Miracle-dz/UniFAR-FTUs huggingface dataset:
Treatment-based classification for low back pain: systematic review with meta-analysis

ABSTRACT Objective To systematically review the effects of treatment-based classification (TBC) in patients with specific and nonspecific acute, subacute and chronic low back pain. Methods The following databases were searched: MEDLINE, EMBASE, PsycINFO, Global Health, CENTRAL, Web of Science, CINAHL, SPORTDiscus, PEDro and WHO from inception up to December 2021. We used the PEDro scale, the TIDieR checklist and the GRADE approach to evaluate the risk of bias, quality on reporting and the certainty of the evidence, respectively. Results Twenty-three trials (pooled n = 2,649) met the inclusion criteria. We have identified a total of 22 comparisons and 134 estimates of treatment effects. There was a very large heterogeneity with regards to the comparison groups. Most of individual trials had low risk of bias with a mean score of 6.8 (SD = 1.3) on a 0–10 scale. The certainty of evidence for most comparisons was low, which indicates that more high quality and robust trials are needed. We were able to pool the data using a meta-analysis approach for only two comparisons (TBC versus mobility exercises in patients with acute low back pain and traction for patients with sciatica). In general, the TBC approach seems to be useful for patients with acute low back pain, sciatica and with spinal stenosis. We strongly suggest readers to carefully read our summary of findings table for further details on each comparison. Conclusion The TBC approach seems to be useful for patients with acute low back pain, sciatica and with spinal stenosis.
UniFormer: Unifying Convolution and Self-Attention for Visual Recognition

It is a challenging task to learn discriminative representation from images and videos, due to large local redundancy and complex global dependency in these visual data. Convolution neural networks (CNNs) and vision transformers (ViTs) have been two dominant frameworks in the past few years. Though CNNs can efficiently decrease local redundancy by convolution within a small neighborhood, the limited receptive field makes it hard to capture global dependency. Alternatively, ViTs can effectively capture long-range dependency via self-attention, while blind similarity comparisons among all the tokens lead to high redundancy. To resolve these problems, we propose a novel Unified transFormer (UniFormer), which can seamlessly integrate the merits of convolution and self-attention in a concise transformer format. Different from the typical transformer blocks, the relation aggregators in our UniFormer block are equipped with local and global token affinity respectively in shallow and deep layers, allowing tackling both redundancy and dependency for efficient and effective representation learning. Finally, we flexibly stack our blocks into a new powerful backbone, and adopt it for various vision tasks from image to video domain, from classification to dense prediction. Without any extra training data, our UniFormer achieves <italic>86.3</italic> top-1 accuracy on ImageNet-1 K classification task. With only ImageNet-1 K pre-training, it can simply achieve state-of-the-art performance in a broad range of downstream tasks. It obtains <italic>82.9/84.8</italic> top-1 accuracy on Kinetics-400/600, <italic>60.9/71.2</italic> top-1 accuracy on Something-Something V1/V2 video classification tasks, <italic>53.8</italic> box AP and <italic>46.4</italic> mask AP on COCO object detection task, <italic>50.8</italic> mIoU on ADE20 K semantic segmentation task, and <italic>77.4</italic> AP on COCO pose estimation task. Moreover, we build an efficient UniFormer with a concise hourglass design of token shrinking and recovering, which achieves <bold>2-4</bold><inline-formula><tex-math notation="LaTeX">$\bm {\times }$</tex-math><alternatives><mml:math><mml:mo>×</mml:mo></mml:math><inline-graphic xlink:href="li-ieq1-3282631.gif"/></alternatives></inline-formula> higher throughput than the recent lightweight models.
Wiener Process of Fractals and Path-Integrals I: Emergent Lorentz Symmetry in Stochastic Process of Quantum Fields

: This is the ﬁrst paper of a series of researches (that is followed by [42, 43]) that aims to interpret the gravitational eﬀects of nature within some consistent stochastic fractal-based (intrinsically conformal) path-integral formulation. In this paper, we initially study the asymptotic behaviors of fractal structure of Weierstrass-like functions by means of Hardy’s criteria for nowhere diﬀerentiability. It is proved that the asymptotic behavior of Fourier-Laplace coeﬃcients of such functions leads to a non-linear diﬀerential equation which in its turn gives rise to an exponentially increasing norm, the so called fractal norm, on the phase space. Then, using the fractal norm the Wiener Brownian process is accomplished for fractal functions on a ﬂat space. By substituting non-local terms with approximated local ones within the derived formula the d’Alembertian operator emerges automatically in the Gaussian terms of the Wiener measure. Hence, it is established that the Lorentz symmetry would be regarded as the ﬁrst order approximate symmetry of nature on a ﬂat space-time manifold based on the stochastic essence of Brownian motion of the background fractal geometry. This leads to a well-deﬁned framework for Feynman’s path-integral in both real and complex formulations. It is shown that the obtained Wiener measure, the so called Wiener fractal measure, gives rise to a consistent path-integral formulation of scalar quantum ﬁeld theories in terms of the Wiener probability measure. Finally, we review some various signiﬁcant aspects of quantum ﬁeld theories (such as renormalizability, RG ﬂow, Wick rotation, regularization, etc.) by means of the analytical properties of Wiener fractal measure. We should emphasize that what makes these achievements innovative is that the mentioned results are based solely on the basics of fractal geometry and stochastic process, with no explicit or implicit imposed presuppositions of quantum physics.
Nearest Class-Center Simplification through Intermediate Layers

Recent advances in theoretical Deep Learning have introduced geometric properties that occur during training, past the Interpolation Threshold -- where the training error reaches zero. We inquire into the phenomena coined Neural Collapse in the intermediate layers of the networks, and emphasize the innerworkings of Nearest Class-Center Mismatch inside the deepnet. We further show that these processes occur both in vision and language model architectures. Lastly, we propose a Stochastic Variability-Simplification Loss (SVSL) that encourages better geometrical features in intermediate layers, and improves both train metrics and generalization.
Multi-Agent Reinforcement Learning for Distributed Joint Communication and Computing Resource Allocation over Cell-Free Massive MIMO-enabled Mobile Edge Computing Network

To support the newly introduced multimedia services with ultra-low latency and extensive computation requirements, resource-constrained end user devices should utilize the ubiquitous computing resources available at network edge for augmenting on-board (local) processing with edge computing. In this regard, the capability of cell-free massive MIMO to provide reliable access links by guaranteeing uniform quality of service without cell edge can be exploited for seamless parallel processing. Taking this into account, we consider a cell-free massive MIMO-enabled mobile edge network to meet the stringent requirements of the advanced services. For the considered mobile edge network, we formulate a joint communication and computing resource allocation (JCCRA) problem with the objective of minimizing energy consumption of the users while meeting the tight delay constraints. We then propose a fully distributed cooperative solution approach based on multiagent deep deterministic policy gradient (MADDPG) algorithm. The simulation results demonstrate that the performance of the proposed distributed approach has converged to that of a centralized deep deterministic policy gradient (DDPG)-based target benchmark, while alleviating the large overhead associated with the latter. Furthermore, it has been shown that our approach significantly outperforms heuristic baselines in terms of energy efficiency, roughly up to 5 times less total energy consumption.
Dark Matter Halos in Interacting Dark Energy Models: Formation History, Density Profile, Spin and Shape

The interacting dark energy (IDE) model, which considers the interaction between dark energy and dark matter, provides a natural mechanism to alleviate the coincidence problem and can also relieve the observational tensions under the ΛCDMmodel. Previous studies have put constraints on IDEmodels by observations of cosmic expansion history, cosmic microwave background and large-scale structures. However, these data are not yet enough to distinguish IDE models from ΛCDM effectively. Because the non-linear structure formation contains rich cosmological information, it can provide additional means to differentiate alternative models. In this paper, based on a set of N-body simulations for IDE models, we investigate the formation histories and properties of dark matter halos, and compare with their ΛCDM counterparts. For the model with dark matter decaying into dark energy and the parameters being the best-fit values from previous constraints, the structure formation is markedly slowed down, and the halos have systematically lower mass, looser internal structure, higher spin and anisotropy. This is inconsistent with the observed structure formation, and thus this model can be safely ruled out from the perspective of non-linear structure formation. Moreover, we find that the ratio of halo concentrations between IDE and ΛCDM counterparts depends sensitively on the interaction parameter and is independent of halo mass. This can act as a powerful probe to constrain IDE models. Our results concretely demonstrate that the interaction of the two dark components can affect the halo formation considerably, and therefore the constraints from non-linear structures are indispensable.
Consistent 3D Hand Reconstruction in Video via Self-Supervised Learning

We present a method for reconstructing accurate and consistent 3D hands from a monocular video. We observe that the detected 2D hand keypoints and the image texture provide important cues about the geometry and texture of the 3D hand, which can reduce or even eliminate the requirement on 3D hand annotation. Accordingly, in this work, we propose <inline-formula><tex-math notation="LaTeX">$\mathrm{{S}^{2}HAND}$</tex-math><alternatives><mml:math><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant="normal">S</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mi> HAND </mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="chen-ieq1-3247907.gif"/></alternatives></inline-formula>, a self-supervised 3D hand reconstruction model, that can jointly estimate pose, shape, texture, and the camera viewpoint from a single RGB input through the supervision of easily accessible 2D detected keypoints. We leverage the continuous hand motion information contained in the unlabeled video data and explore <inline-formula><tex-math notation="LaTeX">$\mathrm{{S}^{2}HAND(V)}$</tex-math><alternatives><mml:math><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant="normal">S</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mi> HAND </mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi mathvariant="normal">V</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href="chen-ieq2-3247907.gif"/></alternatives></inline-formula>, which uses a set of weights shared <inline-formula><tex-math notation="LaTeX">$\mathrm{{S}^{2}HAND}$</tex-math><alternatives><mml:math><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant="normal">S</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mi> HAND </mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="chen-ieq3-3247907.gif"/></alternatives></inline-formula> to process each frame and exploits additional motion, texture, and shape consistency constrains to obtain more accurate hand poses, and more consistent shapes and textures. Experiments on benchmark datasets demonstrate that our self-supervised method produces comparable hand reconstruction performance compared with the recent full-supervised methods in single-frame as input setup, and notably improves the reconstruction accuracy and consistency when using the video training data.
Joint distribution of the cokernels of random p-adic matrices

Abstract In this paper, we study the joint distribution of the cokernels of random p-adic matrices. Let p be a prime and let P 1 ⁢ ( t ) , … , P l ⁢ ( t ) ∈ ℤ p ⁢ [ t ] {P_{1}(t),\ldots,P_{l}(t)\in\mathbb{Z}_{p}[t]} be monic polynomials whose reductions modulo p in 𝔽 p ⁢ [ t ] {\mathbb{F}_{p}[t]} are distinct and irreducible. We determine the limit of the joint distribution of the cokernels cok ⁡ ( P 1 ⁢ ( A ) ) , … , cok ⁡ ( P l ⁢ ( A ) ) {{\operatorname{cok}}(P_{1}(A)),\ldots,{\operatorname{cok}}(P_{l}(A))} for a random n × n {n\times n} matrix A over ℤ p {\mathbb{Z}_{p}} with respect to the Haar measure as n → ∞ {n\rightarrow\infty} . By applying the linearization of a random matrix model, we also provide a conjecture which generalizes this result. Finally, we provide a sufficient condition that the cokernels cok ⁡ ( A ) {{\operatorname{cok}}(A)} and cok ⁡ ( A + B n ) {{\operatorname{cok}}(A+B_{n})} become independent as n → ∞ {n\rightarrow\infty} , where B n {B_{n}} is a fixed n × n {n\times n} matrix over ℤ p {\mathbb{Z}_{p}} for each n and A is a random n × n {n\times n} matrix over ℤ p {\mathbb{Z}_{p}} .
Strong closing property of contact forms and action selecting functors

We introduce a notion of strong closing property of contact forms, inspired by the $C^\infty$ closing lemma for Reeb flows in dimension three. We then prove a sufficient criterion for strong closing property, which is formulated by considering a monoidal functor from a category of manifolds with contact forms to a category of filtered vector spaces. As a potential application of this criterion, we propose a conjecture which says that a standard contact form on the boundary of any symplectic ellipsoid satisfies strong closing property.
A systematic construction of integrable delay-difference and delay-differential analogues of soliton equations

We propose a systematic method for constructing integrable delay-difference and delay-differential analogues of known soliton equations such as the Lotka–Volterra, Toda lattice (TL), and sine-Gordon equations and their multi-soliton solutions. It is carried out by applying a reduction and delay-differential limit to the discrete KP or discrete two-dimensional TL equations. Each of the delay-difference and delay-differential equations has the N-soliton solution, which depends on the delay parameter and converges to an N-soliton solution of a known soliton equation as the delay parameter approaches 0.
Bag of Tricks for Natural Policy Gradient Reinforcement Learning

—Natural policy gradient methods are popular reinforcement learning methods that improve the stability of policy gradient methods by preconditioning the gradient with the inverse of the Fisher-information matrix. However, leveraging natural policy gradient methods in an optimal manner can be very challenging as many implementation details must be set to achieve optimal performance. To the best of the authors’ knowledge, there has not been a study that has investigated strategies for setting these details for natural policy gradient methods to achieve high performance in a comprehensive and systematic manner. To address this, we have implemented and compared strategies that impact performance in natural policy gradient reinforcement learning across ﬁve different second-order approximations. These include varying batch sizes and optimizing the critic network using the natural gradient. Furthermore, insights about the fundamental trade-offs when optimizing for performance (stability, sample efﬁciency, and computation time) were generated. Experimental results indicate that the proposed collection of strategies for performance optimization can improve results by 86% to 181% across the MuJuCo control benchmark, with TENGraD exhibiting the best approximation performance amongst the tested approximations. Code in this study is available at https://github.com/gebob19/ natural-policy-gradient-reinforcement-learning.
LTC-SUM: Lightweight Client-Driven Personalized Video Summarization Framework Using 2D CNN

This paper proposes a novel lightweight thumbnail container-based summarization (LTC-SUM) framework for full feature-length videos. This framework generates a personalized keyshot summary for concurrent users by using the computational resource of the end-user device. State-of-the-art methods that acquire and process entire video data to generate video summaries are highly computationally intensive. In this regard, the proposed LTC-SUM method uses lightweight thumbnails to handle the complex process of detecting events. This significantly reduces computational complexity and improves communication and storage efficiency by resolving computational and privacy bottlenecks in resource-constrained end-user devices. These improvements were achieved by designing a lightweight 2D CNN model to extract features from thumbnails, which helped select and retrieve only a handful of specific segments. Extensive quantitative experiments on a set of full 18 feature-length videos (approximately 32.9 h in duration) showed that the proposed method is significantly computationally efficient than state-of-the-art methods on the same end-user device configurations. Joint qualitative assessments of the results of 56 participants showed that participants gave higher ratings to the summaries generated using the proposed method. To the best of our knowledge, this is the first attempt in designing a fully client-driven personalized keyshot video summarization framework using thumbnail containers for feature-length videos. Our code and trained models are publicly available at https://github.com/iamgmujtaba/LTC-SUM.
What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction

Adversarial examples (AEs) pose severe threats to the applications of deep neural networks (DNNs) to safety-critical domains, e.g., autonomous driving. While there has been a vast body of AE defense solutions, to the best of our knowledge, they all suffer from some weaknesses, e.g., defending against only a subset of AEs or causing a relatively high accuracy loss for legitimate inputs. Moreover, most existing solutions cannot defend against adaptive attacks, wherein attackers are knowledgeable about the defense mechanisms and craft AEs accordingly. In this paper, we propose a novel AE detection framework based on the very nature of AEs, i.e., their semantic information is inconsistent with the discriminative features extracted by the target DNN model. To be specific, the proposed solution, namely ContraNet, models such contradiction by first taking both the input and the inference result to a generator to obtain a synthetic output and then comparing it against the original input. For legitimate inputs that are correctly inferred, the synthetic output tries to reconstruct the input. On the contrary, for AEs, instead of reconstructing the input, the synthetic output would be created to conform to the wrong label whenever possible. Consequently, by measuring the distance between the input and the synthetic output with metric learning, we can differentiate AEs from legitimate inputs. We perform comprehensive evaluations under various AE attack scenarios, and experimental results show that ContraNet outperforms existing solutions by a large margin, especially under adaptive attacks. Moreover, our analysis shows that successful AEs that can bypass ContraNet tend to have much-weakened adversarial semantics. We have also shown that ContraNet can be easily combined with adversarial training techniques to achieve further improved AE defense capabilities.
Learning-Driven Lossy Image Compression; A Comprehensive Survey

In the realm of image processing and computer vision (CV), machine learning (ML) architectures are widely applied. Convolutional neural networks (CNNs) solve a wide range of image processing issues and can solve image compression problem. Compression of images is necessary due to bandwidth and memory constraints. Helpful, redundant, and irrelevant information are three different forms of information found in images. This paper aims to survey recent techniques utilizing mostly lossy image compression using ML architectures including different auto-encoders (AEs) such as convolutional auto-encoders (CAEs), variational auto-encoders (VAEs), and AEs with hyper-prior models, recurrent neural networks (RNNs), CNNs, generative adversarial networks (GANs), principal component analysis (PCA) and fuzzy means clustering. We divide all of the algorithms into several groups based on architecture. We cover still image compression in this survey. Various discoveries for the researchers are emphasized and possible future directions for researchers. The open research problems such as out of memory (OOM), striped region distortion (SRD), aliasing, and compatibility of the frameworks with central processing unit (CPU) and graphics processing unit (GPU) simultaneously are explained. The majority of the publications in the compression domain surveyed are from the previous five years and use a variety of approaches.
Enhanced Motor Imagery-Based Eeg Classification Using A Discriminative Graph Fourier Subspace

Dealing with irregular domains, graph signal processing (GSP) has attracted much attention especially in brain imaging analysis. Motor imagery tasks are extensively utilized in brain-computer interface (BCI) systems that perform classification using features extracted from Electroencephalogram signals. In this paper, a GSP-based approach is presented for two-class motor imagery tasks classification. The proposed method exploits simultaneous diagonalization of two matrices that quantify the covariance structure of graph spectral representation of data from each class, providing a discriminative subspace where distinctive features are extracted from the data. The performance of the proposed method was evaluated on Dataset IVa from BCI Competition III. Experimental results show that the proposed method outperforms two state-of-the-art alternative methods.
GreaseLM: Graph REASoning Enhanced Language Models for Question Answering

Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. However, pretrained language models (LM), the foundation of most modern QA systems, do not robustly represent latent relationships between concepts, which is necessary for reasoning. While knowledge graphs (KG) are often used to augment LMs with structured representations of world knowledge, it remains an open question how to effectively fuse and reason over the KG representations and the language context, which provides situational constraints and nuances. In this work, we propose GreaseLM, a new model that fuses encoded representations from pretrained LMs and graph neural networks over multiple layers of modality interaction operations. Information from both modalities propagates to the other, allowing language context representations to be grounded by structured world knowledge, and allowing linguistic nuances (e.g., negation, hedging) in the context to inform the graph representations of knowledge. Our results on three benchmarks in the commonsense reasoning (i.e., CommonsenseQA, OpenbookQA) and medical question answering (i.e., MedQA-USMLE) domains demonstrate that GreaseLM can more reliably answer questions that require reasoning over both situational constraints and structured knowledge, even outperforming models 8x larger.