Azimuthal distribution of exponential format for particle collective motions in heavy-ion collisions under asynchronous assumption

Particle azimuth distributions are widely studied in heavy-ion collisions. They are often expanded in Fourier series to extract anisotropic flow harmonics simultaneously. It was recently proposed that the different orders of flows could happen asynchronously or noninterdependently. This study extends this idea to an exponential format of the azimuth distribution, which makes it straightforward to extract the asynchronous flow coefficients. We compare these new coefficients to the conventional ones, and find consistency in the leading coefficient and discrepancy in higher-order ones.Comment: 4 pages, 5 figure

Learning Multi-Object Positional Relationships via Emergent Communication

The study of emergent communication has been dedicated to interactive artificial intelligence. While existing work focuses on communication about single objects or complex image scenes, we argue that communicating relationships between multiple objects is important in more realistic tasks, but understudied. In this paper, we try to fill this gap and focus on emergent communication about positional relationships between two objects. We train agents in the referential game where observations contain two objects, and find that generalization is the major problem when the positional relationship is involved. The key factor affecting the generalization ability of the emergent language is the input variation between Speaker and Listener, which is realized by a random image generator in our work. Further, we find that the learned language can generalize well in a new multi-step MDP task where the positional relationship describes the goal, and performs better than raw-pixel images as well as pre-trained image features, verifying the strong generalization ability of discrete sequences. We also show that language transfer from the referential game performs better in the new task than learning language directly in this task, implying the potential benefits of pre-training in referential games. All in all, our experiments demonstrate the viability and merit of having agents learn to communicate positional relationships between multiple objects through emergent communication.Comment: 15 page

An Integrated Enhancement Solution for 24-hour Colorful Imaging

The current industry practice for 24-hour outdoor imaging is to use a silicon camera supplemented with near-infrared (NIR) illumination. This will result in color images with poor contrast at daytime and absence of chrominance at nighttime. For this dilemma, all existing solutions try to capture RGB and NIR images separately. However, they need additional hardware support and suffer from various drawbacks, including short service life, high price, specific usage scenario, etc. In this paper, we propose a novel and integrated enhancement solution that produces clear color images, whether at abundant sunlight daytime or extremely low-light nighttime. Our key idea is to separate the VIS and NIR information from mixed signals, and enhance the VIS signal adaptively with the NIR signal as assistance. To this end, we build an optical system to collect a new VIS-NIR-MIX dataset and present a physically meaningful image processing algorithm based on CNN. Extensive experiments show outstanding results, which demonstrate the effectiveness of our solution.Comment: AAAI 2020 (Oral

LLaMA Rider: Spurring Large Language Models to Explore the Open World

Recently, various studies have leveraged Large Language Models (LLMs) to help decision-making and planning in environments, and try to align the LLMs' knowledge with the world conditions. Nonetheless, the capacity of LLMs to continuously acquire environmental knowledge and adapt in an open world remains uncertain. In this paper, we propose an approach to spur LLMs to explore the open world, gather experiences, and learn to improve their task-solving capabilities. In this approach, a multi-round feedback-revision mechanism is utilized to encourage LLMs to actively select appropriate revision actions guided by feedback information from the environment. This facilitates exploration and enhances the model's performance. Besides, we integrate sub-task relabeling to assist LLMs in maintaining consistency in sub-task planning and help the model learn the combinatorial nature between tasks, enabling it to complete a wider range of tasks through training based on the acquired exploration experiences. By evaluation in Minecraft, an open-ended sandbox world, we demonstrate that our approach LLaMA-Rider enhances the efficiency of the LLM in exploring the environment, and effectively improves the LLM's ability to accomplish more tasks through fine-tuning with merely 1.3k instances of collected data, showing minimal training costs compared to the baseline using reinforcement learning.Comment: 18 page

Increased expression and local accumulation of the Prion Protein, Alzheimer Aβ peptides, superoxide dismutase 1, and Nitric oxide synthases 1 & 2 in muscle in a rabbit model of diabetes

<p>Abstract</p> <p>Background</p> <p>Muscle disease associated with different etiologies has been shown to produce localized accumulations of amyloid and oxidative stress-related proteins that are more commonly associated with neurodegeneration in the brain. In this study we examined changes in muscle tissue in a classic model of diabetes and hyperglycemia in rabbits to determine if similar dysregulation of Alzheimer Aβ peptides, the prion protein (PrP), and superoxide dismutase 1 (SOD1), as well as nitric oxide synthases is produced in muscle in diabetic animals. This wild-type rabbit model includes systemic physiological expression of human-like Alzheimer precursor proteins and Aβ peptides that are considered key in Alzheimer protein studies.</p> <p>Results</p> <p>Diabetes was produced in rabbits by injection of the toxic glucose analogue alloxan, which selectively enters pancreatic beta cells and irreversibly decreases insulin production, similar to streptozotocin. Quadriceps muscle from rabbits 16 wks after onset of diabetes and hyperglycemia were analyzed with biochemical and <it>in situ </it>methods. Immunoblots of whole muscle protein samples demonstrated increased PrP, SOD1, as well as neuronal and inducible Nitric oxide synthases (NOS1 and NOS2) in diabetic muscle. In contrast, we detected little change in Alzheimer Aβ precursor protein expression, or BACE1 and Presenilin 1 levels. However, Aβ peptides measured by ELISA increased several fold in diabetic muscle, suggesting a key role for Aβ cleavage in muscle similar to Alzheimer neurodegeneration in this diabetes model. Histological changes in diabetic muscle included localized accumulations of PrP, Aβ, NOS1 and 2, and SOD1, and evidence of increased central nuclei and cell infiltration.</p> <p>Conclusions</p> <p>The present study provides evidence that several classic amyloid and oxidative stress-related disease proteins coordinately increase in overall expression and form localized accumulations in diabetic muscle. The present study highlights the capacity of this wild-type animal model to produce an array of hallmark pathological features that have also been described in other muscle diseases.</p