Miracle-dz/UniFAR-FTUs huggingface dataset:
Terahertz PHASR Scanner With 2 kHz, 100 ps Time-Domain Trace Acquisition Rate and an Extended Field-of-View Based on a Heliostat Design

Recently, we introduced a Portable HAndheld Spectral Reflection (PHASR) Scanner to allow terahertz time-domain spectroscopic (THz-TDS) imaging in clinical and industrial settings using a fiber-coupled and alignment-free telecentric beam-steering design. The key limitations of the version 1.0 of the PHASR Scanner were its field-of-view (FOV) and speed of time-domain trace acquisition. In this article, we address these limitations by introducing a heliostat geometry for beam scanning to achieve an extended FOV, and by reconfiguring the asynchronous optical sampling system to perform Electronically Controlled OPtical Sampling (ECOPS) measurements. The former change improved the deflection range of the beam, while also drastically reducing the coupling of the two scanning axes, the combination of which resulted in a larger than four-fold increase in the FOV area. The latter change significantly improves the acquisition speed and frequency-domain performance simultaneously by improving measurement efficiency. To accomplish this, we characterized the nonlinear time-axis sampling behavior of the electromechanical system in the ECOPS mode. We proposed methods to model and correct the nonlinear time-axis distortions and tested the performance of the high-speed ECOPS trace acquisition. Therefore, here, we introduce the PHASR Scanner version 2.0, which is capable of imaging a 40 × 27 mm2 FOV with 2000 traces/s over a 100 ps TDS range. This new scanner represents a significant leap toward translating the THz-TDS technology from the lab bench to the bedside for real-time clinical imaging applications.
PFMC: a parallel symbolic model checker for security protocol verification

We present an investigation into the design and implementation of a parallel model checker for security protocol veriﬁcation that is based on a symbolic model of the adversary, where instantiations of concrete terms and messages are avoided until needed to resolve a particular assertion. We propose to build on this naturally lazy approach to parallelise this symbolic state exploration and evaluation. We utilise the concept of strategies in Haskell, which abstracts away from the low-level details of thread management and modularly adds parallel evaluation strategies (encapsulated as a monad in Haskell). We build on an existing symbolic model checker, OFMC, which is already implemented in Haskell. We show that there is a very signiﬁcant speed up of around 3-5 times improvement when moving from the original single-threaded implementation of OFMC to our multi-threaded version, for both the Dolev-Yao attacker model and more general algebraic attacker models. We identify several issues in parallelising the model checker: among oth-ers, controlling growth of memory consumption, balancing lazy vs strict evaluation, and achieving an optimal granularity of parallelism.
Joint Precoding and Artificial Noise Design for MU-MIMO Wiretap Channels

Secure precoding superimposed with artificial noise (AN) is a promising transmission technique to improve security by harnessing the superposition nature of the wireless medium. However, finding a jointly optimal precoding and AN structure is very challenging in downlink multi-user multiple-input multiple-output wiretap channels with multiple eavesdroppers. The major challenge in maximizing the secrecy rate arises from the non-convexity and non-smoothness of the rate function. Traditionally, an alternating optimization framework that identifies beamforming vectors and AN covariance matrix has been adopted; yet this alternating approach has limitations in maximizing the secrecy rate. In this paper, we put forth a novel secure precoding algorithm that jointly and simultaneously optimizes the beams and AN covariance matrix for maximizing the secrecy rate when a transmitter has either perfect or partial channel knowledge of eavesdroppers. To this end, we first establish an approximate secrecy rate in a smooth function. Then, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We present a computationally efficient algorithm to identify the principal eigenvector of the NEP as a suboptimal solution for secure precoding. Simulations demonstrate that the proposed methods improve secrecy rate significantly compared to the existing methods.
Unsupervised energy disaggregation via convolutional sparse coding

In this work, a method for unsupervised energy disaggregation in private households equipped with smart meters is proposed. This method aims to classify power consumption as active or passive, granting the ability to report on the residents' activity and presence without direct interaction. This lays the foundation for applications like non-intrusive health monitoring of private homes. The proposed method is based on minimizing a suitable energy functional, for which the iPALM (inertial proximal alternating linearized minimization) algorithm is employed, demonstrating that various conditions guaranteeing convergence are satisfied. In order to confirm feasibility of the proposed method, experiments on semi-synthetic test data sets and a comparison to existing, supervised methods are provided.
Successor Representation Active Inference

Recent work has uncovered close links between between classical reinforcement learning algorithms, Bayesian ﬁltering, and Active Inference which lets us understand value functions in terms of Bayesian posteriors. An alternative, but less explored, model-free RL algorithm is the successor representation, which expresses the value function in terms of a successor matrix of expected future state occupancies. In this paper, we derive the probabilistic interpretation of the successor representation in terms of Bayesian ﬁltering and thus design a novel active inference agent architecture utilizing successor representations instead of model-based planning. We demonstrate that active inference successor representations have signiﬁcant advantages over current active inference agents in terms of planning horizon and computational cost. Moreover, we demonstrate how the successor representation agent can generalize to changing reward functions such as variants of the expected free energy.
GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulation

Automatic artistic text generation is an emerging topic which receives increasing attention due to its wide applications. The artistic text can be divided into three components, content, font, and texture, respectively. Existing artistic text generation models usually focus on manipulating one aspect of the above components, which is a sub-optimal solution for controllable general artistic text generation. To remedy this issue, we propose a novel approach, namely GenText, to achieve general artistic text style transfer by separably migrating the font and texture styles from the different source images to the target images in an unsupervised manner. Specifically, our current work incorporates three different stages, stylization, destylization, and font transfer, respectively, into a unified platform with a single powerful encoder network and two separate style generator networks, one for font transfer, the other for stylization and destylization. The destylization stage first extracts the font style of the font reference image, then the font transfer stage generates the target content with the desired font style. Finally, the stylization stage renders the resulted font image with respect to the texture style in the reference image. Moreover, considering the difficult data acquisition of paired artistic text images, our model is designed under the unsupervised setting, where all stages can be effectively optimized from unpaired data. Qualitative and quantitative results are performed on artistic text benchmarks, which demonstrate the superior performance of our proposed model. The code with models will become publicly available in the future.
Detecting Harmful Online Conversational Content towards LGBTQIA+ Individuals

Online discussions, panels, talk page edits, etc., often contain harmful conversational content i.e., hate speech, death threats and offensive language, especially towards certain demographic groups. For example, individuals who identify as members of the LGBTQIA+ community and/or BIPOC (Black, Indigenous, People of Color) are at higher risk for abuse and harassment online. In this work, we first introduce a real-world dataset that will enable us to study and understand harmful online conversational content. Then, we conduct several exploratory data analysis experiments to gain deeper insights from the dataset. We later describe our approach for detecting harmful online Anti-LGBTQIA+ conversational content, and finally, we implement two baseline machine learning models (i.e., Support Vector Machine and Logistic Regression), and fine-tune 3 pre-trained large language models (BERT, RoBERTa, and HateBERT). Our findings verify that large language models can achieve very promising performance on detecting online Anti-LGBTQIA+ conversational content detection tasks.
Automated machine learning for borehole resistivity measurements

Deep neural networks (DNNs) offer a real-time solution for the inversion of borehole resistivity measurements to approximate forward and inverse operators. Using extremely large DNNs to approximate the operators is possible, but it demands considerable training time. Moreover, evaluating the network after training also requires a significant amount of memory and processing power. In addition, we may overfit the model. In this work, we propose a scoring function that accounts for the accuracy and size of the DNNs compared to a reference DNNs that provides good approximations for the operators. Using this scoring function, we use DNN architecture search algorithms to obtain a quasi-optimal DNN smaller than the reference network; hence, it requires less computational effort during training and evaluation. The quasi-optimal DNN delivers comparable accuracy to the original large DNN.
Magnetic field back reaction on the matter power spectrum

At lowest order comoving magnetic fields which are frozen-into the expanding cosmic fluid do not evolve in time. At next-to-leading order the induction equation is sourced by the interaction term between the baryon velocity and the magnetic field amplitude which leads to a non-trivial evolution of the comoving magnetic field. Moreover, it induces non-trivial cross correlations between the adiabatic curvature mode and the magnetic mode. This cross correlation together with the evolution of the induced matter perturbation leads to interesting effects on the total matter power spectrum at small scales.
Deep reinforcement learning for key distribution based on quantum repeaters

This work examines secret key rates of key distribution based on quantum repeaters in a broad parameter space of the communication distance and coherence time of the quantum memories. As the first step in this task, a Markov decision process modeling the distribution of entangled quantum states via quantum repeaters is developed. Based on this model, a simulation is implemented, which is employed to determine secret key rates under naively controlled, limited memory storage times for a wide range of parameters. The complexity of the quantum state evolution in a multiple-segment quantum repeater chain motivates the use of deep reinforcement learning to search for optimal solutions for the memory storage time limits - the so-called memory cut-offs. The novel contribution in this work is to explore very general cut-off strategies which dynamically adapt to the state of the quantum repeater. An implementation of this approach is presented, with a particular focus on four-segment quantum repeaters, achieving proof of concept of its validity by finding exemplary solutions that outperform the naive strategies.
Matrix Kendall's tau in High-dimensions: A Robust Statistic for Matrix Factor Model

In this article, we first propose generalized row/column matrix Kendall’s tau for matrix-variate observations that are ubiquitous in areas such as finance and medical imaging. For a random matrix following a matrix-variate elliptically contoured distribution, we show that the eigenspaces of the proposed row/column matrix Kendall’s tau coincide with those of the row/column scatter matrix respectively, with the same descending order of the eigenvalues. We perform eigenvalue decomposition to the generalized row/column matrix Kendall’s tau for recovering the loading spaces of the matrix factor model. We also propose to estimate the pair of the factor numbers by exploiting the eigenvalue-ratios of the row/column matrix Kendall’s tau. Theoretically, we derive the convergence rates of the estimators for loading spaces, factor scores and common components, and prove the consistency of the estimators for the factor numbers without any moment constraints on the idiosyncratic errors. Thorough simulation studies are conducted to show the higher degree of robustness of the proposed estimators over the existing ones. Analysis of a financial dataset of asset returns and a medical imaging dataset associated with COVID-19 illustrate the empirical usefulness of the proposed method.
Robust Data-Driven Control for Systems with Noisy Data

This paper presents a robust data-driven controller design based on the noisy input-output data without assumptions on the statistical properties of the noises. We start with the direct data-representation of system models that take elements from behavioral system theory, followed by analyses of the upper bound of the"modeling"error with the data representation with presence of noises. Some pre-conditioning methods are put into the context based on how the derived bound is structured. We lastly leverage the upper bound to develop robust controllers that ride through the data noises.
Well Posedness and Limit Theorems for a Class of Stochastic Dyadic Models

We consider stochastic inviscid dyadic models with energy-preserving noise. It is shown that the models admit weak solutions which are unique in law. Under a certain scaling limit of the noise, the stochastic models converge weakly to a deterministic viscous dyadic model, for which we provide explicit convergence rates in terms of the parameters of noise. A central limit theorem underlying such scaling limit is also established. In case that the stochastic dyadic model is viscous, we show the phenomenon of dissipation enhancement for suitably chosen noise.
Intrinsic dimension estimation for discrete metrics

Real-world datasets characterized by discrete features are ubiquitous: from categorical surveys to clinical questionnaires, from unweighted networks to DNA sequences. Nevertheless, the most common unsupervised dimensional reduction methods are designed for continuous spaces, and their use for discrete spaces can lead to errors and biases. In this Letter we introduce an algorithm to infer the intrinsic dimension (ID) of datasets embedded in discrete spaces. We demonstrate its accuracy on benchmark datasets, and we apply it to analyze a metagenomic dataset for species fingerprinting, finding a surprisingly small ID, of order 2. This suggests that evolutive pressure acts on a low-dimensional manifold despite the high dimensionality of sequences' space.
Adaptive Partition of Unity Interpolation Method with Moving Patches

The adaptive partition of unity interpolation method, introduced by Aiton and Driscoll, using Chebyshev local interpolants, is explored for interpolating functions with sharp gradients representing two-medium problems. For functions that evolve under vector fields, the partition of unity patches (covers) can be shifted and resized to follow the changing dynamics of local profiles. The method is tested for selected 1D and 2D two-medium problems with linear divergence-free vector fields. In those cases, the volume fraction in each patch contributing to volume conservation throughout the domain can be kept in high accuracy down to machine precisions. Applications that could benefit from the method include volume tracking and multiphase flow modeling.
Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions

: The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use-cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times of its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today's living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 tweets about exoskeletons that were posted in a 5-year period from May 21, 2017, to May 21, 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset. and Artificial Intelligence that may be applied to relevant Twitter data for advancing the field of exoskeleton research, this paper presents 100 Research Questions for researchers to investigate, analyze, ideate, and explore by using this dataset. Future work would involve updating this dataset to incorporate more recent tweets as well as investigating these research questions and developing new ones to advance research and development in this field.
Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges

Facial affect analysis remains a challenging task with its setting transitioned from lab-controlled to in-the-wild situations. In this paper, we present novel frameworks to handle the two challenges in the 4th Affective Behavior Analysis In-The-Wild (ABAW) competition: i) Multi-Task-Learning (MTL) Challenge and ii) Learning from Synthetic Data (LSD) Challenge. For MTL challenge, we adopt the SMM-EmotionNet with a better ensemble strategy of feature vectors. For LSD challenge, we propose respective methods to combat the problems of single labels, imbalanced distribution, fine-tuning limitations, and choice of model architectures. Experimental results on the official validation sets from the competition demonstrated that our proposed approaches outperformed baselines by a large margin. The code is available at https://github.com/sylyoung/ABAW4-HUST-ANT.
First look with JWST spectroscopy: $z \sim 8$ galaxies resemble local analogues

Deep images and near-IR spectra of galaxies in the ﬁeld of the lensing cluster SMACS J0723.3-7327 were recently taken in the Early Release Observations program of JWST. Among these, two NIRSpec spectra of galaxies at z = 7 . 7 and one at z = 8 . 5 were obtained, revealing for the ﬁrst time rest-frame optical emission line spectra of galaxies in the epoch of reionization, including the detection of the important [O iii ] λ 4363 auroral line (see JWST PR 2022-035). We present an analysis of the emission line properties of these galaxies, ﬁnding that these galaxies have a high excitation (as indicated by high ratios of [O iii ] λ 5007 / [O ii ] λ 3727, [Ne iii ] λ 3869 / [O ii ] λ 3727), strong [O iii ] λ 4363, high equivalent widths, and other properties which are typical of low-metallicity star-forming galaxies. Using the direct method we determine oxygen abundances of 12 + log(O / H) ≈ 7 . 9 in two z = 7 . 7 galaxies, and a lower metallicity of 12 + log(O / H) ≈ 7 . 4 − 7 . 5 ( ∼ 5 % solar) in the z = 8 . 5 galaxy using di ﬀ erent strong line methods. More accurate metallicity determinations will require better data. With stellar masses estimated from SED ﬁts, we ﬁnd that the three galaxies lie close to or below the z ∼ 2 mass-metallicity relation. Overall, these ﬁrst galaxy spectra at z ∼ 8 show a strong resemblance of the emission lines properties of galaxies in the epoch of reionization with those of relatively rare local analogues previously studied from the SDSS. Clearly, the ﬁrst JWST observations demonstrate already the incredible power of spectroscopy to reveal properties of galaxies in the early Universe.
Overlooked factors in concept-based explanations: Dataset choice, concept salience, and human capability

Concept-based interpretability methods aim to explain deep neural network model predictions using a predeﬁned set of semantic concepts. These methods evaluate a trained model on a new, “probe” dataset and correlate model predictions with the visual concepts labeled in that dataset. Despite their popularity, they suffer from limitations that are not well-understood and articulated by the literature. In this work, we analyze three commonly overlooked factors in concept-based explanations. First, the choice of the probe dataset has a profound impact on the generated explanations. Our analysis reveals that different probe datasets may lead to very different explanations, and suggests that the explanations are not generalizable outside the probe dataset. Second, we ﬁnd that concepts in the probe dataset are often less salient and harder to learn than the classes they claim to explain, calling into question the correctness of the explanations. We argue that only visually salient concepts should be used in concept-based explanations. Finally, while existing methods use hundreds or even thousands of concepts, our human studies reveal a much stricter upper bound of 32 concepts or less, beyond which the explanations are much less practically useful. Finally, we make suggestions for future development and analysis of concept-based interpretability methods. Code for our analysis and user interface can be found at https://github.com/ princetonvisualai/OverlookedFactors