Public health programs often provide interventions to encourage beneficiary adherence,and effectively allocating interventions is vital for producing the greatest overall health outcomes. Such resource allocation problems are often modeled as restless multi-armed bandits (RMABs) with unknown underlying transition dynamics, hence requiring online reinforcement learning (RL). We present Bayesian Learning for Contextual RMABs (BCoR), an online RL approach for RMABs that novelly combines techniques in Bayesian modeling with Thompson sampling to flexibly model the complex RMAB settings present in public health program adherence problems, such as context and non-stationarity. BCoR's key strength is the ability to leverage shared information within and between arms to learn the unknown RMAB transition dynamics quickly in intervention-scarce settings with relatively short time horizons, which is common in public health applications. Empirically, BCoR achieves substantially higher finite-sample performance over a range of experimental settings, including an example based on real-world adherence data that was developed in collaboration with ARMMAN, an NGO in India which runs a large-scale maternal health program, showcasing BCoR practical utility and potential for real-world deployment.
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The deployment process of a spiking neural network (SNN) can involve partitioning a neural network and mapping partitions onto processing units within the neuromorphic hardware. Searching for optimal deployment schemes presents an NP-hard problem. Optimization of deployment schemes encounters challenges in devising computationally effective cost functions for optimization objectives such as communication time consumption and energy efficiency. These kinds of objectives necessitate consideration of network dynamics shaped by neuron activity patterns, demanding intricate mathematical analyses or simulations for integrating them into a cost model for the deployment of an SNN. The network dynamics are hardware-independent and can be modeled separately from specific hardware configurations. Our approach employs a pairwise Ising-type maximum entropy model, which has shown its effectiveness in accurately reproducing pairwise correlations among components in a system. We utilized this model to capture network dynamics, upon which a cost function is built incorporating hardware-specific parameters. We conducted an extremely preliminary investigation using the SpiNNaker machine. We show that the existing model training can also be computationally complex. Currently, we still lack sufficient evidence to substantiate the effectiveness of our proposed methods. Further efforts is needed to explore integrating network dynamics into SNN deployment.
Deep neural networks conventionally employ end-to-end backpropagation for their training process, which lacks biological credibility and triggers a locking dilemma during network parameter updates, leading to significant GPU memory use. Supervised local learning, which segments the network into multiple local blocks updated by independent auxiliary networks. However, these methods cannot replace end-to-end training due to lower accuracy, as gradients only propagate within their local block, creating a lack of information exchange between blocks. To address this issue and establish information transfer across blocks, we propose a Momentum Auxiliary Network (MAN) that establishes a dynamic interaction mechanism. The MAN leverages an exponential moving average (EMA) of the parameters from adjacent local blocks to enhance information flow. This auxiliary network, updated through EMA, helps bridge the informational gap between blocks. Nevertheless, we observe that directly applying EMA parameters has certain limitations due to feature discrepancies among local blocks. To overcome this, we introduce learnable biases, further boosting performance. We have validated our method on four image classification datasets (CIFAR-10, STL-10, SVHN, ImageNet), attaining superior performance and substantial memory savings. Notably, our method can reduce GPU memory usage by more than 45\% on the ImageNet dataset compared to end-to-end training, while achieving higher performance. The Momentum Auxiliary Network thus offers a new perspective for supervised local learning. Our code is available at: //github.com/JunhaoSu0/MAN.
We propose MToMnet - a Theory of Mind (ToM) neural network for predicting beliefs and their dynamics during human social interactions from multimodal input. ToM is key for effective nonverbal human communication and collaboration, yet, existing methods for belief modelling have not included explicit ToM modelling or have typically been limited to one or two modalities. MToMnet encodes contextual cues (scene videos and object locations) and integrates them with person-specific cues (human gaze and body language) in a separate MindNet for each person. Inspired by prior research on social cognition and computational ToM, we propose three different MToMnet variants: two involving fusion of latent representations and one involving re-ranking of classification scores. We evaluate our approach on two challenging real-world datasets, one focusing on belief prediction, while the other examining belief dynamics prediction. Our results demonstrate that MToMnet surpasses existing methods by a large margin while at the same time requiring a significantly smaller number of parameters. Taken together, our method opens up a highly promising direction for future work on artificial intelligent systems that can robustly predict human beliefs from their non-verbal behaviour and, as such, more effectively collaborate with humans.
Depression Detection and Analysis using Large Language Models on Textual and Audio-Visual Modalities
Depression has proven to be a significant public health issue, profoundly affecting the psychological well-being of individuals. If it remains undiagnosed, depression can lead to severe health issues, which can manifest physically and even lead to suicide. Generally, Diagnosing depression or any other mental disorder involves conducting semi-structured interviews alongside supplementary questionnaires, including variants of the Patient Health Questionnaire (PHQ) by Clinicians and mental health professionals. This approach places significant reliance on the experience and judgment of trained physicians, making the diagnosis susceptible to personal biases. Given that the underlying mechanisms causing depression are still being actively researched, physicians often face challenges in diagnosing and treating the condition, particularly in its early stages of clinical presentation. Recently, significant strides have been made in Artificial neural computing to solve problems involving text, image, and speech in various domains. Our analysis has aimed to leverage these state-of-the-art (SOTA) models in our experiments to achieve optimal outcomes leveraging multiple modalities. The experiments were performed on the Extended Distress Analysis Interview Corpus Wizard of Oz dataset (E-DAIC) corpus presented in the Audio/Visual Emotion Challenge (AVEC) 2019 Challenge. The proposed solutions demonstrate better results achieved by Proprietary and Open-source Large Language Models (LLMs), which achieved a Root Mean Square Error (RMSE) score of 3.98 on Textual Modality, beating the AVEC 2019 challenge baseline results and current SOTA regression analysis architectures. Additionally, the proposed solution achieved an accuracy of 71.43% in the classification task. The paper also includes a novel audio-visual multi-modal network that predicts PHQ-8 scores with an RMSE of 6.51.
Virtual health counselors offer the potential to provide users with information and counseling in complex areas such as disease management and health education. However, ensuring user engagement is challenging, particularly when the volume of information and length of counseling sessions increase. Agenda setting a clinical counseling technique where a patient and clinician collaboratively decide on session topics is an effective approach to tailoring discussions for individual patient needs and sustaining engagement. We explore the effectiveness of agenda setting in a virtual counselor system designed to counsel women for breast cancer genetic testing. In a between subjects study, we assessed three versions of the system with varying levels of user control in the system's agenda setting approach. We found that participants' knowledge improved across all conditions. Although our results showed that any type of agenda setting was perceived as useful, regardless of user control, interviews revealed a preference for more collaboration and user involvement in the agenda setting process. Our study highlights the importance of using patient-centered approaches, such as tailored discussions, when using virtual counselors in healthcare.
Human cancers present a significant public health challenge and require the discovery of novel drugs through translational research. Transcriptomics profiling data that describes molecular activities in tumors and cancer cell lines are widely utilized for predicting anti-cancer drug responses. However, existing AI models face challenges due to noise in transcriptomics data and lack of biological interpretability. To overcome these limitations, we introduce VETE (Variational and Explanatory Transcriptomics Encoder), a novel neural network framework that incorporates a variational component to mitigate noise effects and integrates traceable gene ontology into the neural network architecture for encoding cancer transcriptomics data. Key innovations include a local interpretability-guided method for identifying ontology paths, a visualization tool to elucidate biological mechanisms of drug responses, and the application of centralized large scale hyperparameter optimization. VETE demonstrated robust accuracy in cancer cell line classification and drug response prediction. Additionally, it provided traceable biological explanations for both tasks and offers insights into the mechanisms underlying its predictions. VETE bridges the gap between AI-driven predictions and biologically meaningful insights in cancer research, which represents a promising advancement in the field.
Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body's interaction with electromagnetic signals. Current Wi-Fi sensing applications predominantly employ data-driven learning techniques to associate the fluctuations in the physical properties of the communication channel with the human activity causing them. However, these techniques often lack the desired flexibility and transparency. This paper introduces DeepProbHAR, a neuro-symbolic architecture for Wi-Fi sensing, providing initial evidence that Wi-Fi signals can differentiate between simple movements, such as leg or arm movements, which are integral to human activities like running or walking. The neuro-symbolic approach affords gathering such evidence without needing additional specialised data collection or labelling. The training of DeepProbHAR is facilitated by declarative domain knowledge obtained from a camera feed and by fusing signals from various antennas of the Wi-Fi receivers. DeepProbHAR achieves results comparable to the state-of-the-art in human activity recognition. Moreover, as a by-product of the learning process, DeepProbHAR generates specialised classifiers for simple movements that match the accuracy of models trained on finely labelled datasets, which would be particularly costly.
Understanding causality helps to structure interventions to achieve specific goals and enables predictions under interventions. With the growing importance of learning causal relationships, causal discovery tasks have transitioned from using traditional methods to infer potential causal structures from observational data to the field of pattern recognition involved in deep learning. The rapid accumulation of massive data promotes the emergence of causal search methods with brilliant scalability. Existing summaries of causal discovery methods mainly focus on traditional methods based on constraints, scores and FCMs, there is a lack of perfect sorting and elaboration for deep learning-based methods, also lacking some considers and exploration of causal discovery methods from the perspective of variable paradigms. Therefore, we divide the possible causal discovery tasks into three types according to the variable paradigm and give the definitions of the three tasks respectively, define and instantiate the relevant datasets for each task and the final causal model constructed at the same time, then reviews the main existing causal discovery methods for different tasks. Finally, we propose some roadmaps from different perspectives for the current research gaps in the field of causal discovery and point out future research directions.
Molecular design and synthesis planning are two critical steps in the process of molecular discovery that we propose to formulate as a single shared task of conditional synthetic pathway generation. We report an amortized approach to generate synthetic pathways as a Markov decision process conditioned on a target molecular embedding. This approach allows us to conduct synthesis planning in a bottom-up manner and design synthesizable molecules by decoding from optimized conditional codes, demonstrating the potential to solve both problems of design and synthesis simultaneously. The approach leverages neural networks to probabilistically model the synthetic trees, one reaction step at a time, according to reactivity rules encoded in a discrete action space of reaction templates. We train these networks on hundreds of thousands of artificial pathways generated from a pool of purchasable compounds and a list of expert-curated templates. We validate our method with (a) the recovery of molecules using conditional generation, (b) the identification of synthesizable structural analogs, and (c) the optimization of molecular structures given oracle functions relevant to drug discovery.
Detecting carried objects is one of the requirements for developing systems to reason about activities involving people and objects. We present an approach to detect carried objects from a single video frame with a novel method that incorporates features from multiple scales. Initially, a foreground mask in a video frame is segmented into multi-scale superpixels. Then the human-like regions in the segmented area are identified by matching a set of extracted features from superpixels against learned features in a codebook. A carried object probability map is generated using the complement of the matching probabilities of superpixels to human-like regions and background information. A group of superpixels with high carried object probability and strong edge support is then merged to obtain the shape of the carried object. We applied our method to two challenging datasets, and results show that our method is competitive with or better than the state-of-the-art.
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