Miracle-dz/UniFAR-FTUs huggingface dataset:
An atlas of MUSE observations towards twelve massive lensing clusters

Context. Spectroscopic surveys of massive galaxy clusters reveal the properties of faint background galaxies thanks to the magnification provided by strong gravitational lensing. Aims. We present a systematic analysis of integral-field-spectroscopy observations of 12 massive clusters, conducted with the Multi Unit Spectroscopic Explorer (MUSE). All data were taken under very good seeing conditions (∼0″​​.6) in effective exposure times between two and 15 h per pointing, for a total of 125 h. Our observations cover a total solid angle of ∼23 arcmin2 in the direction of clusters, many of which were previously studied by the MAssive Clusters Survey, Frontier Fields (FFs), Grism Lens-Amplified Survey from Space and Cluster Lensing And Supernova survey with Hubble programmes. The achieved emission line detection limit at 5σ for a point source varies between (0.77–1.5) × 10−18 erg s−1 cm−2 at 7000 Å. Methods. We present our developed strategy to reduce these observational data, detect continuum sources and line emitters in the datacubes, and determine their redshifts. We constructed robust mass models for each cluster to further confirm our redshift measurements using strong-lensing constraints, and identified a total of 312 strongly lensed sources producing 939 multiple images. Results. The final redshift catalogues contain more than 3300 robust redshifts, of which 40% are for cluster members and ∼30% are for lensed Lyman-α emitters. Fourteen percent of all sources are line emitters that are not seen in the available HST images, even at the depth of the FFs (∼29 AB). We find that the magnification distribution of the lensed sources in the high-magnification regime (μ = 2–25) follows the theoretical expectation of N(z) ∝ μ−2. The quality of this dataset, number of lensed sources, and number of strong-lensing constraints enables detailed studies of the physical properties of both the lensing cluster and the background galaxies. The full data products from this work, including the datacubes, catalogues, extracted spectra, ancillary images, and mass models, are made available to the community.
DeepDyve: Dynamic Verification for Deep Neural Networks

Deep neural networks (DNNs) have become one of the enabling technologies in many safety-critical applications, e.g., autonomous driving and medical image analysis. DNN systems, however, suffer from various kinds of threats, such as adversarial example attacks and fault injection attacks. While there are many defense methods proposed against maliciously crafted inputs, solutions against faults presented in the DNN system itself (e.g., parameters and calculations) are far less explored. In this paper, we develop a novel lightweight fault-tolerant solution for DNN-based systems, namely DeepDyve, which employs pre-trained neural networks that are far simpler and smaller than the original DNN for dynamic verification. The key to enabling such lightweight checking is that the smaller neural network only needs to produce approximate results for the initial task without sacrificing fault coverage much. We develop efficient and effective architecture and task exploration techniques to achieve optimized risk/overhead trade-off in DeepDyve. Experimental results show that DeepDyve can reduce 90% of the risks at around 10% overhead.
Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation

In a multi-turn knowledge-grounded dialog, the difference between the knowledge selected at different turns usually provides potential clues to knowledge selection, which has been largely neglected in previous research. In this paper, we propose a difference-aware knowledge selection method. It first computes the difference between the candidate knowledge sentences provided at the current turn and those chosen in the previous turns. Then, the differential information is fused with or disentangled from the contextual information to facilitate final knowledge selection. Automatic, human observational, and interactive evaluation shows that our method is able to select knowledge more accurately and generate more informative responses, significantly outperforming the state-of-the-art baselines.
Learning Representation for Mixed Data Types with a Nonlinear Deep Encoder-Decoder Framework

Representation of data on mixed variables, numerical and categorical types to get suitable feature map is a challenging task as important information lies in a complex non-linear manifold. The feature transformation should be able to incorporate marginal information of the individual variables and complex cross-dependence structure among the mixed type of variables simultaneously. In this work, we propose a novel nonlinear Deep Encoder-Decoder framework to capture the cross-domain information for mixed data types. The hidden layers of the network connect the two types of variables through various non-linear transformations to give latent feature maps. We encode the information on the numerical variables in a number of hidden nonlinear units. We use these units to recreate categorical variables through further nonlinear transformations. A separate and similar network is developed switching the roles of the numerical and categorical variables. The hidden representational units are stacked one next to the others and transformed into a common space using a locality preserving projection. The derived feature maps are used to explore the clusters in the data. Various standard datasets are investigated to show nearly the state of the art performance in clustering using the feature maps with simple K-means clustering.
A Two-Stage Game Framework to Secure Transmission in Two-Tier UAV Networks

The multi-UAV network is promising to extend conventional networks by providing broader coverage, and better reliability. Nevertheless, the broadcast nature of wireless signals, and the broader coverage expose multi-UAV communications to the threats of passive eavesdroppers. Recent studies mainly focus on securing a single legitimate link, or communications between a UAV, and multiple ground users in one/two-UAV-aided networks, while the physical layer secrecy analysis for hierarchical multi-UAV networks is underexplored. In this paper, we investigate a general two-tier UAV network consisting of multiple UAV transmitters (UTs), and multiple UAV receivers (URs) in the presence of multiple UAV eavesdroppers (UEs). To protect all legitimate UT-UR links against UEs at the physical layer, we design a two-stage framework consisting of a UT-UR association stage, and a cooperative transmission stage. Specifically, we formulate the secure transmission problem into a many-to-one matching game followed by an overlapping coalition formation (OCF) game, taking into account the limited capabilities, and the throughput requirements of URs, as well as the transmission power constraints of UTs. A matching algorithm, and an OCF algorithm are proposed to solve these two sequential games whose convergences, and stabilities are guaranteed theoretically. Simulation results show the superiority of our algorithms, and the effectiveness of our two-stage game framework in the terms of secrecy performance.
Provable Finite Data Generalization with Group Autoencoder

Deep Autoencoders (AEs) provide a versatile framework to learn a compressed, interpretable, or structured representation of data. As such, AEs have been used extensively for denoising, compression, data completion as well as pre-training of Deep Networks (DNs) for various tasks such as classification. By providing a careful analysis of current AEs from a spline perspective, we can interpret the input-output mapping, in turn allowing us to derive conditions for generalization and reconstruction guarantee. By assuming a Lie group structure on the data at hand, we are able to derive a novel regularization of AEs, allowing for the first time to ensure the generalization of AEs in the finite training set case. We validate our theoretical analysis by demonstrating how this regularization significantly increases the generalization of the AE on various datasets.
Few-shot learning using pre-training and shots, enriched by pre-trained samples

We use the EMNIST dataset of handwritten digits to test a simple approach for few-shot learning. A fully connected neural network is pre-trained with a subset of the 10 digits and used for few-shot learning with untrained digits. Two basic ideas are introduced: during few-shot learning the learning of the first layer is disabled, and for every shot a previously unknown digit is used together with four previously trained digits for the gradient descend, until a predefined threshold condition is fulfilled. This way we reach about 90% accuracy after 10 shots.
Vehicle Class, Speed, and Roadway Geometry Based Driver Behavior Identification and Classification

Over the past decades, the intense emphasis has been placed on the understanding of car-following behavior and the factors that affect it. The car-following process, however, still remains a very complex field of study in spite of all the efforts. This paper focuses on the study of the effect that the class of the vehicle, leading heavy vehicles in particular, causes on the following vehicle behavior, specifically in terms of the following bumper-to-bumper distance (gap) that the following vehicle keeps from the lead vehicle. This was done by extracting and analyzing different car following episodes in the Next Generation Simulation (NGSIM) dataset for Interstate 80 (I 80) in Emeryville, California, USA. The results of the statistical analysis are compared to that of the synthesized literature of research efforts that have been carried out on the topic, then are further assessed utilizing different calibrated behavioral clusters for the Gazis-Herman-Rothery (GHR) car-following model to address the similarities and differences in car-following behavior between drivers of the same vehicle class. The paper also validates the results of the statistical analysis and highlights possible future implementations.
Parasitoid‐induced transgenerational fecundity compensation in an aphid

Increased reproductive effort by organisms in response to attack by consumers (‘fecundity compensation’) is well documented in both plants and animals, although most examples only involve direct compensation by the individuals exposed to consumers. We used the parasitoid wasp Lysiphlebus orientalis Starý & Rakhshani (Hymenoptera: Braconidae) and the soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), to determine whether reproduction by parasitized aphids can lead to fecundity compensation. Although parasitism by L. orientalis strongly decreased fecundity for parasitized aphids, offspring of parasitized aphids reproduced at a greater rate at maturity than did the offspring of non‐parasitized aphids. Also, parasitized aphids contained fewer but larger embryos developing within them. The presence of these larger embryos may explain how the offspring of parasitized aphids can produce more progeny with no apparent reduction in progeny quality. Mature and nearly mature soybean aphids successfully reproduced after parasitism, a prerequisite for transgenerational fecundity compensation, and L. orientalis showed a preference for these age classes of aphids as hosts when foraging. Transgenerational fecundity compensation may allow for higher than expected densities of both hosts and consumers to persist and may be adaptive for both the host and consumer in this system. The phenomenon could also reduce the strength of biological control interactions.
Content Planning for Neural Story Generation with Aristotelian Rescoring

Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global cohesion. We posit that many of the problems of story generation can be addressed via high-quality content planning, and present a system that focuses on how to learn good plot structures to guide story generation. We utilize a plot-generation language model along with an ensemble of rescoring models that each implement an aspect of good story-writing as detailed in Aristotle's Poetics. We find that stories written with our more principled plot-structure are both more relevant to a given prompt and higher quality than baselines that do not content plan, or that plan in an unprincipled way.
Ultrafast charging in a two-photon Dicke quantum battery

We consider a collection of two level systems, such as qubits, embedded into a microwave cavity as a promising candidate for the realization of high power quantum batteries. In this perspective, the possibility to design devices where the conventional single-photon coupling is suppressed and the dominant interaction is mediated by two-photon processes is investigated, opening the way to an even further enhancement of the charging performance. By solving a Dicke model with both single- and two-photon coupling we determine the range of parameters where the latter unconventional interaction dominates the dynamics of the system leading to better performances both in the charging times and average charging power of the QB compared to the single-photon case. In addition, the scaling of the maximum stored energy, fluctuations and charging power with the finite number of qubits N is inspected. While the energy and fluctuations scale linearly with N, the quadratic growth of the average power leads to a relevant improvement of the charging performance of quantum batteries based on this scheme with respect to the purely single-photon coupling case. Moreover, it is shown that the charging process is progressively faster by increasing the coupling from the weak to the ultra-strong regime.
DEAP Cache: Deep Eviction Admission and Prefetching for Cache

Recent approaches for learning policies to improve caching, target just one out of the prefetching, admission and eviction processes. In contrast, we propose an end to end pipeline to learn all three policies using machine learning. We also take inspiration from the success of pretraining on large corpora to learn specialized embeddings for the task. We model prefetching as a sequence prediction task based on past misses. Following previous works suggesting that frequency and recency are the two orthogonal fundamental attributes for caching, we use an online reinforcement learning technique to learn the optimal policy distribution between two orthogonal eviction strategies based on them. While previous approaches used the past as an indicator of the future, we instead explicitly model the future frequency and recency in a multi-task fashion with prefetching, leveraging the abilities of deep networks to capture futuristic trends and use them for learning eviction and admission. We also model the distribution of the data in an online fashion using Kernel Density Estimation in our approach, to deal with the problem of caching non-stationary data. We present our approach as a "proof of concept" of learning all three components of cache strategies using machine learning and leave improving practical deployment for future work.
Chaos or quasiperiodicity in laboratory insect populations

The pioneering work of May in the mid-seventies showed that similar to the patterns of dynamics observed in many real populations, simple deterministic ecological models can exhibit seemingly random fluctuations in population densities (May 1974, 1976). Such dynamics were termed 'chaotic' by Yorke (Li & Yorke 1975). Since then, the question of whether natural populations really do behave chaotically has been explored extensively, but as yet there appears to be no unequivocal answer (Hastings et al. 1993). Recently, Costantino et al. (1995) reported the findings of their laboratory experiments on the population dynamics of the flour beetle Tribolium castaneum. They demonstrated that the manipulation of demographic parameters (such as adult mortality) in the laboratory population gave rise to transitions in the dynamical behaviour, as predicted by a mathematical model (Dennis et al. 1995). More specifically, it was shown that for high levels of adult mortality, the population dynamics were aperiodic. Costantino et al. did not, however, identify the nature of the aperiodicity and, since this is one of the characteristic properties of a chaotic trajectory, their results are open to misinterpretation as one of the first cases of chaos in a real population (see, for example, Kareiva 1995). Costantino et al. explored a simple three variable discrete-time model that relates the larval (L), pupal (P) and adult (A) numbers at time t + 1 to the number of individuals at time t:
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference

Causal inference studies using textual social media data can provide actionable insights on human behavior. Making accurate causal inferences with text requires controlling for confounding which could otherwise impart bias. Recently, many different methods for adjusting for confounders have been proposed, and we show that these existing methods disagree with one another on two datasets inspired by previous social media studies. Evaluating causal methods is challenging, as ground truth counterfactuals are almost never available. Presently, no empirical evaluation framework for causal methods using text exists, and as such, practitioners must select their methods without guidance. We contribute the first such framework, which consists of five tasks drawn from real world studies. Our framework enables the evaluation of any casual inference method using text. Across 648 experiments and two datasets, we evaluate every commonly used causal inference method and identify their strengths and weaknesses to inform social media researchers seeking to use such methods, and guide future improvements. We make all tasks, data, and models public to inform applications and encourage additional research.
SDST: Successive Decoding for Speech-to-text Translation

End-to-end speech-to-text translation (ST), which directly translates the source language speech to the target language text, has attracted intensive attention recently. However, the combination of speech recognition and machine translation in a single model poses a heavy burden on the direct cross-modal cross-lingual mapping. To reduce the learning difficulty, we propose SDST, an integral framework with \textbf{S}uccessive \textbf{D}ecoding for end-to-end \textbf{S}peech-to-text \textbf{T}ranslation task. This method is verified in two mainstream datasets. Experiments show that our proposed \method improves the previous state-of-the-art methods by big margins.
Neutron stars mergers in a stochastic chemical evolution model: impact of time delay distributions

We study the evolution of the [Eu/Fe] ratio in the Galactic halo by means of a stochastic chemical evolution model considering merging neutron stars as polluters of europium. We improved our previous stochastic chemical evolution model by adding a time delay distribution for the coalescence of the neutron stars, instead of constant delays. The stochastic chemical evolution model can reproduce the trend and the observed spread in the [Eu/Fe] data with neutron star mergers as unique producers if we assume: i) a delay time distribution $\propto t^{-1.5}$, ii) a $M_{Eu}= 1.5$x$10^{-6} M_{\odot}$ per event, iii) progenitors of neutron stars in the range $9-50M_{\odot}$ and iv) a constant fraction of massive stars in the initial mass function (0.02) that produce neutron star mergers. Our best model is obtained by relaxing point iv) and assuming a fraction that varies with metallicity. We confirm that the mixed scenario with both merging neutron stars and supernovae as europium producers can provide a good agreement with the data relaxing the constraints on the distribution time delays for the coalescence of neutron stars. Adopting our best model, we also reproduce the dispersion of [Eu/Fe] at a given metallicity, which depends on the fraction of massive stars that produce neutron star mergers. Future high-resolution spectroscopic surveys, such as 4MOST and WEAVE, will produce the necessary statistics to constrain at best this parameter.
A design framework for actively crosslinked filament networks

Living matter moves, deforms, and organizes itself. In cells this is made possible by networks of polymer filaments and crosslinking molecules that connect filaments to each other and that act as motors to do mechanical work on the network. For the case of highly cross-linked filament networks, we discuss how the material properties of assemblies emerge from the forces exerted by microscopic agents. First, we introduce a phenomenological model that characterizes the forces that crosslink populations exert between filaments. Second, we derive a theory that predicts the material properties of highly crosslinked filament networks, given the crosslinks present. Third, we discuss which properties of crosslinks set the material properties and behavior of highly crosslinked cytoskeletal networks. The work presented here, will enable the better understanding of cytoskeletal mechanics and its molecular underpinnings. This theory is also a first step toward a theory of how molecular perturbations impact cytoskeletal organization, and provides a framework for designing cytoskeletal networks with desirable properties in the lab.
On the X-Ray Spectral Energy Distributions of Star-forming Galaxies: The 0.3–30 keV Spectrum of the Low-metallicity Starburst Galaxy VV 114

Binary population synthesis combined with cosmological models suggests that X-ray emission from star-forming galaxies, consisting primarily of emission from X-ray binaries (XRBs) and the hot interstellar medium (ISM), could be an important, and perhaps dominant, source of heating of the intergalactic medium prior to the epoch of reionization. However, such models rely on empirical constraints for the X-ray spectral energy distributions (SEDs) of star-forming galaxies, which are currently lacking for low-metallicity galaxies. Using a combination of Chandra, XMM-Newton, and NuSTAR observations, we present new constraints on the 0.3–30 keV SED of the low-metallicity starburst galaxy VV 114, which is known to host several ultraluminous X-ray sources (ULXs) with luminosities above 1040 erg s−1. We use an archival Chandra observation of VV 114 to constrain the contributions to the X-ray SED from the major X-ray-emitting components of the galaxy and newly acquired, nearly simultaneous XMM-Newton and NuSTAR observations to extend the spectral model derived from Chandra to cover the 0.3–30 keV range. Using our best-fit galaxy-wide spectral model, we derive the 0.3–30 keV SED of VV 114, which we find is dominated by emission from the XRB population, and in particular ULXs, at energies >1.5 keV, and which we find to have an elevated galaxy-integrated X-ray luminosity per unit star formation rate relative to higher-metallicity star-forming galaxies. We discuss our results in terms of the effect of metallicity on XRB populations and the hot ISM, as well as the importance of X-ray emission from star-forming galaxies in the high-redshift universe.
Nonlocal Conservation Laws of PDEs Possessing Differential Coverings

In his 1892 paper, L. Bianchi noticed, among other things, that quite simple transformations of the formulas that describe the Bäcklund transformation of the sine-Gordon equation lead to what is called a nonlocal conservation law in modern language. Using the techniques of differential coverings, we show that this observation is of a quite general nature. We describe the procedures to construct such conservation laws and present a number of illustrative examples.