Miracle-dz/UniFAR-FTUs huggingface dataset:
Provable Tensor-Train Format Tensor Completion by Riemannian Optimization

The tensor train (TT) format enjoys appealing advantages in handling structural high-order tensors. The recent decade has witnessed the wide applications of TT-format tensors from diverse disciplines, among which tensor completion has drawn considerable attention. Numerous fast algorithms, including the Riemannian gradient descent (RGrad), have been proposed for the TT-format tensor completion. However, the theoretical guarantees of these algorithms are largely missing or sub-optimal, partly due to the complicated and recursive algebraic operations in TT-format decomposition. Moreover, existing results established for the tensors of other formats, for example, Tucker and CP, are inapplicable because the algorithms treating TT-format tensors are substantially different and more involved. In this paper, we provide, to our best knowledge, the first theoretical guarantees of the convergence of RGrad algorithm for TT-format tensor completion, under a nearly optimal sample size condition. The RGrad algorithm converges linearly with a constant contraction rate that is free of tensor condition number without the necessity of re-conditioning. We also propose a novel approach, referred to as the sequential second-order moment method, to attain a warm initialization under a similar sample size requirement. As a byproduct, our result even significantly refines the prior investigation of RGrad algorithm for matrix completion. Lastly, statistically (near) optimal rate is derived for RGrad algorithm if the observed entries consist of random sub-Gaussian noise. Numerical experiments confirm our theoretical discovery and showcase the computational speedup gained by the TT-format decomposition.
A Framework for Supervised Heterogeneous Transfer Learning Using Dynamic Distribution Adaptation and Manifold Regularization

Transfer learning aims to learn classifiers for a target domain by transferring knowledge from a source domain. However, due to two main issues: feature discrepancy and distribution divergence, transfer learning can be a very difficult problem in practice. In this paper, we present a framework called TLF that builds a classifier for the target domain having only a few labeled training records by transferring knowledge from the source domain having many labeled records. While existing methods often focus on one issue and leave the other one for further work, TLF is capable of handling both issues simultaneously. In TLF, we alleviate feature discrepancy by identifying shared label distributions that act as the pivots to bridge the domains. We handle distribution divergence by simultaneously optimizing the structural risk functional, joint distributions between domains, and the manifold consistency underlying marginal distributions. Moreover, for the manifold consistency we exploit its intrinsic properties by identifying <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="rahman-ieq1-3213238.gif"/></alternatives></inline-formula> nearest neighbors of a record, where <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="rahman-ieq2-3213238.gif"/></alternatives></inline-formula> is determined automatically. We evaluate TLF on seven publicly available datasets and compare performances of TLF and fourteen state-of-the-art techniques. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of TLF over the state-of-the-art techniques.
Reinforcement Learning-powered Semantic Communication via Semantic Similarity

—We introduce a new semantic communication mech- anism - SemanticRL, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision. Unlike previous methods that mainly concentrate on the network or structure design, we revisit the learning process and point out the semantic blindness of commonly used objective functions. To address this semantic gap, we introduce a schematic shift that learns from semantic similarity, instead of relying on conventional paired bit-level supervisions like cross entropy and bit error rate. However, developing such a semantic communication system is indeed a nontrivial task considering the non-differentiability of most semantic metrics as well as the instability from noisy channels. To further resolve these issues, we put forward a self-critic reinforcement learning (RL) solution which allows an efﬁcient and stable learning on any user-deﬁned semantic measurement, and take a step further to simultaneously tackle the non-differentiable semantic channel optimization problem via self-critic stochastic iterative updating (SCSIU) training on the decoupled semantic transceiver. We have ﬁrstly tested the proposed method in the challenging European-parliament dataset, which conﬁrms the superiority of our method in revealing the semantic meanings, and better handling the semantic noise. Apart from the experimental results, we further provide an in- depth look at how the semantic model behaves, along with its superb generalization ability in real-life examples. An RL-based image transmission extension is also exempliﬁed, so as to prove the generalization ability and motivate future discussion.
Distinguishing spin pumping from spin rectification in lateral spin pumping device architectures based on doped organic semiconductors

Over the last two decades organic spintronics has developed into a striving field with exciting reports of long spin diffusion lengths and spin relaxation times in organic semiconductors (OSCs). Easily processed and inexpensive, OSCs are considered a potential alternative to inorganic materials for use in spintronic applications. Spin currents have been detected in a wide range of materials, however, there is still uncertainty over the origin of the signals. Recently, we explored spin transport through an organic semiconductor with lateral spin injection and detection architectures, where the injected spin current is detected non-locally via spin-to-charge conversion in an inorganic detector. In this work we show that the widely-used control experiments like linear power dependence and inversion of the signal with the magnetic field are not sufficient evidence of spin transport and can lead to an incorrect interpretation of the signal. Here, we use in-plane angular dependent measurements to separate pure spin signal from parasitic effects arising from spin rectification (SREs). Apart from well established anisotropic magnetoresistance (AMR) and anomalous Hall effect (AHE), we observed a novel effect which we call spurious inverse spin Hall effect (ISHE). It strongly resembles ISHE behaviour, but arises in the ferromagnet rather than the detector meaning this additional effect has to be considered in future work.
The impact of hardware specifications on reaching quantum advantage in the fault tolerant regime

We investigate how hardware specifications can impact the final run time and the required number of physical qubits to achieve a quantum advantage in the fault tolerant regime. Within a particular time frame, both the code cycle time and the number of achievable physical qubits may vary by orders of magnitude between different quantum hardware designs. We start with logical resource requirements corresponding to a quantum advantage for a particular chemistry application, simulating the FeMoco molecule, and explore to what extent slower code cycle times can be mitigated by using additional qubits. We show that in certain situations architectures with considerably slower code cycle times will still be able to reach desirable run times, provided enough physical qubits are available. We utilize various space and time optimization strategies that have been previously considered within the field of error-correcting surface codes. In particular, we compare two distinct methods of parallelization, Game of Surface Code's Units, and AutoCCZ factories, both of which enable one to incrementally speed up the computation until the reaction limited rate is reached. Finally we calculate the number of physical qubits which would be required to break the 256 bit elliptic curve encryption of keys in the Bitcoin network, within the small available time frame in which it would actually pose a threat to do so. It would require approximately 317 million physical qubits to break the encryption within one hour using the surface code, a code cycle time of 1 $ \mu s$, a reaction time of 10 $ \mu s$, and physical gate error of $10^{-3}$. To break the encryption instead within one day it would require 13 million physical qubits.
Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors

In this paper, we explore the capacity of a language model-based method for grammatical error detection in detail. We first show that 5 to 10% of training data are enough for a BERT-based error detection method to achieve performance equivalent to what a non-language model-based method can achieve with the full training data; recall improves much faster with respect to training data size in the BERT-based method than in the non-language model method. This suggests that (i) the BERT-based method should have a good knowledge of the grammar required to recognize certain types of error and that (ii) it can transform the knowledge into error detection rules by fine-tuning with few training samples, which explains its high generalization ability in grammatical error detection. We further show with pseudo error data that it actually exhibits such nice properties in learning rules for recognizing various types of error. Finally, based on these findings, we discuss a cost-effective method for detecting grammatical errors with feedback comments explaining relevant grammatical rules to learners.
Binocular Mutual Learning for Improving Few-shot Classification

Most of the few-shot learning methods learn to transfer knowledge from datasets with abundant labeled data (i.e., the base set). From the perspective of class space on base set, existing methods either focus on utilizing all classes under a global view by normal pretraining, or pay more attention to adopt an episodic manner to train meta-tasks within few classes in a local view. However, the interaction of the two views is rarely explored. As the two views capture complementary information, we naturally think of the compatibility of them for achieving further performance gains. Inspired by the mutual learning paradigm and binocular parallax, we propose a unified framework, namely Binocular Mutual Learning (BML), which achieves the compatibility of the global view and the local view through both intraview and cross-view modeling. Concretely, the global view learns in the whole class space to capture rich inter-class relationships. Meanwhile, the local view learns in the local class space within each episode, focusing on matching positive pairs correctly. In addition, cross-view mutual interaction further promotes the collaborative learning and the implicit exploration of useful knowledge from each other. During meta-test, binocular embeddings are aggregated together to support decision-making, which greatly improve the accuracy of classification. Extensive experiments conducted on multiple benchmarks including cross-domain validation confirm the effectiveness of our method1.
A Tutorial on Learning Disentangled Representations in the Imaging Domain

—Disentangled representation learning has been proposed as an approach to learning general representations. This can be done in the absence of, or with limited, annotations. A good general representation can be readily ﬁne-tuned for new target tasks using modest amounts of data, or even be used directly in unseen domains achieving remarkable performance in the corresponding task. This alleviation of the data and annotation requirements offers tantalising prospects for tractable and affordable applications in computer vision and healthcare. Finally, disentangled representations can offer model explainability and can help us understand the underlying causal relations of the factors of variation, increasing their suitability for real-world deployment. In this tutorial paper, we will offer an overview of the disentangled representation learning, its building blocks and criteria, and discuss applications in computer vision and medical imaging. We conclude our tutorial by presenting the identiﬁed opportunities for the integration of recent machine learning advances into disentanglement, as well as the remaining challenges.
Contaminated Gibbs-Type Priors

Gibbs-type priors are widely used as key components in several Bayesian nonparametric models. By virtue of their flexibility and mathematical tractability, they turn out to be predominant priors in species sampling problems, clustering and mixture modelling. We introduce a new family of processes which extend the Gibbs-type one, by including a contaminant component in the model to account for the presence of anomalies (outliers) or an excess of observations with frequency one. We first investigate the induced random partition, the associated predictive distribution and we characterize the asymptotic behaviour of the number of clusters. All the results we obtain are in closed form and easily interpretable, as a noteworthy example we focus on the contaminated version of the Pitman-Yor process. Finally we pinpoint the advantage of our construction in different applied problems: we show how the contaminant component helps to perform outlier detection for an astronomical clustering problem and to improve predictive inference in a species-related dataset, exhibiting a high number of species with frequency one.
Contrastive Mixup: Self- and Semi-Supervised learning for Tabular Domain

Recent literature in self-supervised has demonstrated significant progress in closing the gap between supervised and unsupervised methods in the image and text domains. These methods rely on domain-specific augmentations that are not directly amenable to the tabular domain. Instead, we introduce Contrastive Mixup, a semi-supervised learning framework for tabular data and demonstrate its effectiveness in limited annotated data settings. Our proposed method leverages Mixup-based augmentation under the manifold assumption by mapping samples to a low dimensional latent space and encourage interpolated samples to have high a similarity within the same labeled class. Unlabeled samples are additionally employed via a transductive label propagation method to further enrich the set of similar and dissimilar pairs that can be used in the contrastive loss term. We demonstrate the effectiveness of the proposed framework on public tabular datasets and real-world clinical datasets.
Ion heat and parallel momentum transport by stochastic magnetic fields and turbulence

The theory of turbulent transport of parallel momentum and ion heat by the interaction of stochastic magnetic fields and turbulence is presented. Attention is focused on determining the kinetic stress and the compressive energy flux. A critical parameter is identified as the ratio of the turbulent scattering rate to the rate of parallel acoustic dispersion. For the parameter large, the kinetic stress takes the form of a viscous stress. For the parameter small, the quasilinear residual stress is recovered. In practice, the viscous stress is the relevant form, and the quasilinear limit is not observable. This is the principal prediction of this paper. A simple physical picture is developed and shown to recover the results of the detailed analysis.
A Guide to Reproducible Research in Signal Processing and Machine Learning

Reproducibility is a growing problem that has been extensively studied among computational researchers and within the signal processing and machine learning research community [1], [2]. A computational experiment is deemed reproducible if the same data and methods are available to replicate quantitative results by any independent researcher, anywhere and at any time, granted they have the required computing power. The advancements in signal processing and machine learning have been made possible by an increase in large-scale computational experiments involving powerful computing resources, massive datasets, and a myriad of new libraries and frameworks (such as NumPy [3], Scikit-learn [4], MATLAB Toolboxes [5], and TensorFlow [6]) that provide a layer of abstraction allowing for rapid implementation of complex algorithms. However, with this changing landscape of signal processing and machine learning research come new obstacles and unseen challenges in creating reproducible experiments. Computational experiments today often incorporate various scripts for preprocessing data, running algorithms, and plotting results, all while utilizing huge datasets which require computing clusters that often take days or weeks to ﬁnish computing with multiple manual interventions needed to successfully produce the desired results. This is contrary to the way computational experiments used to be conducted and the way new researchers are introduced to computational resources in the classroom, where they typically use simple and intuitive interactive computing software consisting of a single script that runs locally on one’s computer [7]. This new paradigm of computational experiments is now requiring the scientiﬁc community to rethink how we publicize and share our code to encapsulate all the necessary information about our experiments and make reproducibility possible. Additionally our extensive dependence on libraries and frameworks leads to brittle codebases that typically only output correct results when executed on the original machine of the researcher. Furthermore, the nature of these data-driven experiments often require careful parameter tuning, random number generations, and data preprocessing, all of which are independent from the main ﬁnding, such as a new algorithm, being implemented or investigated.
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning

The ability to continuously expand knowledge over time and utilize it to rapidly generalize to new tasks is a key feature of human linguistic intelligence. Existing models that pursue rapid generalization to new tasks (e.g., few-shot learning methods), however, are mostly trained in a single shot on fixed datasets, unable to dynamically expand their knowledge; while continual learning algorithms are not specifically designed for rapid generalization. We present a new learning setup, Continual Learning of Few-Shot Learners (CLIF), to address the challenges of both learning settings in a unified setup. CLIF assumes a model learns from a sequence of diverse NLP tasks arriving sequentially, accumulating knowledge for improved generalization to new tasks, while also retaining performance on the tasks learned earlier. We examine how the generalization ability is affected in the continual learning setup, evaluate a number of continual learning algorithms, and propose a novel regularized adapter generation approach. We find that catastrophic forgetting affects generalization ability to a less degree than performance on seen tasks; while continual learning algorithms can still bring considerable benefit to the generalization ability.
Charge-Density-Wave-Induced Bands Renormalization and Energy Gaps in a Kagome Superconductor 
RbV3Sb5

Zhonghao Liu,1, 2, ∗ Ningning Zhao,3 Qiangwei Yin,3 Chunsheng Gong,3 Zhijun Tu,3 Man Li,3 Wenhua Song,3 Zhengtai Liu,1 Dawei Shen,1, 2 Yaobo Huang,4 Kai Liu,3, † Hechang Lei,3, ‡ and Shancai Wang3, § 1State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China 2College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China 3Department of Physics and Beijing Key Laboratory of Opto-Electronic Functional Materials&Micro-Nano Devices, Renmin University of China, Beijing 100872, China 4Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
Perioperative Blood Loss Can Be Reduced If Total Knee Arthroplasty Was Performed in the Si Hour-Period, Compared with the Wei Hour-Period: A Retrospective Cohort Study

Objective To investigate the efficacy and safety of performing primary unilateral total knee arthroplasty (TKA) in the “Si hour-period” meaning 09:00 a.m. to 11:00 a.m. (one of the 12 two-hour periods into which the day was traditionally divided, each being given the name of one of the 12 earthly branches), compared with the “Wei hour-period” (13:00–15:00). Methods Patient documentations were studied for those who underwent a primary unilateral TKA performed by the same surgical team with a tourniquet between January 2018 and January 2021 at our medical center. Eighty-four patients were enrolled and assigned into group A (in Si hour-period) and group B (in Wei hour-period). The main outcomes were total blood cell loss (TBL), hidden blood loss (HBL), visible blood loss (VBL), maximum hemoglobin (Hb) drop, and transfusion rate. Secondary outcomes were length of hospital stay (LOS), postoperative femorotibial mechanical axis (FTMA), FTMA correction, platelet count, plasma D-dimer (D-D), prothrombin time (PT), international normalized ratio (INR), and the incidence of postoperative complications. Results Group A showed statistical significance lower at the mean TBL, the mean HBL, and the maximum Hb drop (95% CI: −352.8 to −46.1,P=0.011, 95% CI: −348.0 to −40.1,P=0.014, and 95% CI: −9.5 to −0.7,P=0.023, respectively) after TKA than group B. The postoperative platelet count of group A was more significant than that of group B (95% CI:3.1 to 52.9, P=0.028). The VBL, transfusion rate, the LOS, postoperative FTMA, FTMA correction, plasma D-D, PT, INR, and the incidence of postoperative complications (wound complications, calf muscular vein thrombosis, infection, pulmonary embolism, and deep vein thrombosis) were similar between the two groups (P > 0.05, respectively). Conclusion Our study shows that blood loss can be reduced when TKA is performed in the “Si hour-period,” which may be due to increasing platelet count, and postoperative complications did not increase, compared with the Wei hour-period. We recommend that the selective operation, such as TKA, should be performed in the “Si hour-period” in clinical practice between the two hour-period.
High Performance Flexible Nonvolatile Memory Based on Vertical Organic Thin Film Transistor

Flexible floating‐gate organic transistor memory (FGOTM) is a potential candidate for emerging memory technologies. Unfortunately, conventional planar FGOTM suffers from weak driving ability and insufficient mechanical flexibility, which limits its commercial application. In this work, a novel flexible vertical FGOTM (VFGOTM) is reported. Benefitting from new vertical architecture, VFGOTM provides ultrashort channel length to afford an extremely high current density. Meanwhile, VFGOTM devices exhibit excellent memory performance and outstanding retention property. The memory properties of VFGOTM devices are comparable or even better than traditional planar FGOTM and much better than the reported organic nonvolatile memory with vertical transistor structures. More importantly, organic nonvolatile memory with vertical transistor structures is investigated for the first time on a flexible substrate. The results show that VFGOTM architecture allows vertical current flow across the channel layer to effectively eliminate the effect of mechanical bending during current transport, which significantly improves the mechanical stability of the flexible VFGOTM. Hence, with a combination of excellent driving ability, memory performance, and mechanical stability, VFGOTM devices meet the practical requirements for high performance memory applications, which have great potential for the application in a wide range of flexible and wearable electronics.