Miracle-dz/UniFAR-FTUs huggingface dataset:
Dreams Are More "Predictable" Than You Think

A consistent body of evidence suggests that dream reports significantly vary from other types of textual transcripts with respect to semantic content. Furthermore, it appears to be a widespread belief in the dream/sleep research community that dream reports constitute rather ``unique'' strings of text. This might be a notable issue for the growing amount of approaches using natural language processing (NLP) tools to automatically analyse dream reports, as they largely rely on neural models trained on non-dream corpora scraped from the web. In this work, I will adopt state-of-the-art (SotA) large language models (LLMs), to study if and how dream reports deviate from other human-generated text strings, such as Wikipedia. Results show that, taken as a whole, DreamBank does not deviate from Wikipedia. Moreover, on average, single dream reports are significantly more predictable than Wikipedia articles. Preliminary evidence suggests that word count, gender, and visual impairment can significantly shape how predictable a dream report can appear to the model.
Optimal Sparse Sliced Inverse Regression via Random Projection

We propose a novel sparse sliced inverse regression method based on random projections in a large $p$ small $n$ setting. Embedded in a generalized eigenvalue framework, the proposed approach finally reduces to parallel execution of low-dimensional (generalized) eigenvalue decompositions, which facilitates high computational efficiency. Theoretically, we prove that this method achieves the minimax optimal rate of convergence under suitable assumptions. Furthermore, our algorithm involves a delicate reweighting scheme, which can significantly enhance the identifiability of the active set of covariates. Extensive numerical studies demonstrate high superiority of the proposed algorithm in comparison to competing methods.
Spectral proper orthogonal decomposition of harmonically forced turbulent flows

Many turbulent flows exhibit time-periodic statistics. These include turbomachinery flows, flows with external harmonic forcing, and the wakes of bluff bodies. Many existing techniques for identifying turbulent coherent structures, however, assume the statistics are statistically stationary. In this paper, we leverage cyclostationary analysis, an extension of the statistically stationary framework to processes with periodically varying statistics, to generalize the spectral proper orthogonal decomposition (SPOD) to the cyclostationary case. The resulting properties of the cyclostationary SPOD (CS-SPOD for short) are explored, a theoretical connection between CS-SPOD and the harmonic resolvent analysis is provided, simplifications for the low and high forcing frequency limits are discussed, and an efficient algorithm to compute CS-SPOD with SPOD-like cost is presented. We illustrate the utility of CS-SPOD using two example problems: a modified complex linearized Ginzburg-Landau model and a high-Reynolds-number turbulent jet.
Communication-robust multi-agent learning by adaptable auxiliary multi-agent adversary generation

Communication can promote coordination in cooperative Multi-Agent Reinforcement Learning (MARL). Nowadays, existing works mainly focus on improving the communication efficiency of agents, neglecting that real-world communication is much more challenging as there may exist noise or potential attackers. Thus the robustness of the communication-based policies becomes an emergent and severe issue that needs more exploration. In this paper, we posit that the ego system^1) trained with auxiliary adversaries may handle this limitation and propose an adaptable method of M ulti -A gent A uxiliary A dversaries Generation for robust C ommunication, dubbed MA3C, to obtain a robust communication-based policy. In specific, we introduce a novel message-attacking approach that models the learning of the auxiliary attacker as a cooperative problem under a shared goal to minimize the coordination ability of the ego system, with which every information channel may suffer from distinct message attacks. Furthermore, as naive adversarial training may impede the generalization ability of the ego system, we design an attacker population generation approach based on evolutionary learning. Finally, the ego system is paired with an attacker population and then alternatively trained against the continuously evolving attackers to improve its robustness, meaning that both the ego system and the attackers are adaptable. Extensive experiments on multiple benchmarks indicate that our proposed MA3C provides comparable or better robustness and generalization ability than other baselines.
Symmetries in polynomial optimization

This chapter investigates how symmetries can be used to reduce the computational complexity in polynomial optimization problems. A focus will be specifically given on the Moment-SOS hierarchy in polynomial optimization, where results from representation theory and invariant theory of groups can be used. In addition, symmetry reduction techniques which are more generally applicable are also presented.
Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains

We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random walk (SRRW) which is less likely to transition to nodes that were highly visited in the past, and more likely to transition to seldom visited nodes. For a class of SRRWs parameterized by a positive real {\alpha}, we prove that the empirical distribution of the process converges almost surely to the the target (stationary) distribution of the underlying Markov chain kernel. We then provide a central limit theorem and derive the exact form of the arising asymptotic co-variance matrix, which allows us to show that the SRRW with a stronger repellence (larger {\alpha}) always achieves a smaller asymptotic covariance, in the sense of Loewner ordering of co-variance matrices. Especially for SRRW-driven MCMC algorithms, we show that the decrease in the asymptotic sampling variance is of the order O(1/{\alpha}), eventually going down to zero. Finally, we provide numerical simulations complimentary to our theoretical results, also empirically testing a version of SRRW with {\alpha} increasing in time to combine the benefits of smaller asymptotic variance due to large {\alpha}, with empirically observed faster mixing properties of SRRW with smaller {\alpha}.
Immersions into Sasakian space forms

We study immersions of Sasakian manifolds into finite and infinite dimensional Sasakian space forms. After proving Calabi's rigidity results in the Sasakian setting, we characterise all homogeneous Sasakian manifolds which admit a (local) Sasakian immersion into a nonelliptic Sasakian space form. Moreover, we give a characterisation of homogeneous Sasakian manifolds which can be embedded into the standard sphere both in the compact and noncompact case.
Topological Kolmogorov complexity and the Berezinskii-Kosterlitz-Thouless mechanism

Topology plays a fundamental role in our understanding of many-body physics, from vortices and solitons in classical field theory, to phases and excitations in quantum matter. Topological phenomena are intimately connected to the distribution of information content - that, differently from ordinary matter, is now governed by non-local degrees of freedom. However, a precise characterization of how topological effects govern the complexity of a many-body state - i.e., its partition function - is presently unclear. In this work, we show how topology and complexity are directly intertwined concepts in the context of classical statistical mechanics. In concrete, we present a theory that shows how the Kolmogorov complexity of a classical partition function sampling carries unique, distinctive features depending on the presence of topological excitations in the system. We confront two-dimensional Ising, Heisenberg, and XY models on several topologies and study the corresponding samplings as high-dimensional manifolds in configuration space, quantifying their complexity via the intrinsic dimension. While for the Ising and Heisenberg models the intrinsic dimension is independent of the real-space topology, for the XY model it depends crucially on temperature: across the Berezkinskii-Kosterlitz-Thouless (BKT) transition, complexity becomes topology dependent. In the BKT phase, it displays a characteristic dependence on the homology of the real-space manifold, and, for $g$-torii, it follows a scaling that is solely genus dependent. We argue that this behavior is intimately connected to the emergence of an order parameter in data space, the conditional connectivity, that displays scaling behavior.
LSAS: Lightweight Sub-attention Strategy for Alleviating Attention Bias Problem

In computer vision, the performance of deep neural networks (DNNs) is highly related to the feature extraction ability, i.e., the ability to recognize and focus on key pixel regions in an image. However, in this paper, we quantitatively and statistically illustrate that DNNs have a serious attention bias problem on many samples from some popular datasets: (1) Position bias: DNNs fully focus on label-independent regions; (2) Range bias: The focused regions from DNN are not completely contained in the ideal region. Moreover, we find that the existing self-attention modules can alleviate these biases to a certain extent, but the biases are still non-negligible. To further mitigate them, we propose a lightweight sub-attention strategy (LSAS), which utilizes high-order sub-attention modules to improve the original self-attention modules. The effectiveness of LSAS is demonstrated by extensive experiments on widely-used benchmark datasets and popular attention networks. We release our code to help other researchers to reproduce the results of LSAS 1
Massively Parallel Tensor Network State Algorithms on Hybrid CPU-GPU Based Architectures

The interplay of quantum and classical simulation and the delicate divide between them is in the focus of massively parallelized tensor network state (TNS) algorithms designed for high performance computing (HPC). In this contribution, we present novel algorithmic solutions together with implementation details to extend current limits of TNS algorithms on HPC infrastructure building on state-of-the-art hardware and software technologies. Benchmark results obtained via large-scale density matrix renormalization group (DMRG) simulations are presented for selected strongly correlated molecular systems addressing problems on Hilbert space dimensions up to $2.88\times10^{36}$.
CharSpan: Utilizing Lexical Similarity to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages

We address the task of machine translation (MT) from extremely low-resource language (ELRL) to English by leveraging cross-lingual transfer from *closely-related* high-resource language (HRL). The development of an MT system for ELRL is challenging because these languages typically lack parallel corpora and monolingual corpora, and their representations are absent from large multilingual language models. Many ELRLs share lexical similarities with some HRLs, which presents a novel modeling opportunity. However, existing subword-based neural MT models do not explicitly harness this lexical similarity, as they only implicitly align HRL and ELRL latent embedding space. To overcome this limitation, we propose a novel, CharSpan, approach based on character-span noise augmentation into the training data of HRL. This serves as a regularization technique, making the model more robust to lexical divergences between the HRL and ELRL, thus facilitating effective cross-lingual transfer. Our method significantly outperformed strong baselines in zero-shot settings on closely related HRL and ELRL pairs from three diverse language families, emerging as the state-of-the-art model for ELRLs.
The Day-After-Tomorrow: On the Performance of Radio Fingerprinting over Time

The performance of Radio Frequency (RF) Fingerprinting (RFF) techniques is negatively impacted when the training data is not temporally close to the testing data. This can limit the practical implementation of physical-layer authentication solutions. To circumvent this problem, current solutions involve collecting training and testing datasets at close time intervals—this being detrimental to the real-life deployment of any physical-layer authentication solution. We refer to this issue as the Day-After-Tomorrow (DAT) effect, being widely attributed to the temporal variability of the wireless channel, which masks the physical-layer features of the transmitter, thus impairing the fingerprinting process. In this work, we investigate the DAT effect shedding light on its root causes. Our results refute previous knowledge by demonstrating that the DAT effect is not solely caused by the variability of the wireless channel. Instead, we prove that it is also due to the power cycling of the radios, i.e., the turning off and on of the radios between the collection of training and testing data. We show that state-of-the-art RFF solutions double their performance when the devices under test are not power cycled, i.e., the accuracy increases from about 0.5 to about 1 in a controlled scenario. Finally, we show how to mitigate the DAT effect in real-world scenarios, through pre-processing of the I-Q samples. Our experimental results show a significant improvement in accuracy, from approximately 0.45 to 0.85. Additionally, we reduce the variance of the results, making the overall performance more reliable.
An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for "In the Wild" Edge Applications

Unsupervised speech models are becoming ubiquitous in the speech and machine learning communities. Upstream models are responsible for learning meaningful representations from raw audio. Later, these representations serve as input to downstream models to solve a number of tasks, such as keyword spotting or emotion recognition. As edge speech applications start to emerge, it is important to gauge how robust these cross-task representations are on edge devices with limited resources and different noise levels. To this end, in this study we evaluate the robustness of four different versions of HuBERT, namely: base, large, and extra-large versions, as well as a recent version termed Robust-HuBERT. Tests are conducted under different additive and convolutive noise conditions for three downstream tasks: keyword spotting, intent classification, and emotion recognition. Our results show that while larger models can provide some important robustness to environmental factors, they may not be applicable to edge applications. Smaller models, on the other hand, showed substantial accuracy drops in noisy conditions, especially in the presence of room reverberation. These findings suggest that cross-task speech representations are not yet ready for edge applications and innovations are still needed.
Parametric Viscous Analysis of Gust Interaction with SD7003 Airfoil

The work develops a high-accuracy numerical viscous analysis of nonlinear interaction of SD7003 airfoil with non-uniform, unsteady incoming flow. The study is focused on the low-Re number unsteady flows typical of MAV applications in which a gust encounter can induce a particularly significant aerodynamic and aeroelastic response. Efficient source models are developed to introduce sharp-edge and time-harmonic gust perturbations with specified amplitude, frequency and duration inside the computational domain through the source terms in the governing momentum equations. Parametric analysis of gust-airfoil interactions for different steady airfoil loads is conducted in comparison with equivalent pitch-ramp and time-harmonic pitching simulations. In addition, all obtained solutions are compared with corresponding predictions based on the inviscid, incompressible unsteady aerodynamic theory. The study reveals complex interaction of inviscid and viscous unsteady forces observed for different gust and pitching excitations, and identifies the degree of similarity between the corresponding gust and pitching airfoil responses.
FedAdapter: Efficient Federated Learning for Modern NLP

Transformer-based pre-trained models have revolutionized NLP for superior performance and generality. Fine-tuning pre-trained models for downstream tasks often requires private data, for which federated learning is the de-facto approach (i.e., FedNLP). However, our measurements show that FedNLP is prohibitively slow due to the large model sizes and the resultant high network/computation cost. Towards practical FedNLP, we identify as the key building blocks adapters, small bottleneck modules inserted at a variety of model layers. A key challenge is to properly configure the depth and width of adapters, to which the training speed and efficiency is highly sensitive. No silver-bullet configuration exists: the optimal choice varies across downstream NLP tasks, desired model accuracy, and mobile resources. To automate adapter configuration, we propose FedAdapter, a framework that enhances the existing FedNLP with two novel designs. First, FedAdapter progressively upgrades the adapter configuration throughout a training session; the principle is to quickly learn shallow knowledge by only training fewer and smaller adapters at the model's top layers, and incrementally learn deep knowledge by incorporating deeper and larger adapters. Second, FedAdapter continuously profiles future adapter configurations by allocating participant devices to trial groups. Extensive experiments show that FedAdapter can reduce FedNLP's model convergence delay to no more than several hours, which is up to 155.5$\times$ faster compared to vanilla FedNLP and 48$\times$ faster compared to strong baselines.
Symmetric teleparallel Horndeski gravity

Horndeski gravity is the most general scalar-tensor theory with one scalar field leading to second-order Euler-Lagrange field equations for the metric and scalar field, and it is based on Riemannian geometry. In this paper, we formulate an analogue version of Horndeski gravity in a symmetric teleparallel geometry which assumes that both the curvature (general) and torsion are vanishing and gravity is only related to nonmetricity. Our setup requires that the Euler-Lagrange equations for not only metric and scalar field but also connection should be at most second order. We find that the theory can be always recast as a sum of the Riemannian Horndeski theory and new terms that are purely teleparallel. Due to the nature of nonmetricity, there are many more possible ways of constructing second-order theories of gravity. In this regard, up to some assumptions, we find the most general $k$-essence extension of Symmetric Teleparallel Horndeski gravity. We also formulate a novel theory containing higher-order derivatives acting on nonmetricity while still respecting the second-order conditions, which can be recast as an extension of Kinetic Gravity Braiding. We finish our study by presenting the FLRW cosmological equations for our model.
Quality assessment of shoulder MRI according to practice parameters of American College of Radiology: A multi-center study in Jordan

Magnetic resonance imaging (MRI) is essential for assessing shoulder conditions. This study aimed to evaluate current shoulder MRI practices in Jordan, including technical parameter patterns, and determine if they adhere to the American College of Radiology (ACR) guidelines. The retrospective analysis included data from 48 eligible participants from 13 MRI centers in March 2021. Descriptive and correlation data analysis were performed using IBM SPSS statistics version_20 and Excel 2013. Most MRI centers (50%) were private outpatient clinics with closed MRI machines above 1 Tesla. Most participants (62.5%) were male, and shoulder pain (47.9%) was the main clinical indication. Most shoulder orientations (68.7%, 33/48) were right shoulders, and the coronal MRI planes (43%, 121/280) were the most common. The alignment percentage for the axial plane was 100%, but MRI artifacts of the shoulder were present in 8.2% of cases (23/280). Dark fluid T1-W coronal sequence was not conducted in 25% of the cases. The percentage of the field view (FOV) within ACR recommendations was 45% (126/281), and slice thickness parameters were 96% (269/281). The recommended pixel area for all sequences was 47.9% (134/280), encompassing all axial, sagittal oblique, and coronal planes. However, crucial parameters, such as FOV and slice thickness, were inadequate and did not meet the ACR guidelines, resulting in suboptimal image quality of shoulder MRI. To improve MRI image quality, it is recommended that MRI technologists receive ongoing education and training on appropriate MRI image parameters.
Broadening participation in science through arts-facilitated experiences at a cultural festival

A key broadening participation challenge for science communication is to reach non-traditional audiences beyond those already interested in science. In this study we test a “Guerilla Science” approach that blends elements of access, by removing barriers to participation, with those of inclusion, by designing participant-centered activities, for reaching an art-interested adult audience at the FIGMENT art festival in New York City. Our results show that participants at Guerilla Science were similar to festival goers overall in their connection to and interest in science, indicating the effectiveness of the approach for engaging non-traditional audiences and those who might not self-select into science activities.
Does adding MRI and CSF-based biomarkers improve cognitive status classification based on cognitive performance questionnaires?

Background Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns about the accuracy of cognitive assessments. MRI and CSF biomarkers may provide improved classification, but the potential improvement in classification in population-based studies is relatively unknown. Methods Data come from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We examined whether the addition of MRI and CSF biomarkers improved cognitive status classification based on cognitive status questionnaires (MMSE). We estimated several multinomial logistic regression models with different combinations of MMSE and CSF/MRI biomarkers. Based on these models, we also predicted prevalence of each cognitive status category using a model with MMSE only and a model with MMSE + MRI + CSF measures and compared them to diagnosed prevalence. Results Our analysis showed a slight improvement in variance explained (pseudo-R2) between the model with MMSE only and the model including MMSE and MRI/CSF biomarkers; the pseudo-R2 increased from . 401 to . 445. Additionally, in evaluating differences in predicted prevalence for each cognitive status, we found a small improvement in the predicted prevalence of cognitively normal individuals between the MMSE only model and the model with MMSE and CSF/MRI biomarkers (3.1% improvement). We found no improvement in the correct prediction of dementia prevalence. Conclusion MRI and CSF biomarkers, while important for understanding dementia pathology in clinical research, were not found to substantially improve cognitive status classification based on cognitive status performance, which may limit adoption in population-based surveys due to costs, training, and invasiveness associated with their collection.