Type 2 diabetes mellitus represents a prevalent and widespread global health concern, necessitating a comprehensive assessment of its risk factors. This study aimed towards learning whether there is any differential impact of age, Lifestyle, BMI and Waist to height ratio on the risk of Type 2 diabetes mellitus in males and females in Kolkata, West Bengal, India based on a sample observed from the out-patient consultation department of Belle Vue Clinic in Kolkata. Various machine learning models like Logistic Regression, Random Forest, and Support Vector Classifier, were used to predict the risk of diabetes, and performance was compared based on different predictors. Our findings indicate a significant age-related increase in risk of diabetes for both males and females. Although exercising and BMI was found to have significant impact on the risk of Type 2 diabetes in males, in females both turned out to be statistically insignificant. For both males and females, predictive models based on WhtR demonstrated superior performance in risk assessment compared to those based on BMI. This study sheds light on the gender-specific differences in the risk factors for Type 2 diabetes, offering valuable insights that can be used towards more targeted healthcare interventions and public health strategies.

Misconfigurations are the major causes of software failures. Existing configuration validation techniques rely on manually written rules or test cases, which are expensive to implement and maintain, and are hard to be comprehensive. Leveraging machine learning (ML) and natural language processing (NLP) for configuration validation is considered a promising direction, but has been facing challenges such as the need of not only large-scale configuration data, but also system-specific features and models which are hard to generalize. Recent advances in Large Language Models (LLMs) show the promises to address some of the long-lasting limitations of ML/NLP-based configuration validation techniques. In this paper, we present an exploratory analysis on the feasibility and effectiveness of using LLMs like GPT and Codex for configuration validation. Specifically, we take a first step to empirically evaluate LLMs as configuration validators without additional fine-tuning or code generation. We develop a generic LLM-based validation framework, named Ciri, which integrates different LLMs. Ciri devises effective prompt engineering with few-shot learning based on both valid configuration and misconfiguration data. Ciri also validates and aggregates the outputs of LLMs to generate validation results, coping with known hallucination and nondeterminism of LLMs. We evaluate the validation effectiveness of Ciri on five popular LLMs using configuration data of six mature, widely deployed open-source systems. Our analysis (1) confirms the potential of using LLMs for configuration validation, (2) understands the design space of LLMbased validators like Ciri, especially in terms of prompt engineering with few-shot learning, and (3) reveals open challenges such as ineffectiveness in detecting certain types of misconfigurations and biases to popular configuration parameters.

Eery year, landslides cause economic damages worth 20 billion US dollars [1], and between 2004 and 2019 non-seismic landslides alone caused about 70, 000 fatalities worldwide [2]. Within the first two months of 2023, we have seen reports of devastating landslides in São Paulo, Brazil [3], Southern Peru [4], and New Zealand [5], injuring many and killing approximately 70 people. Adding to this, recent studies count over one million landslide occurrences with annual volumes estimated at fifty-six billion cubic meters globally [6], presenting a risk to sixty million people [7]. With the increase in urbanization, global climate change, and environmental change trends, the frequency of landslides and the associated risks will keep increasing globally over time [7]. In line with this, landslides are anticipated to evolve and remobilize with increased frequency under changing climatic conditions on a decadal scale [8, 9]. Our ability to identify hazards from emerging landslides and dynamically assess impact areas is essential in averting risk to rapidly urbanizing communities and adapting to changing environmental conditions [10, 7]. To address the rising landslide risk, predictive models for hazard, risk, and early warning systems are developed which assist in forecasting landslide occurrences and locating landslide-prone regions to mitigate the associated impacts [11]. However, the efficacy of these models is contingent on the quality of the underlying landslide databases.

Polarization has increased substantially in political discourse, contributing to a widening partisan divide. In this paper, we analyzed large-scale, real-world language use in Reddit communities (294,476,146 comments) and in news outlets (6,749,781 articles) to uncover psychological dimensions along which partisan language is divided. Using word embedding models that captured semantic associations based on co-occurrences of words in vast textual corpora, we identified patterns of affective polarization present in natural political discourse. We then probed the semantic associations of words related to seven political topics (e.g., abortion, immigration) along the dimensions of morality (moral-to-immoral), threat (threatening-to-safe), and valence (pleasant-to-unpleasant). Across both Reddit communities and news outlets, we identified a small but systematic divergence in the moral associations of words between text sources with different partisan leanings. Moral associations of words were highly correlated between conservative and liberal text sources (average $\rho$ = 0.96), but the differences remained reliable to enable us to distinguish text sources along partisan lines with above 85% classification accuracy. These findings underscore that despite a shared moral understanding across the political spectrum, there are consistent differences that shape partisan language and potentially exacerbate political polarization. Our results, drawn from both informal interactions on social media and curated narratives in news outlets, indicate that these trends are widespread. Leveraging advanced computational techniques, this research offers a fresh perspective that complements traditional methods in political attitudes.

Lung cancer is highly lethal, emphasizing the critical need for early detection. However, identifying lung nodules poses significant challenges for radiologists, who rely heavily on their expertise for accurate diagnosis. To address this issue, computer-aided diagnosis (CAD) systems based on machine learning techniques have emerged to assist doctors in identifying lung nodules from computed tomography (CT) scans. Unfortunately, existing networks in this domain often suffer from computational complexity, leading to high rates of false negatives and false positives, limiting their effectiveness. To address these challenges, we present an innovative model that harnesses the strengths of both convolutional neural networks and vision transformers. Inspired by object detection in videos, we treat each 3D CT image as a video, individual slices as frames, and lung nodules as objects, enabling a time-series application. The primary objective of our work is to overcome hardware limitations during model training, allowing for efficient processing of 2D data while utilizing inter-slice information for accurate identification based on 3D image context. We validated the proposed network by applying a 10-fold cross-validation technique to the publicly available Lung Nodule Analysis 2016 dataset. Our proposed architecture achieves an average sensitivity criterion of 97.84% and a competition performance metrics (CPM) of 96.0% with few parameters. Comparative analysis with state-of-the-art advancements in lung nodule identification demonstrates the significant accuracy achieved by our proposed model.

Bias detection in text is imperative due to its role in reinforcing negative stereotypes, disseminating misinformation, and influencing decisions. Current language models often fall short in generalizing beyond their training sets. In response, we introduce the Contextualized Bi-Directional Dual Transformer (CBDT) Classifier. This novel architecture utilizes two synergistic transformer networks: the Context Transformer and the Entity Transformer, aiming for enhanced bias detection. Our dataset preparation follows the FAIR principles, ensuring ethical data usage. Through rigorous testing on various datasets, CBDT showcases its ability in distinguishing biased from neutral statements, while also pinpointing exact biased lexemes. Our approach outperforms existing methods, achieving a 2-4\% increase over benchmark performances. This opens avenues for adapting the CBDT model across diverse linguistic and cultural landscapes.

Prompt-tuning has emerged as an attractive paradigm for deploying large-scale language models due to its strong downstream task performance and efficient multitask serving ability. Despite its wide adoption, we empirically show that prompt-tuning is vulnerable to downstream task-agnostic backdoors, which reside in the pretrained models and can affect arbitrary downstream tasks. The state-of-the-art backdoor detection approaches cannot defend against task-agnostic backdoors since they hardly converge in reversing the backdoor triggers. To address this issue, we propose LMSanitator, a novel approach for detecting and removing task-agnostic backdoors on Transformer models. Instead of directly inverting the triggers, LMSanitator aims to invert the predefined attack vectors (pretrained models' output when the input is embedded with triggers) of the task-agnostic backdoors, which achieves much better convergence performance and backdoor detection accuracy. LMSanitator further leverages prompt-tuning's property of freezing the pretrained model to perform accurate and fast output monitoring and input purging during the inference phase. Extensive experiments on multiple language models and NLP tasks illustrate the effectiveness of LMSanitator. For instance, LMSanitator achieves 92.8% backdoor detection accuracy on 960 models and decreases the attack success rate to less than 1% in most scenarios.

Deep learning techniques have implemented many unconditional image generation (UIG) models, such as GAN, Diffusion model, etc. The extremely realistic images (also known as AI-Generated Content, AIGC for short) produced by these models bring urgent needs for intellectual property protection such as data traceability and copyright certification. An attacker can steal the output images of the target model and use them as part of the training data to train a private surrogate UIG model. The implementation mechanisms of UIG models are diverse and complex, and there is no unified and effective protection and verification method at present. To address these issues, we propose a two-stage unified watermark verification mechanism with high-binding effects for such models. In the first stage, we use an encoder to invisibly write the watermark image into the output images of the original AIGC tool, and reversely extract the watermark image through the corresponding decoder. In the second stage, we design the decoder fine-tuning process, and the fine-tuned decoder can make correct judgments on whether the suspicious model steals the original AIGC tool data. Experiments demonstrate our method can complete the verification work with almost zero false positive rate under the condition of only using the model output images. Moreover, the proposed method can achieve data steal verification across different types of UIG models, which further increases the practicality of the method.

In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL). PSL subsumes a wide range of important applications, such as open-set face recognition, speaker verification, image retrieval and person re-identification. The goal of PSL is to learn a pairwise similarity function assigning a higher similarity score to positive pairs (i.e., a pair of samples with the same label) than to negative pairs (i.e., a pair of samples with different label). We start by identifying a key desideratum for PSL, and then discuss how existing methods can achieve this desideratum. We then propose a surprisingly simple proxy-free method, called SimPLE, which requires neither feature/proxy normalization nor angular margin and yet is able to generalize well in open-set recognition. We apply the proposed method to three challenging PSL tasks: open-set face recognition, image retrieval and speaker verification. Comprehensive experimental results on large-scale benchmarks show that our method performs significantly better than current state-of-the-art methods.

Regarding the rising number of people suffering from mental health illnesses in today's society, the importance of mental health cannot be overstated. Wearable sensors, which are increasingly widely available, provide a potential way to track and comprehend mental health issues. These gadgets not only monitor everyday activities but also continuously record vital signs like heart rate, perhaps providing information on a person's mental state. Recent research has used these sensors in conjunction with machine learning methods to identify patterns relating to different mental health conditions, highlighting the immense potential of this data beyond simple activity monitoring. In this research, we present a novel algorithm called the Hybrid Random forest - Neural network that has been tailored to evaluate sensor data from depressed patients. Our method has a noteworthy accuracy of 80\% when evaluated on a special dataset that included both unipolar and bipolar depressive patients as well as healthy controls. The findings highlight the algorithm's potential for reliably determining a person's depression condition using sensor data, making a substantial contribution to the area of mental health diagnostics.