pwc-archive/papers-with-abstracts huggingface dataset:
Unambiguosly expressing expectations about the content of prokaryotic genomes
Authors: Giorgio Gonnella
Date: 2023-02-06 00:00:00
Tasks: Articles

In recent years, the sequencing, assembling and annotation of prokaryotic genomes has become increasingly easy and cheap. Thus it becomes increasingly feasible and interesting to perform comparative genomics analyses of new genomes to those of related organisms. Thereby related organisms can be defined by different criteria, such as taxonomy or phenotype. Expectations regarding the contents of genomes are often expressed in scientific articles describing group of organisms. Evaluating such expectations, when a new genome becomes available, requires analysing the text snippets which express such expectations, extracting the logical elements of the text and enabling a formal expression, more suitable for further automated analyses. Hereby we present a theoretical framework, alongside practical consideration for expressing expectations about the content of genomes, with the purpose of enabling such comparative genomics analyses. The components of the framework include a system for the definition of groups of organisms, supported by a Prokaryotic Group Types Ontology, a system for the definition of genomic contents, supported by a Prokaryotic Genomic Contents Definition Ontology. Finally we discuss how the combination of these two systems may enable an unambiguous definition of absolute and relative genome content expectation rules.
Shapeception: Unravelling Brain Activity during Animated Shape Perception and Mentalization
Authors: Varad Srivastava, Minaxi Goel
Date: 2023-02-06 00:00:00

In this paper, we investigate the brain activity elicited during perception of animated shapes as stimuli, which have been found to evoke mental state attributions. Contrary to a previous study, we incorporated the participants' responses in the analysis, and observed robust activations in mPFC, which has been found to play an important role in understanding other's and one's own nature. From our analyses, TPOj was observed showing robust activation during the task as well as functionally connected to AA and LTC, which lead to speculation that empathy might co-occur with mentalizing in the task and that humans might be able to empathize with these interacting shapes, in spite of lacking human features. Along with this, in one of our analyses, we were able to localize a region close to the pSTS, where the activation depicted the participants' 'ability to mentalize'. Based on our observations, we modelled the prediction of mentalization and propose our model as an approach towards developing a brain-activity based model to detect ToM (Theory of Mind) difficulties, which could be useful in research about disorders like Autism Spectrum Disorder (ASD) as well as assessment of mentalization-based treatments. Additionally, we use our findings to reiterate how the Resting State might not always act as a good control condition and that control conditions should be task-specific.
Principles and Guidelines for Sharing Biomedical Data for Secondary Use: The University of Chicago Perspective
Authors: Robert L. Grossman, Maryellen L. Giger, Julie A. Johnson, Jeremy D. Marks, Jessica P. Ridgway, Julian Solway, Walter M. Stadler
Date: 2023-02-05 00:00:00

Academic medical centers are generating an increasing amount of biomedical data and there is an increasing demand for biomedical data for research purposes by research projects, research consortia, companies, and other third parties. At the same time, as the number of patients grows and the amount of data per patient grows, there is an increasing possibility that some information about some patients may become available if the data is shared with third parties and the third parties have a data breach or violate the terms of the data use agreement. Balancing the importance of research that may result in improved patient outcomes with the importance of protecting patient data is challenging. The article discusses the principles, considerations about risks and mitigating risks, and guidelines used at the University of Chicago used for making decisions about sharing biomedical data with third parties.
Vaccination in a two-strain model with cross-immunity and antibody-dependent enhancement
Authors: Lorena C. Bulhosa, Juliane F. Oliveira
Date: 2023-02-04 00:00:00

Dengue and Zika incidence data and the latest research have raised questions about how dengue vaccine strategies might be impacted by the emergence of Zika virus. Existing antibodies to one virus might temporarily protect or promote infection by the other through antibody-dependent enhancement (ADE). With this condition, understanding the dynamics of propagation of these two viruses is of great importance when implementing vaccines. In this work, we analyze the effect of vaccination against one strain, in a two-strain model that accounts for cross-immunity and ADE. Using basic and invasion reproductive numbers, we examined the dynamics of the model and provide conditions to ensure the stability of the disease-free equilibrium. We provide conditions on cross-immunity, ADE and vaccination rate under which the vaccination could ensure the global stability of the disease-free equilibrium. The results indicate scenarios in which vaccination against one strain may improve or worsen the control of the other, as well as contribute to the eradication or persistence of one or both viruses in the population.
Facts of US Firm Scale and Growth 1970-2019: An Illustrated Guide
Authors: Robert Parham
Date: 2023-02-05 00:00:00

This work analyzes data on all public US firms in the 50 year period 1970-2019, and presents 18 stylized facts of their scale, income, growth, return, investment, and dynamism. Special attention is given to (i) identifying distributional forms; and (ii) scale effects -- systematic difference between firms based on their scale of operations. Notable findings are that the Difference-of-Log-Normals (DLN) distribution has a central role in describing firm data, scale-dependent heteroskedasticity is rampant, and small firms are systematically different from large firms.
The Fifteen Puzzle- A New Approach through Hybridizing Three Heuristics Methods
Authors: Dler O. Hasan, Aso M. Aladdin, Hardi Sabah Talabani, Tarik Ahmed Rashid, Seyedali Mirjalili
Date: 2023-01-06 00:00:00

Fifteen Puzzle problem is one of the most classical problems that have captivated mathematical enthusiasts for centuries. This is mainly because of the huge size of the state space with approximately 1013 states that have to be explored and several algorithms have been applied to solve the Fifteen Puzzle instances. In this paper, to deal with this large state space, Bidirectional A* (BA*) search algorithm with three heuristics, such as Manhattan distance (MD), linear conflict (LC), and walking distance (WD) has been used to solve the Fifteen Puzzle problems. The three mentioned heuristics will be hybridized in a way that can dramatically reduce the number of generated states by the algorithm. Moreover, all those heuristics require only 25KB of storage but help the algorithm effectively reduce the number of generated states and expand fewer nodes. Our implementation of BA* search can significantly reduce the space complexity, and guarantee either optimal or near-optimal solutions.1
A Mathematical Approach to Order Book Modeling
Authors: Frederic Abergel, Aymen Jedidi
Date: 2010-10-25 00:00:00

Motivated by the desire to bridge the gap between the microscopic description of price formation (agent-based modeling) and the stochastic differential equations approach used classically to describe price evolution at macroscopic time scales, we present a mathematical study of the order book as a multidimensional continuous-time Markov chain and derive several mathematical results in the case of independent Poissonian arrival times. In particular, we show that the cancellation structure is an important factor ensuring the existence of a stationary distribution and the exponential convergence towards it. We also prove, by means of the functional central limit theorem (FCLT), that the rescaled-centered price process converges to a Brownian motion. We illustrate the analysis with numerical simulation and comparison against market data.
Towards a Deeper Understanding of Concept Bottleneck Models Through End-to-End Explanation
Authors: Jack Furby, Daniel Cunnington, Dave Braines, Alun Preece
Date: 2023-02-07 00:00:00

Concept Bottleneck Models (CBMs) first map raw input(s) to a vector of human-defined concepts, before using this vector to predict a final classification. We might therefore expect CBMs capable of predicting concepts based on distinct regions of an input. In doing so, this would support human interpretation when generating explanations of the model's outputs to visualise input features corresponding to concepts. The contribution of this paper is threefold: Firstly, we expand on existing literature by looking at relevance both from the input to the concept vector, confirming that relevance is distributed among the input features, and from the concept vector to the final classification where, for the most part, the final classification is made using concepts predicted as present. Secondly, we report a quantitative evaluation to measure the distance between the maximum input feature relevance and the ground truth location; we perform this with the techniques, Layer-wise Relevance Propagation (LRP), Integrated Gradients (IG) and a baseline gradient approach, finding LRP has a lower average distance than IG. Thirdly, we propose using the proportion of relevance as a measurement for explaining concept importance.
Saliency-Aware Neural Architecture Search
Authors: Ramtin Hosseini, Pengtao Xie
Date: 2022-11-01 00:00:00
Proceeding: NIPS 2022 11
Tasks: Neural Architecture Search

Recently a wide variety of NAS methods have been proposed and achieved considerable success in automatically identifying highly-performing architectures of
neural networks for the sake of reducing the reliance on human experts. Existing
NAS methods ignore the fact that different input data elements (e.g., image pixels)
have different importance (or saliency) in determining the prediction outcome.
They treat all data elements as being equally important and therefore lead to suboptimal performance. To address this problem, we propose an end-to-end framework
which dynamically detects saliency of input data, reweights data using saliency
maps, and searches architectures on saliency-reweighted data. Our framework is
based on four-level optimization, which performs four learning stages in a unified
way. At the first stage, a model is trained with its architecture tentatively fixed. At
the second stage, saliency maps are generated using the trained model. At the third
stage, the model is retrained on saliency-reweighted data. At the fourth stage, the
model is evaluated on a validation set and the architecture is updated by minimizing
the validation loss. Experiments on several datasets demonstrate the effectiveness
of our framework
Long Horizon Temperature Scaling
Authors: Andy Shih, Dorsa Sadigh, Stefano Ermon
Date: 2023-02-07 00:00:00
Tasks: Multiple-choice

Temperature scaling is a popular technique for tuning the sharpness of a model distribution. It is used extensively for sampling likely generations and calibrating model uncertainty, and even features as a controllable parameter to many large language models in deployment. However, autoregressive models rely on myopic temperature scaling that greedily optimizes the next token. To address this, we propose Long Horizon Temperature Scaling (LHTS), a novel approach for sampling from temperature-scaled joint distributions. LHTS is compatible with all likelihood-based models, and optimizes for the long horizon likelihood of samples. We derive a temperature-dependent LHTS objective, and show that finetuning a model on a range of temperatures produces a single model capable of generation with a controllable long horizon temperature parameter. We experiment with LHTS on image diffusion models and character/language autoregressive models, demonstrating advantages over myopic temperature scaling in likelihood and sample quality, and showing improvements in accuracy on a multiple choice analogy task by $10\%$.
Temporal Robustness against Data Poisoning
Authors: Wenxiao Wang, Soheil Feizi
Date: 2023-02-07 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Data Poisoning

Data poisoning considers cases when an adversary manipulates the behavior of machine learning algorithms through malicious training data. Existing threat models of data poisoning center around a single metric, the number of poisoned samples. In consequence, if attackers can poison more samples than expected with affordable overhead, as in many practical scenarios, they may be able to render existing defenses ineffective in a short time. To address this issue, we leverage timestamps denoting the birth dates of data, which are often available but neglected in the past. Benefiting from these timestamps, we propose a temporal threat model of data poisoning with two novel metrics, earliness and duration, which respectively measure how long an attack started in advance and how long an attack lasted. Using these metrics, we define the notions of temporal robustness against data poisoning, providing a meaningful sense of protection even with unbounded amounts of poisoned samples when the attacks are temporally bounded. We present a benchmark with an evaluation protocol simulating continuous data collection and periodic deployments of updated models, thus enabling empirical evaluation of temporal robustness. Lastly, we develop and also empirically verify a baseline defense, namely temporal aggregation, offering provable temporal robustness and highlighting the potential of our temporal threat model for data poisoning.
Auditing Gender Presentation Differences in Text-to-Image Models
Authors: Yanzhe Zhang, Lu Jiang, Greg Turk, Diyi Yang
Date: 2023-02-07 00:00:00

Text-to-image models, which can generate high-quality images based on textual input, have recently enabled various content-creation tools. Despite significantly affecting a wide range of downstream applications, the distributions of these generated images are still not fully understood, especially when it comes to the potential stereotypical attributes of different genders. In this work, we propose a paradigm (Gender Presentation Differences) that utilizes fine-grained self-presentation attributes to study how gender is presented differently in text-to-image models. By probing gender indicators in the input text (e.g., "a woman" or "a man"), we quantify the frequency differences of presentation-centric attributes (e.g., "a shirt" and "a dress") through human annotation and introduce a novel metric: GEP. Furthermore, we propose an automatic method to estimate such differences. The automatic GEP metric based on our approach yields a higher correlation with human annotations than that based on existing CLIP scores, consistently across three state-of-the-art text-to-image models. Finally, we demonstrate the generalization ability of our metrics in the context of gender stereotypes related to occupations.
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation
Authors: Qiwen Cui, Kaiqing Zhang, Simon S. Du
Date: 2023-02-07 00:00:00
Tasks: Multi-agent Reinforcement Learning

We propose a new model, independent linear Markov game, for multi-agent reinforcement learning with a large state space and a large number of agents. This is a class of Markov games with independent linear function approximation, where each agent has its own function approximation for the state-action value functions that are marginalized by other players' policies. We design new algorithms for learning the Markov coarse correlated equilibria (CCE) and Markov correlated equilibria (CE) with sample complexity bounds that only scale polynomially with each agent's own function class complexity, thus breaking the curse of multiagents. In contrast, existing works for Markov games with function approximation have sample complexity bounds scale with the size of the \emph{joint action space} when specialized to the canonical tabular Markov game setting, which is exponentially large in the number of agents. Our algorithms rely on two key technical innovations: (1) utilizing policy replay to tackle non-stationarity incurred by multiple agents and the use of function approximation; (2) separating learning Markov equilibria and exploration in the Markov games, which allows us to use the full-information no-regret learning oracle instead of the stronger bandit-feedback no-regret learning oracle used in the tabular setting. Furthermore, we propose an iterative-best-response type algorithm that can learn pure Markov Nash equilibria in independent linear Markov potential games. In the tabular case, by adapting the policy replay mechanism for independent linear Markov games, we propose an algorithm with $\widetilde{O}(\epsilon^{-2})$ sample complexity to learn Markov CCE, which improves the state-of-the-art result $\widetilde{O}(\epsilon^{-3})$ in Daskalakis et al. 2022, where $\epsilon$ is the desired accuracy, and also significantly improves other problem parameters.
HumanMAC: Masked Motion Completion for Human Motion Prediction
Authors: Ling-Hao Chen, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, Tongliang Liu
Date: 2023-02-07 00:00:00
Proceeding: ICCV 2023 1
Tasks: Denoising, Human motion prediction, motion prediction, Prediction

Human motion prediction is a classical problem in computer vision and computer graphics, which has a wide range of practical applications. Previous effects achieve great empirical performance based on an encoding-decoding style. The methods of this style work by first encoding previous motions to latent representations and then decoding the latent representations into predicted motions. However, in practice, they are still unsatisfactory due to several issues, including complicated loss constraints, cumbersome training processes, and scarce switch of different categories of motions in prediction. In this paper, to address the above issues, we jump out of the foregoing style and propose a novel framework from a new perspective. Specifically, our framework works in a masked completion fashion. In the training stage, we learn a motion diffusion model that generates motions from random noise. In the inference stage, with a denoising procedure, we make motion prediction conditioning on observed motions to output more continuous and controllable predictions. The proposed framework enjoys promising algorithmic properties, which only needs one loss in optimization and is trained in an end-to-end manner. Additionally, it accomplishes the switch of different categories of motions effectively, which is significant in realistic tasks, e.g., the animation task. Comprehensive experiments on benchmarks confirm the superiority of the proposed framework. The project page is available at https://lhchen.top/Human-MAC.
Flow Matching on General Geometries
Authors: Ricky T. Q. Chen, Yaron Lipman
Date: 2023-02-07 00:00:00

We propose Riemannian Flow Matching (RFM), a simple yet powerful framework for training continuous normalizing flows on manifolds. Existing methods for generative modeling on manifolds either require expensive simulation, are inherently unable to scale to high dimensions, or use approximations for limiting quantities that result in biased training objectives. Riemannian Flow Matching bypasses these limitations and offers several advantages over previous approaches: it is simulation-free on simple geometries, does not require divergence computation, and computes its target vector field in closed-form. The key ingredient behind RFM is the construction of a relatively simple premetric for defining target vector fields, which encompasses the existing Euclidean case. To extend to general geometries, we rely on the use of spectral decompositions to efficiently compute premetrics on the fly. Our method achieves state-of-the-art performance on many real-world non-Euclidean datasets, and we demonstrate tractable training on general geometries, including triangular meshes with highly non-trivial curvature and boundaries.
Ethical Considerations for Responsible Data Curation
Authors: Jerone T. A. Andrews, Dora Zhao, William Thong, Apostolos Modas, Orestis Papakyriakopoulos, Alice Xiang
Date: 2023-02-07 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Diversity, Fairness

Human-centric computer vision (HCCV) data curation practices often neglect privacy and bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed through nonconsensual web scraping lack crucial metadata for comprehensive fairness and robustness evaluations. Current remedies are post hoc, lack persuasive justification for adoption, or fail to provide proper contextualization for appropriate application. Our research focuses on proactive, domain-specific recommendations, covering purpose, privacy and consent, and diversity, for curating HCCV evaluation datasets, addressing privacy and bias concerns. We adopt an ante hoc reflective perspective, drawing from current practices, guidelines, dataset withdrawals, and audits, to inform our considerations and recommendations.
An Expert System to Diagnose Spinal Disorders
Authors: Seyed Mohammad Sadegh Dashti, Seyedeh Fatemeh Dashti
Date: 2023-02-07 00:00:00
Tasks: Diagnostic

Objective: Until now, traditional invasive approaches have been the only means being leveraged to diagnose spinal disorders. Traditional manual diagnostics require a high workload, and diagnostic errors are likely to occur due to the prolonged work of physicians. In this research, we develop an expert system based on a hybrid inference algorithm and comprehensive integrated knowledge for assisting the experts in the fast and high-quality diagnosis of spinal disorders. Methods: First, for each spinal anomaly, the accurate and integrated knowledge was acquired from related experts and resources. Second, based on probability distributions and dependencies between symptoms of each anomaly, a unique numerical value known as certainty effect value was assigned to each symptom. Third, a new hybrid inference algorithm was designed to obtain excellent performance, which was an incorporation of the Backward Chaining Inference and Theory of Uncertainty. Results: The proposed expert system was evaluated in two different phases, real-world samples, and medical records evaluation. Evaluations show that in terms of real-world samples analysis, the system achieved excellent accuracy. Application of the system on the sample with anomalies revealed the degree of severity of disorders and the risk of development of abnormalities in unhealthy and healthy patients. In the case of medical records analysis, our expert system proved to have promising performance, which was very close to those of experts. Conclusion: Evaluations suggest that the proposed expert system provides promising performance, helping specialists to validate the accuracy and integrity of their diagnosis. It can also serve as an intelligent educational software for medical students to gain familiarity with spinal disorder diagnosis process, and related symptoms.