Statistical Theory of Asteroid Escape Rates

Transition states in phase space are identified and shown to regulate the rate of escape of asteroids temporarily captured in circumplanetary orbits. The transition states, similar to those occurring in chemical reaction dynamics, are then used to develop a statistical semianalytical theory for the rate of escape of asteroids temporarily captured by Mars. Theory and numerical simulations are found to agree to better than 1%. These calculations suggest that further development of transition state theory in celestial mechanics, as an alternative to large-scale numerical simulations, will be a fruitful approach to mass transport calculations

Breaking a Chaotic Cryptographic Scheme Based on Composition Maps

Recently, a chaotic cryptographic scheme based on composition maps was proposed. This paper studies the security of the scheme and reports the following findings: 1) the scheme can be broken by a differential attack with $6+\lceil\log_L(MN)\rceil$ chosen-plaintext, where $MN$ is the size of plaintext and $L$ is the number of different elements in plain-text; 2) the scheme is not sensitive to the changes of plaintext; 3) the two composition maps do not work well as a secure and efficient random number source.Comment: 9 pages, 7 figure

Assessing the Generalizability of code2vec Token Embeddings

Many Natural Language Processing (NLP) tasks, such as sentiment analysis or syntactic parsing, have benefited from the development of word embedding models. In particular, regardless of the training algorithms, the learned embeddings have often been shown to be generalizable to different NLP tasks. In contrast, despite recent momentum on word embeddings for source code, the literature lacks evidence of their generalizability beyond the example task they have been trained for. In this experience paper, we identify 3 potential downstream tasks, namely code comments generation, code authorship identification, and code clones detection, that source code token embedding models can be applied to. We empirically assess a recently proposed code token embedding model, namely code2vec’s token embeddings. Code2vec was trained on the task of predicting method names, and while there is potential for using the vectors it learns on other tasks, it has not been explored in literature. Therefore, we fill this gap by focusing on its generalizability for the tasks we have identified. Eventually, we show that source code token embeddings cannot be readily leveraged for the downstream tasks. Our experiments even show that our attempts to use them do not result in any improvements over less sophisticated methods. We call for more research into effective and general use of code embeddings

A randomized trial of a lab-embedded discourse intervention to improve research ethics.

We report a randomized trial of a research ethics training intervention designed to enhance ethics communication in university science and engineering laboratories, focusing specifically on authorship and data management. The intervention is a project-based research ethics curriculum that was designed to enhance the ability of science and engineering research laboratory members to engage in reason giving and interpersonal communication necessary for ethical practice. The randomized trial was fielded in active faculty-led laboratories at two US research-intensive institutions. Here, we show that laboratory members perceived improvements in the quality of discourse on research ethics within their laboratories and enhanced awareness of the relevance and reasons for that discourse for their work as measured by a survey administered over 4 mo after the intervention. This training represents a paradigm shift compared with more typical module-based or classroom ethics instruction that is divorced from the everyday workflow and practices within laboratories and is designed to cultivate a campus culture of ethical science and engineering research in the very work settings where laboratory members interact

Adversarial Attacks on Code Models with Discriminative Graph Patterns

Pre-trained language models of code are now widely used in various software engineering tasks such as code generation, code completion, vulnerability detection, etc. This, in turn, poses security and reliability risks to these models. One of the important threats is \textit{adversarial attacks}, which can lead to erroneous predictions and largely affect model performance on downstream tasks. Current adversarial attacks on code models usually adopt fixed sets of program transformations, such as variable renaming and dead code insertion, leading to limited attack effectiveness. To address the aforementioned challenges, we propose a novel adversarial attack framework, GraphCodeAttack, to better evaluate the robustness of code models. Given a target code model, GraphCodeAttack automatically mines important code patterns, which can influence the model's decisions, to perturb the structure of input code to the model. To do so, GraphCodeAttack uses a set of input source codes to probe the model's outputs and identifies the \textit{discriminative} ASTs patterns that can influence the model decisions. GraphCodeAttack then selects appropriate AST patterns, concretizes the selected patterns as attacks, and inserts them as dead code into the model's input program. To effectively synthesize attacks from AST patterns, GraphCodeAttack uses a separate pre-trained code model to fill in the ASTs with concrete code snippets. We evaluate the robustness of two popular code models (e.g., CodeBERT and GraphCodeBERT) against our proposed approach on three tasks: Authorship Attribution, Vulnerability Prediction, and Clone Detection. The experimental results suggest that our proposed approach significantly outperforms state-of-the-art approaches in attacking code models such as CARROT and ALERT

Cost-Effective Use of Silver Dressings for the Treatment of Hard-to-Heal Chronic Venous Leg Ulcers

Aim To estimate the cost-effectiveness of silver dressings using a health economic model based on time-to-wound-healing in hard-to-heal chronic venous leg ulcers (VLUs). Background Chronic venous ulceration affects 1–3% of the adult population and typically has a protracted course of healing, resulting in considerable costs to the healthcare system. The pathogenesis of VLUs includes excessive and prolonged inflammation which is often related to critical colonisation and early infection. The use of silver dressings to control this bioburden and improve wound healing rates remains controversial. Methods A decision tree was constructed to evaluate the cost-effectiveness of treatment with silver compared with non-silver dressings for four weeks in a primary care setting. The outcomes: ‘Healed ulcer’, ‘Healing ulcer’ or ‘No improvement’ were developed, reflecting the relative reduction in ulcer area from baseline to four weeks of treatment. A data set from a recent meta-analysis, based on four RCTs, was applied to the model. Results Treatment with silver dressings for an initial four weeks was found to give a total cost saving (£141.57) compared with treatment with non-silver dressings. In addition, patients treated with silver dressings had a faster wound closure compared with those who had been treated with non-silver dressings. Conclusion The use of silver dressings improves healing time and can lead to overall cost savings. These results can be used to guide healthcare decision makers in evaluating the economic aspects of treatment with silver dressings in hard-to-heal chronic VLUs