Session Types in a Linearly Typed Multi-Threaded Lambda-Calculus

We present a formalization of session types in a multi-threaded lambda-calculus (MTLC) equipped with a linear type system, establishing for the MTLC both type preservation and global progress. The latter (global progress) implies that the evaluation of a well-typed program in the MTLC can never reach a deadlock. As this formulated MTLC can be readily embedded into ATS, a full-fledged language with a functional programming core that supports both dependent types (of DML-style) and linear types, we obtain a direct implementation of session types in ATS. In addition, we gain immediate support for a form of dependent session types based on this embedding into ATS. Compared to various existing formalizations of session types, we see the one given in this paper is unique in its closeness to concrete implementation. In particular, we report such an implementation ready for practical use that generates Erlang code from well-typed ATS source (making use of session types), thus taking great advantage of the infrastructural support for distributed computing in Erlang.Comment: This is the original version of the paper on supporting programming with dyadic session types in AT

Theory of electron transport in normal metal/superconductor junctions

On the basis of the Keldysh method of non-equilibrium systems, we develop a theory of electron tunneling in normal-metal/superconductor junctions. By using the tunneling Hamiltonian model (being appropriate for the tight-binding systems), the tunneling current can be exactly obtained in terms of the equilibrium Green functions of the normal metal and the superconductor. We calculate the conductance of various junctions. The discrepancy between the present treatment and the well-known scheme by Blonder, Tinkham, and Klapwijk is found for some junctions of low interfacial potential barrier.Comment: 5 pages, 4 figure

Undergraduates’ Behavioral Intention to Use E-Guests to Facilitate Online Learning in The Public Universities in Chongqing, China

Purpose: This study evaluates the determinants that significantly affect undergraduate design students’ behavioral intentions to invite e-guests in online education from three essential public universities in Chongqing, China. A conceptual framework proposes the relationship between self-efficacy, perceived enjoyment, perceived ease of use, perceived usefulness, attitude, social influence, and behavioral intention. Research design, data, and methodology: A quantitative approach was used with 495 samples, and a questionnaire was distributed to undergraduate students at three target universities. The sampling techniques are judgmental stratified random and convenience sampling. Content validity was reserved by index of item objective congruence (IOC) at a score of 0.6 or over. Pilot test of 30 samples was approved by Cronbach’s Alpha reliability test at a score of 0.7 and above. Confirmatory Factor Analysis (CFA) and the Structural Equation Model were utilized for statistical analysis (SEM), as well as evaluations of the goodness of model fit, correlation validity, and reliability of each factor. Results: Seven hypotheses have been established to accomplish the research objectives, with the attitude has the strongest effect on behavioral intention. Conclusion: It is recommended that the administrations of public universities should enhance the critical determinants of effective implementation of e-guests in online learning to enhance students’ behavioral intentions

Multi-Graph Convolution Network for Pose Forecasting

Recently, there has been a growing interest in predicting human motion, which involves forecasting future body poses based on observed pose sequences. This task is complex due to modeling spatial and temporal relationships. The most commonly used models for this task are autoregressive models, such as recurrent neural networks (RNNs) or variants, and Transformer Networks. However, RNNs have several drawbacks, such as vanishing or exploding gradients. Other researchers have attempted to solve the communication problem in the spatial dimension by integrating Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) models. These works deal with temporal and spatial information separately, which limits the effectiveness. To fix this problem, we propose a novel approach called the multi-graph convolution network (MGCN) for 3D human pose forecasting. This model simultaneously captures spatial and temporal information by introducing an augmented graph for pose sequences. Multiple frames give multiple parts, joined together in a single graph instance. Furthermore, we also explore the influence of natural structure and sequence-aware attention to our model. In our experimental evaluation of the large-scale benchmark datasets, Human3.6M, AMSS and 3DPW, MGCN outperforms the state-of-the-art in pose prediction.Comment: arXiv admin note: text overlap with arXiv:2110.04573 by other author

Hypothermia treatment ameliorated cyclin-dependent kinase 5-mediated inflammation in ischemic stroke and improved outcomes in ischemic stroke patients

OBJECTIVES: The inflammatory response is a key mechanism of neuronal damage and loss during acute ischemic stroke. Hypothermia has shown promise as a treatment for ischemic stroke. In this study, we investigated the molecular signaling pathways in ischemic stroke after hypothermia treatment. METHODS: Cyclin-dependent kinase 5 (CDK5) was overexpressed or silenced in cultured cells. Nuclear transcription factor-kB (NF-kB) activity was assessed by measurement of the luciferase reporter gene. An ischemic stroke model was established in Sprague–Dawley (SD) rats using the suture-occluded method. Animals were assigned to three groups: sham operation control, ischemic stroke, and ischemic stroke + hypothermia treatment groups. Interleukin 1b (IL-1b) levels in the culture supernatant and blood samples were assessed by ELISA. Protein expression was measured by Western blotting. RESULTS: In HEK293 cells and primary cortical neuronal cultures exposed to hypothermia, CDK5 overexpression was associated with increased IL-1b, caspase 1, and NF-kB levels. In both a murine model of stroke and in patients, increased IL-1b levels were observed after stroke, and hypothermia treatment was associated with lower IL-1b levels. Furthermore, hypothermia-treated patients showed significant improvement in neurophysiological functional outcome. CONCLUSIONS: Overall, hypothermia offers clinical benefit, most likely through its effects on the inflammatory response

PromptCARE: Prompt Copyright Protection by Watermark Injection and Verification

Large language models (LLMs) have witnessed a meteoric rise in popularity among the general public users over the past few months, facilitating diverse downstream tasks with human-level accuracy and proficiency. Prompts play an essential role in this success, which efficiently adapt pre-trained LLMs to task-specific applications by simply prepending a sequence of tokens to the query texts. However, designing and selecting an optimal prompt can be both expensive and demanding, leading to the emergence of Prompt-as-a-Service providers who profit by providing well-designed prompts for authorized use. With the growing popularity of prompts and their indispensable role in LLM-based services, there is an urgent need to protect the copyright of prompts against unauthorized use. In this paper, we propose PromptCARE, the first framework for prompt copyright protection through watermark injection and verification. Prompt watermarking presents unique challenges that render existing watermarking techniques developed for model and dataset copyright verification ineffective. PromptCARE overcomes these hurdles by proposing watermark injection and verification schemes tailor-made for prompts and NLP characteristics. Extensive experiments on six well-known benchmark datasets, using three prevalent pre-trained LLMs (BERT, RoBERTa, and Facebook OPT-1.3b), demonstrate the effectiveness, harmlessness, robustness, and stealthiness of PromptCARE.Comment: To Appear in the 45th IEEE Symposium on Security and Privacy 2024, code is available at: https://github.com/grasses/PromptCAR