Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction

The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist healthcare professionals in clinical decision-making. Existing methods focus on measuring the similarity between patients using deep neural networks to capture the hidden feature structures. However, the higher-order relationships are ignored, such as patient characteristics (e.g., diagnosis codes) and their causal effects on downstream clinical predictions. In this paper, we propose a novel Hypergraph Convolutional Network that allows the representation of non-pairwise relationships among diagnosis codes in a hypergraph to capture the hidden feature structures so that fine-grained patient similarity can be calculated for personalized mortality risk prediction. Evaluation using a publicly available eICU Collaborative Research Database indicates that our method achieves superior performance over the state-of-the-art models on mortality risk prediction. Moreover, the results of several case studies demonstrated the effectiveness and robustness of the model decisions.
Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning

In this paper, we investigate the operation of an aerial manipulator system, namely an Unmanned Aerial Vehicle (UAV) equipped with a controllable arm with two degrees of freedom to carry out actuation tasks on the fly. Our solution is based on employing a Q-learning method to control the trajectory of the tip of the arm, also called end-effector. More specifically, we develop a motion planning model based on Time To Collision (TTC), which enables a quadrotor UAV to navigate around obstacles while ensuring the manipulator's reachability. Additionally, we utilize a model-based Q-learning model to independently track and control the desired trajectory of the manipulator's end-effector, given an arbitrary baseline trajectory for the UAV platform. Such a combination enables a variety of actuation tasks such as high-altitude welding, structural monitoring and repair, battery replacement, gutter cleaning, skyscrapper cleaning, and power line maintenance in hard-to-reach and risky environments while retaining compatibility with flight control firmware. Our RL-based control mechanism results in a robust control strategy that can handle uncertainties in the motion of the UAV, offering promising performance. Specifically, our method achieves 92% accuracy in terms of average displacement error (i.e. the mean distance between the target and obtained trajectory points) using Q-learning with 15,000 episodes
Fall Detection using Knowledge Distillation Based Long short-term memory for Offline Embedded and Low Power Devices

This paper presents a cost-effective, low-power approach to unintentional fall detection using knowledge distillation-based LSTM (Long Short-Term Memory) models to significantly improve accuracy. With a primary focus on analyzing time-series data collected from various sensors, the solution offers real-time detection capabilities, ensuring prompt and reliable identification of falls. The authors investigate fall detection models that are based on different sensors, comparing their accuracy rates and performance. Furthermore, they employ the technique of knowledge distillation to enhance the models' precision, resulting in refined accurate configurations that consume lower power. As a result, this proposed solution presents a compelling avenue for the development of energy-efficient fall detection systems for future advancements in this critical domain.
Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges

Deep reinforcement learning (DRL), leveraging Deep Learning (DL) in reinforcement learning, has shown significant potential in achieving human-level autonomy in a wide range of domains, including robotics, computer vision, and computer games. This potential justifies the enthusiasm and growing interest in DRL in both academia and industry. However, the community currently focuses mostly on the development phase of DRL systems, with little attention devoted to DRL deployment. In this paper, we propose an empirical study on Stack Overflow (SO), the most popular Q&A forum for developers, to uncover and understand the challenges practitioners faced when deploying DRL systems. Specifically, we categorized relevant SO posts by deployment platforms: server/cloud, mobile/embedded system, browser, and game engine. After filtering and manual analysis, we examined 357 SO posts about DRL deployment, investigated the current state, and identified the challenges related to deploying DRL systems. Then, we investigate the prevalence and difficulty of these challenges. Results show that the general interest in DRL deployment is growing, confirming the study’s relevance and importance. Results also show that DRL deployment is more difficult than other DRL issues. Additionally, we built a taxonomy of 31 unique challenges in deploying DRL to different platforms. On all platforms, RL environment-related challenges are the most popular, and communication-related challenges are the most difficult among practitioners. We hope our study inspires future research and helps the community overcome the most common and difficult challenges practitioners face when deploying DRL systems.
Matter relative to quantum hypersurfaces

We explore the canonical description of a scalar field as a parameterized field theory on an extended phase space that includes additional embedding fields that characterize spacetime hypersurfaces $\mathsf{X}$ relative to which the scalar field is described. This theory is quantized via the Dirac prescription and physical states of the theory are used to define conditional wave functionals $|\psi_\phi[\mathsf{X}]\rangle$ interpreted as the state of the field relative to the hypersurface $\mathsf{X}$, thereby extending the Page-Wootters formalism to quantum field theory. It is shown that this conditional wave functional satisfies the Tomonaga-Schwinger equation, thus demonstrating the formal equivalence between this extended Page-Wootters formalism and standard quantum field theory. We also construct relational Dirac observables and define a quantum deparameterization of the physical Hilbert space leading to a relational Heisenberg picture, which are both shown to be unitarily equivalent to the Page-Wootters formalism. Moreover, by treating hypersurfaces as quantum reference frames, we extend recently developed quantum frame transformations to changes between classical and nonclassical hypersurfaces. This allows us to exhibit the transformation properties of a quantum field under a larger class of transformations, which leads to a frame-dependent particle creation effect.
On the Consistency of Average Embeddings for Item Recommendation

A prevalent practice in recommender systems consists of averaging item embeddings to represent users or higher-level concepts in the same embedding space. This paper investigates the relevance of such a practice. For this purpose, we propose an expected precision score, designed to measure the consistency of an average embedding relative to the items used for its construction. We subsequently analyze the mathematical expression of this score in a theoretical setting with specific assumptions, as well as its empirical behavior on real-world data from music streaming services. Our results emphasize that real-world averages are less consistent for recommendation, which paves the way for future research to better align real-world embeddings with assumptions from our theoretical setting.
Differences between journal and conference in computer science: a bibliometric view based on Bayesian network

Abstract Purpose This paper aims to investigate the differences between conference papers and journal papers in the field of computer science based on Bayesian network. Design/methodology/approach This paper investigated the differences between conference papers and journal papers in the field of computer science based on Bayesian network, a knowledge-representative framework that can model relationships among all variables in the network. We defined the variables required for Bayesian networks modeling, calculated the values of each variable based Aminer dataset (a literature data set in the field of computer science), learned the Bayesian network and derived some findings based on network inference. Findings The study found that conferences are more attractive to senior scholars, the academic impact of conference papers is slightly higher than journal papers, and it is uncertain whether conference papers are more innovative than journal papers. Research limitations The study was limited to the field of computer science and employed Aminer dataset as the sample. Further studies involving more diverse datasets and different fields could provide a more complete picture of the matter. Practical implications By demonstrating that Bayesian networks can effectively analyze issues in Scientometrics, the study offers valuable insights that may enhance researchers’ understanding of the differences between journal and conference in computer science. Originality/value Academic conferences play a crucial role in facilitating scholarly exchange and knowledge dissemination within the field of computer science. Several studies have been conducted to examine the distinctions between conference papers and journal papers in terms of various factors, such as authors, citations, h-index and others. Those studies were carried out from different (independent) perspectives, lacking a systematic examination of the connections and interactions between multiple perspectives. This paper supplements this deficiency based on Bayesian network modeling.
Event-based Social Networks : Linking the Online and Offline Social Worlds

With the rapid growth of event-based social networks (EBSNs) like Meetup, the demand for event recommendation becomes increasingly urgent. In EBSNs, event recommendation plays a central role in recommending the most relevant events to users who are likely to participate in. Different from traditional recommendation problems, event recommendation encounters three new types of information, i.e., heterogenous online+offline social relationships, geographical features of events and implicit rating data from users. Yet combining the three types of data for offline event recommendation has not been considered. Therefore, we present a Bayesian latent factor model that can unify these data for event recommendation. Experimental results on real-world data sets show the performance of our method. Introduction Recent years have witnessed increased development and popularity of event-based social networks (EBSNs), such as Meetup, Plancast and Douban Event. The services allow users to organize, participate, comment and share events such as cocktail parties, seminars and concerts. To enhance user experience on these services, event recommendation systems are studied lately that aims to recommend the most relevant events to users who are likely to participate. The recommendation provides convenience to both event organizers and participants. For the participants, they can easily find the events they are interested in. For the organizers, their events can attract potential users who share similar interests. e1 e2 e3 e4 . e|E| 1 e|E| u1 0 0 0 0 . 1 1 u2 1 0 0 1 . 1 0 . u|U| 1 0 0 1 1 . 0 1 u|U| 0 1 0 0 . 0 0 Figure 1: An illustration of user-event ratings. Different from existing recommendation problems, event recommendation in EBSNs meets new characteristics. Copyright c 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Event-based Social Networks: Linking the Online and Offline Social Worlds Xingjie Liu? , Qi He†, Yuanyuan Tian†, Wang-Chien Lee? , John McPherson†, Jiawei Han⇧ ? The Pennsylvania State University, † IBM Almaden Research Center, ⇧ University of Illinois at Urbana-Champaign ? {xzl106, wlee}@cse.psu. edu, † {heq, ytian, jmcphers}@us.ibm. com,⇧ hanj@cs.uiuc. edu
Considering Distributed Processing in the Query Optimizer

Distributed database systems gain relevance both in industry and academia. However, existing research on query optimization for relational database systems focuses largely on systems running on a single machine. Work on distributed systems neglects available workload information in database systems. In this work, we present optimization strategies to fully leverage the potential of distributed systems running on modern cloud architectures with fast networks. We focus on the optimal assignment of tasks to compute nodes and the joint optimization of join ordering and distribution layout of data. Furthermore, we introduce distributed plans and simulation-based evaluations using a new cost model for computation time.
Improving Data Minimization through Decentralized Data Architectures

In this research project, we investigate an alternative to the standard cloud-centralized data architecture. Specifically, we aim to leave part of the application data under the control of the individual data owners in decentralized personal data stores. Our primary goal is to increase data minimization, i. e. , enabling more sensitive personal data to be under the control of its owners while providing a straightforward and efficient framework to design architectures that allow applications to run and data to be analyzed. To serve this purpose, the centralized part of the schema contains aggregating views over this decentralized data. We propose to design a declarative language that extends SQL, for architects to specify different kinds of tables and views at the schema level, along with sensitive columns and their minimum granularity level of their aggregations. Local updates need to be reflected in the centralized views while ensuring privacy throughout intermediate calculations; for this we pursue the integration of distributed materialized view maintenance and multi-party computation (MPC) techniques. We finally aim to implement this system, where the personal data stores could either live in mobile devices or encrypted cloud storage, in order to evaluate its performance properties.
Systematic Review of Environmental and Psychosocial Risk Factors associated with Attention Deficit Hyperactivity Disorder, Oppositional Defiant Disorder, and Conduct Disorder in Children and Adolescents

Abstract Background In the majority of cases, attention deficit hyperactivity disorder (ADHD) is accompanied by one or more comorbid disorders, with the oppositional defiant disorder (ODD) being one of the most frequently diagnosed comorbid disorders. There is a lack of systematic reviews addressing the evidence for an association between the independent environmental and psychosocial risk factors associated with ADHD, ODD, and Conduct Disorder (CD). Objective This study aims to determine the link between ADHD and ODD/CD, specifically in terms of the most up-to-date environmental and psychosocial risk factors in the development of these illnesses. Results Eleven studies were included in this systematic review. Among these, ten studies involved environmental risk factors, and only one involved socioeconomic risk factors as exposure. Of the ten studies highlighting the environmental risk factors, six studies reported perinatal risk factors, three reported Atopic diseases as exposure measures, and one involved exposure to energy and coffee drinks. We have found that the most common risk factors associated with ADHD, ODD and CD in Europe and North America were Perinatal risk factors. In contrast, the risk factors of Atopic diseases were more common in Asia. Conclusions Most of the studies included in our systematic review fall within the scope of environmental risk factors were perinatal risk factors and atopic diseases are the most common risk factors. However, only one article highlighted the association of socioeconomic risk factors as an exposure. Our review results suggest the need for more research focused on psychosocial risk factors for ADHD and comorbid ODD/CD. Further research is required with the primary objective of investigating this association in greater depth and examining the possible mechanisms at varying levels is needed.
Computing Provenance Using the Negated Chase

Since different challenges of data processing are interconnected, we describe them in a unified manner using a classic algorithm of database theory: the Chase. Computing the origin of query results is one of the challenges considered in this research project. Previously, the Chase has been used to calculate why-provenance of simple conjunctive queries. However, applying the Chase to more realistic scenarios requires an extension of the algorithm, for example with negation. This work reveals opportunities for the extended Chase by calculating both why-and why-not provenance of conjunctive queries with negation.
Machine Learning for Arterial Blood Pressure Prediction

High blood pressure is a major risk factor for cardiovascular disease, necessitating accurate blood pressure (BP) measurement. Clinicians measure BP with an invasive arterial catheter or via a non-invasive arm or finger cuff. However, the former can cause discomfort to the patient and is unsuitable outside Intensive Care Unit (ICU). While cuff-based devices, despite being non-invasive, fails to provide continuous measurement, and they measure from peripheral blood vessels whose BP waveforms differ significantly from those proximal to the heart. Hence, there is an urgent need to develop a measurement protocol for converting easily measured non-invasive data into accurate BP values. Addressing this gap, we propose a non-invasive approach to predict BP from arterial area and blood flow velocity signals measured from a Philips ultrasound transducer (XL-143) applied to large arteries close to heart. We developed the protocol and collected data from 72 subjects. The shape of BP (relative BP) can be theoretically calculated from these wave-forms,
TAYSIR Competition: Transformer+RNN: Algorithms to Yield Simple and Interpretable Representations

This article presents the content of the competition Transformers+ rnn : Algorithms to Yield Simple and Interpretable Representations (TAYSIR, the Arabic word for ‘simple’), which was an on-line challenge on extracting simpler models from already trained neural networks held in Spring 2023. These neural nets were trained on sequential catego-rial/symbolic data. Some of these data were artificial, some came from real world problems (such as Natural Language Processing, Bioinformatics, and Software Engineering). The trained models covered a large spectrum of architectures, from Simple Recurrent Neural Network (SRN) to Transformers, including Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM). No constraint was given on the surrogate models submitted by the participants: any model working on sequential data was accepted. Two tracks were proposed: neural networks trained on Binary Classification tasks, and on Language Modeling tasks. The evaluation of the surrogate models took into account both the simplicity of the extracted model and the quality of the approximation of the original model.
Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding

In this work, we show how to combine two popular paradigms for causal discovery from observational data in the presence of latent confounders in order to arrive at a much more informative causal model. Building on the seminal constraint-based causal discovery algorithm, FCI, we exploit the power of direct cause-effect pair identification to uncover new relationships, which can subsequently be propagated to find even more causal links in the rest of the model. This idea has been explored before, but until now always under the assumption of no latent confounders. Using our new causal direction criterion (CDC), we can finally drop this limitation. We derive inference rules for orienting additional cause-effect relations and show how to minimize the number of tests during the CDC search. In our experimental evaluations over a range of simulated data sets, the resulting FCI-CDC algorithm increases recall by between 5%-10% compared to vanilla FCI, without loss in precision.
Beady: interactive beadwork design and construction

Beadwork is the art of connecting beads together by wires. While common beadwork is two-dimensional (2D), three-dimensional (3D) beadwork is also popular in oriental regions such as Japan and China. However, the design and construction of 3D beadwork is very difficult. The final shape is defined by the complicated three-dimensional interaction between beads and wires, thus making it very difficult to design manually. One also needs to specify an appropriate wire path to hold the beads together and to manually insert the wire into the beads one by one following the path to construct the beadwork. Careful observation of existing beadwork structures shows several geometrically interesting problems, which make beadwork design an interesting technical challenge.
Counterfactually Guided Policy Transfer in Clinical Settings

Reliably transferring treatment policies learned in one clinical environment to another is currently limited by challenges related to domain shift. In this paper we address off-policy learning for sequential decision making under domain shift -- a scenario susceptible to catastrophic overconfidence -- which is highly relevant to a high-stakes clinical settings where the target domain may also be data-scarce. We propose a two-fold counterfactual regularization procedure to improve off-policy learning, addressing domain shift and data scarcity. First, we utilize an informative prior derived from a data-rich source environment to indirectly improve drawing counterfactual example observations. Then, these samples are then used to learn a policy for the target domain, regularized by the source policy through KL-divergence. In simulated sepsis treatment, our counterfactual policy transfer procedure significantly improves the performance of a learned treatment policy.
Fair and Private Data Preprocessing through Microaggregation

Privacy protection for personal data and fairness in automated decisions are fundamental requirements for responsible Machine Learning. Both may be enforced through data preprocessing and share a common target: data should remain useful for a task, while becoming uninformative of the sensitive information. The intrinsic connection between privacy and fairness implies that modifications performed to guarantee one of these goals, may have an effect on the other, e.g., hiding a sensitive attribute from a classification algorithm might prevent a biased decision rule having such attribute as a criterion. This work resides at the intersection of algorithmic fairness and privacy. We show how the two goals are compatible, and may be simultaneously achieved, with a small loss in predictive performance. Our results are competitive with both state-of-the-art fairness correcting algorithms and hybrid privacy-fairness methods. Experiments were performed on three widely used benchmark datasets: Adult Income, COMPAS, and German Credit.