OpenRaiser/Intern-Atlas huggingface dataset:
Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review
Venue: arXiv cs.CL 2023
Year: 2023

Background: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. Methodology: After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: 1) medical note classification, 2) clinical entity recognition, 3) text summarisation, 4) deep learning (DL) and transfer learning architecture, 5) information extraction, 6) Medical language translation and 7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Result and Discussion: EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. Conclusion: We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification.
Combination of Measurement Data and Domain Knowledge for Simulation of Halbach Arrays with Bayesian Inference
Venue: arXiv cs.CE 2023
Year: 2023

Accelerator magnets made from blocks of permanent magnets in a zero-clearance configuration are known as Halbach arrays. The objective of this work is the fusion of knowledge from different measurement sources (material and field) and domain knowledge (magnetostatics) to obtain an updated magnet model of a Halbach array. From Helmholtz-coil measurements of the magnetized blocks, a prior distribution of the magnetization is estimated. Measurements of the magnetic flux density are used to derive, by means of Bayesian inference, a posterior distribution. The method is validated on simulated data and applied to measurements of a dipole of the FASER detector. The updated magnet model of the FASER dipole describes the magnetic flux density one order of magnitude better than the prior magnet model.
Data-Driven Update of B(H) Curves of Iron Yokes in Normal Conducting Accelerator Magnets
Venue: arXiv cs.CE 2023
Year: 2023

Constitutive equations are used in electromagnetic field simulations to model a material response to applied fields or forces. The $B(H)$ characteristic of iron laminations depends on thermal and mechanical stresses that may have occurred during the manufacturing process. Data-driven modelling and updating of the $B(H)$ characteristic are therefore well known necessities. In this work the $B(H)$ curve of an iron yoke of an accelerator magnet is updated based on observed magnetic flux density data by solving a non-linear inverse problem. The inverse problem is regularized by restricting the solution to the function space that is spanned by the truncated Karhunen Loeve expansion of a stochastic $B(H)$-curve model based on material measurements. It is shown that this method is able to retrieve a previously selected ground truth $B(H)$-curve. With the update of the $B(H)$ characteristic, the numerical model gains predictive capacities for excitation currents that were not included in the data.
Multi-Objective Hull Form Optimization with CAD Engine-based Deep Learning Physics for 3D Flow Prediction
Venue: arXiv cs.LG 2023
Year: 2023

In this work, we propose a built-in Deep Learning Physics Optimization (DLPO) framework to set up a shape optimization study of the Duisburg Test Case (DTC) container vessel. We present two different applications: (1) sensitivity analysis to detect the most promising generic basis hull shapes, and (2) multi-objective optimization to quantify the trade-off between optimal hull forms. DLPO framework allows for the evaluation of design iterations automatically in an end-to-end manner. We achieved these results by coupling Extrality's Deep Learning Physics (DLP) model to a CAD engine and an optimizer. Our proposed DLP model is trained on full 3D volume data coming from RANS simulations, and it can provide accurate and high-quality 3D flow predictions in real-time, which makes it a good evaluator to perform optimization of new container vessel designs w.r.t the hydrodynamic efficiency. In particular, it is able to recover the forces acting on the vessel by integration on the hull surface with a mean relative error of 3.84\% \pm 2.179\% on the total resistance. Each iteration takes only 20 seconds, thus leading to a drastic saving of time and engineering efforts, while delivering valuable insight into the performance of the vessel, including RANS-like detailed flow information. We conclude that DLPO framework is a promising tool to accelerate the ship design process and lead to more efficient ships with better hydrodynamic performance.
Design Considerations and Robustness to Parameter Uncertainty in Wire-Wrapped Cam Mechanisms
Venue: arXiv cs.RO 2023
Year: 2023

Collaborative robots must simultaneously be safe enough to operate in close proximity to human operators and powerful enough to assist users in industrial tasks such as lifting heavy equipment. The requirement for safety necessitates that collaborative robots are designed with low-powered actuators. However, some industrial tasks may require the robot to have high payload capacity and/or long reach. For collaborative robot designs to be successful, they must find ways of addressing these conflicting design requirements. One promising strategy for navigating this tradeoff is through the use of static balancing mechanisms to offset the robot's self weight, thus enabling the selection of lower-powered actuators. In this paper, we introduce a novel, 2 degree of freedom static balancing mechanism based on spring-loaded, wire-wrapped cams. We also present an optimization-based cam design method that guarantees the cams stay convex, ensures the springs stay below their extensions limits, and minimizes sensitivity to unmodeled deviations from the nominal spring constant. Additionally, we present a model of the effect of friction between the wire and the cam. Lastly, we show experimentally that the torque generated by the cam mechanism matches the torque predicted in our modeling approach. Our results also suggest that the effects of wire-cam friction are significant for non-circular cams.
Designing Individualized Policy and Technology Interventions to Improve Gig Work Conditions
Venue: arXiv cs.HC 2023
Year: 2023

The gig economy is characterized by short-term contract work completed by independent workers who are paid to perform "gigs", and who have control over when, whether and how they conduct work. Gig economy platforms (e.g., Uber, Lyft, Instacart) offer workers increased job opportunities, lower barriers to entry, and improved flexibility. However, growing evidence suggests that worker well-being and gig work conditions have become significant societal issues. In designing public-facing policies and technologies for improving gig work conditions, inherent tradeoffs exist between offering individual flexibility and when attempting to meet all community needs. In platform-based gig work, contractors pursue the flexibility of short-term tasks, but policymakers resist segmenting the population when designing policies to support their work. As platforms offer an ever-increasing variety of services, we argue that policymakers and platform designers must provide more targeted and personalized policies, benefits, and protections for platform-based workers, so that they can lead more successful and sustainable gig work careers. We present in this paper relevant legal and scholarly evidence from the United States to support this position, and make recommendations for future innovations in policy and technology.
Apolitical Intelligence? Auditing Delphi's responses on controversial political issues in the US
Venue: arXiv cs.CY 2023
Year: 2023

As generative language models are deployed in ever-wider contexts, concerns about their political values have come to the forefront with critique from all parts of the political spectrum that the models are biased and lack neutrality. However, the question of what neutrality is and whether it is desirable remains underexplored. In this paper, I examine neutrality through an audit of Delphi [arXiv:2110.07574], a large language model designed for crowdsourced ethics. I analyse how Delphi responds to politically controversial questions compared to different US political subgroups. I find that Delphi is poorly calibrated with respect to confidence and exhibits a significant political skew. Based on these results, I examine the question of neutrality from a data-feminist lens, in terms of how notions of neutrality shift power and further marginalise unheard voices. These findings can hopefully contribute to a more reflexive debate about the normative questions of alignment and what role we want generative models to play in society.
GT-TSCH: Game-Theoretic Distributed TSCH Scheduler for Low-Power IoT Networks
Venue: arXiv cs.NI 2023
Year: 2023

Time-Slotted Channel Hopping (TSCH) is a synchronous medium access mode of the IEEE 802.15.4e standard designed for providing low-latency and highly-reliable end-to-end communication. TSCH constructs a communication schedule by combining frequency channel hopping with Time Division Multiple Access (TDMA). In recent years, IETF designed several standards to define general mechanisms for the implementation of TSCH. However, the problem of updating the TSCH schedule according to the changes of the wireless link quality and node's traffic load left unresolved. In this paper, we use non-cooperative game theory to propose GT-TSCH, a distributed TSCH scheduler designed for low-power IoT applications. By considering selfish behavior of nodes in packet forwarding, GT-TSCH updates the TSCH schedule in a distributed approach with low control overhead by monitoring the queue length, the place of the node in the Directed Acyclic Graph (DAG) topology, the quality of the wireless link, and the data packet generation rate. We prove the existence and uniqueness of Nash equilibrium in our game model and we find the optimal number of TSCH Tx timeslots to update the TSCH slotframe. To examine the performance of our contribution, we implement GT-TSCH on Zolertia Firefly IoT motes and the Contiki-NG Operating System (OS). The evaluation results reveal that GT-TSCH improves performance in terms of throughput and end-to-end delay compared to the state-of-the-art method.
Auditing Predictive Models for Intersectional Biases
Venue: arXiv cs.LG 2023
Year: 2023

Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we propose Conditional Bias Scan (CBS), a flexible auditing framework for detecting intersectional biases in classification models. CBS identifies the subgroup for which there is the most significant bias against the protected class, as compared to the equivalent subgroup in the non-protected class, and can incorporate multiple commonly used fairness definitions for both probabilistic and binarized predictions. We show that this methodology can detect previously unidentified intersectional and contextual biases in the COMPAS pre-trial risk assessment tool and has higher bias detection power compared to similar methods that audit for subgroup fairness.
An overview on the evaluated video retrieval tasks at TRECVID 2022
Venue: arXiv cs.AI 2023
Year: 2023

The TREC Video Retrieval Evaluation (TRECVID) is a TREC-style video analysis and retrieval evaluation with the goal of promoting progress in research and development of content-based exploitation and retrieval of information from digital video via open, tasks-based evaluation supported by metrology. Over the last twenty-one years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID has been funded by NIST (National Institute of Standards and Technology) and other US government agencies. In addition, many organizations and individuals worldwide contribute significant time and effort. TRECVID 2022 planned for the following six tasks: Ad-hoc video search, Video to text captioning, Disaster scene description and indexing, Activity in extended videos, deep video understanding, and movie summarization. In total, 35 teams from various research organizations worldwide signed up to join the evaluation campaign this year. This paper introduces the tasks, datasets used, evaluation frameworks and metrics, as well as a high-level results overview.
Anticipatory Thinking Challenges in Open Worlds: Risk Management
Venue: arXiv cs.AI 2023
Year: 2023

Anticipatory thinking drives our ability to manage risk - identification and mitigation - in everyday life, from bringing an umbrella when it might rain to buying car insurance. As AI systems become part of everyday life, they too have begun to manage risk. Autonomous vehicles log millions of miles, StarCraft and Go agents have similar capabilities to humans, implicitly managing risks presented by their opponents. To further increase performance in these tasks, out-of-distribution evaluation can characterize a model's bias, what we view as a type of risk management. However, learning to identify and mitigate low-frequency, high-impact risks is at odds with the observational bias required to train machine learning models. StarCraft and Go are closed-world domains whose risks are known and mitigations well documented, ideal for learning through repetition. Adversarial filtering datasets provide difficult examples but are laborious to curate and static, both barriers to real-world risk management. Adversarial robustness focuses on model poisoning under the assumption there is an adversary with malicious intent, without considering naturally occurring adversarial examples. These methods are all important steps towards improving risk management but do so without considering open-worlds. We unify these open-world risk management challenges with two contributions. The first is our perception challenges, designed for agents with imperfect perceptions of their environment whose consequences have a high impact. Our second contribution are cognition challenges, designed for agents that must dynamically adjust their risk exposure as they identify new risks and learn new mitigations. Our goal with these challenges is to spur research into solutions that assess and improve the anticipatory thinking required by AI agents to manage risk in open-worlds and ultimately the real-world.
Improving Log-Cumulant Based Estimation of Roughness Information in SAR imagery
Venue: arXiv cs.CV 2023
Year: 2023

Synthetic Aperture Radar (SAR) image understanding is crucial in remote sensing applications, but it is hindered by its intrinsic noise contamination, called speckle. Sophisticated statistical models, such as the $\mathcal{G}^0$ family of distributions, have been employed to SAR data and many of the current advancements in processing this imagery have been accomplished through extracting information from these models. In this paper, we propose improvements to parameter estimation in $\mathcal{G}^0$ distributions using the Method of Log-Cumulants. First, using Bayesian modeling, we construct that regularly produce reliable roughness estimates under both $\mathcal{G}^0_A$ and $\mathcal{G}^0_I$ models. Second, we make use of an approximation of the Trigamma function to compute the estimated roughness in constant time, making it considerably faster than the existing method for this task. Finally, we show how we can use this method to achieve fast and reliable SAR image understanding based on roughness information.
Fast Maximum $k$-Plex Algorithms Parameterized by Small Degeneracy Gaps
Venue: arXiv cs.DS 2023
Year: 2023

Given a graph, a $k$-plex is a set of vertices in which each vertex is not adjacent to at most $k-1$ other vertices in the set. The maximum $k$-plex problem, which asks for the largest $k$-plex from the given graph, is an important but computationally challenging problem in applications such as graph mining and community detection. So far, there are many practical algorithms, but without providing theoretical explanations on their efficiency. We define a novel parameter of the input instance, $g_k(G)$, the gap between the degeneracy bound and the size of the maximum $k$-plex in the given graph, and present an exact algorithm parameterized by this $g_k(G)$, which has a worst-case running time polynomial in the size of the input graph and exponential in $g_k(G)$. In real-world inputs, $g_k(G)$ is very small, usually bounded by $O(\log{(|V|)})$, indicating that the algorithm runs in polynomial time. We further extend our discussion to an even smaller parameter $cg_k(G)$, the gap between the community-degeneracy bound and the size of the maximum $k$-plex, and show that without much modification, our algorithm can also be parameterized by $cg_k(G)$. To verify the empirical performance of these algorithms, we carry out extensive experiments to show that these algorithms are competitive with the state-of-the-art algorithms. In particular, for large $k$ values such as $15$ and $20$, our algorithms dominate the existing algorithms. Finally, empirical analysis is performed to illustrate the effectiveness of the parameters and other key components in the implementation.
Learning Scene Flow With Skeleton Guidance For 3D Action Recognition
Venue: arXiv cs.CV 2023
Year: 2023

Among the existing modalities for 3D action recognition, 3D flow has been poorly examined, although conveying rich motion information cues for human actions. Presumably, its susceptibility to noise renders it intractable, thus challenging the learning process within deep models. This work demonstrates the use of 3D flow sequence by a deep spatiotemporal model and further proposes an incremental two-level spatial attention mechanism, guided from skeleton domain, for emphasizing motion features close to the body joint areas and according to their informativeness. Towards this end, an extended deep skeleton model is also introduced to learn the most discriminant action motion dynamics, so as to estimate an informativeness score for each joint. Subsequently, a late fusion scheme is adopted between the two models for learning the high level cross-modal correlations. Experimental results on the currently largest and most challenging dataset NTU RGB+D, demonstrate the effectiveness of the proposed approach, achieving state-of-the-art results.
On minimum $t$-claw deletion in split graphs
Venue: arXiv cs.DS 2023
Year: 2023

For $t\geq 3$, $K_{1, t}$ is called $t$-claw. In minimum $t$-claw deletion problem (\texttt{Min-$t$-Claw-Del}), given a graph $G=(V, E)$, it is required to find a vertex set $S$ of minimum size such that $G[V\setminus S]$ is $t$-claw free. In a split graph, the vertex set is partitioned into two sets such that one forms a clique and the other forms an independent set. Every $t$-claw in a split graph has a center vertex in the clique partition. This observation motivates us to consider the minimum one-sided bipartite $t$-claw deletion problem (\texttt{Min-$t$-OSBCD}). Given a bipartite graph $G=(A \cup B, E)$, in \texttt{Min-$t$-OSBCD} it is asked to find a vertex set $S$ of minimum size such that $G[V \setminus S]$ has no $t$-claw with the center vertex in $A$. A primal-dual algorithm approximates \texttt{Min-$t$-OSBCD} within a factor of $t$. We prove that it is $\UGC$-hard to approximate with a factor better than $t$. We also prove it is approximable within a factor of 2 for dense bipartite graphs. By using these results on \texttt{Min-$t$-OSBCD}, we prove that \texttt{Min-$t$-Claw-Del} is $\UGC$-hard to approximate within a factor better than $t$, for split graphs. We also consider their complementary maximization problems and prove that they are $\APX$-complete.
Synchronization of primitive automata
Venue: arXiv cs.FL 2023
Year: 2023

We exhibit new conditions under which a primitive automaton is synchronizing. In particular, we show that the primitivity of an automaton forces its synchronizability whenever the automaton has either a letter of defect 1 or a word of rank 2.
Lesion Detection on Leaves using Class Activation Maps
Venue: arXiv cs.CV 2023
Year: 2023

Lesion detection on plant leaves is a critical task in plant pathology and agricultural research. Identifying lesions enables assessing the severity of plant diseases and making informed decisions regarding disease control measures and treatment strategies. To detect lesions, there are studies that propose well-known object detectors. However, training object detectors to detect small objects such as lesions can be problematic. In this study, we propose a method for lesion detection on plant leaves utilizing class activation maps generated by a ResNet-18 classifier. In the test set, we achieved a 0.45 success rate in predicting the locations of lesions in leaves. Our study presents a novel approach for lesion detection on plant leaves by utilizing CAMs generated by a ResNet classifier while eliminating the need for a lesion annotation process.
Co-creating a globally interpretable model with human input
Venue: arXiv cs.HC 2023
Year: 2023

We consider an aggregated human-AI collaboration aimed at generating a joint interpretable model. The model takes the form of Boolean decision rules, where human input is provided in the form of logical conditions or as partial templates. This focus on the combined construction of a model offers a different perspective on joint decision making. Previous efforts have typically focused on aggregating outcomes rather than decisions logic. We demonstrate the proposed approach through two examples and highlight the usefulness and challenges of the approach.
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning
Venue: arXiv cs.LG 2023
Year: 2023

Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected and labeled datasets, which eliminates the time-consuming data collection in online RL. However, offline RL still bears a large burden of specifying/handcrafting extrinsic rewards for each transition in the offline data. As a remedy for the labor-intensive labeling, we propose to endow offline RL tasks with a few expert data and utilize the limited expert data to drive intrinsic rewards, thus eliminating the need for extrinsic rewards. To achieve that, we introduce \textbf{C}alibrated \textbf{L}atent g\textbf{U}idanc\textbf{E} (CLUE), which utilizes a conditional variational auto-encoder to learn a latent space such that intrinsic rewards can be directly qualified over the latent space. CLUE's key idea is to align the intrinsic rewards consistent with the expert intention via enforcing the embeddings of expert data to a calibrated contextual representation. We instantiate the expert-driven intrinsic rewards in sparse-reward offline RL tasks, offline imitation learning (IL) tasks, and unsupervised offline RL tasks. Empirically, we find that CLUE can effectively improve the sparse-reward offline RL performance, outperform the state-of-the-art offline IL baselines, and discover diverse skills from static reward-free offline data.