Hasil untuk "Auxiliary sciences of history"

Euclid Collaboration L. R. Ecker, M. Fabricius, S. Seitz et al.

We present 72 additional galaxy-galaxy strong lenses that complement the sample discovered in the Euclid Quick Release 1 data (63.1 deg^2) of the Strong Lens Discovery Engine (SLDE) papers A-E. It is shown that previous pre-selection of potential lenses, which excluded objects from the Gaia catalogue, led to missing several bright and low-redshift strong lenses, adding more than 10% new strong lens candidates compared to the previous search. In total, the catalogue includes 38"grade A"(confident) and 34"grade B"(probable) candidates. These lenses are identified through a combination of two independent searches for bright nearby objects: one based on machine-learning models followed by expert visual inspection, and the other based solely on expert visual inspection, targeting objects not included in the initial machine-learning selection (a limitation identified only after extensive visual inspection). With these additional strong lens candidates, we augment the expected number of high-confidence candidates in the Euclid Wide Survey from previous forecasts to 120000. Detailed semi-automated lens modelling confirms at least 41 systems out of 72, a fraction consistent with that found in SLDE A (315 out of 488). These include: multiple edge-on disc lenses; sources with arcs near the lens centre;"red sources"; and an edge-on disk galaxy lensing a galaxy merger, producing two sets of lensed features, an Einstein ring and a doubly imaged component. The median redshift of these systems is $\Delta$ z ~ 0.3 lower than that of the SLDE A sample.

Euclid Collaboration X. Xu, R. Chen, T. Li et al.

We present an end-to-end, iterative pipeline for efficient identification of strong galaxy--galaxy lensing systems, applied to the Euclid Q1 imaging data. Starting from VIS catalogues, we reject point sources, apply a magnitude cut (I$_E$ $\leq$ 24) on deflectors, and run a pixel-level artefact/noise filter to build 96 $\times$ 96 pix cutouts; VIS+NISP colour composites are constructed with a VIS-anchored luminance scheme that preserves VIS morphology and NISP colour contrast. A VIS-only seed classifier supplies clear positives and typical impostors, from which we curate a morphology-balanced negative set and augment scarce positives. Among the six CNNs studied initially, a modified VGG16 (GlobalAveragePooling + 256/128 dense layers with the last nine layers trainable) performs best; the training set grows from 27 seed lenses (augmented to 1809) plus 2000 negatives to a colour dataset of 30,686 images. After three rounds of iterative fine-tuning, human grading of the top 4000 candidates ranked by the final model yields 441 Grade A/B candidate lensing systems, including 311 overlapping with the existing Q1 strong-lens catalogue, and 130 additional A/B candidates (9 As and 121 Bs) not previously reported. Independently, the model recovers 740 out of 905 (81.8%) candidate Q1 lenses within its top 20,000 predictions, considering off-centred samples. Candidates span I$_E$ $\simeq$ 17--24 AB mag (median 21.3 AB mag) and are redder in Y$_E$--H$_E$ than the parent population, consistent with massive early-type deflectors. Each training iteration required a week for a small team, and the approach easily scales to future Euclid releases; future work will calibrate the selection function via lens injection, extend recall through uncertainty-aware active learning, explore multi-scale or attention-based neural networks with fast post-hoc vetters that incorporate lens models into the classification.

C. O'Riordan, L. J. Oldham, A. Nersesian et al.

We report the discovery of a complete Einstein ring around the elliptical galaxy NGC 6505, at z = 0.042. This is the first strong gravitational lens discovered in Euclid and the first in an NGC object from any survey. The combination of the low redshift of the lens galaxy, the brightness of the source galaxy (IE = 18.1 lensed, IE = 21.3 unlensed), and the completeness of the ring make this an exceptionally rare strong lens, unidentified until its observation by Euclid. We present deep imaging data of the lens from the Euclid Visible Camera (VIS) and Near-Infrared Spectrometer and Photometer (NISP) instruments, as well as resolved spectroscopy from the Keck Cosmic Web Imager (KCWI). The Euclid imaging in particular presents one of the highest signal-to-noise ratio optical/near-infrared observations of a strong gravitational lens to date. From the KCWI data we measure a source redshift of z = 0.406. Using data from the Dark Energy Spectroscopic Instrument (DESI) we measure a velocity dispersion for the lens galaxy of σ⋆ = 303 ± 15 km s−1. We model the lens galaxy light in detail, revealing angular structure that varies inside the Einstein ring. After subtracting this light model from the VIS observation, we model the strongly lensed images, finding an Einstein radius of 2.″5, corresponding to 2.1 kpc at the redshift of the lens. This is small compared to the effective radius of the galaxy, Reff ∼ 12.″3. Combining the strong lensing measurements with analysis of the spectroscopic data we estimate a dark matter fraction inside the Einstein radius of fDM = (11.1−3.5+5.4)% and a stellar initial mass-function (IMF) mismatch parameter of αIMF = 1.26−0.08+0.05, indicating a heavier-than-Chabrier IMF in the centre of the galaxy.

Huiling Zhang, Minghua Han, Yapeng He et al.

Recently, the incidence of anxiety and depression in inflammatory bowel disease (IBD) patients has gradually increased. Critically, psychological comorbidities not only compromise quality of life but independently predict adverse IBD outcomes including heightened relapse risk and treatment non-adherence. Therefore, the aim is to conduct a systematic review and meta-analysis on the influencing factors of anxiety and depression in IBD patients and to provide a scientific basis for the effective prevention. We searched PubMed, Embase, PsycINFO, Web of Science and Cochrane Library until 30 January 2024. Included studies were cross-sectional or cohort designs. Quality was assessed using Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality. Data analysis used Stata 16.0 with fixed/random-effects models. Publication bias was assessed via Begg test and funnel plots. Twenty factors (e.g. age, marriage, education) were extracted. From 11, 755 citations, 28 studies (58,064 patients) met criteria. Overall, factors influencing anxiety in IBD patients include gender (OR = 1.85, 95% CI: 1.68-2.04), disease activity (OR = 1.56, 95% CI: 1.16-2.09), IBD-related surgery (OR = 0.64, 95% CI: 0.37-0.79) and non-white ethnicity (OR = 0.74, 95% CI: 0.62-0.88). Factors associated with depression in patients include gender (OR = 1.74, 95% CI: 1.62-1.86), disease activity (OR = 1.89, 95% CI: 1.50-2.39), IBD-related surgery (OR = 1.52, 95% CI: 1.20-1.92), male gender (OR = 1.59, 95% CI: 1.33-1.90), perianal disease (OR = 1.85, 95% CI: 1.35-2.55), higher education (OR = 1.56, 95% CI: 1.22-1.98), steroid use (OR = 2.05, 95% CI: 1.22-3.45), non-white ethnicity (OR = 0.72, 95% CI: 0.62-0.84) and family history (OR = 1.62, 95% CI: 1.33-1.97). Disease activity has a relatively high impact on the emotion of patients with inflammatory bowel disease, and gender differences and side effects of therapeutic drugs also play an auxiliary role. Therefore, early intervention should be carried out for the existence of modifiable risk factors of anxiety and depression in IBD patients.

Pingping Dong, Xiao-Lin Wang, Indranil Bose et al.

Accurate and reliable prediction has profound implications for a wide range of applications, such as hospital admissions, inventory control, and route planning. In this study, we focus on an instance of spatiotemporal learning problems—traffic prediction—to demonstrate an advanced deep learning model developed for making accurate and reliable predictions. Despite the significant progress in traffic prediction, limited studies have incorporated both explicit (e.g., road network topology) and implicit (e.g., causality-related traffic phenomena and impact of exogenous factors) traffic patterns simultaneously to improve prediction performance. Meanwhile, the variable nature of traffic states necessitates quantifying the uncertainty of model predictions in a statistically principled way; however, extant studies offer no provable guarantee on the statistical validity of confidence intervals in reflecting their actual likelihood of containing the ground truth. In this paper, we propose an end-to-end traffic prediction framework that leverages three primary components to generate accurate and reliable traffic predictions: dynamic causal structure learning for discovering implicit traffic patterns from massive traffic data, causally aware spatiotemporal multigraph convolutional network (CASTMGCN) for learning spatiotemporal dependencies, and conformal prediction for uncertainty quantification. In particular, CASTMGCN fuses several graphs that characterize different important aspects of traffic networks (including physical road structure, time-lagged causal effect, and contemporaneous causal relationships) and an auxiliary graph that captures the effect of exogenous factors on the road network. On this basis, a conformal prediction approach tailored to spatiotemporal data is further developed for quantifying the uncertainty in node-wise traffic predictions over varying prediction horizons. Experimental results on two real-world traffic data sets of varying scale demonstrate that the proposed method outperforms several state-of-the-art models in prediction accuracy; moreover, it generates more efficient prediction regions than several other methods while strictly satisfying the statistical validity in coverage. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This paper was supported by the Hong Kong Research Grants Council [Grant PolyU 25206422], the Research Committee of The Hong Kong Polytechnic University [Project Code G-UARJ, Student Account Code RM5Y], and the National Natural Science Foundation of China [Grants 62406269, 72021002, and 72201180]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0891 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0891 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Gregorio Paci, Sergey N. Solodukhin

We study the conformal field theory defined by the fourth-order operator on four-dimensional manifolds with boundaries, reformulating it through an auxiliary field so that the dynamics become second order. Within this framework, we compute the heat kernel of $\Box^2$ in flat space exactly, together with the associated Seeley-DeWitt coefficients for a broad class of non-standard boundary conditions. On curved backgrounds, we further construct the Weyl-invariant completion of the auxiliary field action with boundary terms and identify the corresponding conformal boundary conditions. Finally, we compute the boundary charges in the trace anomaly from the displacement operator correlators.

Yu Shi, Hao Li, Bram Adams et al.

Automated program repair (APR) has recently shifted toward large language models and agent-based systems, yet most systems rely on local snapshot context, overlooking repository history. Prior work shows that repository history helps repair single-line bugs, since the last commit touching the buggy line is often the bug-introducing one. In this paper, we investigate whether repository history can also improve agentic APR systems at scale, especially for complex multi-hunk bugs. We present HAFixAgent, a History-Aware Bug-Fixing Agent that injects blame-derived repository heuristics into its repair loop. A preliminary study on 854 Defects4J (Java) and 501 BugsInPy (Python) bugs motivates our design, showing that bug-relevant history is widely available across both benchmarks. Using the same LLM (DeepSeek-V3.2-Exp) for all experiments, including replicated baselines, we show: (1) Effectiveness: HAFixAgent outperforms RepairAgent (+56.6\%) and BIRCH-feedback (+47.1\%) on Defects4J. Historical context further improves repair by +4.4\% on Defects4J and +38.6\% on BugsInPy, especially on single-file multi-hunk (SFMH) bugs. (2) Robustness: under noisy fault localization (+1/+3/+5 line shifts), history provides increasing resilience, maintaining 40 to 56\% success on SFMH bugs where the non-history baseline collapses to 0\%. (3) Efficiency: history does not significantly increase agent steps or token costs on either benchmark.

Euclid Collaboration A. Nersesian, Abdurro’uf, M. Baes et al.

We analyzed the spatially resolved and global star formation histories (SFHs) for a sample of 25 TNG50- SKIRT Atlas galaxies to assess the feasibility of reconstructing accurate SFHs from Euclid-like data. This study provides a proof of concept for extracting the spatially resolved SFHs of local galaxies with Euclid, highlighting the strengths and limitations of SFH modeling in the context of next-generation galaxy surveys. We used the spectral energy distribution (SED) fitting code Prospector to model both spatially resolved and global SFHs using parametric and nonparametric configurations. The input consisted of mock ultraviolet--near-infrared photometry derived from the TNG50 cosmological simulation and processed with the radiative transfer code SKIRT We show that nonparametric SFHs provide a more effective approach to mitigating the outshining effect by recent star formation, offering improved accuracy in the determination of galaxy stellar properties. Also, we find that the nonparametric SFH model at resolved scales closely recovers the stellar mass formation times (within 0.1 dex) and the ground truth values from TNG50, with an absolute average bias of $0.03$ dex in stellar mass and $0.01$ dex in both specific star formation rate and mass-weighted age. In contrast, larger offsets are estimated for all stellar properties and formation times when using a simple τ-model SFH, at both resolved and global scales, highlighting its limitations. These results emphasize the critical role of nonparametric SFHs in both global and spatially resolved analyses, as they better capture the complex evolutionary pathways of galaxies and avoid the biases inherent in simple parametric models.

T. Petrova

Volodymyr Biletskyi and Myroslava Kovbuz dedicated the new publication 'Donetsk regional branch of Shevchenko Scientific Society' (2023) to the 150th anniversary of Shevchenko Scientific Society in Ukraine. The publication reveals the importance of the source that presents the latest historiography of the Ukrainian science, in particular, the development trends and multifaceted activities of the Donetsk Regional Branch of Shevchenko Scientific Society (1997), as well as the results of fundamental scientific research of its members, dedicated to the interests of the national state in the modern period. It is emphasized that the Donetsk Regional Branch of Shevchenko Scientific Society as a public organization and a legal entity has been playing the role of the South-Eastern center of Shevchenko Scientific Society for almost three decades. The substantive description of the Branch's achievements was highly appreciated; its powerful potential, stability, steadfastness and resistance to the Russian aggressive influence were testified. The need to qualify the publication as a reference work was determined, and it was noted that it has the distinct features of a scientific auxiliary biobibliographical manual. It was concluded that the information about the activities of the Donetsk regional branch of Shevchenko Scientific Society, which is presented in the source, can be the fundamental fragment in the reconstruction of the general history of the Shevchenko Scientific Society in Ukraine.

Euclid Collaboration N. Allen, P. Oesch, R. Bowler et al.

The evolution of the rest-frame ultraviolet luminosity function (UV LF) is a powerful probe of early star formation and stellar mass build-up. At z>6, its bright end (MUV6 Lyman break galaxies (LBGs) and constrain the UV LF's bright end. With NIR coverage extending to 2um, Euclid can detect galaxies out to z = 13. We present forecasts for the number densities of z>6 galaxies expected in the final EDF dataset. Using synthetic photometry from spectral energy distribution (SED) templates of z = 5--15 galaxies, z = 1--4 interlopers, and Milky Way MLT dwarfs, we explore optimal selection methods for high-z LBGs. A combination of S/N cuts with SED fitting (from optical to MIR) yields the highest-fidelity sample, recovering>76% of input z>6 LBGs while keeping low-z contamination10 sources. Based on empirical double power-law LF models, we expect>100,000 LBGs at z = 6-12 and>100 at z>12 in the final Euclid release. In contrast, steeper Schechter models predict no z>12 detections. We also present two ultra-luminous (MUV9, highlighting Euclid's power to constrain the UV LF's bright end and identify the most luminous early galaxies for follow-up.

Yi He, Yi Yang, Defu Lian et al.

Forecasting the market risk for publicly traded companies is a critical task for market participants. Financial economics research demonstrates that the textual information contained in corporate disclosures, such as earnings conference call transcripts, can effectively predict a firm’s future risk. This finding has inspired a growing body of research focused specifically on transcript-based approaches to risk forecasting. However, earnings transcripts are typically long documents with thousands of words. Prior transcript-based risk forecasting studies that represent the entire transcript as one text sequence often fail to capture risk-relevant information and fall short in risk forecasting. In this work, we propose a novel divide-and-contrast machine learning method for predicting risks from earnings conference call transcripts. We exploit the semistructured nature of an earnings transcript and decompose it into several semantically coherent conversation units, ranging from the finest grained question–answer pair level to the coarsest grained transcript level. We then propose contrastive learning objectives as an auxiliary task to the risk forecasting objective, facilitating the learning of risk-relevant information from the earnings transcripts. We conduct experiments on a data set of U.S. market earnings call transcripts. The experimental results show that our proposed divide-and-contrast method substantially outperforms state-of-the-art methods by significantly reducing errors in risk forecasting. This paper sheds light on extracting informative insights from lengthy financial documents to support informed decision making. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0195 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0195 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Polina Shvanyukova

This study uses corpus linguistic methods to extract top keywords and analyze concordance lines in the majority and dissenting opinions in the U. S. Supreme Court ruling concerning Dobbs v. Jackson released on 24th June 2022. The combination of a quantitative analysis with manual inspection of selected concordances offers linguistic evidence of a deep ideological divide between the majority and dissenting justices that underpins the reality of partisan judicial decision-making.

Pengsheng Liu, Linan Zheng, Jiale Chen et al.

The historical interaction sequences of users plays a crucial role in training recommender systems that can accurately predict user preferences. However, due to the arbitrariness of user behavior, the presence of noise in these sequences poses a challenge to predicting their next actions in recommender systems. To address this issue, our motivation is based on the observation that training noisy sequences and clean sequences (sequences without noise) with equal weights can impact the performance of the model. We propose a novel self-supervised Auxiliary Task Joint Training (ATJT) method aimed at more accurately reweighting noisy sequences in recommender systems. Specifically, we strategically select subsets from users' original sequences and perform random replacements to generate artificially replaced noisy sequences. Subsequently, we perform joint training on these artificially replaced noisy sequences and the original sequences. Through effective reweighting, we incorporate the training results of the noise recognition model into the recommender model. We evaluate our method on three datasets using a consistent base model. Experimental results demonstrate the effectiveness of introducing self-supervised auxiliary task to enhance the base model's performance.

Yuming Zhang, Shouxin Zhang, Peizhe Wang et al.

Deep neural networks (DNNs) typically employ an end-to-end (E2E) training paradigm which presents several challenges, including high GPU memory consumption, inefficiency, and difficulties in model parallelization during training. Recent research has sought to address these issues, with one promising approach being local learning. This method involves partitioning the backbone network into gradient-isolated modules and manually designing auxiliary networks to train these local modules. Existing methods often neglect the interaction of information between local modules, leading to myopic issues and a performance gap compared to E2E training. To address these limitations, we propose the Multilaminar Leap Augmented Auxiliary Network (MLAAN). Specifically, MLAAN comprises Multilaminar Local Modules (MLM) and Leap Augmented Modules (LAM). MLM captures both local and global features through independent and cascaded auxiliary networks, alleviating performance issues caused by insufficient global features. However, overly simplistic auxiliary networks can impede MLM's ability to capture global information. To address this, we further design LAM, an enhanced auxiliary network that uses the Exponential Moving Average (EMA) method to facilitate information exchange between local modules, thereby mitigating the shortsightedness resulting from inadequate interaction. The synergy between MLM and LAM has demonstrated excellent performance. Our experiments on the CIFAR-10, STL-10, SVHN, and ImageNet datasets show that MLAAN can be seamlessly integrated into existing local learning frameworks, significantly enhancing their performance and even surpassing end-to-end (E2E) training methods, while also reducing GPU memory consumption.

Salimon V.Yu.

The article deals with the views of the leader of the “centre-left” François-Denis Tronchet on the interference of the legislative and executive branches of power in the political system of revolutionary France and the place of the king in it. One of the important problems facing the deputies of the National Constituent Assembly was to change the form of government that existed under the Old Regime, in which all power was in the same hands. The position of Tronchet, who thought in the spirit of the liberal majority for the abolition of absolutism and the subordination of the monarch to the law, at the same time, differed from the supporters of the influential royal power of Mirabeau, Lafayette, Le Chapelier, and from the adherents of the powerful parliament of Barnave, Duport, Alexander and Charles Lameth. Unlike the moderate deputies of the Assembly and the “left”, the representative of the “centre-left” expressed balanced judgments about the parity cooperation between the branches of power insisting on the significant role of the head of the executive branch. The Varennes crisis changed the views of many politicians. Tronchet was one of the main heralds of the liberal majority, who consolidated their intention to strengthen the executive branch of power in the person of the king and his agents, to complete the Constitution, to stabilize the situation in the country, to prevent the further radicalization of the revolution and the spread of republican mentality.

Jaehyung Kim, Jinwoo Shin, Dongyeop Kang

The development of largely human-annotated benchmarks has driven the success of deep neural networks in various NLP tasks. To enhance the effectiveness of existing benchmarks, collecting new additional input-output pairs is often too costly and challenging, particularly considering their marginal impact on improving the current model accuracy. Instead, additional or complementary annotations on the existing input texts in the benchmarks can be preferable as an efficient way to pay the additional human cost. In this paper, we investigate task-specific preferences between pairs of input texts as a new alternative way for such auxiliary data annotation. From 'pair-wise' comparisons with respect to the task, the auxiliary preference learning enables the model to learn an additional informative training signal that cannot be captured with 'instance-wise' task labels. To this end, we propose a novel multi-task learning framework, called prefer-to-classify (P2C), which can enjoy the cooperative effect of learning both the given classification task and the auxiliary preferences. Here, we provide three different ways to collect preference signals in practice: (a) implicitly extracting from annotation records (for free, but often unavailable), (b) collecting explicitly from crowd workers (high paid), or (c) pre-trained large language models such as GPT-3 (low paid). Given existing classification NLP benchmarks, we demonstrate that the proposed auxiliary preference learning via P2C on them is effective in improving text classifiers. Our codes are publicly available.

Shumin Zhou, Hailan Ma, Sen Kuang et al.

Due to its property of not requiring prior knowledge of the environment, reinforcement learning has significant potential for quantum control problems. In this work, we investigate the effectiveness of continuous control policies based on deep deterministic policy gradient. To solve the sparse reward signal in quantum learning control problems, we propose an auxiliary task-based deep reinforcement learning (AT-DRL) for quantum control. In particular, we first design a guided reward function based on the fidelity of quantum states that enables incremental fidelity improvement. Then, we introduce the concept of an auxiliary task whose network shares parameters with the main network to predict the reward provided by the environment (called the main task). The auxiliary task learns synchronously with the main task, allowing one to select the most relevant features of the environment, thus aiding the agent in comprehending how to achieve the desired state. The numerical simulations demonstrate that the proposed AT-DRL can provide a solution to the sparse reward in quantum systems, and has great potential in designing control pulses that achieve efficient quantum state preparation.

Vasile Chirica, Bogdan Minea

Includes a summary in English.

Irène Dos Santos

In Portugal, until recently, the official memory of the Empire has overlooked the violent pasts under the dictatorship and decolonization – colonial war, repatriation - less unifying for the national collective identity. The first part of this article focuses on the societal and academic shift resulting from the emergence of memorializing counter-narratives. The aim is to identify the players in these processes and illustrate how, in this new political relationship with the past, the memory of dictatorship can be imbricated with that of decolonization. Studying current research — historiography, postcolonial studies — also highlights divergences in Portuguese academia on the role of history and memory — postmemory in particular — in the interpretation of such past events. The second part looks back on two case studies with a heuristic potential to deconstruct the very homogeneous representations of the history of Portuguese colonization and decolonization of Angola, and decompartmentalize national accounts. This relates to two individuals whose family histories link them to the Portuguese colonial presence in Angola, involved in writing about the past through eye-witness accounts or scientific research: an exiled in France, grandson of a colonial administrator and son of an anticolonial and antifascist militant actions; and the daughter of an interracial couple of retornados undertaking research on Angola. These case studies also reveal the complexity of social, political and ethno-racial affiliations in this postcolonial post-imperial context and challenge the social hierarchization inherited from the past and the silence surrounding this heritage.

Alexandru Andronic

Includes a summary in English.