Miracle-dz/UniFAR-FTUs huggingface dataset:
Moderate Deviations of Hitting Times for Trajectories of Sums of Independent and Identically Distributed Random Variables

In this paper we establish a moderate deviation principle of the hitting times for trajectories of sums of independent and identically distributed random variables. The main idea of proof is to convert the moderate deviations over a small time interval into the moderate deviations at a point, then utilize the moderate deviations for trajectories of sums of independent random variables given by Hu in \cite{Hu2001} to get the moderate deviations at a point. By the upper bounds of large deviations for trajectories of sums of independent random variables given by Schuette in \cite{ Schuette1994}, we can prove that our convert doesn't influence the rate function of the moderate deviation.
Energy-Efficient Analog Beamforming for RF-WET with Charging Time Constraint

Internet of Things (IoT) sustainability may hinge on radio frequency wireless energy transfer (RF-WET). However, energy-efficient charging strategies are still needed, motivating our work. Specifically, this letter proposes a time division scheme to efficiently charge low-power devices in an IoT network. For this, a multi-antenna power beacon (PB) drives the devices' energy harvesting circuit to the highest power conversion efficiency point via energy beamforming, thus achieving minimum energy consumption. Herein, we adopt the analog multi-antenna architecture due to its low complexity, cost, and energy consumption. The proposal includes a simple yet accurate model for the transfer characteristic of the energy harvesting circuit, enabling the optimization framework. The results evince the effectiveness of our RF-WET strategy over a benchmark scheme where the PB charges all the IoT devices simultaneously. Furthermore, the performance increases with the number of PB antennas.
Causal Inference from Text: Unveiling Interactions between Variables

Adjusting for latent covariates is crucial for estimating causal effects from observational textual data. Most existing methods only account for confounding covariates that affect both treatment and outcome, potentially leading to biased causal effects. This bias arises from insufficient consideration of non-confounding covariates, which are relevant only to either the treatment or the outcome. In this work, we aim to mitigate the bias by unveiling interactions between different variables to disentangle the non-confounding covariates when estimating causal effects from text. The disentangling process ensures covariates only contribute to their respective objectives, enabling independence between variables. Additionally, we impose a constraint to balance representations from the treatment group and control group to alleviate selection bias. We conduct experiments on two different treatment factors under various scenarios, and the proposed model significantly outperforms recent strong baselines. Furthermore, our thorough analysis on earnings call transcripts demonstrates that our model can effectively disentangle the variables, and further investigations into real-world scenarios provide guidance for investors to make informed decisions.
Generalized test utilities for long-tail performance in extreme multi-label classification

Extreme multi-label classification (XMLC) is the task of selecting a small subset of relevant labels from a very large set of possible labels. As such, it is characterized by long-tail labels, i.e., most labels have very few positive instances. With standard performance measures such as precision@k, a classifier can ignore tail labels and still report good performance. However, it is often argued that correct predictions in the tail are more"interesting"or"rewarding,"but the community has not yet settled on a metric capturing this intuitive concept. The existing propensity-scored metrics fall short on this goal by confounding the problems of long-tail and missing labels. In this paper, we analyze generalized metrics budgeted"at k"as an alternative solution. To tackle the challenging problem of optimizing these metrics, we formulate it in the expected test utility (ETU) framework, which aims to optimize the expected performance on a fixed test set. We derive optimal prediction rules and construct computationally efficient approximations with provable regret guarantees and robustness against model misspecification. Our algorithm, based on block coordinate ascent, scales effortlessly to XMLC problems and obtains promising results in terms of long-tail performance.
S3AD: Semi-supervised Small Apple Detection in Orchard Environments

Crop detection is integral for precision agriculture applications such as automated yield estimation or fruit picking. However, crop detection, e.g., apple detection in orchard environments remains challenging due to a lack of large-scale datasets and the small relative size of the crops in the image. In this work, we address these challenges by reformulating the apple detection task in a semi-supervised manner. To this end, we provide the large, high-resolution dataset MAD comprising 105 labeled images with 14,667 annotated apple instances and 4,440 unlabeled images. Utilizing this dataset, we also propose a novel Semi-Supervised Small Apple Detection system S$^3$AD based on contextual attention and selective tiling to improve the challenging detection of small apples, while limiting the computational overhead. We conduct an extensive evaluation on MAD and the MSU dataset, showing that S$^3$AD substantially outperforms strong fully-supervised baselines, including several small object detection systems, by up to $14.9\%$. Additionally, we exploit the detailed annotations of our dataset w.r. t. apple properties to analyze the influence of relative size or level of occlusion on the results of various systems, quantifying current challenges.
Formal construction of some exchangeable structures

We show that exchangeable structures such as Polya urn model and Chinese restaurant process can be constructed from sets with a real number of elements. From this construction, the exchangeability of these structures becomes obvious and the calculations on them become extremely simple. Moreover, the usual calculations on these structures, which involve induction, integrals and Jacobian determinants, now can be done by simple combinatorial calculations.
Global error estimates of high-order fully decoupled schemes for the Cahn-Hilliard-Navier-Stokes model of Two-Phase Incompressible Flows

In this paper we construct new fully decoupled and high-order implicit-explicit (IMEX) schemes for the two-phase incompressible flows based on the new generalized scalar auxiliary variable approach with optimal energy approximation (EOP-GSAV) for Cahn-Hilliard equation and consistent splitting method for Navier-Stokes equation. These schemes are linear, fully decoupled, unconditionally energy stable, only require solving a sequence of elliptic equations with constant coefficients at each time step, and provide a new technique to preserve the consistency between original energy and modified energy. We derive that numerical solutions of these schemes are uniformly bounded without any restriction on time step size. Furthermore, we carry out a rigorous error analysis for the first-order scheme and establish optimal global error estimates for the phase function, velocity and pressure in two and three-dimensional cases. Numerical examples are presented to validate the proposed schemes.
Observation of mulitply imaged quasars with the 4-m ILMT

Gravitationally lensed quasars (GLQs) are known to potentially provide an independent way of determining the value of the Hubble-Lema\^{i}tre parameter $H_{0}$, to probe the dark matter content of lensing galaxies and to resolve tiny structures in distant active galactic nuclei. That is why multiply imaged quasars are one of the main drivers for a photometric monitoring with the 4-m International Liquid Mirror Telescope (ILMT). We would like to answer the following questions -- how many multiply imaged quasars should we be able to detect with the ILMT? And how to derive accurate magnitudes of the GLQ images? Our estimation of the possible number of multiply imaged quasars is $15$, although optimistic forecasts predict up to $50$ of them. We propose to use the adaptive PSF fitting method for accurate flux measurements of the lensed images. During preliminary observations in spring 2022 we were able to detect the quadruply imaged quasar - SDSS J1251+2935 in the $\it{i}$ and $\it{r}$ spectral bands.
Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data

Recent advances in supervised deep learning techniques have demonstrated the possibility to remotely measure human physiological vital signs (e.g., photoplethysmograph, heart rate) just from facial videos. However, the performance of these methods heavily relies on the availability and diversity of real labeled data. Yet, collecting large-scale real-world data with high-quality labels is typically challenging and resource intensive, which also raises privacy concerns when storing personal bio-metric data. Synthetic video-based datasets (e.g., SCAMPS \cite{mcduff2022scamps}) with photo-realistic synthesized avatars are introduced to alleviate the issues while providing high-quality synthetic data. However, there exists a significant gap between synthetic and real-world data, which hinders the generalization of neural models trained on these synthetic datasets. In this paper, we proposed several measures to add real-world noise to synthetic physiological signals and corresponding facial videos. We experimented with individual and combined augmentation methods and evaluated our framework on three public real-world datasets. Our results show that we were able to reduce the average MAE from 6.9 to 2.0.
LCM-LoRA: A Universal Stable-Diffusion Acceleration Module

Latent Consistency Models (LCMs) have achieved impressive performance in accelerating text-to-image generative tasks, producing high-quality images with minimal inference steps. LCMs are distilled from pre-trained latent diffusion models (LDMs), requiring only ~32 A100 GPU training hours. This report further extends LCMs' potential in two aspects: First, by applying LoRA distillation to Stable-Diffusion models including SD-V1.5, SSD-1B, and SDXL, we have expanded LCM's scope to larger models with significantly less memory consumption, achieving superior image generation quality. Second, we identify the LoRA parameters obtained through LCM distillation as a universal Stable-Diffusion acceleration module, named LCM-LoRA. LCM-LoRA can be directly plugged into various Stable-Diffusion fine-tuned models or LoRAs without training, thus representing a universally applicable accelerator for diverse image generation tasks. Compared with previous numerical PF-ODE solvers such as DDIM, DPM-Solver, LCM-LoRA can be viewed as a plug-in neural PF-ODE solver that possesses strong generalization abilities. Project page: https://github.com/luosiallen/latent-consistency-model.
Dilute measurement-induced cooling into many-body ground states

Cooling a quantum system to its ground state is important for the characterization of non-trivial interacting systems, and in the context of a variety of quantum information platforms. In principle, this can be achieved by employing measurement-based passive steering protocols, where the steering steps are predetermined and are not based on measurement readouts. However, measurements, i.e., coupling the system to auxiliary quantum degrees of freedom, is rather costly, and protocols in which the number of measurements scales with system size will have limited practical applicability. Here, we identify conditions under which measurement-based cooling protocols can be taken to the dilute limit. For two examples of frustration-free one-dimensional spin chains, we show that steering on a single link is sufficient to cool these systems into their unique ground states. We corroborate our analytical arguments with finite-size numerical simulations and discuss further applications.
The detection of potassic alteration by gamma-ray spectrometry—Recognition of alteration related to mineralization

Canadian case histories document the use of airborne and ground gamma‐ray spectrometry to detect and map potassium alteration associated with different styles of mineralization. These include: volcanic‐hosted massive sulfides (Cu‐Pb‐Zn), Pilley’s Island, Newfoundland; polymetallic, magmatic‐hydrothermal deposits (Au‐Co‐Cu‐Bi‐W‐As), Lou Lake, Northwest Territories; and porphyry Cu‐Au‐(Mo) deposits at Mt. Milligan, British Columbia and Casino, Yukon Territory. Mineralization in two of these areas was discovered using airborne gamma‐ray spectrometry. In each case history, alteration produces potassium anomalies that can be distinguished from normal lithologic potassium variations by characteristic lows in eTh/K ratios. Interpretations incorporating airborne and ground spectrometry, surficial and bedrock geochemistry and petrology show that gamma‐ray spectrometric patterns provide powerful guides to mineralization. This information complements magnetic, electromagnetic, geological, and conventional geochemica.
Quiescence for the exceptional Bianchi cosmologies

Cosmologies of the lower Bianchi types, i.e. except those of type VIII or IX, admit a two-dimensional Abelian subgroup of the isometry group, the $G_2$. In orthogonal perfect fluid cosmologies of all lower Bianchi types except for type VI$_{-1/9}$ the $G_2$ acts orthogonally-transitively, which is closely related to an eventual cessation of the oscillations and thus to a quiescent singularity. But due to a degeneracy in the momentum constraints, such cosmologies of type VI$_{-1/9}$ do not necessarily have this property. As a consequence, the dynamics of type VI$_{-1/9}$ orthogonal perfect fluid cosmologies have the same degrees of freedom as those of the higher types VIII and IX and their dynamics are expected to be markedly different compared to those of the other lower Bianchi types. In this article we take a different approach to quiescence, namely the presence of an orthogonal stiff fluid. On the one hand, this completes the analysis of the initial singularity for all Bianchi orthogonal stiff fluid cosmologies. On the other hand, this allows us to get a grasp of the underlying dynamics of type VI$_{-1/9}$ perfect fluid cosmologies, in particular the effect of orthogonal transitivity as well as possible (asymptotic) polarization conditions. In particular, we show that a generic type VI$_{-1/9}$ cosmology with an orthogonal stiff fluid has similar asymptotics as a generic Bianchi type VIII or IX cosmology with an orthogonal stiff fluid. The only exceptions to this genericity are solutions satisfying (asymptotic) polarization conditions, and solutions for which the $G_2$ acts orthogonally-transitively. Only in those cases may the limits of the eigenvalues of the expansion-normalized Weingarten map be negative.
Universality for graphs with bounded density

A graph $G$ is $\textit{universal}$ for a (finite) family $\mathcal{H}$ of graphs if every $H \in \mathcal{H}$ is a subgraph of $G$. For a given family $\mathcal{H}$, the goal is to determine the smallest number of edges an $\mathcal{H}$-universal graph can have. With the aim of unifying a number of recent results, we consider a family of graphs with bounded density. In particular, we construct a graph with $O_d\left( n^{2 - 1/(\lceil d \rceil + 1)} \right)$ edges which contains every $n$-vertex graph with density at most $d \in \mathbb{Q}$ ($d \ge 1$), which is close to a lower bound $\Omega(n^{2 - 1/d - o(1)})$ obtained by counting lifts of a carefully chosen (small) graph. When restricting the maximum degree of such graphs to be constant, we obtain a near-optimal universality. If we further assume $d \in \mathbb{N}$, we get an asymptotically optimal construction.
Challenges and Opportunities in the Co-design of Convolutions and RISC-V Vector Processors

The RISC-V "V" extension introduces vector processing to the RISC-V architecture. Unlike most SIMD extensions, it supports long vectors which can result in significant improvement of multiple applications. In this paper, we present our ongoing research to implement and optimize a vectorized Winograd algorithm used in convolutional layers on RISC-V Vector(RISC-VV) processors. Our study identifies effective techniques for optimizing the kernels of Winograd on RISC-VV using intrinsic instructions, and showcases how certain instructions offer better performance. Our co-design findings suggest that the Winograd algorithm benefits from vector lengths up to 2048 bits and cache sizes up to 64MB. We use our experience with Winograd to highlight potential enhancements for the standard that would simplify code generation and aid low-level programming. Finally, we share our experience from experimenting with forks of gem5 for RISC-VV and stress the importance of a mature software ecosystem, to facilitate design space exploration and architectural optimization. Our study identifies effective techniques for optimizing the kernels of Winograd on RISC-VV using the available intrinsic instructions and showcases that certain instructions offer better performance to the vectorized algorithm. Furthermore, our co-design study reveals that the Winograd algorithm benefits from vector lengths up to 2048 bits and cache sizes up to 64MB.
JWST Measurements of 13C, 18O, and 17O in the Atmosphere of Super-Jupiter VHS 1256 b

Isotope ratios have recently been measured in the atmospheres of directly imaged and transiting exoplanets from ground-based observations. The arrival of JWST allows us to characterize exoplanetary atmospheres in further detail and opens up wavelengths inaccessible from the ground. In this work we constrain the carbon and oxygen isotopes 13C, 18O, and 17O from CO in the atmosphere of the directly imaged companion VHS 1256 b through retrievals of the ∼4.1–5.3 μm NIRSpec G395H/F290LP observations from the early-release science program (ERS 1386). We detect and constrain 13C16O, 12C18O, and 12C17O at 32σ, 16σ, and 10σ confidence respectively, thanks to the very high signal-to-noise observations. We find the ratio of abundances are more precisely constrained than their absolute values, with 12C/13C=62−2+2 , in between previous measurements for companions (∼30) and isolated brown dwarfs (∼100). The oxygen isotope ratios are 16O/18O=425−28+33 and 16O/17O=1010−100+120 . All of the ratios are lower than the local interstellar medium and solar system, suggesting that abundances of the more minor isotopes are enhanced compared to the primary. This could be driven by isotope fractionation in protoplanetary disks, which can potentially alter the carbon and oxygen ratios through isotope selective photodissociation, gas/ice partitioning, and isotopic exchange reactions. In addition to CO, we constrain 1H 216O and 12C16O2 (the primary isotopologues of both species), but find only upper limits on 12C1H4 and 14N1H3. This work highlights the power of JWST to constrain isotopes in exoplanet atmospheres, with great promise in determining formation histories in the future.
On the Behavior of Audio-Visual Fusion Architectures in Identity Verification Tasks

We train an identity verification architecture and evaluate modifications to the part of the model that combines audio and visual representations, including in scenarios where one input is missing in either of two examples to be compared. We report results on the Voxceleb1-E test set that suggest averaging the output embeddings improves error rate in the full-modality setting and when a single modality is missing, and makes more complete use of the embedding space than systems which use shared layers and discuss possible reasons for this behavior.
How are Italian and Spanish cities tackling climate change? A local comparative study

Cities are widely recognised as being pivotal to fight climate change. They magnify the drivers of climate change, experience the impacts and also concentrate the highest room for action. Although urban areas are broadly claimed to be climate leaders, there is no archetype of right actions given the highly contextual differences among them. Yet, the how and why cities respond to global environmental challenges in the context of increasingly competitive economies needs further research. In this paper we aim at advancing in this regard by assessing the state of the art on urban climate actions in two European Mediterranean Countries: Spain and Italy that face similar climate change challenges. Based on an extensive review of documents, we analyse mitigation and adaptation plans of 26 Spanish and 32 Italian Urban Audit cities, as representative samples. Our results show relevant differences between Spanish and Italian cities in terms of the starting time of their climate actions as well their implementation. We concur with existing literature in that mitigation is more advanced than adaptation actions and take evidence in both countries and we also demonstrate that international and national networking initiatives are being instrumental in engaging cities in climate action.
How reproducible is best-worst scaling for human evaluation? A reproduction of ‘Data-to-text Generation with Macro Planning’

This paper is part of the larger ReproHum project, where different teams of researchers aim to reproduce published experiments from the NLP literature. Specifically, ReproHum focuses on the reproducibility of human evaluation studies, where participants indicate the quality of different outputs of Natural Language Generation (NLG) systems. This is necessary because without reproduction studies, we do not know how reliable earlier results are. This paper aims to reproduce the second human evaluation study of Puduppully & Lapata (2021), while another lab is attempting to do the same. This experiment uses best-worst scaling to determine the relative performance of different NLG systems. We found that the worst performing system in the original study is now in fact the best performing system across the board. This means that we cannot fully reproduce the original results. We also carry out alternative analyses of the data, and discuss how our results may be combined with the other reproduction study that is carried out in parallel with this paper.
An automated tool with human supervision to adapt difficult texts into Plain Language

In this paper, we present an automated tool with human supervision to write in plain language or to adapt difficult texts into plain language. It can be used on a web version and as a plugin for Word/Outlook plugins. At the publication date, it is only available in the French language. This tool has been developed for 3 years and has been used by 400 users from private companies and from public administrations. Text simplification is automatically performed with the manual approval of the user, at the lexical, syntactic, and discursive levels. Screencast of the demo can be found at the following link: https://www.youtube. com/watch? v=wXVtjfKO9FI.