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Chaotic dynamics are ubiquitous in many real-world systems, ranging from biological and industrial processes to climate dynamics and the spread of viruses. These systems are characterized by high sensitivity to initial conditions, making it challenging to predict their future behavior confidently. In this study, we propose a novel deep-learning framework that addresses this challenge by directly exploiting the long-term compounding of local prediction errors during model training, aiming to extend the time horizon for reliable predictions of chaotic systems. Our approach observes the future trajectories of initial errors at a time horizon, modeling the evolution of the loss to that point through the use of two major components: 1) a recurrent architecture (Error Trajectory Tracing) designed to trace the trajectories of predictive errors through phase space, and 2) a training regime, Horizon Forcing, that pushes the model’s focus out to a predetermined time horizon. We validate our method on three classic chaotic systems and six real-world time series prediction tasks with chaotic characteristics. The results show that our approach outperforms the state-of-the-art methods. 

Despite recent progress in Multiple Object Tracking (MOT), several obstacles such as occlusions, similar objects, and complex scenes remain an open challenge. Meanwhile, a systematic study of the cost-performance tradeoff for the popular tracking-by-detection paradigm is still lacking. This paper introduces SMILEtrack, an innovative object tracker that effectively addresses these challenges by integrating an efficient object detector with a Siamese network-based Similarity Learning Module (SLM). The technical contributions of SMILETrack are twofold. First, we propose an SLM that calculates the appearance similarity between two objects, overcoming the limitations of feature descriptors in Separate Detection and Embedding (SDE) models. The SLM incorporates a Patch Self-Attention (PSA) block inspired by the vision Transformer, which generates reliable features for accurate similarity matching. Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames, further enhancing MOT performance. Together, these innovations help SMILETrack achieve an improved trade-off between the cost (e.g., running speed) and performance (e.g., tracking accuracy) over several existing state-of-the-art benchmarks, including the popular BYTETrack method. SMILETrack outperforms BYTETrack by 0.4-0.8 MOTA and 2.1-2.2 HOTA points on MOT17 and MOT20 datasets. Code is available at http://github.com/pingyang1117/SMILEtrack_official. 

Sex is determined by multiple factors derived from somatic and germ cells in vertebrates. We have identifiedamhy,dmrt1,gsdfas male andfoxl2,foxl3,cyp19a1aas female sex determination pathway genes in Nile tilapia. However, the relationship among these genes is largely unclear. Here, we found that the gonads ofdmrt1;cyp19a1adouble mutants developed as ovaries or underdeveloped testes with no germ cells irrespective of their genetic sex. In addition, the gonads ofdmrt1;cyp19a1a;cyp19a1btriple mutants still developed as ovaries. The gonads offoxl3;cyp19a1adouble mutants developed as testes, while the gonads ofdmrt1;cyp19a1a;foxl3triple mutants eventually developed as ovaries. In contrast, the gonads ofamhy;cyp19a1a,gsdf;cyp19a1a,amhy;foxl2,gsdf;foxl2double andamhy;cyp19a1a;cyp19a1b,gsdf;cyp19a1a;cyp19a1btriple mutants developed as testes with spermatogenesis via up-regulation ofdmrt1in both somatic and germ cells. The gonads ofamhy;foxl3andgsdf;foxl3double mutants developed as ovaries but with germ cells in spermatogenesis due to up-regulation ofdmrt1. Taking the respective ovary and underdeveloped testis ofdmrt1;foxl3anddmrt1;foxl2double mutants reported previously into consideration, we demonstrated that oncedmrt1mutated, the gonad could not be rescued to functional testis by mutating any female pathway gene. The sex reversal caused by mutation of male pathway genes other thandmrt1, including its upstreamamhyand downstreamgsdf, could be rescued by mutating female pathway gene. Overall, our data suggested thatdmrt1is the only male pathway gene tested indispensable for sex determination and functional testis development in tilapia. 

Abstract The direct detection of core-collapse supernova (SN) progenitor stars is a powerful way of probing the last stages of stellar evolution. However, detections in archival Hubble Space Telescope images are limited to about one detection per year. Here, we explore whether we can increase the detection rate by using data from ground-based wide-field surveys. Due to crowding and atmospheric blurring, progenitor stars can typically not be identified in preexplosion images alone. Instead, we combine many pre-SN and late-time images to search for the disappearance of the progenitor star. As a proof of concept, we implement our search of ZTF data. For a few hundred images, we achieve limiting magnitudes of ∼23 mag in thegandrbands. However, no progenitor stars or long-lived outbursts are detected for 29 SNe withinz≤ 0.01, and the ZTF limits are typically several magnitudes less constraining than detected progenitors in the literature. Next, we estimate progenitor detection rates for the Legacy Survey of Space and Time (LSST) with the Vera C. Rubin telescope by simulating a population of nearby SNe. The background from bright host galaxies reduces the nominal LSST sensitivity by, on average, 0.4 mag. Over the 10 yr survey, we expect the detection of ∼50 red supergiant progenitors and several yellow and blue supergiants. The progenitors of Type Ib and Ic SNe will be detectable if they are brighter than −4.7 or −4.0 mag in the LSSTiband, respectively. In addition, we expect the detection of hundreds of pre-SN outbursts depending on their brightness and duration.