Miracle-dz/UniFAR-FTUs huggingface dataset:
True-Time Delay-Based Hybrid Precoding Under Time Delay Constraints in Wideband THz Massive MIMO Systems

—The beam squint effect that arises in the wideband Terahertz (THz) massive multiple-input multiple-output (MIMO) communication produces a serious array gain loss. True-time delay (TTD)-based hybrid precoding has been considered to compensate for the beam squint effect. By ﬁxing the phase shifter (PS) precoder, a common strategy has been designing TTD precoder under the assumption of unbounded time delay values. In this paper, we present a new approach to the problem of beam squint compensation, based on the joint optimization of the TTD and PS precoders under per TTD device time delay constraints. We ﬁrst derive a lower bound of the achievable rate and show that in the large system limit the ideal analog precoder that completely compensates for the beam squint is equivalent to the one that maximizes the achievable rate lower bound. Unlike the prior approaches, our approach does not require the unbounded time delay assumption; the range of time delay values that a TTD can produce is strictly limited in our approach. Instead of focusing on the design of TTD values only, we jointly optimize both the TTD and PS values to effectively cope with the practical time delay constraints. Taking the advantage of the proposed joint TTD and PS precoder optimization approach, we quantify the minimum number of TTDs required to produce a predeﬁned array gain performance. The simulation results illustrate the substantially improved performance with the array gain performance guarantee of the proposed joint optimization method.
A multi-tracer empirically-driven approach to line-intensity mapping lightcones

Line-intensity mapping (LIM) is an emerging technique to probe the large-scale structure of the Universe. By targeting the integrated intensity of specific spectral lines, it captures the emission from all sources and is sensitive to the astrophysical processes that drive galaxy evolution. Relating these processes to the underlying distribution of matter introduces observational and theoretical challenges, such as observational contamination and highly non-Gaussian fields, which motivate the use of simulations to better characterize the signal. In this work we present SkyLine, a computational framework to generate realistic mock LIM observations that include observational features and foreground contamination, as well as a variety of self-consistent tracer catalogs. We apply our framework to generate realizations of LIM maps from the MultiDark Planck 2 simulations coupled to the UniverseMachine galaxy formation model. We showcase the potential of our scheme by exploring the voxel intensity distribution and the power spectrum of emission lines such as 21 cm, CO, CII, and Lyman-$\alpha$, their mutual cross-correlations, and cross-correlations with galaxy clustering. We additionally present cross-correlations between LIM and sub-millimeter extragalactic tracers of large-scale structure such as the cosmic infrared background and the thermal Sunyaev-Zel'dovich effect, as well as quantify the impact of galactic foregrounds, line interlopers and instrument noise on LIM observations. These simulated products will be crucial in quantifying the true information content of LIM surveys and their cross-correlations in the coming decade, and to develop strategies to overcome the impact of contaminants and maximize the scientific return from LIM experiments.
Hope Speech Detection on Social Media Platforms

Ever since personal computers became widely available in the consumer market, the amount of harmful content on the internet has significantly expanded. In simple terms, harmful content is anything online which causes a person distress or harm. It may include hate speech, violent content, threats, non-hope speech, etc. The online content must be positive, uplifting and supportive. Over the past few years, many studies have focused on solving this problem through hate speech detection, but very few focused on identifying hope speech. This paper discusses various machine learning approaches to identify a sentence as Hope Speech, Non-Hope Speech, or a Neutral sentence. The dataset used in the study contains English YouTube comments and is released as a part of the shared task "EACL-2021: Hope Speech Detection for Equality, Diversity, and Inclusion". Initially, the dataset obtained from the shared task had three classes: Hope Speech, non-Hope speech, and not in English; however, upon deeper inspection, we discovered that dataset relabelling is required. A group of undergraduates was hired to perform the entire dataset's relabeling task. We experimented with conventional machine learning models (such as Naïve Bayes, logistic regression and support vector machine) and pre-trained models (such as BERT) on relabelled data. According to the experimental results, the relabelled data has achieved a better accuracy for Hope speech identification than the original data set.
Guided waves filtering with warped curvelets

Lamb wave testing for SHM is complicated by multimodal propagation and by reflections. In this paper, the effectiveness of the decomposition inWarped Curvelet Frames for the analysis of guided ultrasonic waves is studied. The transformation acts by expanding the analyzed signal into a tight frame of basis functions named Curvelets. The curvelet transform may be effectively combined with warping procedures to analyze wave propagation in dispersive media. Experimental results show that, thanks to the spatial and temporal localization of curvelets, it is possible to decompose waves that are overlapped both in the time/space and in the frequency/wavenumber domain.
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks

Designing powerful adversarial attacks is of paramount importance for the evaluation of $\ell_p$-bounded adversarial defenses. Projected Gradient Descent (PGD) is one of the most effective and conceptually simple algorithms to generate such adversaries. The search space of PGD is dictated by the steepest ascent directions of an objective. Despite the plethora of objective function choices, there is no universally superior option and robustness overestimation may arise from ill-suited objective selection. Driven by this observation, we postulate that the combination of different objectives through a simple loss alternating scheme renders PGD more robust towards design choices. We experimentally verify this assertion on a synthetic-data example and by evaluating our proposed method across 25 different $\ell_{\infty}$-robust models and 3 datasets. The performance improvement is consistent, when compared to the single loss counterparts. In the CIFAR-10 dataset, our strongest adversarial attack outperforms all of the white-box components of AutoAttack (AA) ensemble, as well as the most powerful attacks existing on the literature, achieving state-of-the-art results in the computational budget of our study ($T=100$, no restarts).
Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation

Human evaluation is the foundation upon which the evaluation of both summarization systems and automatic metrics rests. However, existing human evaluation studies for summarization either exhibit a low inter-annotator agreement or have insufficient scale, and an in-depth analysis of human evaluation is lacking. Therefore, we address the shortcomings of existing summarization evaluation along the following axes: (1) We propose a modified summarization salience protocol, Atomic Content Units (ACUs), which is based on fine-grained semantic units and allows for a high inter-annotator agreement. (2) We curate the Robust Summarization Evaluation (RoSE) benchmark, a large human evaluation dataset consisting of 22,000 summary-level annotations over 28 top-performing systems on three datasets. (3) We conduct a comparative study of four human evaluation protocols, underscoring potential confounding factors in evaluation setups. (4) We evaluate 50 automatic metrics and their variants using the collected human annotations across evaluation protocols and demonstrate how our benchmark leads to more statistically stable and significant results. The metrics we benchmarked include recent methods based on large language models (LLMs), GPTScore and G-Eval. Furthermore, our findings have important implications for evaluating LLMs, as we show that LLMs adjusted by human feedback (e.g., GPT-3.5) may overfit unconstrained human evaluation, which is affected by the annotators’ prior, input-agnostic preferences, calling for more robust, targeted evaluation methods.
Generative structured normalizing flow Gaussian processes applied to spectroscopic data

In this work, we propose a novel generative model for map- ping inputs to structured, high-dimensional outputs using structured conditional normalizing ﬂows and Gaussian pro- cess regression. The model is motivated by the need to char-acterize uncertainty in the input/output relationship when making inferences on new data. In particular, in the physical sciences, limited training data may not adequately char- acterize future observed data; it is critical that models adequately indicate uncertainty, particularly when they may be asked to extrapolate. In our proposed model, structured conditional normalizing ﬂows provide parsimonious latent repre- sentations that relate to the inputs through a Gaussian process, providing exact likelihood calculations and uncertainty that naturally increases away from the training data inputs. We demonstrate the methodology on laser-induced breakdown spectroscopy data from the ChemCam instrument onboard the Mars rover Curiosity. ChemCam was designed to recover the chemical composition of rock and soil samples by mea-suring the spectral properties of plasma atomic emissions in- duced by a laser pulse. We show that our model can generate realistic spectra conditional on a given chemical composition and that we can use the model to perform uncertainty quantiﬁcation of chemical compositions for new observed spectra. Based on our results, we anticipate that our proposed modeling approach may be useful in other scientiﬁc domains with high-dimensional, complex structure where it is important to quantify predictive uncertainty.
Identifying and Explaining Safety-critical Scenarios for Autonomous Vehicles via Key Features

Ensuring the safety of autonomous vehicles (AVs) is of utmost importance, and testing them in simulated environments is a safer option than conducting in-field operational tests. However, generating an exhaustive test suite to identify critical test scenarios is computationally expensive as the representation of each test is complex and contains various dynamic and static features, such as the AV under test, road participants (vehicles, pedestrians, and static obstacles), environmental factors (weather and light), and the road’s structural features (lanes, turns, road speed, etc.). In this paper, we present a systematic technique that uses Instance Space Analysis (ISA) to identify the significant features of test scenarios that affect their ability to reveal the unsafe behaviour of AVs. ISA identifies the features that best differentiate safety-critical scenarios from normal driving and visualises the impact of these features on test scenario outcomes (safe/unsafe) in 2D. This visualisation helps to identify untested regions of the instance space and provides an indicator of the quality of the test suite in terms of the percentage of feature space covered by testing. To test the predictive ability of the identified features, we train five Machine Learning classifiers to classify test scenarios as safe or unsafe. The high precision, recall, and F1 scores indicate that our proposed approach is effective in predicting the outcome of a test scenario without executing it and can be used for test generation, selection, and prioritisation.
GAS-NeXt: Few-Shot Cross-Lingual Font Generator

Generating new fonts is a time-consuming and labor-intensive task, especially in a language with a huge amount of characters like Chinese. Various deep learning models have demonstrated the ability to efficiently generate new fonts with a few reference characters of that style, but few models support cross-lingual font generation. This paper presents GAS-NeXt, a novel few-shot cross-lingual font generator based on AGIS-Net and Font Translator GAN, and improve the performance metrics such as Fr\'echet Inception Distance (FID), Structural Similarity Index Measure(SSIM), and Pixel-level Accuracy (pix-acc). Our approaches include replacing the original encoder and decoder with the idea of layer attention and context-aware attention from Font Translator GAN, while utilizing the shape, texture, and local discriminators of AGIS-Net. In our experiment on English-to-Chinese font translation, we observed better results in fonts with distinct local features than conventional Chinese fonts compared to results obtained from Font Translator GAN. We also validate our method on multiple languages and datasets.
Neural signal propagation atlas of C. elegans

A fundamental problem in neuroscience is understanding how a network's properties dictate its function. Connectomics provides one avenue to predict nervous system function. To test this explicitly, we systematically measure signal propagation in 23,427 pairs of neurons across the head of the nematode Caenorhabditis elegans by direct optogenetic activation and simultaneous whole-brain calcium imaging. We measure the sign (excitatory or inhibitory), strength, temporal properties, and causal direction of signal propagation between these neurons to create a functional atlas. We find that signal propagation differs from predictions based on anatomy. Using mutants, we show that extrasynaptic signaling not visible from anatomy contributes to this difference. We identify many instances of dense-core-vesicle dependent signaling on seconds-or-less timescales that evoke acute calcium transients often where no direct wired connection exists but where relevant neuropeptides and receptors are expressed. We propose that here extrasynaptically released neuropeptides serve a similar function as that of classical neurotransmitters. Finally, our measured signal propagation atlas better predicts neural dynamics of spontaneous activity than does anatomy. We conclude that both synaptic and extrasynaptic signaling drive neural dynamics on short timescales and that measurement of evoked signal propagation are critical for interpreting neural function.
The Visco-elastic Properties of Network Structure II. Structural Viscosity

Viscosity behaviors of concentrated polymer solution are treated by making use of the model of weak Gaussian network. If chain-breakage coefficient, i.e. the probability of breakage of a chain in the network is independent of elongation of the chain, such system shows the Newtonian flow. Proper choice of chain-breakage coefficient, for example, that of square well type, yields a typical rate dependence of viscosity; the larger the rate of shearing flow, λ, the smaller the value of viscosity coefficient, µ. The tensile viscosity, µ * , is also considered, which in general increases with the rate of stretching, λ * , for small values of the rate. The so-called Trouton's formula, µ * =3µ is satisfied only in the limit of zero values of rates λ and λ * .
JAX-Accelerated Neuroevolution of Physics-informed Neural Networks: Benchmarks and Experimental Results

– This paper introduces the use of evolutionary algorithms for solving differential equations. The solution is obtained by optimizing a deep neural network whose loss function is defined by the residual terms from the differential equations. Recent studies have used stochastic gradient descent (SGD) variants to train these physics-informed neural networks (PINNs), but these methods can struggle to find accurate solutions due to optimization challenges. When solving differential equations, it is important to find the globally optimum parameters of the network, rather than just finding a solution that works well during training. SGD only searches along a single gradient direction, so it may not be the best approach for training PINNs with their accompanying complex optimization landscapes. In contrast, evolutionary algorithms perform a parallel exploration of different solutions in order to avoid getting stuck in local optima and can potentially find more accurate solutions. However, evolutionary algorithms can be slow, which can make them difficult to use in practice. To address this, we provide a set of five benchmark problems with associated performance metrics and baseline results to support the development of evolutionary algorithms for enhanced PINN training. As a baseline, we evaluate the performance and speed of using the widely adopted Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for solving PINNs. We provide the loss and training time for CMA-ES run on TensorFlow (both with and without GPU acceleration), and CMA-ES and SGD run on JAX (with GPU acceleration) for the five benchmark problems. Our results show that JAX-accelerated evolutionary algorithms, particularly CMA-ES, can be a useful approach for solving differential equations. We hope that our work will support the exploration and development of alternative optimization algorithms for the complex task of optimizing PINNs.
Analytical Engines With Context-Rich Processing: Towards Efficient Next-Generation Analytics

As modern data pipelines continue to collect, produce, and store a variety of data formats, extracting and combining value from traditional and context-rich sources such as strings, text, video, audio, and logs becomes a manual process where such formats are unsuitable for RDBMS. To tap into the dark data, domain experts analyze and extract insights and integrate them into the data repositories. This process can involve out-of-DBMS, ad-hoc analysis, and processing resulting in ETL, engineering effort, and suboptimal performance. While AI systems based on ML models can automate the analysis process, they often further generate context-rich answers. Using multiple sources of truth, for either training the models or in the form of knowledge bases, further exacerbates the problem of consolidating the data of interest.We envision an analytical engine co-optimized with components that enable context-rich analysis. Firstly, as the data from different sources or resulting from model answering cannot be cleaned ahead of time, we propose using online data integration via model-assisted similarity operations. Secondly, we aim for a holistic pipeline cost- and rule-based optimization across relational and model-based operators. Thirdly, with increasingly heterogeneous hardware and equally heterogeneous workloads ranging from traditional relational analytics to generative model inference, we envision a system that just-in-time adapts to the complex analytical query requirements. To solve increasingly complex analytical problems, ML offers attractive solutions that must be combined with traditional analytical processing and benefit from decades of database community research to achieve scalability and performance effortless for the end user.
Representation of Ideal Magnetohydrodynamic Modes

One of the most fundamental properties of ideal magnetohydrodynamics is the condition that plasma motion cannot change magnetic topology. The conventional representation of ideal magnetohydrodynamic modes by perturbing a toroidal equilibrium field through δ Β = ∇ X (xi X B) ensures that δ B • ∇ ψ = 0 at a resonance, with ψ labelling an equilibrium flux surface. Also useful for the analysis of guiding center orbits in a perturbed field is the representation δ Β = ∇ X αB. These two representations are equivalent, but the vanishing of δ B • ∇ψ at a resonance is necessary but not sufficient for the preservation of field line topology, and a indiscriminate use of either perturbation in fact destroys the original equilibrium flux topology. It is necessary to find the perturbed field to all orders in xi to conserve the original topology. The effect of using linearized perturbations on stability and growth rate calculations is discussed
Active Learning for Quantum Mechanical Measurements

The experimental evaluation of many quantum mechanical quantities requires the estimation of several directly measurable observables, such as local observables. Due to the necessity to repeat experiments on individual quantum systems in order to estimate expectation values of observables, the question arises how many repetitions to allocate to a given directly measurable observable. We show that an active learning scheme can help to improve such allocations, and the resultant decrease in experimental repetitions required to evaluate a quantity with the desired accuracy increases with the size of the underlying quantum mechanical system.
Magnetic field evolution and reconnection in low resistivity plasmas

The mathematics and physics of each of the three aspects of magnetic field evolution—topology, energy, and helicity—are remarkably simple and clear. When the resistivity η is small compared to an imposed evolution, a/v, timescale, which means Rm≡μ0va/η≫1, magnetic field-line chaos dominates the evolution of field-line topology in three-dimensional systems. Chaos has no direct role in the dissipation of energy. A large current density, jη≡vB/η, is required for energy dissipation to be on a comparable timescale to the topological evolution. Nevertheless, chaos plus Alfvén wave damping explain why both timescales tend to be approximately an order of magnitude longer than the evolution timescale a/v. Magnetic helicity is injected onto tubes of field lines when boundary flows have vorticity. Chaos can spread but not destroy magnetic helicity. Resistivity has a negligible effect on helicity accumulation when Rm≫1. Helicity accumulates within a tube of field lines until the tube erupts and moves far from its original location.
Counterfactual Explanations for Support Vector Machine Models

We tackle the problem of computing counterfactual explanations —minimal changes to the features that ﬂip an undesirable model prediction. We propose a solution to this question for linear Support Vector Machine (SVMs) models. Moreover, we introduce a way to account for weighted actions that allow for more changes in certain features than others. In particular, we show how to ﬁnd counterfactual explanations with the purpose of increasing model interpretability. These explanations are valid, change only actionable features, are close to the data distribution, sparse, and take into account correlations between features. We cast this as a mixed integer programming optimization problem . Additionally, we introduce two novel scale-invariant cost functions for assessing the qual-ityofcounterfactualexplanationsandusethem to evaluate the quality of our approach with a real medical dataset. Finally, we build a support vector machine model to predict whether law students will pass the Bar exam using protected features, and used our algorithms to uncover the inherent biases of the SVM.
DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems

Iterative detection and decoding (IDD) is known to achieve near-capacity performance in multi-antenna wireless systems. We propose deep-unfolded interleaved detection and decoding (DUIDD), a new paradigm that reduces the complexity of IDD while achieving even lower error rates. DUIDD interleaves the inner stages of the data detector and channel decoder, which expedites convergence and reduces complexity. Furthermore, DUIDD applies deep unfolding to automatically optimize algorithmic hyperparameters, soft-information exchange, message damping, and state forwarding. We demonstrate the efficacy of DUIDD using NVIDIA's Sionna link-level simulator in a 5G-near multi-user MIMO-OFDM wireless system with a novel low-complexity soft-input soft-output data detector, an optimized low-density parity-check decoder, and channel vectors from a commercial ray-tracer. Our results show that DUIDD outperforms classical IDD both in terms of block error rate and computational complexity.