Commonsense Knowledge Assisted Deep Learning with Application to Size-Related Fine-Grained Object Detection

This paper addresses fine-grained object detection in scenarios with limited computing resources, such as edge computing. In particular, we focus on a scenario where a single image contains objects of the same category but varying sizes, and we desire an algorithm that can not only recognize the physical class of objects but also detect their size. Deep learning (DL), particularly through the use of deep neural networks (DNNs), has become the primary approach to object detection. However, obtaining accurate fine-grained detection requires a large DNN model and a significant amount of annotated data, presenting a challenge to solve our problem particularly for resource-constrained scenarios. To this end, we propose an approach that utilizes commonsense knowledge to assist a coarse-grained object detector in achieving accurate size-related fine-grained detection results. Specifically, we introduce a commonsense knowledge inference module (CKIM) that processes the coarse-grained labels produced by a benchmark coarse-grained DL detector to generate size-related fine-grained labels. Our CKIM explores both crisp-rule and fuzzy-rule based inference methods, with the latter being employed to handle ambiguity in the target size-related labels. We implement our method based on two modern DL detectors, including Mobilenet-SSD, and YOLOv7-tiny. Experimental results demonstrate that our approach achieves accurate fine-grained detections with a reduced amount of annotated data, and smaller model size. Our code is available at https://github.com/ZJLAB-AMMI/CKIM.Comment: 15 page

Effective p-wave interaction and topological superfluids in s-wave quantum gases

P-wave interaction in cold atoms may give rise to exotic topological superfluids. However, the realization of p-wave interaction in cold atom system is experimentally challenging. Here we propose a simple scheme to synthesize effective $p$-wave interaction in conventional $s$-wave interacting quantum gases. The key idea is to load atoms into spin-dependent optical lattice potential. Using two concrete examples involving spin-1/2 fermions, we show how the original system can be mapped into a model describing spinless fermions with nearest neighbor p-wave interaction, whose ground state can be a topological superfluid that supports Majorana fermions under proper conditions. Our proposal has the advantage that it does not require spin-orbit coupling or loading atoms onto higher orbitals, which is the key in earlier proposals to synthesize effective $p$-wave interaction in $s$-wave quantum gases, and may provide a completely new route for realizing $p$-wave topological superfluids.Comment: 5 pages, 4 figure

A Model of Two-Way Selection System for Human Behavior

We propose a model of two-way selection system. It appears in the processes like choosing a mate between men and women, making contracts between job hunters and recruiters, and trading between buyers and sellers. In this paper, we propose a model of two-way selection system, and present its analytic solution for the expectation of successful matching total and the regular pattern that the matching rate trends toward an inverse proportion to either the ratio between the two sides or the ratio of the state total to the smaller people number. The proposed model is verified by empirical data of the matchmaking fairs. Results indicate that the model well predicts this typical real-world two- way selection behavior to the bounded error extent, thus it is helpful for understanding the dynamics mechanism of the real-world two-way selection system.Comment: 8 pages, 4 figure

Enabling Efficient Interaction between an Algorithm Agent and an LLM: A Reinforcement Learning Approach

Large language models (LLMs) encode a vast amount of world knowledge acquired from massive text datasets. Recent studies have demonstrated that LLMs can assist an algorithm agent in solving complex sequential decision making tasks in embodied environments by providing high-level instructions. However, interacting with LLMs can be time-consuming, as in many practical scenarios, they require a significant amount of storage space that can only be deployed on remote cloud server nodes. Additionally, using commercial LLMs can be costly since they may charge based on usage frequency. In this paper, we explore how to enable efficient and cost-effective interactions between the agent and an LLM. We propose a reinforcement learning based mediator model that determines when it is necessary to consult LLMs for high-level instructions to accomplish a target task. Experiments on 4 MiniGrid environments that entail planning sub-goals demonstrate that our method can learn to solve target tasks with only a few necessary interactions with an LLM, significantly reducing interaction costs in testing environments, compared with baseline methods. Experimental results also suggest that by learning a mediator model to interact with the LLM, the agent's performance becomes more robust against both exploratory and stochastic environments.Comment: 10 page

1,2-Bis[5-(2,2′-dicyano­vinyl)-2-n-pentyl-3-thien­yl]-3,3,4,4,5,5-hexa­fluoro­cyclo­pent-1-ene: a new photochromic diaryl­ethene compound

The title compound, C31H26F6N4S2, is a new photochromic dithienylethene with dicyano­vinyl subsitituents. In the crystal structure, the mol­ecule adopts a photoactive anti­parallel conformation, with two n-pentyl groups located on opposite sides of the cyclo­pentene ring. The cyclo­pentene ring assumes an envelope conformation. The distance between the two reactive C atoms on the thio­phene rings is 3.834 (7) Å. One of the n-pentyl groups is disordered over two positions; the site occupancy factors are ca 0.7 and 0.3

On the high strain rate behavior of 63-37 Sn-Pb eutectic solders with temperature effects

This study presents experimental results performed on samples of Eutectic solder material (63 wt. % Sn 37 wt. % Pb) loaded at high strain rates and elevated temperatures. The tests were performed at high strain rates using Split Hopkinson Pressure Bar (SHPB). The strain rates were in the range of 400 s-1to 1300 s-1. Heating unit was added to conventional SHPB to vary sample' s initial temperature conditions. Tests were conducted at three initial temperatures, i.e. room temperature, 60 °C and 120 °C for compressive mode. The effects of temperature on the behavior of material were compared. Transient temperature changes during dynamic loading conditions are calculated by an analytical approach using measured stress-strain data for plastic work. Test results were fitted into the Johnson-Cook model (JC model). In addition, dynamic tests were performed in tension mode using Split Hopkinson Tensile Bar (SHTB) at room temperature