Miracle-dz/UniFAR-FTUs huggingface dataset:
Labeling Multipath via Reconfigurable Intelligent Surface

Reconfigurable intelligent surface (RIS) has shown promise in providing apparent benefits in wireless communication and positioning. Most of the existing research focuses on the ability of RIS to adjust the direction of propagation. In this paper, we present another application based on RIS, named multipath labeling, which intends to inject a label on propagation paths through the RISs. Each labeled path contains spatial knowledge between the RIS and the receiver, thus opening the door for sensing the surrounding world by RISs. The critical challenge is how the labeled paths can be extracted and distinguish from other paths, especially with multipath effects. To address this challenge, we present a complete labeling procedure consisting of channel flipping, parameter extraction, and RIS association. Simulations under a practice ray-tracing model reveal the feasibility of the proposed labeling technique even under the sub 6GHz channels with severe multipath effect. We also apply the multipath labeling technique to localization, which assists user equipment in obtaining its precise location through only a single base station.
DVMark: A Deep Multiscale Framework for Video Watermarking

Video watermarking embeds a message into a cover video in an imperceptible manner, which can be retrieved even if the video undergoes certain modifications or distortions. Traditional watermarking methods are often manually designed for particular types of distortions and thus cannot simultaneously handle a broad spectrum of distortions. To this end, we propose a robust deep learning-based solution for video watermarking that is end-to-end trainable. Our model consists of a novel multiscale design where the watermarks are distributed across multiple spatial-temporal scales. Extensive evaluations on a wide variety of distortions show that our method outperforms traditional video watermarking methods as well as deep image watermarking models by a large margin. We further demonstrate the practicality of our method on a realistic video-editing application.
Designing Optimal Key Lengths and Control Laws for Encrypted Control Systems Based on Sample Identifying Complexity and Deciphering Time

In the state-of-the-art literature on cryptography and control theory, there has been no systematic methodology of constructing cyber–physical systems that can achieve the desired control performance while being protected against eavesdropping attacks. In this article, we tackle this challenging problem. We first propose two novel notions referred to as sample identifying complexity and sample deciphering time in an encrypted control framework. The former explicitly captures the relation between the dynamical characteristics of control systems and the level of identifiability of the systems while the latter shows the relation between the computation time for the identification and the key length of a cryptosystem. Based on these two tractable new notions, we propose a systematic method for designing both of an optimal key length to prevent system identification with a given precision within a given life span of systems and of an optimal controller to maximize both of the control performance and the difficulty of the identification. The efficiency of the proposed method in terms of security level and realtime-ness is investigated through numerical simulations. To the best of our knowledge, this article first connects the relationship between the security of cryptography and dynamical systems from a control-theoretic perspective.
A novel bivariate autoregressive model for predicting and forecasting irregularly observed time series

In several disciplines it is common to find time series measured at irregular observational times. In particular, in astronomy there are a large number of surveys that gather information over irregular time gaps and in more than one passband. Some examples are Pan-STARRS, ZTF and also the LSST. However, current commonly used time series models that estimate the time dependency in astronomical light curves consider the information of each band separately (e.g, CIAR, IAR and CARMA models) disregarding the dependency that might exist between different passbands. In this paper we propose a novel bivariate model for irregularly sampled time series, called the bivariate irregular autoregressive (BIAR) model. The BIAR model assumes an autoregressive structure on each time series, it is stationary, and it allows to estimate the autocorrelation, the cross-correlation and the contemporary correlation between two unequally spaced time series. We implemented the BIAR model on light curves, in the g and r bands, obtained from the ZTF alerts processed by the ALeRCE broker. We show that if the light curves of the two bands are highly correlated, the model has more accurate forecast and prediction using the bivariate model than a similar method that uses only univariate information. Further, the estimated parameters of the BIAR are useful to characterize Long Period Variable Stars and to distinguish between classes of stochastic objects, providing promising features that can be used for classification purposes.
Calibrating LiDAR and Camera using Semantic Mutual information

We propose an algorithm for automatic, targetless, extrinsic calibration of a LiDAR and camera system using semantic information. We achieve this goal by maximizing mutual information (MI) of semantic information between sensors, leveraging a neural network to estimate semantic mutual information, and matrix exponential for calibration computation. Using kernel-based sampling to sample data from camera measurement based on LiDAR projected points, we formulate the problem as a novel differentiable objective function which supports the use of gradient-based optimization methods. We also introduce an initial calibration method using 2D MI-based image registration. Finally, we demonstrate the robustness of our method and quantitatively analyze the accuracy on a synthetic dataset and also evaluate our algorithm qualitatively on KITTI360 and RELLIS-3D benchmark datasets, showing improvement over recent comparable approaches.
TOI-1431b/MASCARA-5b: A Highly Irradiated Ultrahot Jupiter Orbiting One of the Hottest and Brightest Known Exoplanet Host Stars

We present the discovery of a highly irradiated and moderately inflated ultrahot Jupiter, TOI-1431b/MASCARA-5 b (HD 201033b), first detected by NASA’s Transiting Exoplanet Survey Satellite mission (TESS) and the Multi-site All-Sky Camera (MASCARA). The signal was established to be of planetary origin through radial velocity measurements obtained using SONG, SOPHIE, FIES, NRES, and EXPRES, which show a reflex motion of K = 294.1 ± 1.1 m s−1. A joint analysis of the TESS and ground-based photometry and radial velocity measurements reveals that TOI-1431b has a mass of M p = 3.12 ± 0.18 M J (990 ± 60 M ⊕), an inflated radius of R p = 1.49 ± 0.05 R J (16.7 ± 0.6 R ⊕), and an orbital period of P = 2.650237 ± 0.000003 days. Analysis of the spectral energy distribution of the host star reveals that the planet orbits a bright (V = 8.049 mag) and young ( 0.29−0.19+0.32 Gyr) Am type star with Teff=7690−250+400 K, resulting in a highly irradiated planet with an incident flux of 〈F〉=7.24−0.64+0.68× 109 erg s−1 cm−2 ( 5300−470+500S⊕ ) and an equilibrium temperature of T eq = 2370 ± 70 K. TESS photometry also reveals a secondary eclipse with a depth of 127−5+4 ppm as well as the full phase curve of the planet’s thermal emission in the red-optical. This has allowed us to measure the dayside and nightside temperature of its atmosphere as T day = 3004 ± 64 K and T night = 2583 ± 63 K, the second hottest measured nightside temperature. The planet’s low day/night temperature contrast (∼420 K) suggests very efficient heat transport between the dayside and nightside hemispheres. Given the host star brightness and estimated secondary eclipse depth of ∼1000 ppm in the K band, the secondary eclipse is potentially detectable at near-IR wavelengths with ground-based facilities, and the planet is ideal for intensive atmospheric characterization through transmission and emission spectroscopy from space missions such as the James Webb Space Telescope and the Atmospheric Remote-sensing Infrared Exoplanet Large-survey.
A non-abelian exterior product of Hom-Leibniz algebras

In this paper, we introduce a non-abelian exterior product of Hom-Leibniz algebras and investigate its relative to the Hopf’s formula. We also construct an eight-term exact sequence in the homology of Hom-Leibniz algebras. Finally, we relate the notion of capability of a Hom-Leibniz algebra to its exterior product.
Hot Carrier Degradation in MOSFETs at Cryogenic Temperatures Down to 4.2 K

Wide attention has been focused on cryogenic CMOS (cryo-CMOS) operation because of its promising improvement of devices’ and circuits’ performance and wide application prospects. However, hot carrier degradation (HCD) limits the long-term reliability of cryo-CMOS. This article investigates HCD in 0.18 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> bulk CMOS at cryogenic temperature down to 4.2 K. Particularly, the relationship between HCD and the current overshoot phenomenon and the influence of substrate bias on HCD are discussed. Besides, we predict the lifetime of the device at 77 K and 4.2 K. It is concluded that cryogenic NMOS cannot reach the ten years’ commercial standard lifetime at standard <inline-formula> <tex-math notation="LaTeX">$V_{DD}$ </tex-math></inline-formula>. And it is predicted that the reliability requirements can be reached when <inline-formula> <tex-math notation="LaTeX">$V_{DD} \leq 1.768$ </tex-math></inline-formula> V and 1.734 V at 77 K and 4.2 K, respectively. Differently, the lifetime of PMOS is long enough even at low temperatures.
Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

Gaze estimation reveals where a person is looking. It is an important clue for understanding human intention. The recent development of deep learning has revolutionized many computer vision tasks, the appearance-based gaze estimation is no exception. However, it lacks a guideline for designing deep learning algorithms for gaze estimation tasks. In this paper, we present a comprehensive review of the appearance-based gaze estimation methods with deep learning. We summarize the processing pipeline and discuss these methods from four perspectives: deep feature extraction, deep neural network architecture design, personal calibration as well as device and platform. Since the data pre-processing and post-processing methods are crucial for gaze estimation, we also survey face/eye detection method, data rectification method, 2D/3D gaze conversion method, and gaze origin conversion method. To fairly compare the performance of various gaze estimation approaches, we characterize all the publicly available gaze estimation datasets and collect the code of typical gaze estimation algorithms. We implement these codes and set up a benchmark of converting the results of different methods into the same evaluation metrics. This paper not only serves as a reference to develop deep learning-based gaze estimation methods but also a guideline for future gaze estimation research. Implemented methods and data processing codes are available at http://phi-ai.org/GazeHub.
DC3: A learning method for optimization with hard constraints

Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the use of deep networks as cheap"approximate solvers. "Unfortunately, naive deep learning approaches typically cannot enforce the hard constraints of such problems, leading to infeasible solutions. In this work, we present Deep Constraint Completion and Correction (DC3), an algorithm to address this challenge. Specifically, this method enforces feasibility via a differentiable procedure, which implicitly completes partial solutions to satisfy equality constraints and unrolls gradient-based corrections to satisfy inequality constraints. We demonstrate the effectiveness of DC3 in both synthetic optimization tasks and the real-world setting of AC optimal power flow, where hard constraints encode the physics of the electrical grid. In both cases, DC3 achieves near-optimal objective values while preserving feasibility.
Optimal Regional Tracking Control of Time-Fractional Diffusion Systems

In this paper, we aim to explore optimal regional trajectory tracking control problems of the anomalous subdif-fusion processes governed by time-fractional diffusion systems under the Neumann boundary conditions. Using eigenvalue theory of the system operator and the semigroup theory, we explore the existence and some estimates of the mild solution to the considered system. An approach on finding solution to the optimal problem that minimizes the regional trajectory tracking error and the corresponding control cost over a finite space and time domain is then explored via the Hilbert uniqueness method (HUM). The obtained results not only can be directly used to investigate the systems that are not controllable on the whole domain, but also yield an explicit expression of the control signal in terms of the desired trajectory. Most importantly, it is worth noting that our results in this paper are still novel even for the special case when the order of fractional derivative is equal to one. Finally, we provide a numerical example to illustrate our theoretical results.
Modeling Risk via Realized HYGARCH Model

In this paper, we propose the realized Hyperbolic GARCH model for the joint-dynamics of lowfrequency returns and realized measures that generalizes the realized GARCH model of Hansen et al.(2012) as well as the FLoGARCH model introduced by Vander Elst (2015). This model is sufficiently flexible to capture both long memory and asymmetries related to leverage effects. In addition, we will study the strictly and weak stationarity conditions of the model. To evaluate its performance, experimental simulations, using the Monte Carlo method, are made to forecast the Value at Risk (VaR) and the Expected Shortfall (ES). These simulation studies show that for ES and VaR forecasting, the realized Hyperbolic GARCH (RHYGARCH-GG) model with Gaussian-Gaussian errors provide more adequate estimates than the realized Hyperbolic GARCH model with student- Gaussian errors.
Interacting-fermion dynamics in Majorana phase space

The problem of fermion dynamics is studied using the Q-function for fermions. This is a probabilistic phase-space representation, which we express using Majorana operators, so that the phase-space variable is a real antisymmetric matrix. We consider a general interaction Hamiltonian with four Majorana operators and arbitrary properties. Our model includes the Majorana Hubbard and Fermi Hubbard Hamiltonians, as well as general quantum field theories of interacting fermions. Using the Majorana Q-function we derive a generalized Fokker-Planck equation, with results for the drift and diffusion terms. The diffusion term is proved to be traceless, which gives a dynamical interpretation as a forwards-backwards stochastic process. This approach leads to a model of quantum measurement in terms of an ontology with real vacuum fluctuations.
Boolean Reasoning-Based Biclustering for Shifting Pattern Extraction

Biclustering is a powerful approach to search for patterns in data, as it can be driven by a function that measures the quality of diverse types of patterns of interest. However, due to its computational complexity, the exploration of the search space is usually guided by an algorithmic strategy, sometimes introducing random factors that simplify the computational cost (e.g. greedy search or evolutionary computation). Shifting patterns are specially interesting as they account constant fluctuations in data, i.e. they capture situations in which all the values in the pattern move up or down for one dimension maintaining the range amplitude for all the dimensions. This behaviour is very common in nature, e.g. in the analysis of gene expression data, where a subset of genes might go up or down for a subset of patients or experimental conditions, identifying functionally coherent categories. Boolean reasoning was recently revealed as an appropriate methodology to address the search for constant biclusters. In this work, this direction is extended to search for more general biclusters that include shifting patterns. The mathematical foundations are described in order to associate Boolean concepts with shifting patterns, and the methodology is presented to show that the induction of shifting patterns by means
Explore BiLSTM-CRF-Based Models for Open Relation Extraction

Extracting multiple relations from text sentences is still a challenge for current Open Relation Extraction (Open RE) tasks. In this paper, we develop several Open RE models based on the bidirectional LSTM-CRF (BiLSTM-CRF) neural network and different contextualized word embedding methods. We also propose a new tagging scheme to solve overlapping problems and enhance models' performance. From the evaluation results and comparisons between models, we select the best combination of tagging scheme, word embedder, and BiLSTM-CRF network to achieve an Open RE model with a remarkable extracting ability on multiple-relation sentences.
Automatic Description Construction for Math Expression via Topic Relation Graph

Math expressions are important parts of scientific and educational documents, but some of them may be challenging for junior scholars or students to understand. Nevertheless, constructing textual descriptions for math expressions is nontrivial. In this paper, we explore the feasibility to automatically construct descriptions for math expressions. But there are two challenges that need to be addressed: 1) finding relevant documents since a math equation understanding usually requires several topics, but these topics are often explained in different documents. 2) the sparsity of the collected relevant documents making it difficult to extract reasonable descriptions. Different documents mainly focus on different topics which makes model hard to extract salient information and organize them to form a description of math expressions. To address these issues, we propose a hybrid model (MathDes) which contains two important modules: Selector and Summarizer. In the Selector, a Topic Relation Graph (TRG) is proposed to obtain the relevant documents which contain the comprehensive information of math expressions. TRG is a graph built according to the citations between expressions. In the Summarizer, a summarization model under the Integer Linear Programming (ILP) framework is proposed. This module constructs the final description with the help of a timeline that is extracted from TRG. The experimental results demonstrate that our methods are promising for this task and outperform the baselines in all aspects.
Holography, 1-form symmetries, and confinement

We study confinement in 4d N = 1 SU(N) Super-Yang Mills (SYM) from a holographic point of view, focusing on the 1-form symmetry and its relation to chiral symmetry breaking. In the 5d supergravity dual, obtained by truncation of the Klebanov-Strassler solution, we identify the topological couplings that determine the 1-form symmetry and its ’t Hooft anomalies. One such coupling is a mixed 0-form/1-form symmetry anomaly closely related to chiral symmetry breaking in gapped confining vacua. From the dual gravity description we also identify the infra-red (IR) 4d topological field theory (TQFT), which realises chiral symmetry breaking and matches the mixed anomaly. Finally, complementing this, we derive the chiral and mixed anomalies from the Little String Theory realization of pure SYM.
Generative modeling of spatio-temporal weather patterns with extreme event conditioning

Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots or assume 1D time-series; few are able to capture spatio-temporal processes simultaneously. Beyond this, Earth-systems data often exhibit highly irregular and complex patterns, for example caused by extreme weather events. Because of climate change, these phenomena are only increasing in frequency. Here, we proposed a novel GAN-based approach for generating spatio-temporal weather patterns conditioned on detected extreme events. Our approach augments GAN generator and discriminator with an encoded extreme weather event segmentation mask. These segmentation masks can be created from raw input using existing event detection frameworks. As such, our approach is highly modular and can be combined with custom GAN architectures. We highlight the applicability of our proposed approach in experiments with real-world surface radiation and zonal wind data.
Faint Active Galactic Nuclei Favor Unexpectedly Long Inter-band Time Lags

Inconsistent conclusions are obtained from recent active galactic nuclei (AGNs) accretion disk inter-band time-lag measurements. While some works show that the measured time lags are significantly larger (by a factor of ∼3) than the theoretical predictions of the Shakura & Sunyaev disk (SSD) model, others find that the time-lag measurements are consistent with (or only slightly larger than) that of the SSD model. These conflicting observational results might be symptoms of our poor understanding of AGN accretion physics. Here we show that sources with larger-than-expected time lags tend to be less luminous AGNs. Such a dependence is unexpected if the inter-band time lags are attributed to the light-travel-time delay of the illuminating variable X-ray photons to the static SSD. If, instead, the measured inter-band lags are related not only to the static SSD but also to the outer broad emission-line regions (BLRs; e.g., the blended broad emission lines and/or diffuse continua), our result indicates that the contribution of the non-disk BLR to the observed ultraviolet/optical continuum decreases with increasing luminosity (L), i.e., an anti-correlation resembling the well-known Baldwin effect. Alternatively, we argue that the observed dependence might be a result of coherent disk thermal fluctuations as the relevant thermal timescale, τ TH ∝ L 0.5. With future accurate measurements of inter-band time lags, the above two scenarios can be distinguished by inspecting the dependence of inter-band time lags upon either the BLR components in the variable spectra or the timescales.
A Low-Complexity MIMO Channel Estimator with Implicit Structure of a Convolutional Neural Network

A convolutional neural network estimator which learns the minimum mean squared error channel estimator for single-antenna users was recently proposed. We generalize the architecture to the estimation of MIMO channels with multiple-antenna users and incorporate assumptions, such as a high number of antennas and a single propagation cluster, which reduces the computational complexity tremendously. Learning is used in this context to combat the mismatch between the assumptions and real scenarios with a limited number of antennas and many propagation clusters. We derive a high-level description of the estimator for arbitrary choices of the pilot sequence. It turns out that the proposed estimator has the implicit structure of a two-layered convolutional neural network, where the derived quantities can be relaxed to learnable parameters. We show that by using discrete Fourier transform based pilots the number of network parameters decreases significantly and the online run time of the estimator is reduced considerably, where we can achieve linearithmic order of complexity in the number of antennas. Numerical results display performance gains compared to state-of-the-art algorithms from the field of compressive sensing or covariance estimation of the same or higher computational complexity. The simulation code is available online.