Miracle-dz/UniFAR-FTUs huggingface dataset:
A Brief Review of Explainable Artificial Intelligence in Healthcare

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated.
Cavity-Mediated Collective Momentum-Exchange Interactions

Quantum simulation and sensing hold great promise for providing new insights into nature, from understanding complex interacting systems to searching for undiscovered physics. Large ensembles of laser-cooled atoms interacting via infinite-range photon mediated interactions are a powerful platform for both endeavours. Here, we realize for the first time momentum-exchange interactions in which atoms exchange their momentum states via collective emission and absorption of photons from a common cavity mode. The momentum-exchange interaction leads to an observed all-to-all Ising-like interaction in a matter-wave interferometer, which is useful for entanglement generation. A many-body energy gap also emerges, effectively binding interferometer matter-wave packets together to suppress Doppler dephasing, akin to M\"ossbauer spectroscopy. The tunable momentum-exchange interaction provides a new capability for quantum interaction-enhanced matter-wave interferometry and for realizing exotic behaviors including simulations of superconductors and dynamical gauge fields.
Role of Transients in Two-Bounce Non-Line-of-Sight Imaging

The goal of non-line-of-sight (NLOS) imaging is to image objects occluded from the camera's field of view using multiply scattered light. Recent works have demonstrated the feasibility of two-bounce (2B) NLOS imaging by scanning a laser and measuring cast shadows of occluded objects in scenes with two relay surfaces. In this work, we study the role of time-of-flight (ToF) measurements, i.e. transients, in 2B-NLOS under multiplexed illumination. Specifically, we study how ToF information can reduce the number of measurements and spatial resolution needed for shape reconstruction. We present our findings with respect to tradeoffs in (1) temporal resolution, (2) spatial resolution, and (3) number of image captures by studying SNR and recoverability as functions of system parameters. This leads to a formal definition of the mathematical constraints for 2B lidar. We believe that our work lays an analytical ground- workfor design of future NLOS imaging systems, especially as ToF sensors become increasingly ubiquitous.
IterativePFN: True Iterative Point Cloud Filtering

The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud filtering or denoising. State-of-the-art learning based methods focus on training neural networks to infer filtered displacements and directly shift noisy points onto the underlying clean surfaces. In high noise conditions, they iterate the filtering process. However, this iterative filtering is only done at test time and is less effective at ensuring points converge quickly onto the clean surfaces. We propose IterativePFN (iterative point cloud filtering network), which consists of multiple IterationModules that model the true iterative filtering process internally, within a single network. We train our IterativePFn network using a novel loss function that utilizes an adaptive ground truth target at each iteration to capture the relationship between intermediate filtering results during training. This ensures that the filtered results converge faster to the clean surfaces. Our method is able to obtain better performance compared to state-of-the-art methods. The source code can be found at: https://github.com/ddsediri/IterativePFN
Imitation Learning from Nonlinear MPC via the Exact Q-Loss and its Gauss-Newton Approximation

This work presents a novel loss function for learning nonlinear Model Predictive Control policies via Imitation Learning. Standard approaches to Imitation Learning neglect information about the expert and generally adopt a loss function based on the distance between expert and learned controls. In this work, we present a loss based on the Q-function directly embedding the performance objectives and constraint satisfaction of the associated Optimal Control Problem (OCP). However, training a Neural Network with the Q-loss requires solving the associated OCP for each new sample. To alleviate the computational burden, we derive a second Q-loss based on the Gauss-Newton approximation of the OCP resulting in a faster training time. We validate our losses against Behavioral Cloning, the standard approach to Imitation Learning, on the control of a nonlinear system with constraints. The final results show that the Q-function-based losses significantly reduce the amount of constraint violations while achieving comparable or better closed-loop costs.
MS-DCANet: A MLP-based Multi-Scale Feature Framework For COVID-19 Infection Segmentation From Medical Images

Coronavirus Disease 2019 (COVID-19) spread rapidly around the world, causing a series of severe health crises. Automated segmentation of lung infections based on Deep Convolutional Neural Network (DCNN) from medical images such as CT, X-ray, etc, displayed a huge potential for accurate diagnosis and quantitative analysis. Most COVID-19 medical images show blurred boundaries, dense noise points, low contrast, and significant variation in the shape and size of lesions. Although various models based on UNet have been proposed, more optimisation is required to obtain accurate segmentation and meet complex computational needs. Furthermore, the existing COVID-19 infections segmentation DCNN based methods are only suitable for single modality medical images. To solve these problems, this paper proposes a symmetric Encoder-Decoder segmentation framework named MS-DCANet. We introduce Tokenized MLP block, a novel attention scheme that uses a shift-window mechanism similar to the Transformer to acquire self-attention and achieve local-to-global semantic dependency. MS-DCANet also uses several Dual Channel blocks and a Res-ASPP block to expand the receptive field and extract multi-scale features. In a large number of experiments on COVID-19 datasets using both X-ray and CT images, MS-DCANet achieved state-of-the-art performance compared with other UNet models. MS-DCANet can also improve trade-off accuracy and complexity. To prove the proposed model’s strong generalisability, we also apply MS-DCANet to the segmentation of skin tumours from dermoscopy images and hand bone from X-ray images with satisfactory results.
Decoupling Dynamic Monocular Videos for Dynamic View Synthesis

The challenge of dynamic view synthesis from dynamic monocular videos, i.e., synthesizing novel views for free viewpoints given a monocular video of a dynamic scene captured by a moving camera, mainly lies in accurately modeling the dynamic objects of a scene using limited 2D frames, each with a varying timestamp and viewpoint. Existing methods usually require pre-processed 2D optical flow and depth maps by off-the-shelf methods to supervise the network, making them suffer from the inaccuracy of the pre-processed supervision and the ambiguity when lifting the 2D information to 3D. In this paper, we tackle this challenge in an unsupervised fashion. Specifically, we decouple the motion of the dynamic objects into object motion and camera motion, respectively regularized by proposed unsupervised surface consistency and patch-based multi-view constraints. The former enforces the 3D geometric surfaces of moving objects to be consistent over time, while the latter regularizes their appearances to be consistent across different viewpoints. Such a fine-grained motion formulation can alleviate the learning difficulty for the network, thus enabling it to produce not only novel views with higher quality but also more accurate scene flows and depth than existing methods requiring extra supervision.
Cross-Domain Image Captioning with Discriminative Finetuning

Neural captioners are typically trained to mimic human-generated references without optimizing for any specific communication goal, leading to problems such as the generation of vague captions. In this paper, we show that fine-tuning an out-of-the-box neural captioner with a self-supervised discriminative communication objective helps to recover a plain, visually descriptive language that is more informative about image contents. Given a target image, the system must learn to produce a description that enables an out-of-the-box text-conditioned image retriever to iden-tify such image among a set of candidates. We experiment with the popular ClipCap captioner; also replicating the main results with BLIP. In terms of similarity to ground-truth human descriptions, the captions emerging from dis-criminative finetuning lag slightly behind those generated by the non-finetuned model, when the latter is trained and tested on the same caption dataset. However, when the model is used without further tuning to generate captions for out-of-domain datasets, our discriminatively-finetuned captioner generates descriptions that resemble human ref-erences more than those produced by the same captioner wihtout finetuning. We further show that, on the Concep-tual Captions dataset, discriminatively finetuned captions are more helpful than either vanilla ClipCap captions or ground-truth captions for human annotators tasked with an image discrimination task.11Our code is available at https://github.com/facebookresearch/EGG/tree/main/egg/zoo/discriminative_captioner.
A Grammar of Hypotheses for Visualization, Data, and Analysis

We present a grammar for expressing hypotheses in visual data analysis to formalize the previously abstract notion of"analysis tasks. "Through the lens of our grammar, we lay the groundwork for how a user's data analysis questions can be operationalized and automated as a set of hypotheses (a hypothesis space). We demonstrate that our grammar-based approach for analysis tasks can provide a systematic method towards unifying three disparate spaces in visualization research: the hypotheses a dataset can express (a data hypothesis space), the hypotheses a user would like to refine or verify through analysis (an analysis hypothesis space), and the hypotheses a visualization design is capable of supporting (a visualization hypothesis space). We illustrate how the formalization of these three spaces can inform future research in visualization evaluation, knowledge elicitation, analytic provenance, and visualization recommendation by using a shared language for hypotheses. Finally, we compare our proposed grammar-based approach with existing visual analysis models and discuss the potential of a new hypothesis-driven theory of visual analytics.
Marginalization and Collapsibility in Graphical Interaction Models

The behaviour of a graphical interaction model under marginalization is discussed. A graphical interaction model is called collapsible onto a set of variables if the class of marginal distributions is the same as that implied by the related subgraph. The necessary and sufficient condition for collapsibility is found and it is shown that collapsibility is equivalent to a range of other important statistical properties of the model.
Two-dimensional tunable photonic crystal formed in a liquid-crystal/polymer composite: Threshold behavior and morphology

A two-dimensional (2D) transverse square lattice is formed using holographic methods in a liquid-crystal/polymer composite through a photoinduced diffusion process. The polymer morphology is investigated using scanning electron microscopy. The kinetics of formation is described within the framework of a 2D diffusion model, and a simple phenomenological description captures the nature of the Freedericksz transition, allowing the estimation of the surface anchoring strength W≈3×10−4 J/m2.
An infinite number of conservation laws for coupled nonlinear evolution equations

The n‐dimensional Zakharov‐Shabat eigenvalue problem and the corresponding time dependencey of the vector eigenfuncions are considered. It is known that certain coupled systems of nonlinear partial differential equatons are equivalent to the time invariance of the spectrum. Here, for any such coupled system, and infinite sequence of conservation laws is explicitly derived. As an example, this result is applied to the equaions describing three resonantly interacting nonlinear wave envelopes.
of the 18th USENIX Symposium on Networked Systems Design and Implementation

This paper presents DST, a decentralized scalar timestamp scheme to scale distributed transactions using multi-version concurrency control (MVCC). DST is efﬁcient in storage and network by being a scalar timestamp but requiring no centralized timestamp service for coordination, which may become a scalability bottleneck. The key observation is that concurrency control (CC) protocols like OCC and 2PL already imply a serializable order among concurrent read-write transactions through conﬂicting database tuples. To this end, DST piggybacks on CC protocols to maintain the timestamp ordering with low cost and no new scalability bottleneck for read-write transactions. DST further provides snapshot reads with bounded staleness by using a hybrid scalar timestamp (physical clock and logical counter). To demonstrate the generality of DST, we provide a general guideline for the integration of DST and further show the effectiveness by using three representative transactional systems (i.e., DrTM+R, MySQL cluster, and R OCOCO ) with different CC protocols. Experimental results show that DST can achieve more than 95% of optimal performance (using Read Committed) without compromising correctness. With DST, DrTM+R achieves up to 1.8X higher peak throughput for TPC-E and outperforms other timestamp schemes by 6.3X for TPC-C . DST also leads up to 1.9X and 2.1X speedup on TPC-C for MySQL cluster and R OCOCO , respectively.
The effect of the abdomen deformation on the longitudinal stability of flying insects

In this paper, we derive longitudinal nonlinear equations of motion of a hovering insect with deformable abdomen to investigate the effect of the abdominal motion to the longitudinal dynamics. The blade-element theory, which is based on experimentally obtained aerodynamic coefficients, is used for the periodic force and moment excitation to the system. Here, we focus on the role of the deformable abdomen to investigate whether or not the flexible body is a decisive factor to the longitudinal flight dynamic stability. Three cases: 1) rigid connection between the thorax and abdomen, 2) flexible connection, and 3) active connection with a feedback control, are compared to check the role of the abdomen deformation on the longitudinal flight dynamic stability, by examining eigenvalues of the linearized system model of each case. The results show that an active control of the abdominal angle can stabilize the longitudinal flight dynamics of the insect modeled in this study.
Introduction to S and S-Plus

From the Publisher: Accessing the power of S. designed to make the language of choice for high-end research statisticians more accessible, this book demonstrates how to get the most out of the unique power of S to display, manipulate, analyze, and graph data. An Introduction to S and S-PLUS presents ideas and methods in a straightforward manner to extend computerized research possibilities for anyone involved in data management, manipulation and presentation of statistical computing, statistical modeling, or graphics. To encourage effective self-study and hands-on experimentation, the author accompanies his presentation of every concept with an example, either using data sets that are distributed with S or easily created from them. It should not be necessary to enter any data to recreate the examples in the book. Throughout, the focus is on the unique features of S (and its commercial version, S-PLUS) that do not have exact parallels in other languages. Among the topics examined are functional programming concepts, production of custom graphics, and the writing of functions.
α‐Mangostin induces oxidative damage, mitochondrial dysfunction, and apoptosis in a triple‐negative breast cancer model

Triple‐negative breast cancer (TNBC) does not express estrogen receptor, progesterone receptor, and human epidermal growth factor receptor; therefore, TNBC lacks targeted therapy, and chemotherapy is the only available treatment for this illness but causes side effects. A putative strategy for the treatment of TNBC could be the use of the polyphenols such as α‐Mangostin (α‐M), which has shown anticancerogenic effects in different cancer models and can modulate the inflammatory and prooxidant state in several pathological models. The redox state, oxidative stress (OS), and oxidative damage are highly related to cancer development and its treatment. Thus, this study aimed to evaluate the effects of α‐M on redox state, mitochondrial metabolism, and apoptosis in 4T1 mammary carcinoma cells. We found that α‐M decreases both protein levels and enzymatic activity of catalase, and increases reactive oxygen species, oxidized proteins and glutathione disulfide, which demonstrates that α‐M induces oxidative damage. We also found that α‐M promotes mitochondrial dysfunction by abating basal respiration, the respiration ligated to oxidative phosphorylation (OXPHOS), and the rate control of whole 4T1 cells. Additionally, α‐M also decreases the levels of OXPHOS subunits of mitochondrial complexes I, II, III, and adenosine triphosphate synthase, the activity of mitochondrial complex I as well as the levels of peroxisome proliferator‐activated receptor‐gamma co‐activator 1α, showing a mitochondrial mass reduction. Then, oxidative damage and mitochondrial dysfunction induced by α‐M induce apoptosis of 4T1 cells, which is evidenced by B cell lymphoma 2 decrease and caspase 3 cleavage. Taken together, our results suggest that α‐M induces OS and mitochondrial dysfunction, resulting in 4T1 cell death through apoptotic mechanisms.
Yeast PIC-Mediator structure with RNA polymerase II C-terminal domain

Significance At gene promoters, RNA polymerase II (Pol II) assembles into a preinitiation complex (PIC) which includes the coactivator Mediator. Here, we describe the PIC-Mediator complex from yeast at unprecedented resolution and provide a reference structure for initiation complexes. We obtain insights into the atomic details of Mediator and its interaction with Pol II. Moreover, we observe three Mediator-bound fragments of the C-terminal domain (CTD) of Pol II which stabilize Mediator conformation within the PIC and significantly extend prior information from the human system. These peptide regions correspond to almost 50% of the yeast CTD and thus the minimal length required for cell viability. Their location and conformation are in part extremely conserved, highlighting their central role in transcription.
Childhood Residential Mobility and Mental and Physical Health in Later Life: Findings From the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study

The study objective was to investigate the effects of childhood residential mobility on older adult physical and mental health. In REasons for Geographic and Racial Differences in Stroke (REGARDS) Study, we used linear regression models to investigate if number of moves during childhood predicted mental and physical health (SF-12 MCS, PCS), adjusting for demographic covariates, childhood socioeconomic status (SES), childhood social support, and adverse childhood experiences (ACEs). We investigated interaction by age, race, childhood SES, and ACEs. People who moved more during childhood had poorer MCS scores, β = −0.10, SE = 0.05, p = 0.03, and poorer PCS scores, β = −0.25, SE = 0.06, p < 0.0001. Effects of moves on PCS were worse for Black people compared to White people (p = 0.06), those with low childhood SES compared to high childhood SES (p = 0.02), and high ACEs compared to low ACEs (p = 0.01). As family instability accompanying residential mobility, family poverty, and adversity disproportionately affect health, Black people may be especially disadvantaged.
Optimizing sleep across the menopausal transition

Abstract Women frequently experience sleep disturbances, particularly night-time awakenings, as they transition menopause and enter postmenopause. Sleep is essential for optimal functioning and health. Persistent and distressing sleep disturbances across menopause can negatively impact daytime functioning and productivity, and increase risk for mental and physical health conditions. While multiple factors can disturb sleep, two unique factors in the context of menopause are vasomotor symptoms and the changing reproductive hormone environment. Vasomotor symptoms are associated with sleep disturbances and contribute significantly to awakenings and amount of time spent awake during the night. Even after accounting for vasomotor and depressive symptoms, lower estradiol and higher follicle stimulating hormone levels, indicative of menopause, are associated with sleep disturbance, particularly awakenings, suggesting that the hormone environment may directly affect sleep. Management strategies for clinically significant menopausal sleep disturbances include cognitive behavioral therapy for insomnia, which is effective and durable in treating menopausal insomnia. Hormone therapy alleviates sleep disturbances, particularly in the presence of disruptive vasomotor symptoms. Sleep disturbances have a significant impact on women’s functioning and health, and there is a need for further research of the underlying mechanisms to advance effective preventative and treatment strategies that ensure optimal health and well-being of midlife women.