Stratified Transfer Learning for Cross-domain Activity Recognition

In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready version

An integral gated mode single photon detector at telecom wavelengths

We demonstrate an integral gated mode single photon detector at telecom wavelengths. The charge number of an avalanche pulse rather than the peak current is monitored for single-photon detection. The transient spikes in conventional gated mode operation are canceled completely by integrating, which enables one to improve the performance of single photon detector greatly with the same avalanche photodiode. This method has achieved a detection efficiency of 29.9% at the dark count probability per gate equal to 5.57E-6/gate (1.11E-6/ns) at 1550nm.Comment: word to PDF, 3 pages with 4 figure

Red teaming GPT-4V: are GPT-4V safe against uni/multi-modal jailbreak attacks?

Various jailbreak attacks have been proposed to red-team Large Language Models (LLMs) and revealed the vulnerable safeguards of LLMs. Besides, some methods are not limited to the textual modality and extend the jailbreak attack to Multimodal Large Language Models (MLLMs) by perturbing the visual input. However, the absence of a universal evaluation benchmark complicates the performance reproduction and fair comparison. Besides, there is a lack of comprehensive evaluation of closed-source state-of-the-art (SOTA) models, especially MLLMs, such as GPT-4V. To address these issues, this work first builds a comprehensive jailbreak evaluation dataset with 1445 harmful questions covering 11 different safety policies. Based on this dataset, extensive red-teaming experiments are conducted on 11 different LLMs and MLLMs, including both SOTA proprietary models and open-source models. We then conduct a deep analysis of the evaluated results and find that (1) GPT4 and GPT-4V demonstrate better robustness against jailbreak attacks compared to open-source LLMs and MLLMs. (2) Llama2 and Qwen-VL-Chat are more robust compared to other open-source models. (3) The transferability of visual jailbreak methods is relatively limited compared to textual jailbreak methods. The dataset and code can be found here

Stop reasoning! When multimodal LLMs with chain-of-thought reasoning meets adversarial images

Recently, Multimodal LLMs (MLLMs) have shown a great ability to understand images. However, like traditional vision models, they are still vulnerable to adversarial images. Meanwhile, Chain-of-Thought (CoT) reasoning has been widely explored on MLLMs, which not only improves model’s performance, but also enhances model’s explainability by giving intermediate reasoning steps. Nevertheless, there is still a lack of study regarding MLLMs’ adversarial robustness with CoT and an understanding of what the rationale looks like when MLLMs infer wrong answers with adversarial images. Our research evaluates the adversarial robustness of MLLMs when employing CoT reasoning, finding that CoT marginally improves adversarial robustness against existing attack methods. Moreover, we introduce a novel stop-reasoning attack technique that effectively bypasses the CoT-induced robustness enhancements. Finally, we demonstrate the alterations in CoT reasoning when MLLMs confront adversarial images, shedding light on their reasoning process under adversarial attacks

A Survey on Evaluation of Large Language Models

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, educations, natural and social sciences, agent applications, and other areas. Secondly, we answer the `where' and `how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey.Comment: 23 page

Deficiency of FLCN in Mouse Kidney Led to Development of Polycystic Kidneys and Renal Neoplasia

The Birt–Hogg–Dubé (BHD) disease is a genetic cancer syndrome. The responsible gene, BHD, has been identified by positional cloning and thought to be a novel tumor suppressor gene. BHD mutations cause many types of diseases including renal cell carcinomas, fibrofolliculomas, spontaneous pneumothorax, lung cysts, and colonic polyps/cancers. By combining Gateway Technology with the Ksp-Cre gene knockout system, we have developed a kidney-specific BHD knockout mouse model. BHDflox/flox/Ksp-Cre mice developed enlarged kidneys characterized by polycystic kidneys, hyperplasia, and cystic renal cell carcinoma. The affected BHDflox/flox/Ksp-Cre mice died of renal failure at approximate three weeks of age, having blood urea nitrogen levels over tenfold higher than those of BHD flox/+/Ksp-Cre and wild-type littermate controls. We further demonstrated that these phenotypes were caused by inactivation of BHD and subsequent activation of the mTOR pathway. Application of rapamycin, which inhibits mTOR activity, to the affected mice led to extended survival and inhibited further progression of cystogenesis. These results provide a correlation of kidney-targeted gene inactivation with renal carcinoma, and they suggest that the BHD product FLCN, functioning as a cyst and tumor suppressor, like other hamartoma syndrome–related proteins such as PTEN, LKB1, and TSC1/2, is a component of the mTOR pathway, constituting a novel FLCN-mTOR signaling branch that regulates cell growth/proliferation

Electronic imaging applications in mobile healthcare

Information technology is changing healthcare systems in revolutionary ways; there can be no health care reform without an information revolution. One information technology that is transforming healthcare systems is mobile technology. As it develops and matures, mobile technology is having a significant impact on healthcare, and emerging mobile technologies are attracting significant attention as well as investment of time and effort among researchers and industrial developers. The combination of mobile technology with healthcare has produced an important research area called mHealth. In 2011, U.S. Secretary of Health and Human Services, Kathleen Sebelius, referred to mHealth as the biggest technology breakthrough of our time and maintained that its use would address our greatest national challenge. Based on related research, mobile health is projected to be a 26 billion dollar industry by 2017