divergence operator on 1-forms or symmetric 2-tensors. Suitably interpreted, these are instances of (weighted) totally characteristic differential operators on a compact manifold with boundary whose principal symbols are surjective but not injective. We study the equation $P u=f$ when $f$ has a generalized Taylor expansion at $r=\infty$, that is, a full asymptotic expansion into terms with radial dependence $r^{-i z}(\log r)^k$ with $(z,k)\in\mathbb{C}\times\mathbb{N}_0$ up to rapidly decaying remainders. We construct a solution $u$ whose asymptotic behavior at $r=\infty$ is optimal in that the index set of exponents $(z,k)$ arising in its asymptotic expansion is as small as possible. On the flipside, we show that there is an infinite-dimensional nullspace of $P$ consisting of smooth tensors whose expansions at $r=\infty$ contain nonzero terms $r^{-i z}(\log r)^k$ for any desired index set of $(z,k)\in\mathbb{C}\times\mathbb{N}_0$. Applications include sharp solvability results for the divergence equation on 1-forms or symmetric 2-tensors on asymptotically Euclidean spaces, as well as a regularity improvement in a gluing construction for the constraint equations in general relativity recently introduced by the author.
Do as You Say: Consistency Detection of Data Practice in Program Code and Privacy Policy in Mini-App

Mini-app is an emerging form of mobile application that combines web technology with native capabilities. Its features, e.g., no need to download and no installation, have made it popular rapidly. However, privacy issues that violate the laws or regulations are breeding in the swiftly expanding mini-app ecosystem. The consistency between what the mini-app does about the data in the program code and what it declares in its privacy policy description is important. But no work has systematically investigated the privacy problem of the mini-app before. In this paper, to our best knowledge, we are the first to conduct the compliance detection of data practice and policy description in mini-apps. In this paper, we first customize a taint analysis method based on data entity dependency network to adapt to the characteristics of the JavaScript language in the mini-apps. Then, we transform data types and data operations to data practices in program codes and privacy policies, so as to finish a fine-grained consistency matching model.We crawl 100,000 mini-apps on WeChat client in the wild and extract 2,998 with a privacy policy. Among them, only 318 meet the consistency requirements, 2,680 are inconsistent, and the proportion of inconsistencies is as high as 89.4%. The inconsistency in the mini-app is very serious. Based on 6 real-world cases analyzed, in order to reduce this potential data leakage risk, we suggest that the developer should reduce the collection of irrelevant information and the straightforward use of templates, and the platform should provide data flow detection tools and privacy policy writing support.
Visuo-Tactile-Based Slip Detection Using A Multi-Scale Temporal Convolution Network

Humans can accurately determine whether the object in hand has slipped or not by visual and tactile perception. However, it is still a challenge for robots to detect in-hand object slip through visuo-tactile fusion. To address this issue, a novel visuo-tactile fusion deep neural network is proposed to detect slip, which is a time-dependent continuous action. By using the multi-scale temporal convolution network (MS-TCN) to extract the temporal features of visual and tactile data, the slip can be detected effectively. In this paper, a 7-dregree-of-freedom (7-DoF) robot manipulator equipped with a camera and a tactile sensor is used for data collection on 50 daily objects with different shapes, materials, sizes, and weights. Therefore, a dataset is built, where the grasping data of 40 objects and 10 objects are used for network training and testing, respectively. The detection accuracy is 96.96% based on the proposed model. Also, the proposed model is compared with a visuo-tactile fusion deep neural network (DNN) based on long short-term memory network (LSTM) on the collected dataset and a public dataset using the GelSight tactile sensor. The results demonstrate that the proposed model performs better on both dataset. The proposed model can help robots grasp daily objects reliably. In addition, it can be used in grasping force control, grasping policy generation and dexterous manipulation.
Simultaneously realizing thermal and electromagnetic cloaking by multi-physical null medium

Simultaneously manipulating multiple physical fields plays an important role in the increasingly complex integrated systems, aerospace equipment, biochemical productions, etc. For on-chip systems with high integration level (e.g., electronic/photonic chips and radio-frequency/microwave circuits), where both electromagnetic information/energy transporting and heat dissipation/recovery need to be considered, the precise and efficient control of the propagation of electromagnetic waves and heat fluxes simultaneously is particularly important. In this study, we propose a graphical designing method based on thermal-electromagnetic null medium to simultaneously control the propagation of electromagnetic waves and thermal fields according to the pre-designed paths. A thermal-electromagnetic cloak, which can create a cloaking effect on both electromagnetic waves and thermal fields simultaneously, is designed by thermal-electromagnetic surface transformation and verified by both numerical simulations and experimental measurements. The thermal-electromagnetic surface transformation proposed in this study provides a new methodology for simultaneous controlling on electromagnetic and temperature fields, which can be used to realize a series of novel thermal-electromagnetic devices such as thermal-electromagnetic shifter, splitter, bender, multiplexer and mode converter. The designed thermal-electromagnetic cloak opens up new ways to create a concealed region where any object within it does not create any disturbance to the external electromagnetic waves and temperature fields simultaneously, which may have significant applications in improving thermal-electromagnetic compatibility problem, protecting of thermal-electromagnetic sensitive components, and improving efficiency of energy usage for complex on-chip systems.
Moving Target Detection via Multi-IRS-Aided OFDM Radar

An intelligent reflecting surface (IRS) consists of passive reflective elements capable of altering impinging waveforms. The IRS-aided radar systems have recently been shown to improve detection and estimation performance by exploiting the target information collected via non-line-of-sight paths. However, the waveform design problem for an IRS-aided radar has remained relatively unexplored. In this paper, we consider a multi-IRS-aided orthogonal frequency-division multiplexing (OFDM) radar and study the theoretically achievable accuracy of target detection. In addition, we jointly design the OFDM signal and IRS phase-shifts to optimize the target detection performance via an alternating optimization approach. To this end, we formulate the IRS phase-shift design problem as a unimodular bi-quadratic program which is tackled by a computationally cost-effective approach based on power-method-like iterations. Numerical experiments illustrate that our proposed joint design of IRS phase-shifts and the OFDM code improves the detection performance in comparison with conventional OFDM radar.
Quantum theory of the intrinsic orbital magnetoelectric effect in itinerant electron systems at finite temperatures

Magnetization can be induced by an electric field in systems without inversion symmetry $\mathcal{P}$ and time-reversal symmetry $\mathcal{T}$. This phenomenon is called the magnetoelectric (ME) effect. The spin ME effect has been actively studied in multiferroics. The orbital ME effect also exists and has been mainly discussed in topological insulators at zero temperature. In this paper, we study the intrinsic orbital ME response in metals at finite temperature using the Kubo formula. The intrinsic response originates from the Fermi sea and does not depend on the dissipation. Especially in systems with $\mathcal{PT}$-symmetry, the extrinsic orbital ME effect becomes zero, and the intrinsic ME effect is dominant. We apply the response tensor obtained in this work to a $\mathcal{PT}$-symmetric model Hamiltonian with antiferromagnetic loop current order demonstrating that the intrinsic ME effect is enhanced around the Dirac points.
Symbol-Level Precoding for Integrated Sensing and Communications: A Faster-Than-Nyquist Approach

In this letter, we propose a novel symbol-level precoding (SLP) method for a multi-user multi-input multi-output (MU-MIMO) downlink Integrated Sensing and Communications (ISAC) system based on faster-than-Nyquist (FTN) signaling. Our method minimizes the minimum mean squared error (MMSE) for target parameter estimation while guaranteeing per-user quality-of-service by exploiting constructive interference (CI) techniques. We tackle the non-convex problem using an efficient successive convex approximation (SCA) method. Numerical results demonstrate that our FTN-ISAC-SLP design significantly outperforms conventional benchmarks in both communication and sensing performance.
Solution for the EPO CodeFest on Green Plastics: Hierarchical multi-label classification of patents relating to green plastics using deep learning

This work aims at hierarchical multi-label patents classification for patents disclosing technologies related to green plastics. This is an emerging field for which there is currently no classification scheme, and hence, no labeled data is available, making this task particularly challenging. We first propose a classification scheme for this technology and a way to learn a machine learning model to classify patents into the proposed classification scheme. To achieve this, we come up with a strategy to automatically assign labels to patents in order to create a labeled training dataset that can be used to learn a classification model in a supervised learning setting. Using said training dataset, we come up with two classification models, a SciBERT Neural Network (SBNN) model and a SciBERT Hierarchical Neural Network (SBHNN) model. Both models use a BERT model as a feature extractor and on top of it, a neural network as a classifier. We carry out extensive experiments and report commonly evaluation metrics for this challenging classification problem. The experiment results verify the validity of our approach and show that our model sets a very strong benchmark for this problem. We also interpret our models by visualizing the word importance given by the trained model, which indicates the model is capable to extract high-level semantic information of input documents. Finally, we highlight how our solution fulfills the evaluation criteria for the EPO CodeFest and we also outline possible directions for future work. Our code has been made available at https://github.com/epo/CF22-Green-Hands
JND-Based Perceptual Optimization For Learned Image Compression

Recently, learned image compression schemes have achieved remarkable improvements in image fidelity (e.g., PSNR and MS-SSIM) compared to conventional hybrid image coding ones due to their high-efficiency non-linear transform, end-to-end optimization frameworks, etc. However, few of them take the Just Noticeable Difference (JND) characteristic of the Human Visual System (HVS) into account and optimize learned image compression towards perceptual quality. To address this issue, a JND-based perceptual quality loss is proposed. Considering that the amounts of distortion in the compressed image at different training epochs under different Quantization Parameters (QPs) are different, we develop a distortion-aware adjustor. After combining them together, we can better assign the distortion in the compressed image with the guidance of JND to preserve the high perceptual quality. All these designs enable the proposed method to be flexibly applied to various learned image compression schemes with high scalability and plug-and-play advantages. Experimental results on the Kodak dataset demonstrate that the proposed method has led to better perceptual quality than the baseline model under the same bit rate.