Miracle-dz/UniFAR-FTUs huggingface dataset:
Variation in Metropolitan Travel Behavior by Sex and Ethnicity

Researchers have long observed that travel behavior varies systematically by sex and by ethnicity. However, the underlying causes of these differences and their policy implications have been subject to ongoing debate. In particular, there is an extensive literature comparing and contrasting the travel patterns of men and women, though relatively few studies have explicitly examined the combined influence of sex and ethnicity on travel behavior—the focus of this study. This research uses recently published data from the Nationwide Personal Transportation Survey (NPTS) to examine patterns of metropolitan travel by sex and ethnicity along three dimensions: (1) the choice of travel mode, (2) commuting to and from work, and (3) the purpose of travel. The authors use both cross-tabulations of data and multi-variate analyses (using both weighted and unweighted data) to explore ethnic and sex variations in travel behavior for metropolitan trips under 75 miles in length and 180 minutes in duration. In a nutshell, they find that race/ethnicity appears to be a more important influence than sex on mode choice and commuting behavior, although sex differences persist, especially by household type. Their analysis of non-work travel, however, reveals sharp distinctions between men and women across ethnic groups. Despite significant increases in paid labor force participation by women over the last third of the twentieth century, these data suggest that women continue to shoulder far more responsibility than men for maintaining households (and for household-serving travel). As a consequence, the authors find that women, regardless of race/ethnicity, are more likely than men to chain trips together into tours. This has important implications for urban transportation planning and policy. For example, the relative inflexibility of fixed-route transit service is often poorly suited to chaining multiple trips together across a metropolitan area. Public transit systems may need to develop new, more flexible forms to better adapt to the needs of trip-chaining travelers.
Correlating High-energy IceCube Neutrinos with 5BZCAT Blazars and RFC Sources

We investigate the possibility that blazars in the Roma-BZCAT Multifrequency Catalogue of Blazars (5BZCAT) are sources of the high-energy astrophysical neutrinos detected by the IceCube Neutrino Observatory, as recently suggested by Buson et al. Although we can reproduce their ∼4. 5σ result, which applies to 7 yr of neutrino data in the southern sky, we find no significant correlation with 5BZCAT sources when extending the search to the northern sky, where IceCube is most sensitive to astrophysical signals. To further test this scenario, we use a larger sample consisting of 10 yr of neutrino data recently released by the IceCube Collaboration, this time finding no significant correlation in neither the southern nor the northern sky. These results suggest that the strong correlation reported by Buson et al. using 5BZCAT could be due to a statistical fluctuation and possibly the spatial and flux nonuniformities in the blazar sample. We perform some additional correlation tests using the more uniform, flux-limited, and blazar-dominated Radio Fundamental Catalogue and find a ∼3. 2σ equivalent p-value when correlating it with the 7 yr southern neutrino sky. However, this correlation disappears completely when extending the analysis to the northern sky and when analyzing 10 yr of all-sky neutrino data. Our findings support a scenario where the contribution of the whole blazar class to the IceCube signal is relevant but not dominant, in agreement with most previous studies.
Connectivity and Interference in Device-to-Device Networks in Poisson-Voronoi Cities

To study the overall connectivity in device-to-device networks in cities, we incorporate a signal-to-interference-plus-noise connectivity model into a Poisson-Voronoi tessellation model representing the streets of a city. Relays are located at crossroads (or street intersections), whereas (user) devices are scattered along streets. Between any two adjacent relays, we assume data can be transmitted either directly between the relays or through users, given they share a common street. Our simulation results reveal that the network connectivity is ensured when the density of users (on the streets) exceeds a certain critical value. But then the network connectivity disappears when the user density exceeds a second critical value. The intuition is that for longer streets, where direct relay-to-relay communication is not possible, users are needed to transmit data between relays, but with too many users the interference becomes too strong, eventually reducing the overall network connectivity. This observation on the user density evokes previous results based on another wireless network model, where transmitter-receivers were scattered across the plane. This effect disappears when interference is removed from the model, giving a variation of the classic Gilbert model and recalling the lesson that neglecting interference in such network models can give overly optimistic results. For physically reasonable model parameters, we show that crowded streets (with more than six users on a typical street) lead to a sudden drop in connectivity. We also give numerical results outlining a relationship between the user density and the strength of any interference reduction techniques.
Extreme Tidal Stripping May Explain the Overmassive Black Hole in Leo I: A Proof of Concept

A recent study found dynamical evidence of a supermassive black hole of ∼3 × 106 M ⊙ at the center of Leo I, the most distant dwarf spheroidal galaxy of the Milky Way. This black hole, comparable in mass to the Milky Way’s Sgr A*, places the system >2 orders of magnitude above the standard M •–M ⋆ relation. We investigate the possibility, from a dynamical standpoint, that Leo I’s stellar system was originally much more massive and, thus, closer to the relation. Extreme tidal disruption from one or two close passages within the Milky Way’s virial radius could have removed most of its stellar mass. A simple analytical model suggests that the progenitor of Leo I could have experienced a mass loss in the range 32%–57% from a single pericenter passage, depending on the stellar velocity dispersion estimate. This mass-loss percentage increases to the range 66%–78% if the pericenter occurs at the minimum distance allowed by current orbital reconstructions. Detailed N-body simulations show that the mass loss could reach ∼90% with up to two passages, again with pericenter distances compatible with the minimum value allowed by Gaia data. Despite very significant uncertainties in the properties of Leo I, we reproduce its current position and velocity dispersion, as well as the final stellar mass enclosed in 1 kpc (∼5 × 106 M ⊙) within a factor <2. The most recent tidal stream is directed along our line of sight toward Leo I, making it difficult to detect. Evidence from this extreme tidal disruption event could be present in current Gaia data in the form of extended tidal streams.
Towards frugal unsupervised detection of subtle abnormalities in medical imaging

Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. This problem can be addressed through unsupervised anomaly detection (UAD) methods, which identify features that do not match with a reference model of normal profiles. Artificial neural networks have been extensively used for UAD but they do not generally achieve an optimal trade-o$\hookleftarrow$ between accuracy and computational demand. As an alternative, we investigate mixtures of probability distributions whose versatility has been widely recognized for a variety of data and tasks, while not requiring excessive design e$\hookleftarrow$ort or tuning. Their expressivity makes them good candidates to account for complex multivariate reference models. Their much smaller number of parameters makes them more amenable to interpretation and e cient learning. However, standard estimation procedures, such as the Expectation-Maximization algorithm, do not scale well to large data volumes as they require high memory usage. To address this issue, we propose to incrementally compute inferential quantities. This online approach is illustrated on the challenging detection of subtle abnormalities in MR brain scans for the follow-up of newly diagnosed Parkinsonian patients. The identified structural abnormalities are consistent with the disease progression, as accounted by the Hoehn and Yahr scale.
Classifying the CP properties of the ggH coupling in H+2j production

The Higgs-gluon interaction is crucial for LHC phenomenology. To improve the constraints on the CP structure of this coupling, we investigate Higgs production with two jets using machine learning. In particular, we exploit the CP sensitivity of the so far neglected phase space region that differs from the typical vector boson fusion-like kinematics. Our results suggest that significant improvements in current experimental limits are possible. We also discuss the most relevant observables and how CP violation in the Higgs-gluon interaction can be disentangled from CP violation in the interaction between the Higgs boson and massive vector bosons. Assuming the absence of CP-violating Higgs interactions with coloured beyond-the-Standard-Model states, our projected limits on a CP-violating top-Yukawa coupling are stronger than more direct probes like top-associated Higgs production and limits from a global fit.
Curtain Characterization of Sublinearly Morse Geodesics in CAT(0) Spaces

We show that the sublinear Morse boundary of every CAT(0) space continuously injects into the Gromov boundary of a hyperbolic space, which was not previously known even for all CAT(0) cube complexes. Our work utilizes the curtain machinery introduced by Petyt-Spriano-Zalloum. Curtains are more general combinatorial analogues of hyperplanes in cube complexes, and we develop multiple curtain characterizations of the sublinear Morse property along the way. Our results answer multiple questions of Petyt-Spriano-Zalloum.
PESTO: Pitch Estimation with Self-supervised Transposition-equivariant Objective

In this paper, we address the problem of pitch estimation using Self Supervised Learning (SSL). The SSL paradigm we use is equivariance to pitch transposition, which enables our model to accurately perform pitch estimation on monophonic audio after being trained only on a small unlabeled dataset. We use a lightweight ($<$ 30k parameters) Siamese neural network that takes as inputs two different pitch-shifted versions of the same audio represented by its Constant-Q Transform. To prevent the model from collapsing in an encoder-only setting, we propose a novel class-based transposition-equivariant objective which captures pitch information. Furthermore, we design the architecture of our network to be transposition-preserving by introducing learnable Toeplitz matrices. We evaluate our model for the two tasks of singing voice and musical instrument pitch estimation and show that our model is able to generalize across tasks and datasets while being lightweight, hence remaining compatible with low-resource devices and suitable for real-time applications. In particular, our results surpass self-supervised baselines and narrow the performance gap between self-supervised and supervised methods for pitch estimation.
Existence and Completeness of Bounded Disturbance Observers: A Set-Membership Viewpoint

This paper investigates the boundedness of the Disturbance Observer (DO) for linear discrete-time systems. In contrast to previous studies that focus on analyzing and/or designing observer gains, our analysis and synthesis approach is based on a set-membership viewpoint. From this viewpoint, a necessary and sufficient existence condition of bounded DOs is first established, which can be easily verified. Furthermore, a set-membership filter-based DO is developed, and its completeness is proved; thus, our proposed DO is bounded if and only if bounded DOs exist. We also prove that the proposed DO has the capability to achieve the worst-case optimality, which can provide a benchmark for the design of DOs. Finally, numerical simulations are performed to corroborate the effectiveness of the theoretical results.
The Secrets of Non-Blind Poisson Deconvolution

Non-blind image deconvolution has been studied for several decades but most of the existing work focuses on blur instead of noise. In photon-limited conditions, however, the excessive amount of shot noise makes traditional deconvolution algorithms fail. In searching for reasons why these methods fail, we present a systematic analysis of the Poisson non-blind deconvolution algorithms reported in the literature, covering both classical and deep learning methods. We compile a list of five “secrets” highlighting the do's and don'ts when designing algorithms. Based on this analysis, we build a proof-of-concept method by combining the five secrets. We find that the new method performs on par with some of the latest methods while outperforming some older ones.
Language Models for Novelty Detection in System Call Traces

Due to the complexity of modern computer systems, novel and unexpected behaviors frequently occur. Such deviations are either normal occurrences, such as software updates and new user activities, or abnormalities, such as misconfigurations, latency issues, intrusions, and software bugs. Regardless, novel behaviors are of great interest to developers, and there is a genuine need for efficient and effective methods to detect them. Nowadays, researchers consider system calls to be the most fine-grained and accurate source of information to investigate the behavior of computer systems. Accordingly, this paper introduces a novelty detection methodology that relies on a probability distribution over sequences of system calls, which can be seen as a language model. Language models estimate the likelihood of sequences, and since novelties deviate from previously observed behaviors by definition, they would be unlikely under the model. Following the success of neural networks for language models, three architectures are evaluated in this work: the widespread LSTM, the state-of-the-art Transformer, and the lower-complexity Longformer. However, large neural networks typically require an enormous amount of data to be trained effectively, and to the best of our knowledge, no massive modern datasets of kernel traces are publicly available. This paper addresses this limitation by introducing a new open-source dataset of kernel traces comprising over 2 million web requests with seven distinct behaviors. The proposed methodology requires minimal expert hand-crafting and achieves an F-score and AuROC greater than 95% on most novelties while being data- and task-agnostic. The source code and trained models are publicly available on GitHub while the datasets are available on Zenodo.
A study on fault diagnosis in nonlinear dynamic systems with uncertainties

In this draft, fault diagnosis in nonlinear dynamic systems is addressed. The objective of this work is to establish a framework, in which not only model-based but also data-driven and machine learning based fault diagnosis strategies can be uniformly handled. Instead of the well-established input-output and the associated state space models, stable image and kernel representations are adopted in our work as the basic process model forms. Based on it, the nominal system dynamics can then be modelled as a lower-dimensional manifold embedded in the process data space. To achieve a reliable fault detection as a classification problem, projection technique is a capable tool. For nonlinear dynamic systems, we propose to construct projection systems in the well-established framework of Hamiltonian systems and by means of the normalised image and kernel representations. For nonlinear dynamic systems, process data form a non-Euclidean space. Consequently, the norm-based distance defined in Hilbert space is not suitable to measure the distance from a data vector to the manifold of the nominal dynamics. To deal with this issue, we propose to use a Bregman divergence, a measure of difference between two points in a space, as a solution. Moreover, for our purpose of achieving a performance-oriented fault detection, the Bregman divergences adopted in our work are defined by Hamiltonian functions. This scheme not only enables to realise the performance-oriented fault detection, but also uncovers the information geometric aspect of our work. The last part of our work is devoted to the kernel representation based fault detection and uncertainty estimation that can be equivalently used for fault estimation. It is demonstrated that the projection onto the manifold of uncertainty data, together with the correspondingly defined Bregman divergence, is also capable for fault detection.
PromptTTS 2: Describing and Generating Voices with Text Prompt

Speech conveys more information than text, as the same word can be uttered in various voices to convey diverse information. Compared to traditional text-to-speech (TTS) methods relying on speech prompts (reference speech) for voice variability, using text prompts (descriptions) is more user-friendly since speech prompts can be hard to find or may not exist at all. TTS approaches based on the text prompt face two main challenges: 1) the one-to-many problem, where not all details about voice variability can be described in the text prompt, and 2) the limited availability of text prompt datasets, where vendors and large cost of data labeling are required to write text prompts for speech. In this work, we introduce PromptTTS 2 to address these challenges with a variation network to provide variability information of voice not captured by text prompts, and a prompt generation pipeline to utilize the large language models (LLM) to compose high quality text prompts. Specifically, the variation network predicts the representation extracted from the reference speech (which contains full information about voice variability) based on the text prompt representation. For the prompt generation pipeline, it generates text prompts for speech with a speech language understanding model to recognize voice attributes (e.g., gender, speed) from speech and a large language model to formulate text prompts based on the recognition results. Experiments on a large-scale (44K hours) speech dataset demonstrate that compared to the previous works, PromptTTS 2 generates voices more consistent with text prompts and supports the sampling of diverse voice variability, thereby offering users more choices on voice generation. Additionally, the prompt generation pipeline produces high-quality text prompts, eliminating the large labeling cost. The demo page of PromptTTS 2 is available online.
Resurgent large genus asymptotics of intersection numbers

In this paper, we present a novel approach for computing the large genus asymptotics of intersection numbers. Our strategy is based on a resurgent analysis of the $n$-point functions of such intersection numbers, which are computed via determinantal formulae, and relies on the presence of a quantum curve. With this approach, we are able to extend the recent results of Aggarwal for Witten-Kontsevich intersection numbers with the computation of all subleading corrections, proving a conjecture of Guo-Yang, and to obtain new results on $r$-spin and Theta-class intersection numbers.
Do You Trust ChatGPT? - Perceived Credibility of Human and AI-Generated Content

This paper examines how individuals perceive the credibility of content originating from human authors versus content generated by large language models, like the GPT language model family that powers ChatGPT, in different user interface versions. Surprisingly, our results demonstrate that regardless of the user interface presentation, participants tend to attribute similar levels of credibility. While participants also do not report any different perceptions of competence and trustworthiness between human and AI-generated content, they rate AI-generated content as being clearer and more engaging. The findings from this study serve as a call for a more discerning approach to evaluating information sources, encouraging users to exercise caution and critical thinking when engaging with content generated by AI systems.
Screening of Pneumonia and Urinary Tract Infection at Triage using TriNet

Due to the steady rise in population demographics and longevity, emergency department visits are increasing across North America. As more patients visit the emergency department, traditional clinical workflows become overloaded and inefficient, leading to prolonged wait-times and reduced healthcare quality. One of such workflows is the triage medical directive, impeded by limited human workload, inaccurate diagnoses and invasive over-testing. To address this issue, we propose TriNet: a machine learning model for medical directives that automates first-line screening at triage for conditions requiring downstream testing for diagnosis confirmation. To verify screening potential, TriNet was trained on hospital triage data and achieved high positive predictive values in detecting pneumonia (0.86) and urinary tract infection (0.93). These models outperform current clinical benchmarks, indicating that machine-learning medical directives can offer cost-free, non-invasive screening with high specificity for common conditions, reducing the risk of over-testing while increasing emergency department efficiency.
A trace inequality for Euclidean gravitational path integrals (and a new positive action conjecture)

The AdS/CFT correspondence states that certain conformal field theories are equivalent to string theories in a higher-dimensional anti-de Sitter space. One aspect of the correspondence is an equivalence of density matrices or, if one ignores normalizations, of positive operators. On the CFT side of the correspondence, any two positive operators $A,B$ will satisfy the trace inequality $\operatorname{Tr}(AB) \leq \operatorname{Tr}(A) \operatorname{Tr}(B)$. This relation holds on any Hilbert space ${\cal H}$ and is deeply associated with the fact that the algebra $B({\cal H})$ of bounded operators on ${\cal H}$ is a type I von Neumann factor. Holographic bulk theories must thus satisfy a corresponding condition, which we investigate below. In particular, we argue that the Euclidean gravitational path integral respects this inequality at all orders in the semi-classical expansion and with arbitrary higher-derivative corrections. The argument relies on a conjectured property of the classical gravitational action, which in particular implies a positive action conjecture for quantum gravity wavefunctions. We prove this conjecture for Jackiw-Teitelboim gravity and we also motivate it for more general theories.
Self-Supervised Video Transformers for Isolated Sign Language Recognition

This paper presents an in-depth analysis of various self-supervision methods for isolated sign language recognition (ISLR). We consider four recently introduced transformer-based approaches to self-supervised learning from videos, and four pre-training data regimes, and study all the combinations on the WLASL2000 dataset. Our findings reveal that MaskFeat achieves performance superior to pose-based and supervised video models, with a top-1 accuracy of 79.02% on gloss-based WLASL2000. Furthermore, we analyze these models' ability to produce representations of ASL signs using linear probing on diverse phonological features. This study underscores the value of architecture and pre-training task choices in ISLR. Specifically, our results on WLASL2000 highlight the power of masked reconstruction pre-training, and our linear probing results demonstrate the importance of hierarchical vision transformers for sign language representation.