Federated Learning with Intermediate Representation Regularization

In contrast to centralized model training that involves data collection, federated learning (FL) enables remote clients to collaboratively train a model without exposing their private data. However, model performance usually degrades in FL due to the heterogeneous data generated by clients of diverse characteristics. One promising strategy to maintain good performance is by limiting the local training from drifting far away from the global model. Previous studies accomplish this by regularizing the distance between the representations learned by the local and global models. However, they only consider representations from the early layers of a model or the layer preceding the output layer. In this study, we introduce FedIntR, which provides a more fine-grained regularization by integrating the representations of intermediate layers into the local training process. Specifically, FedIntR computes a regularization term that encourages the closeness between the intermediate layer representations of the local and global models. Additionally, FedIntR automatically determines the contribution of each layer's representation to the regularization term based on the similarity between local and global representations. We conduct extensive experiments on various datasets to show that FedIntR can achieve equivalent or higher performance compared to the state-of-the-art approaches. Our code is available at https://github.com/YLTun/FedIntR.Comment: IEEE BigComp 202

Edge assisted crime prediction and evaluation framework for machine learning algorithms

The growing global populations, particularly in major cities, have created new problems, notably in terms of public safety regulation and optimization. As a result, in this paper, a strategy is provided for predicting crime occurrences in a city based on historical events and demographic observation. In particular, this study proposes a crime prediction and evaluation framework for machine learning algorithms of the network edge. Thus, a complete analysis of four distinct sorts of crimes, such as murder, rapid trial, repression of women and children, and narcotics, validates the efficiency of the proposed framework. The complete study and implementation process have shown a visual representation of crime in various areas of country. The total work is completed by the selection, assessment, and implementation of the Machine Learning (ML) model, and finally, proposed the crime prediction. Criminal risk is predicted using classification models for a particular time interval and place. To anticipate occurrences, ML methods such as Decision Trees, Neural Networks, K-Nearest Neighbors, and Impact Learning are being utilized, and their performance is compared based on the data processing and modification used. A maximum accuracy of 81% is obtained for Decision Tree algorithm during the prediction of crime. The findings demonstrate that employing Machine Learning techniques aids in the prediction of criminal events, which has aided in the enhancement of public security

Chemsex among gay, bisexual, and other men who have sex with men in Singapore and the challenges ahead: a qualitative study

Background: Sexualised substance use, or 'chemsex' has been shown to be a major factor driving the syndemic of HIV/AIDS in communities of gay, bisexual, and other men who have sex with men (GBMSM) around the world. However, there is a paucity of research on chemsex among GBMSM in Singapore due to punitive drug laws and the criminalisation of sexual behaviour between men. This qualitative descriptive study is the first to explore perceptions towards, motivators to engaging in, and the barriers to addressing the harms associated with chemsex among GBMSM in Singapore. Methods: We conducted 30 semi-structured in-depth interviews with self-identifying GBMSM between the ages of 18–39 in Singapore following a purposive sampling strategy. Interview topics included participants' perceptions of drug use among GBMSM in Singapore, perceptions towards chemsex, reasons for drug use and chemsex, and recommendations to address the harms associated with chemsex in Singapore. Interviews were audio-recorded, transcribed, coded, and analysed using thematic analysis. Results: Participants reported that it was common to encounter chemsex among GBMSM in Singapore as it could be easily accessed or initiated using social networking phone apps. Enhancement and prolongation of sexual experiences, fear of rejection from sexual partners and peers, and its use as a means of coping with societal rejection were three main reasons cited for engaging in chemsex. The impact of punitive drug laws on disclosure and stigmatisation of GBMSM who use drugs were reported to be key barriers towards addressing chemsex. Participants suggested using gay-specific commercial venues as avenues for awareness and educational campaigns, and social media to reach out to younger GBMSM. Conclusions: This study highlights the complexities behind chemsex use among GBMSM in Singapore, and the range of individual to institutional factors to be addressed. We recommend that community-based organisations and policy-makers find ways to destigmatise discussion of chemsex and provide safe spaces to seek help for drug use

One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era

OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence (AGI). Since its official release in November 2022, ChatGPT has quickly attracted numerous users with extensive media coverage. Such unprecedented attention has also motivated numerous researchers to investigate ChatGPT from various aspects. According to Google scholar, there are more than 500 articles with ChatGPT in their titles or mentioning it in their abstracts. Considering this, a review is urgently needed, and our work fills this gap. Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges. Moreover, we present an outlook on how ChatGPT might evolve to realize general-purpose AIGC (a.k.a. AI-generated content), which will be a significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated ([email protected]

A Study on the Possibility of Measuring Sludge Sedimentation Using Contrast Detection Characteristics of CdS Photoresistors

Although operators periodically measure the sludge volume index (SVI) to stabilize the bioreactor and solid–liquid separation during the wastewater treatment process, there is a problem of inconsistency attributed to the subjective judgment of the operator. This study aims to investigate the possibility of securing objective data by employing CdS (cadmium–sulfur) photoresistors for SVI measurements. The sedimentation velocity of settling sludge was measured using LED (Light Emitting Diode) lights at the same level as the installed CdS photoresistors. As a result of the experiment, the settling velocity of sludge in the CdS photoresistors’ installation position H1 to H8 (non-flocculent settling), H9 to H12 (discrete flocculent settling) and H13 to H18 (zone settling and compressive settling), was 0.594 mm/s, 0.180 mm/s and 0.056 mm/s, respectively. Through this study, it was confirmed that measuring sludge sedimentation using the CdS photoresistors is possible. If the measurement of solid matter in sludge using several sludge sedimentation measurements is enabled in the future, it will be possible to develop calculation algorithms to measure the SVI

The Mixture of Gotu Kola, Cnidium Fruit, and Goji Berry Enhances Memory Functions by Inducing Nerve-Growth-Factor-Mediated Actions Both In Vitro and In Vivo

Nerve growth factor (NGF), a typical neurotrophin, has been characterized by the regulation of neuronal cell differentiation and survival involved in learning and memory functions. NGF has a main role in neurite extension and synapse formation by activating the cyclic adenosine monophosphate-response-element-binding protein (CREB) in the hippocampus. The purpose of this study was to determine whether a mixture of Gotu Kola, Cnidium fruit, and Goji berry (KYJ) enhances memory function by inducing NGF-mediated actions both in vitro and in vivo. The KYJ combination increased NGF concentration and neurite length in C6 glioma and N2a neuronal cells, respectively. Additionally, we discovered memory-enhancing effects of KYJ through increased NGF-mediated synapse maturation, CREB phosphorylation, and cell differentiation in the mouse hippocampus. These findings suggest that this combination may be a potential nootropic cognitive enhancer via the induction of NGF and NGF-dependent activities

A Case of Graves’ Disease Accompanied with Acute Hepatitis A Virus Infection

Concurrent presentation of acute hepatitis A virus (HAV) infection and Graves’ disease has not been reported in literature worldwide. Although there is no well-established mechanism that explains the induction of Graves’ disease by HAV to date, our case suggests that HAV infection may be responsible for inducing Graves’ disease. A healthy 27-year-old female presented fever, palpitation, and diarrhea, and she was subsequently diagnosed as acute HAV infection. Concurrently, she showed hyperthyroidism, and the diagnosis was made as Graves’ disease. She had never had symptoms that suggested hyperthyroidism, and previous thyroid function test was normal. Acute HAV infection was recovered by conservative management, however, thyroid dysfunction was maintained even after normalization of liver enzymes. Methimazole was used to treat Graves’ disease. We report a case of concurrent acute HAV infection and Graves’ disease in a patient without preexisting thyroid disease. This suggests that HAV infection may be a trigger for an autoimmune thyroid disease in susceptible individuals

Beclin1-induced Autophagy Abrogates Radioresistance of Lung Cancer Cells by Suppressing Osteopontin

Osteopontin (OPN) serves as an indicator of resistance to radiotherapy. However, the role of OPN in the development of acquired radioresistance in human lung cancer cells has not yet been fully elucidated. Therefore, the potential importance of OPN as a marker of lung cancer with a potential significant role in the development of radioresistance against repeated radiotherapy has prompted us to define the pathways by which OPN regulates lung cancer cell growth. In addition, autophagy has been reported to play a key role in the radiosensitization of cancer cells. Here, we report that increased OPN expression through induction of nuclear p53 following irradiation was inhibited by exogenous beclin-1 (BECN1). Our results clearly show that BECN1 gene expression led to induction of autophagy and inhibition of cancer cell growth and angiogenesis. Our results suggest that the induction of autophagy abrogated the radioresistance of the cancer cells. Interestingly, we showed that knockdown of OPN by lentivirus-mediated shRNA induced the autophagy of human lung cancer cell. Taken together, these results suggest that OPN and BECN1 can be molecular targets for overcoming radioresistance by controlling autophagy