Aditya Bharat Soni, Rajat Ghosh, Vaishnavi Bhargava
et al.
Software testing is crucial for ensuring the correctness and reliability of software systems. Automated generation of issue reproduction tests from natural language issue descriptions enhances developer productivity by simplifying root cause analysis, promotes test-driven development -- "test first, write code later", and can be used for improving the effectiveness of automated issue resolution systems like coding agents. Existing methods proposed for this task predominantly rely on closed-source LLMs, with limited exploration of open models. To address this, we propose SWE-Tester -- a novel pipeline for training open-source LLMs to generate issue reproduction tests. First, we curate a high-quality training dataset of 41K instances from 2.6K open-source GitHub repositories and use it to train LLMs of varying sizes and families. The fine-tuned models achieve absolute improvements of up to 10\% in success rate and 21\% in change coverage on SWT-Bench Verified. Further analysis shows consistent improvements with increased inference-time compute, more data, and larger models. These results highlight the effectiveness of our framework for advancing open-source LLMs in this domain.
Robotic reproduction of oil paintings using soft brushes and pigments requires force-sensitive control of deformable tools, prediction of brushstroke effects, and multi-step stroke planning, often without human step-by-step demonstrations or faithful simulators. Given only a sequence of target oil painting images, can a robot infer and execute the stroke trajectories, forces, and colors needed to reproduce it? We present IMPASTO, a robotic oil-painting system that integrates learned pixel dynamics models with model-based planning. The dynamics models predict canvas updates from image observations and parameterized stroke actions; a receding-horizon model predictive control optimizer then plans trajectories and forces, while a force-sensitive controller executes strokes on a 7-DoF robot arm. IMPASTO integrates low-level force control, learned dynamics models, and high-level closed-loop planning, learns solely from robot self-play, and approximates human artists' single-stroke datasets and multi-stroke artworks, outperforming baselines in reproduction accuracy. Project website: https://impasto-robopainting.github.io/
ABSTRACTOdourant receptors (ORs) are not restricted only to the nose, but also occur in many other organs and tissues, including the reproductive system. In fact, ORs are the most heavily expressed in testis than in any other extra‐nasal tissue. Accumulating evidence suggests that olfactory and reproductive systems are both structurally and functionally linked and that these interconnections can influence various aspects of reproduction. In this article, we first review our current understanding of these interconnections and then collate accumulated evidence on the presence of ORs in the male reproductive system and sperm cells. We then investigate the potential role of female reproductive tract odourants in sperm chemotaxis and selection. Finally, since the existing evidence especially for sperm odor sensing capability and its physiological function are controversial, we also review potential reasons for the controversy and propose some ways to resolve the debate. Collectively, we conclude that reproductive odourant signaling may play an important, although currently largely unclear role in many key processes directly related to male fertility. However, since we lack holistic understanding of the functional significance of ORs and odor sensing pathways of the male reproductive system, more empirical research is warranted.
Sound field reproduction with undistorted sound quality and precise spatial localization is desirable for automotive audio systems. However, the complexity of automotive cabin acoustic environment often necessitates a trade-off between sound quality and spatial accuracy. To overcome this limitation, we propose Spatial Power Map Net (SPMnet), a learning-based sound field reproduction method that improves both sound quality and spatial localization in complex environments. We introduce a spatial power map (SPM) constraint, which characterizes the angular energy distribution of the reproduced field using beamforming. This constraint guides energy toward the intended direction to enhance spatial localization, and is integrated into a multi-channel equalization framework to also improve sound quality under reverberant conditions. To address the resulting non-convexity, deep optimization that use neural networks to solve optimization problems is employed for filter design. Both in situ objective and subjective evaluations confirm that our method enhances sound quality and improves spatial localization within the automotive cabin. Furthermore, we analyze the influence of different audio materials and the arrival angles of the virtual sound source in the reproduced sound field, investigating the potential underlying factors affecting these results.
Gyeongwon James Kim, Alex Wilf, Louis-Philippe Morency
et al.
Recent progress in autonomous code generation has fueled excitement around AI agents capable of accelerating scientific discovery by running experiments. However, there is currently no benchmark that evaluates whether such agents can implement scientific ideas when given varied amounts of code as a starting point, interpolating between reproduction (running code) and from-scratch replication (fully re-implementing and running code). We introduce AutoExperiment, a benchmark that evaluates AI agents' ability to implement and run machine learning experiments based on natural language descriptions in research papers. In each task, agents are given a research paper, a codebase with key functions masked out, and a command to run the experiment. The goal is to generate the missing code, execute the experiment in a sandboxed environment, and reproduce the results. AutoExperiment scales in difficulty by varying the number of missing functions $n$, ranging from partial reproduction to full replication. We evaluate state-of-the-art agents and find that performance degrades rapidly as $n$ increases. Agents that can dynamically interact with the environment (e.g. to debug their code) can outperform agents in fixed "agentless" harnesses, and there exists a significant gap between single-shot and multi-trial success rates (Pass@1 vs. Pass@5), motivating verifier approaches to our benchmark. Our findings highlight critical challenges in long-horizon code generation, context retrieval, and autonomous experiment execution, establishing AutoExperiment as a new benchmark for evaluating progress in AI-driven scientific experimentation. Our data and code are open-sourced at https://github.com/j1mk1m/AutoExperiment .
Personalized binaural audio reproduction is the basis of realistic spatial localization, sound externalization, and immersive listening, directly shaping user experience and listening effort. This survey reviews recent advances in deep learning for this task and organizes them by generation mechanism into two paradigms: explicit personalized filtering and end-to-end rendering. Explicit methods predict personalized head-related transfer functions (HRTFs) from sparse measurements, morphological features, or environmental cues, and then use them in the conventional rendering pipeline. End-to-end methods map source signals directly to binaural signals, aided by other inputs such as visual, textual, or parametric guidance, and they learn personalization within the model. We also summarize the field's main datasets and evaluation metrics to support fair and repeatable comparison. Finally, we conclude with a discussion of key applications enabled by these technologies, current technical limitations, and potential research directions for deep learning-based spatial audio systems.
IntroductionWith ongoing global lifestyle changes and economic development, the prevalence of hyperuricemia has steadily increased. Elevated levels of serum uric acid (SUA) have been linked to gestational diabetes mellitus (GDM); however, this relationship has not yet been specifically evaluated in women undergoing assisted reproductive technology (ART). Therefore, this study aimed to analyze the relationship between pre-pregnancy SUA as well as SUA to serum creatinine (SCr) ratio and GDM in women undergoing ART.MethodsThis retrospective cohort study was carried out at the Reproductive and Genetic Hospital of CITIC-Xiangya in Changsha, Hunan, China, and included 1027 women who underwent their first ART treatment between 2017 and 2018. SUA levels were measured during the baseline visit prior to any ART procedures, and GDM incidence was recorded based on screening results from the oral glucose tolerance test.ResultsGDM was diagnosed in 172 (16.7%) of the 1027 patients. When comparing SUA quintiles, significant differences were observed in GDM incidence, and several other parameters (including pre-pregnancy weight, BMI, blood glucose, blood pressure, SCr, lipid parameters, anti-Müllarian Hormone, follicle stimulating hormone, and testosterone). SUA was independently associated with GDM incidence after adjusting for potential confounding factors in multivariate analysis (OR 1.004, p = 0.003). Moreover, the SUA/SCr ratio displayed an even stronger association (OR 1.226, p = 0.003).ConclusionPre-pregnancy SUA levels – and particularly the SUA/SCr ratio – were significantly associated with GDM among women undergoing ART.
Diseases of the endocrine glands. Clinical endocrinology
Ratna Dewi, Tongku Nizwan Siregar, Amalia Sutriana
et al.
Background: Morinda citrifolia L. possesses antioxidant activity that can ameliorate the decline in semen quality of male rats due to exposure to cigarette smoke. Objectives: This study intend to assess the effectiveness of M._citrifolia leaves extract in ameliorating male infertility associated with oxidative dysregulation induced by exposure to tobacco smoke. Methods: The animals used in the study were evenly and randomly divided into five groups, each containing five rats. Group X1 served as the normal control without any treatment, whereas group X2 comprised rats that were exclusively subjected to cigarette smoke exposure. Groups X3, X4, and X5 were exposed to cigarette smoke and subsequently administered M. citrifolia leaves extract orally via a nasogastric tube at doses of 100, 200, and 300 mg/kg BW, respectively, for a period of 52 days. Twenty-four hours after the final treatment, blood samples were collected to examine FSH, LH, and testosterone levels using ELISA technique. Semen was collected from the cauda epididymis to analyze the quality of spermatozoa. Results: The administration of M. citrifolia leaves extract improved sperm concentration, progressive motility, and viability, while sperm morphological abnormalities were not affected by the extract (P = 0.618). FSH concentration decreased following M. citrifolia leaves extract administration, particularly at dose of 100, and 200 mg/kg BW. LH concentration increased significantly after treatment with 100_mg/kg BWof M. citrifolia leaves extract and testosterone levels improved after treated with leaves extract of M. citrifolia (P <0.001). Conclusions: Methanol extract of M. citrifolia leaves enhance sperm quality and testosterone levels but does not affect FSH and LH concentrations in male rats exposed to cigarette smoke.