Miracle-dz/UniFAR-FTUs huggingface dataset:
Autologous followed by allogeneic versus tandem-autologous transplantation in high-risk, newly diagnosed multiple myeloma: a systematic review and meta-analysis

ABSTRACT Objectives: High-risk multiple myeloma (HRMM) is associated with poor survival, despite many advances in antimyeloma strategies. Autologous followed by allogeneic stem cell transplantation (auto-allo-SCT) has yielded controversial results compared to tandem autologous stem cell transplantation (auto-SCT) in patients with HRMM. We conducted this meta-analysis to compare the efficacy and safety of auto-allo-SCT and tandem-auto-SCT in patients with HRMM. Methods: Embase, Cochrane Library, and PubMed databases were searched until March 2023. Prospective or retrospective studies comparing the effects of auto-allo-SCT and tandem-auto-SCT were included. Hazard ratios (HRs) and 95% confidence intervals (CIs) for time-to-event outcomes, and odds ratios (ORs) and 95%CIs for dichotomous outcomes were pooled using random-effects models. Results: Three studies involving 491 patients were included. Despite auto-allo-SCT seemed to be associated with improvements in progression-free survival (PFS) (HR [95%CI], 0.71 [0.51–1.00]) and complete response (CR) (OR [95%CI], 3.16 [1.67–5.99]), and reduced relapse/progression rates (47% vs. 55%) in comparison with tandem-auto-SCT, no marked improvement in overall survival (OS). In comparison to tandem-auto-SCT, patients assigned to auto-allo-SCT exhibited a higher risk of transplant-related mortality (TRM) (11.9% vs. 4.1%) and non-relapse mortality (NRM) (12.3% vs. 3.1%). Conclusion: Auto-allo-SCT seemed to be associated with improvements in PFS and CR when compared to tandem-auto-SCT in patients with HRMM, but it did not lead to a significant improvement in OS. Furthermore, patients in the auto-allo-SCT group were at a higher risk of developing TRM and NRM. Auto-allo-SCT transplantation should not be routinely incorporated into HRMM therapy but rather should be considered investigational.
Prognostic impact of monocyte-to-lymphocyte ratio in coronary heart disease: a systematic review and meta-analysis

Objective Inflammatory biomarkers are novel tools to assess the prognosis of different cardiovascular diseases. We evaluated the impact of the monocyte-to-lymphocyte ratio (MLR) on clinical outcomes in patients with coronary heart disease (CHD). Methods We systematically screened English-language articles in PubMed, Scopus, and Web of Science to 31 August 2022. Relevant articles reporting the MLR and its association with clinical outcomes (major adverse cardiovascular events (MACE), coronary artery disease (CAD) severity, mortality, cardiac rupture, subclinical CAD, acute coronary syndrome (ACS) prediction, thin-cap fibroatheroma, no-reflow phenomenon, MLR-related differences in percutaneous coronary intervention, heart failure hospitalization, and depression) in patients with CHD were collected for further analysis. Results Nineteen articles were selected. The mean MLR was 0.34. A higher MLR was significantly associated with an increased risk of MACE among patients with CHD. The MLR was an independent predictor of MACE in patients with ACS. No significant association was found for CAD severity. A complementary analysis was not performed because of few studies focusing on the other predefined endpoints. Conclusions The MLR is a simple and widely available tool to predict MACE in patients with CHD. This biomarker can be utilized in emergency settings to prioritize high-risk patients and optimize therapeutic interventions.
Tracking dynamic flow: Decoding flow fluctuations through performance in a fine motor control task

Flow, an optimal mental state merging action and awareness, significantly impacts our emotion, performance, and well-being. However, capturing its swift fluctuations on a fine timescale is challenging due to the sparsity of the existing flow detecting tools. Here we present a fine fingertip force control (F3C) task to induce flow, wherein the task challenge is set at a compatible level with personal skill, and to quantitatively track the flow state variations from synchronous motor control performance. We extract eight performance metrics from fingertip force sequence and reveal their significant differences under distinct flow states. Further, we built a learning-based flow decoder that aims to predict the continuous flow intensity during the user experiment through the selected performance metrics, taking the self-reported flow as the label. Cross-validation shows that the predicted flow intensity reaches significant correlation with the self-reported flow intensity (r=0.81). Based on the decoding results, we observe rapid oscillations in flow fluctuations during the intervals between sparse self-reporting probes. This study showcases the feasibility of tracking intrinsic flow variations with high temporal resolution using task performance measures and may serve as foundation for future work aiming to take advantage of flow' s dynamics to enhance performance and positive emotions.
Physics-informed Neural Network for Acoustic Resonance Analysis

This study proposes the physics-informed neural network (PINN) framework to solve the wave equation for acoustic resonance analysis. ResoNet, the analytical model proposed in this study, minimizes the loss function for periodic solutions, in addition to conventional PINN loss functions, thereby effectively using the function approximation capability of neural networks, while performing resonance analysis. Additionally, it can be easily applied to inverse problems. Herein, the resonance in a one-dimensional acoustic tube was analyzed. The effectiveness of the proposed method was validated through the forward and inverse analyses of the wave equation with energy-loss terms. In the forward analysis, the applicability of PINN to the resonance problem was evaluated by comparison with the finite-difference method. The inverse analysis, which included the identification of the energy loss term in the wave equation and design optimization of the acoustic tube, was performed with good accuracy.
FreSh: A Lock-Free Data Series Index

We present FreSh, a lock-free data series index that exhibits good performance (while being robust). FreSh is based on Refresh, which is a generic approach we have developed for supporting lock-freedom in an efficient way on top of any locality-aware data series index. We believe Refresh is of independent interest and can be used to get well-performed lock-free versions of other locality-aware blocking data structures. For developing FreSh, we first studied in depth the design decisions of current state-of-the-art data series indexes, and the principles governing their performance. This led to a theoretical framework, which enables the development and analysis of data series indexes in a modular way. The framework allowed us to apply Refresh, repeatedly, to get lock-free versions of the different phases of a family of data series indexes. Experiments with several synthetic and real datasets illustrate that FreSh achieves performance that is as good as that of the state-of-the-art blocking in-memory data series index. This shows that the helping mechanisms of FreSh are light-weight, respecting certain principles that are crucial for performance in locality-aware data structures.
A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis

Time series data, including univariate and multivariate ones, are characterized by unique composition and complex multi-scale temporal variations. They often require special consideration of decomposition and multi-scale modeling to analyze. Existing deep learning methods on this best fit to univariate time series only, and have not sufficiently considered sub-series modeling and decomposition completeness. To address these challenges, we propose MSD-Mixer, a Multi-Scale Decomposition MLP-Mixer, which learns to explicitly decompose and represent the input time series in its different layers. To handle the multi-scale temporal patterns and multivariate dependencies, we propose a novel temporal patching approach to model the time series as multi-scale patches, and employ MLPs to capture intra- and inter-patch variations and channel-wise correlations. In addition, we propose a novel loss function to constrain both the mean and the autocorrelation of the decomposition residual for better decomposition completeness. Through extensive experiments on various real-world datasets for five common time series analysis tasks, we demonstrate that MSD-Mixer consistently and significantly outperforms other state-of-the-art algorithms with better efficiency.
Exploring Fairness in Pre-trained Visual Transformer based Natural and GAN Generated Image Detection Systems and Understanding the Impact of Image Compression in Fairness

It is not only sufficient to construct computational models that can accurately classify or detect fake images from real images taken from a camera, but it is also important to ensure whether these computational models are fair enough or produce biased outcomes that can eventually harm certain social groups or cause serious security threats. Exploring fairness in forensic algorithms is an initial step towards correcting these biases. Since visual transformers are recently being widely used in most image classification based tasks due to their capability to produce high accuracies, this study tries to explore bias in the transformer based image forensic algorithms that classify natural and GAN generated images. By procuring a bias evaluation corpora, this study analyzes bias in gender, racial, affective, and intersectional domains using a wide set of individual and pairwise bias evaluation measures. As the generalizability of the algorithms against image compression is an important factor to be considered in forensic tasks, this study also analyzes the role of image compression on model bias. Hence to study the impact of image compression on model bias, a two phase evaluation setting is followed, where a set of experiments is carried out in the uncompressed evaluation setting and the other in the compressed evaluation setting.
On the Evaluation of Generative Models in Distributed Learning Tasks

The evaluation of deep generative models including generative adversarial networks (GANs) and diffusion models has been extensively studied in the literature. While the existing evaluation methods mainly target a centralized learning problem with training data stored by a single client, many applications of generative models concern distributed learning settings, e.g. the federated learning scenario, where training data are collected by and distributed among several clients. In this paper, we study the evaluation of generative models in distributed learning tasks with heterogeneous data distributions. First, we focus on the Fr\'echet inception distance (FID) and consider the following FID-based aggregate scores over the clients: 1) FID-avg as the mean of clients' individual FID scores, 2) FID-all as the FID distance of the trained model to the collective dataset containing all clients' data. We prove that the model rankings according to the FID-all and FID-avg scores could be inconsistent, which can lead to different optimal generative models according to the two aggregate scores. Next, we consider the kernel inception distance (KID) and similarly define the KID-avg and KID-all aggregations. Unlike the FID case, we prove that KID-all and KID-avg result in the same rankings of generative models. We perform several numerical experiments on standard image datasets and training schemes to support our theoretical findings on the evaluation of generative models in distributed learning problems.
Direct numerical simulation of Taylor–Couette flow with vertical asymmetric rough walls

Abstract Direct numerical simulations are performed to explore the effects of the rotating direction of the vertically asymmetric rough wall on the transport properties of Taylor–Couette (TC) flow, up to a Taylor number of ${Ta} = 2.39\times 10^{7}$. It is shown that, compared with the smooth wall, the rough wall with vertical asymmetric strips can enhance the dimensionless torque ${Nu}_{\omega }$. More importantly, at high Ta, clockwise rotation of the inner rough wall (where the fluid is sheared by the steeper slope side of the strips) results in a significantly greater torque enhancement compared to counter-clockwise rotation (where the fluid is sheared by the smaller slope side of the strips), due to the larger convective contribution to the angular velocity flux. However, the rotating direction has a negligible effect on the torque at low Ta. The larger torque enhancement caused by the clockwise rotation of the vertically asymmetric rough wall at high Ta is then explained by the stronger coupling between the rough wall and the bulk, attributed to the larger biased azimuthal velocity towards the rough wall at the mid-gap of the TC system, the increased turbulence intensity manifested by larger Reynolds stress and a thinner boundary layer, and the more significant contribution of the pressure force on the surface of the rough wall to the torque.
Skateboarding stance and handedness: A brief analysis of relationship, proportions and influences

The aim of this paper is to show the relationship that lies in the fact of a person being right or left handed, in their skateboarding stance. Starting from the null hypothesis that there is no relationship, the Pearson's X^2 with Yates correction tests, as well as its respective p-value will be used to test the hypothesis. It will also be calculated and analyzed the residuals, Cramer's V and the Risk and Odds Ratios, with their respective confidence intervals to know the intensity of the association.
Genuine multipartite entanglement without fully controllable measurements

Standard procedures for entanglement detection assume that experimenters can exactly implement specific quantum measurements. Here, we depart from such idealizations and investigate, in both theory and experiment, the detection of genuine multipartite entanglement using measurements that are subject to small imperfections. We show how to correct well-known examples of multipartite entanglement witnesses to account for measurement imperfection, thereby excluding the possibility of false positives. We proceed with a tabletop four-partite photonic experiment and demonstrate first how a small amount of alignment error can undermine the conclusions drawn from uncorrected entanglement witnesses, and then the conclusions of the corrected data analysis. Furthermore, as our approach is tailored for a quantum device that is trusted but not flawlessly controlled, we show its advantages in terms of noise resilience as compared to device-independent models. Our work contributes to verifying quantum properties of multipartite systems when taking into account knowledge of imperfections in the verification devices.
Holistic Parking Slot Detection with Polygon-Shaped Representations

Current parking slot detection in advanced driver-assistance systems (ADAS) primarily relies on ultrasonic sen-sors. This method has several limitations such as the need to scan the entire parking slot before detecting it, the incapacity of detecting multiple slots in a row, and the difficulty of classifying them. Due to the complex visual environment, vehicles are equipped with surround view camera systems to detect vacant parking slots. Previous research works in this field mostly use image-domain models to solve the problem. These two-stage approaches separate the 2D detection and 3D pose estimation steps using camera calibration. In this paper, we propose one-step Holistic Parking Slot Network (HPS-Net), a tailor-made adaptation of the You Only Look Once (YOLO)v4 algorithm. This camera-based approach directly outputs the four vertex coordinates of the parking slot in topview domain, instead of a bounding box in raw camera images. Several visible points and shapes can be proposed from different angles. A novel regression loss function named polygon-corner Generalized Intersection over Union (GIoU) for polygon vertex position optimization is also proposed to manage the slot orientation and to distinguish the entrance line. Experiments show that HPS-Net can detect various vacant parking slots with a F1-score of 0.92 on our internal Valeo Parking Slots Dataset (VPSD) and 0.99 on the public dataset PS2.0. It provides a satisfying generalization and robustness in various parking scenarios, such as indoor (F1: 0.86) or paved ground (F1: 0.91). Moreover, it achieves a realtime detection speed of 17 FPS on Nvidia Drive AGX Xavier. A demo video can be found at https://streamable.com/75j7sj.
IntentDial: An Intent Graph based Multi-Turn Dialogue System with Reasoning Path Visualization

Intent detection and identification from multi-turn dialogue has become a widely explored technique in conversational agents, for example, voice assistants and intelligent customer services. The conventional approaches typically cast the intent mining process as a classification task. Although neural classifiers have proven adept at such classification tasks, the issue of neural network models often impedes their practical deployment in real-world settings. We present a novel graph-based multi-turn dialogue system called , which identifies a user's intent by identifying intent elements and a standard query from a dynamically constructed and extensible intent graph using reinforcement learning. In addition, we provide visualization components to monitor the immediate reasoning path for each turn of a dialogue, which greatly facilitates further improvement of the system.
Learning a Hierarchical Planner from Humans in Multiple Generations

A typical way in which a machine acquires knowledge from humans is by programming. Compared to learning from demonstrations or experiences, programmatic learning allows the machine to acquire a novel skill as soon as the program is written, and, by building a library of programs, a machine can quickly learn how to perform complex tasks. However, as programs often take their execution contexts for granted, they are brittle when the contexts change, making it difficult to adapt complex programs to new contexts. We present natural programming, a library learning system that combines programmatic learning with a hierarchical planner. Natural programming maintains a library of decompositions, consisting of a goal, a linguistic description of how this goal decompose into sub-goals, and a concrete instance of its decomposition into sub-goals. A user teaches the system via curriculum building, by identifying a challenging yet not impossible goal along with linguistic hints on how this goal may be decomposed into sub-goals. The system solves for the goal via hierarchical planning, using the linguistic hints to guide its probability distribution in proposing the right plans. The system learns from this interaction by adding newly found decompositions in the successful search into its library. Simulated studies and a human experiment (n=360) on a controlled environment demonstrate that natural programming can robustly compose programs learned from different users and contexts, adapting faster and solving more complex tasks when compared to programmatic baselines.
The Asymptotic Rank Conjecture and the Set Cover Conjecture are not Both True

Strassen's asymptotic rank conjecture [Progr. Math. 120 (1994)] claims a strong submultiplicative upper bound on the rank of a three-tensor obtained as an iterated Kronecker product of a constant-size base tensor. The conjecture, if true, most notably would put square matrix multiplication in quadratic time. We note here that some more-or-less unexpected algorithmic results in the area of exponential-time algorithms would also follow. Specifically, we study the so-called set cover conjecture, which states that for any $\epsilon>0$ there exists a positive integer constant $k$ such that no algorithm solves the $k$-Set Cover problem in worst-case time $\mathcal{O}((2-\epsilon)^n|\mathcal F|\operatorname{poly}(n))$. The $k$-Set Cover problem asks, given as input an $n$-element universe $U$, a family $\mathcal F$ of size-at-most-$k$ subsets of $U$, and a positive integer $t$, whether there is a subfamily of at most $t$ sets in $\mathcal F$ whose union is $U$. The conjecture was formulated by Cygan et al. in the monograph Parameterized Algorithms [Springer, 2015] but was implicit as a hypothesis already in Cygan et al. [CCC 2012, ACM Trans. Algorithms 2016], there conjectured to follow from the Strong Exponential Time Hypothesis. We prove that if the asymptotic rank conjecture is true, then the set cover conjecture is false. Using a reduction by Krauthgamer and Trabelsi [STACS 2019], in this scenario we would also get a $\mathcal{O}((2-\delta)^n)$-time randomized algorithm for some constant $\delta>0$ for another well-studied problem for which no such algorithm is known, namely that of deciding whether a given $n$-vertex directed graph has a Hamiltonian cycle.
Value-Biased Maximum Likelihood Estimation for Model-based Reinforcement Learning in Discounted Linear MDPs

We consider the infinite-horizon linear Markov Decision Processes (MDPs), where the transition probabilities of the dynamic model can be linearly parameterized with the help of a predefined low-dimensional feature mapping. While the existing regression-based approaches have been theoretically shown to achieve nearly-optimal regret, they are computationally rather inefficient due to the need for a large number of optimization runs in each time step, especially when the state and action spaces are large. To address this issue, we propose to solve linear MDPs through the lens of Value-Biased Maximum Likelihood Estimation (VBMLE), which is a classic model-based exploration principle in the adaptive control literature for resolving the well-known closed-loop identification problem of Maximum Likelihood Estimation. We formally show that (i) VBMLE enjoys $\widetilde{O}(d\sqrt{T})$ regret, where $T$ is the time horizon and $d$ is the dimension of the model parameter, and (ii) VBMLE is computationally more efficient as it only requires solving one optimization problem in each time step. In our regret analysis, we offer a generic convergence result of MLE in linear MDPs through a novel supermartingale construct and uncover an interesting connection between linear MDPs and online learning, which could be of independent interest. Finally, the simulation results show that VBMLE significantly outperforms the benchmark method in terms of both empirical regret and computation time.
Guarantees for Self-Play in Multiplayer Games via Polymatrix Decomposability

Self-play is a technique for machine learning in multi-agent systems where a learning algorithm learns by interacting with copies of itself. Self-play is useful for generating large quantities of data for learning, but has the drawback that the agents the learner will face post-training may have dramatically different behavior than the learner came to expect by interacting with itself. For the special case of two-player constant-sum games, self-play that reaches Nash equilibrium is guaranteed to produce strategies that perform well against any post-training opponent; however, no such guarantee exists for multiplayer games. We show that in games that approximately decompose into a set of two-player constant-sum games (called constant-sum polymatrix games) where global $\epsilon$-Nash equilibria are boundedly far from Nash equilibria in each subgame (called subgame stability), any no-external-regret algorithm that learns by self-play will produce a strategy with bounded vulnerability. For the first time, our results identify a structural property of multiplayer games that enable performance guarantees for the strategies produced by a broad class of self-play algorithms. We demonstrate our findings through experiments on Leduc poker.