Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition

The prevalence of violence in daily life poses significant threats to individuals' physical and mental well-being. Using surveillance cameras in public spaces has proven effective in proactively deterring and preventing such incidents. However, concerns regarding privacy invasion have emerged due to their widespread deployment. To address the problem, we leverage Dynamic Vision Sensors (DVS) cameras to detect violent incidents and preserve privacy since it captures pixel brightness variations instead of static imagery. We introduce the Bullying10K dataset, encompassing various actions, complex movements, and occlusions from real-life scenarios. It provides three benchmarks for evaluating different tasks: action recognition, temporal action localization, and pose estimation. With 10,000 event segments, totaling 12 billion events and 255 GB of data, Bullying10K contributes significantly by balancing violence detection and personal privacy persevering. And it also poses a challenge to the neuromorphic dataset. It will serve as a valuable resource for training and developing privacy-protecting video systems. The Bullying10K opens new possibilities for innovative approaches in these domains.Comment: Accepted at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmark

Receiver Algorithms for Single-Carrier OSM Based High-Rate Indoor Visible Light Communications

In intensity-modulation and direct-detection (IM/DD) multiple-input and multiple-output (MIMO) visible light communication (VLC) systems, spatial subchannels are usually correlated, and spatial modulation is a good choice to achieve the advantages of MIMO technology. Peak-to-average power ratio (PAPR) is a key issue in VLCs due to the limited linear dynamic range of light emitting diodes (LEDs). Single-carrier communication systems have a lower PAPR than orthogonal frequency division multiplexing (OFDM) communication systems. However, it is challenging to design a single-carrier spatial modulation for high-rate transmissions because of the time domain intersymbol interference. This paper develops an optical spatial modulation (OSM) scheme based on bipolar pulse amplitude modulation (PAM) and spatial elements for high-rate indoor VLC systems. Multiple data streams can be transmitted simultaneously in the proposed scheme. Based on the transmit strategy, we develop a low-complexity receiver algorithm that achieves better bit-error rate performance than reference schemes, and the proposed OSM scheme has a much lower PAPR than OFDM based OSM schemes. When the spatial subchannels are highly correlated, a spatial area division strategy is applied, and the receiver algorithm is investigated. The symbol-error rate expression of the proposed OSM scheme is derived, and the computational complexity is analyzed

Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

State-of-the-art Text-to-Image models like Stable Diffusion and DALLE$\cdot$2 are revolutionizing how people generate visual content. At the same time, society has serious concerns about how adversaries can exploit such models to generate unsafe images. In this work, we focus on demystifying the generation of unsafe images and hateful memes from Text-to-Image models. We first construct a typology of unsafe images consisting of five categories (sexually explicit, violent, disturbing, hateful, and political). Then, we assess the proportion of unsafe images generated by four advanced Text-to-Image models using four prompt datasets. We find that these models can generate a substantial percentage of unsafe images; across four models and four prompt datasets, 14.56% of all generated images are unsafe. When comparing the four models, we find different risk levels, with Stable Diffusion being the most prone to generating unsafe content (18.92% of all generated images are unsafe). Given Stable Diffusion's tendency to generate more unsafe content, we evaluate its potential to generate hateful meme variants if exploited by an adversary to attack a specific individual or community. We employ three image editing methods, DreamBooth, Textual Inversion, and SDEdit, which are supported by Stable Diffusion. Our evaluation result shows that 24% of the generated images using DreamBooth are hateful meme variants that present the features of the original hateful meme and the target individual/community; these generated images are comparable to hateful meme variants collected from the real world. Overall, our results demonstrate that the danger of large-scale generation of unsafe images is imminent. We discuss several mitigating measures, such as curating training data, regulating prompts, and implementing safety filters, and encourage better safeguard tools to be developed to prevent unsafe generation.Comment: To Appear in the ACM Conference on Computer and Communications Security, November 26, 202

Optimising the Use of TRIzol-extracted Proteins in Surface Enhanced Laser Desorption/ Ionization (SELDI) Analysis

BACKGROUND: Research with clinical specimens is always hampered by the limited availability of relevant samples, necessitating the use of a single sample for multiple assays. TRIzol is a common reagent for RNA extraction, but DNA and protein fractions can also be used for other studies. However, little is known about using TRIzol-extracted proteins in proteomic research, partly because proteins extracted from TRIzol are very resistant to solubilization. RESULTS: To facilitate the use of TRIzol-extracted proteins, we first compared the ability of four different common solubilizing reagents to solubilize the TRIzol-extracted proteins from an osteosarcoma cell line, U2-OS. Then we analyzed the solubilized proteins by Surface Enhanced Laser Desorption/ Ionization technique (SELDI). The results showed that solubilization of TRIzol-extracted proteins with 9.5 M Urea and 2% CHAPS ([3-[(3-cholamidopropyl)-dimethylammonio]propanesulfonate]) (UREA-CHAPS) was significantly better than the standard 1% SDS in terms of solubilization efficiency and the number of detectable ion peaks. Using three different types of SELDI arrays (CM10, H50, and IMAC-Cu), we demonstrated that peak detection with proteins solubilized by UREA-CHAPS was reproducible (r > 0.9). Further SELDI analysis indicated that the number of ion peaks detected in TRIzol-extracted proteins was comparable to a direct extraction method, suggesting many proteins still remain in the TRIzol protein fraction. CONCLUSION: Our results suggest that UREA-CHAPS performed very well in solubilizing TRIzol-extracted proteins for SELDI applications. Protein fractions left over after TRIzol RNA extraction could be a valuable but neglected source for proteomic or biochemical analysis when additional samples are not available

Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

State-of-the-art Text-to-Image models like Stable Diffusion and DALLE·2 are revolutionizing how people generate visual content. At the same time, society has serious concerns about how adversaries can exploit such models to generate problematic or unsafe images. In this work, we focus on demystifying the generation of unsafe images and hateful memes from Text-to-Image models. We first construct a typology of unsafe images consisting of five categories (sexually explicit, violent, disturbing, hateful, and political). Then, we assess the proportion of unsafe images generated by four advanced Text-to-Image models using four prompt datasets. We find that Text-to-Image models can generate a substantial percentage of unsafe images; across four models and four prompt datasets, 14.56% of all generated images are unsafe. When comparing the four Text-to-Image models, we find different risk levels, with Stable Diffusion being the most prone to generating unsafe content (18.92% of all generated images are unsafe). Given Stable Diffusion’s tendency to generate more unsafe content, we evaluate its potential to generate hateful meme variants if exploited by an adversary to attack a specific individual or community. We employ three image editing methods, DreamBooth, Textual Inversion, and SDEdit, which are supported by Stable Diffusion to generate variants. Our evaluation result shows that 24% of the generated images using DreamBooth are hateful meme variants that present the features of the original hateful meme and the target individual/community; these generated images are comparable to hateful meme variants collected from the real world. Overall, our results demonstrate that the danger of large-scale generation of unsafe images is imminent. We discuss several mitigating measures, such as curating training data, regulating prompts, and implementing safety filters, and encourage better safeguard tools to be developed to prevent unsafe generation

Practice of pharmaceutical services and prescription analysis in internet-based psychiatric hospitals during COVID-19 pandemic in Wuxi, China

ObjectiveTo study the practice of pharmaceutical services in internet-based psychiatric hospitals, and to analyze the prescriptions to ensure the safety and efficacy of internet-based medication in Wuxi, China.MethodsAll 1,259 internet-based prescriptions from our hospital in 2022 were collected, and data on patients’ age, gender, diagnosis, medications used, medication types, dosage forms, rationality of medication use, and reasons for irrationality were analyzed through descriptive statistics.ResultsIn the electronic prescriptions of internet-based psychiatric hospitals, females accounted for the majority (64.50%), with a female-to-male ratio of 1.82:1. Middle-aged and young adults accounted for the majority of patients (57.50%). There were 47 diagnosed diseases involved, with 89 types of medications used and 1,938prescriptions issued. Among them, there were 78 types of western medicine with 1,876 prescriptions (96.80%), and 11 types of traditional Chinese medicine with 62 prescriptions (3.20%). The main medications used were anti-anxiety and antidepressant medications (44.94%) and psychiatric medications (42.21%). The dosage forms were all oral, with tablets (78.53%), capsules (17.54%), and solution preparations (2.17%) being the top three in frequency. According to the prescription review results, the initial pass rate of internet-based system review was 64.26%. After intervention by the internet-based system and manual review by pharmacist reviewers, the final pass rate of internet-based prescriptions reached 99.76%.ConclusionThe practice of pharmaceutical services and prescription analysis in internet-based psychiatric hospitals could significantly improve medication rationality, which fills the research gap in this field. In addition, it promotes the transformation of pharmaceutical service models

Wip1-dependent modulation of macrophage migration and phagocytosis

Macrophage accumulation within the vascular wall is a hallmark of atherosclerosis. Controlling macrophage conversion into foam cells remains a major challenge for treatment of atherosclerotic diseases. Here, we show that Wip1, a member of the PP2C family of Ser/Thr protein phosphatases, modulates macrophage migration and phagocytosis associated with atherosclerotic plaque formation. Wip1 deficiency increases migratory and phagocytic activities of the macrophage under stress conditions. Enhanced migration of Wip1-/- macrophages is mediated by Rac1-GTPase and PI3K/AKT signalling pathways. Elevated phagocytic ability of Wip1-/- macrophages is linked to CD36 plasma membrane recruitment that is regulated by AMPK activity. Our study identifies Wip1 as an intrinsic negative regulator of macrophage chemotaxis. We propose that Wip1-dependent control of macrophage function may provide avenues for preventing or eliminating plaque formation in atherosclerosis

Will the US Economy Recover in 2010? A Minimal Spanning Tree Study

We calculated the cross correlations between the half-hourly times series of the ten Dow Jones US economic sectors over the period February 2000 to August 2008, the two-year intervals 2002--2003, 2004--2005, 2008--2009, and also over 11 segments within the present financial crisis, to construct minimal spanning trees (MSTs) of the US economy at the sector level. In all MSTs, a core-fringe structure is found, with consumer goods, consumer services, and the industrials consistently making up the core, and basic materials, oil and gas, healthcare, telecommunications, and utilities residing predominantly on the fringe. More importantly, we find that the MSTs can be classified into two distinct, statistically robust, topologies: (i) star-like, with the industrials at the center, associated with low-volatility economic growth; and (ii) chain-like, associated with high-volatility economic crisis. Finally, we present statistical evidence, based on the emergence of a star-like MST in Sep 2009, and the MST staying robustly star-like throughout the Greek Debt Crisis, that the US economy is on track to a recovery.Comment: elsarticle class, includes amsmath.sty, graphicx.sty and url.sty. 68 pages, 16 figures, 8 tables. Abridged version of the manuscript presented at the Econophysics Colloquim 2010, incorporating reviewer comment

Correction: Symptom improvement in children with autism spectrum disorder following bumetanide administration is associated with decreased GABA/glutamate ratios.

An important detail was omitted in the Method of the original Article, I.E, The CARS and other evaluations were conducted 'blind' to condition (Bumetanide or no treatment) by experienced clinicians. This has now been updated in the HTML and PDF versions of this Article

Correction: Symptom improvement in children with autism spectrum disorder following bumetanide administration is associated with decreased GABA/glutamate ratios

An important detail was omitted in the Method of the original Article, I.E, The CARS and other evaluations were conducted ‘blind' to condition (Bumetanide or no treatment) by experienced clinicians. This has now been updated in the HTML and PDF versions of this Article