Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Recommendation with Implicit Feedback via Eliminating the Effects of Unexpected Behaviors

In the implicit feedback recommendation, incorporating short-term preference into recommender systems has attracted increasing attention in recent years. However, unexpected behaviors in historical interactions like clicking some items by accident don't well reflect users' inherent preferences. Existing studies fail to model the effects of unexpected behaviors, thus achieve inferior recommendation performance. In this paper, we propose a Multi-Preferences Model (MPM) to eliminate the effects of unexpected behaviors. MPM first extracts the users' instant preferences from their recent historical interactions by a fine-grained preference module. Then an unexpected-behaviors detector is trained to judge whether these instant preferences are biased by unexpected behaviors. We also integrate user's general preference in MPM. Finally, an output module is performed to eliminate the effects of unexpected behaviors and integrates all the information to make a final recommendation. We conduct extensive experiments on two datasets of a movie and an e-retailing, demonstrating significant improvements in our model over the state-of-the-art methods. The experimental results show that MPM gets a massive improvement in HR@10 and NDCG@10, which relatively increased by 3.643% and 4.107% compare with AttRec model on average. We publish our code at https://github.com/chenjie04/MPM/.
A projection-based synthesis of non-uniform array

A novel synthesis method for a nonuniform antenna array is presented, based on the intersection approach and the projection concept. The method makes it possible to deal with general pattern requirements and lends itself to an effective implementation. The synthesis procedure was tested on a number of significant test cases, and typical test results are reported. <<ETX>>
Transferable End-to-end Room Layout Estimation via Implicit Encoding

We study the problem of estimating room layouts from a single panorama image. Most former works have two stages: feature extraction and parametric model fitting. Here we propose an end-to-end method that directly predicts parametric layouts from an input panorama image. It exploits an implicit encoding procedure that embeds parametric layouts into a latent space. Then learning a mapping from images to this latent space makes end-to-end room layout estimation possible. However end-to-end methods have several notorious drawbacks despite many intriguing properties. A widely raised criticism is that they are troubled with dataset bias and do not transfer to unfamiliar domains. Our study echos this common belief. To this end, we propose to use semantic boundary prediction maps as an intermediate domain. It brings significant performance boost on four benchmarks (Structured3D, PanoContext, S3DIS, and Matterport3D), notably in the zero-shot transfer setting. Code, data, and models will be released.
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee

Policy optimization methods are one of the most widely used classes of Reinforcement Learning (RL) algorithms. However, theoretical understanding of these methods remains insufficient. Even in the episodic (time-inhomogeneous) tabular setting, the state-of-the-art theoretical result of policy-based method in \citet{shani2020optimistic} is only $\tilde{O}(\sqrt{S^2AH^4K})$ where $S$ is the number of states, $A$ is the number of actions, $H$ is the horizon, and $K$ is the number of episodes, and there is a $\sqrt{SH}$ gap compared with the information theoretic lower bound $\tilde{\Omega}(\sqrt{SAH^3K})$. To bridge such a gap, we propose a novel algorithm Reference-based Policy Optimization with Stable at Any Time guarantee (\algnameacro), which features the property"Stable at Any Time". We prove that our algorithm achieves $\tilde{O}(\sqrt{SAH^3K} + \sqrt{AH^4K})$ regret. When $S>H$, our algorithm is minimax optimal when ignoring logarithmic factors. To our best knowledge, RPO-SAT is the first computationally efficient, nearly minimax optimal policy-based algorithm for tabular RL.
Worldline instantons for nonlinear Breit-Wheeler pair production and Compton scattering

Worldline instantons have previously been used to study the probability of Schwinger pair production (both the exponential and pre-exponential parts) and photon-stimulated pair production (the exponential part). Previous studies obtained the pair-production probability on the probability level by using unitarity, i.e. the imaginary part of the effective action for Schwinger pair production or the imaginary part of the polarization tensor for photon-stimulated pair production. The corresponding instantons are closed loops in the complex plane. Here we show how to use instantons on the amplitude level, which means open instanton lines with start and end points representing fermions at asymptotic times. The amplitude is amputated with LSZ using, in general, field-dependent asymptotic states. We show how to use this formalism for photon-stimulated/Breit-Wheeler pair production and nonlinear Compton scattering.
Theory of drift-enabled control in nonlocal magnon transport

Electrically injected and detected nonlocal magnon transport has emerged as a versatile method for transporting spin as well as probing the spin excitations in a magnetic insulator. We examine the role of drift currents in this phenomenon as a method for controlling the magnon propagation length. Formulating a phenomenological description, we identify the essential requirements for existence of magnon drift. Guided by this insight, we examine magnetic field gradient, asymmetric contribution to dispersion, and temperature gradient as three representative mechanisms underlying a finite magnon drift velocity, finding temperature gradient to be particularly effective.
Translational Concept Embedding for Generalized Compositional Zero-shot Learning

Generalized compositional zero-shot learning means to learn composed concepts of attribute-object pairs in a zero-shot fashion, where a model is trained on a set of seen concepts and tested on a combined set of seen and unseen concepts. This task is very challenging because of not only the gap between seen and unseen concepts but also the contextual dependency between attributes and objects. This paper introduces a new approach, termed translational concept embedding, to solve these two difficulties in a unified framework. It models the effect of applying an attribute to an object as adding a translational attribute feature to an object prototype. We explicitly take into account of the contextual dependency between attributes and objects by generating translational attribute features conditionally dependent on the object prototypes. Furthermore, we design a ratio variance constraint loss to promote the model's generalization ability on unseen concepts. It regularizes the distances between concepts by utilizing knowledge from their pretrained word embeddings. We evaluate the performance of our model under both the unbiased and biased concept classification tasks, and show that our model is able to achieve good balance in predicting unseen and seen concepts.
Melody Harmonization with Controllable Harmonic Rhythm

Melody harmonization, namely generating a chord progression for a user-given melody, remains a challenging task to this day. Although previous neural network-based systems can effectively generate an appropriate chord progression for a melody, few studies focus on controllable melody harmonization, and none of them can generate flexible harmonic rhythms. To achieve harmonic rhythm-controllable melody harmonization, we propose AutoHarmonizer, a neural network-based melody harmonization system that can generate denser or sparser chord progressions with the use of a new sampling method for controllable generation proposed in this paper. This system mainly consists of two parts: a harmonic rhythm model provides coarse-grained chord onset information, while a chord model generates specific pitches for chords based on the given melody and the corresponding harmonic rhythm sequence previously generated. To evaluate the performance of AutoHarmo-nizer, we use nine metrics to compare the chord progressions from humans, the system proposed in this paper and the baseline. Experimental results show that AutoHarmonizer not only generates harmonic rhythms comparable to the human level, but generates chords with overall better quality than baseline at different settings. In addition, we use AutoHarmonizer to harmonize the Session Dataset (which were originally chordless), and ended with 40,925 traditional Irish folk songs with harmonies, named the Session Lead Sheet Dataset , which is the largest lead sheet dataset to date.
Local Strong Convexity of Source Localization and Error Bound for Target Tracking under Time-of-Arrival Measurements

In this paper, we consider a time-varying optimization approach to the problem of tracking a moving target using noisy time-of-arrival (TOA) measurements. Specifically, we formulate the problem as that of sequential TOA-based source localization and apply online gradient descent (OGD) to it to generate the position estimates of the target. To analyze the tracking performance of OGD, we first revisit the classic least-squares formulation of the (static) TOA-based source localization problem and elucidate its estimation and geometric properties. In particular, under standard assumptions on the TOA measurement model, we establish a bound on the distance between an optimal solution to the least-squares formulation and the true target position. Using this bound, we show that the loss function in the formulation, albeit non-convex in general, is locally strongly convex at its global minima. To the best of our knowledge, these results are new and can be of independent interest. By combining them with existing techniques from online strongly convex optimization, we then establish the first non-trivial bound on the cumulative target tracking error of OGD. Our numerical results corroborate the theoretical findings and show that OGD can effectively track the target at different noise levels.
Bialgebras, Frobenius algebras and associative Yang-Baxter equations for Rota-Baxter algebras

Rota-Baxter operators and bialgebras go hand in hand in their applications, such as in the Connes-Kreimer approach to renormalization and the operator approach to the classical Yang-Baxter equation. We establish a bialgebra structure that is compatible with the Rota-Baxter operator, called the Rota-Baxter antisymmetric infinitesimal (ASI) bialgebra. This bialgebra is characterized by generalizations of matched pairs of algebras and double constructions of Frobenius algebras to the context of Rota-Baxter algebras. The study of the coboundary case leads to an enrichment of the associative Yang-Baxter equation (AYBE) to Rota-Baxter algebras. Antisymmetric solutions of the equation are used to construct Rota-Baxter ASI bialgebras. The notions of an O-operator on a Rota-Baxter algebra and a Rota-Baxter dendriform algebra are also introduced to produce solutions of the AYBE in Rota-Baxter algebras and thus to provide Rota-Baxter ASI bialgebras. An unexpected byproduct is that a Rota-Baxter ASI bialgebra of weight zero gives rise to a quadri-bialgebra instead of bialgebra constructions for the dendriform algebra.
Fake News Detection Tools and Methods - A Review

In the past decade, the social networks platforms and micro-blogging sites such as Facebook, Twitter, Instagram, and Weibo have become an integral part of our day-to-day activities and is widely used all over the world by billions of users to share their views and circulate information in the form of messages, pictures, and videos. These are even used by government agencies to spread important information through their verified Facebook accounts and official Twitter handles, as they can reach a huge population within a limited time window. However, many deceptive activities like propaganda and rumor can mislead users on a daily basis. In these COVID times, fake news and rumors are very prevalent and are shared in a huge number which has created chaos in this tough time. And hence, the need for Fake News Detection in the present scenario is inevitable. In this paper, we survey the recent literature about different approaches to detect fake news over the Internet. In particular, we firstly discuss fake news and the various terms related to it that have been considered in the literature. Secondly, we highlight the various publicly available datasets and various online tools that are available and can debunk Fake News in real-time. Thirdly, we describe fake news detection methods based on two broader areas i.e., its content and the social context. Finally, we provide a comparison of various techniques that are used to debunk fake news.
Chaos and sub-diffusion in wave packets with periodically kicked interactions

We study the quantum dynamics of a peculiar driven system, a Bose gas subjected to periodicallykicked interactions. In the limit of infinitely short kicks, this system was recently shown to exhibit a fast exponential spreading of the wave function. Here we examine this problem for kicks or arbitrary duration and show that, in this case, the spreading is not exponential but rather sub-diffusive at long time. This phenomenon stems from the competition between the kinetic and interaction energies within the kicks, which is absent in the limit of delta kicks. Our analysis further shows that the breakdown of exponential spreading occurs at relatively short times even for extremely short kicks, suggesting that, in practice, sub-diffusion should be more the rule than the exception in this system.
Byzantine Fault-Tolerant Causal Ordering

Byzantine fault-tolerant causal ordering of messages in asynchronous systems is useful to many applications. Although the problem has been studied for broadcast communication, it has not been examined for unicasts or multicasts in asynchronous systems. In this paper, we use execution histories to prove that it is impossible to solve causal ordering for both unicasts and multicasts in an asynchronous system with one or more Byzantine processes. In view of these impossibility results, we propose the Channel Sync Algorithms to provide causal order of unicasts and multicasts under the Byzantine failure model in synchronous systems, which have a known upper bound on message latency. The Channel Sync Algorithms operate under the synchronous system model, but are inherently asynchronous and offer a high degree of concurrency as lock-step communication is not assumed.
Deducing Optimal Classification Algorithm for Heterogeneous Fabric

For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed the supervised machine learning runs for five different algorithms. For heterogeneous rock fabric, we identified Random Forest, among others, to be the appropriate algorithm.
Efficacy and safety evaluation of acupuncture in the treatment of impaired glucose regulation

Abstract Background: Impaired of glucose regulation belongs to the stage of prediabetes, which is a state of glucose metabolism between diabetes and normal blood glucose. The prevalence of prediabetes in people over 20 years old in China is significantly higher than that in diabetic patients. If no measures are taken to prevent the transition from prediabetes to diabetes, the number of diabetic patients in China will further increase. This study conducted a meta-analysis of the effectiveness of acupuncture in the treatment of impaired glucose regulation by collecting relevant literatures. Methods: Nine electronic databases: PubMed, EMBASE, Cochrane library, Web of Science, Google Scholar, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, Chinese Scientific and Journal Database, Wan Fang database, and 2 clinical trials register platforms: Chinese Clinical Trial Registry, ClinicalTrials.gov (www.ClinicalTrials.gov/) will be searched for randomized clinical trails of acupuncture for impaired glucose regulation. The screening process will be developed by 2 independent reviewers, and meta-analysis will be performed with RevMan (V5.3.5) software. Results: This meta-analysis further confirmed the benefits of acupuncture in the treatment of impaired of glucose regulation. Conclusion: This study will provide a high-quality evidence of the efficacy and safety of acupuncture on patients with impaired glucose regulation. PROSPERO registration number: INPLASY202170058. Ethics and dissemination: This systematics review will evaluate the efficacy and safety of acupuncture in the treatment of impaired of glucose regulation. Since all the data included were published, the systematic review did not require ethical approval.
Alternative Anchorage Methodology for Exposed Geomembrane Installations with Flexible Solar Photovoltaic Panels

This paper provides an alternative design and construction methodology for an exposed geomembrane cover at risk of uplift by wind. The alternative anchorage can be used with both exposed geomembrane covers and exposed geomembrane covers hosting flexible laminated photovoltaic (PV) solar panels. The exposed geomembrane cover can be designed using earth anchors consisting of corrosion resistant materials in lieu of the traditional soil-filled anchor trenches and/or anchor benches (trench/bench) to secure the exposed geomembrane against wind action. Penetrations made by the earth anchor alternative can be covered by flexible solar panels adhered onto a carrier geomembrane. The earth anchor penetrations are covered by the carrier geomembrane and solar panels, thereby creating a uniform hydraulic barrier and sustainable cover system. It is anticipated that this alternative anchorage method may reduce many of the issues that arise from constructing on-slope anchor trenches (e.g., constructability, drainage from benches, and exposure and handling of the existing buried waste).
A Minimal GPU Stack for Client ML

GPUReplay is a novel way for deploying GPU-accelerated computation on mobile and embedded devices. It addresses high complexity of a modern GPU stack for deployment ease and security. The idea is to record GPU executions on the full GPU stack ahead of time and replay the executions on new input at run time. We address key challenges towards making GR feasible, sound, and practical to use. The resultant replayer is a drop-in replacement of the original GPU stack. It is tiny (50 KB of executable), robust (replaying long executions without divergence), portable (running in a commodity OS, in TEE, and baremetal), and quick to launch (speeding up startup by up to two orders of magnitude). We show that GPUReplay works with a variety of integrated GPU hardware, GPU APIs, ML frameworks, and 33 neural network (NN) implementations for inference or training. The code is available at https://github.com/bakhi/GPUReplay .
Privacy-Preserving and Sustainable Contact Tracing Using Batteryless Bluetooth Low-Energy Beacons

Contact tracing is the technology choice of reference to address the COVID-19 pandemic. Many of the current approaches have severe privacy and security issues and fail to offer a sustainable contact tracing infrastructure. We address these issues introducing an innovative, privacy-preserving, sustainable, and experimentally tested architecture that leverages batteryless Bluetooth low-energy beacons.
Analytical tool requirements for power system restoration

This paper is one of a series presented by the IEEE Power System Restoration Working Group (SRWG) on behalf of the System Operation Subcommittee with the intent of focusing industry attention on power system restoration. In this paper a set of analytical tools is specified which together describe the static transient and dynamic behavior or a power system during restoration. These tools are identified and described for restoration planning, training and operation. Their applications cover all stages of restoration including pre-disturbance condition, post-disturbance status, post-restoration target system, and minimization of unserved loads. The paper draws on the previous reports by the SRWG. >
Describing Non–Lane-Based Motorcycle Movements in Motorcycle-Only Traffic Flow

The motorcycle is the main transport mode for commuters in Vietnam because it costs less than a car and has greater mobility on congested city roads. A better understanding of the characteristics of motorcycle traffic flow can be helpful in formulating proposals for traffic management policies. Under congested conditions, motorcycles are observed to change their directions and speeds quite often. This study focuses on the zigzag movements of motorcycles in motorcycle-only traffic, which are termed “non–lane-based movements. ” Research was conducted on the mechanisms of non–lane-based movements with a consideration of how two behaviors of a subject motorcycle—acceleration and deceleration—were affected by the velocity changes of lead motorcycles. A concept of safety space was introduced to explain these behaviors. Calibration data for the proposed model were extracted from video clips taken at road segments in Ho Chi Minh City, Vietnam. By calculating the root mean square error of the estimated value and the field value, the proposed model reproduced the speed and direction of motorcycles with high reliability. A computer simulation was used to reproduce two basic types of non–lane-based movements—oblique following and swerving—and to verify the difference in the speed–flow relationship between lane-based and non–lane-based movements.
Consultation patterns and frequent attenders in UK primary care from 2000 to 2019: a retrospective cohort analysis of consultation events across 845 general practices

Objective To describe the distribution of consultations at the practice level and examine whether increases are uniform or driven by people who consult more frequently. Design Retrospective cohort study. Setting UK general practice data from the Clinical Practice Research Datalink (CPRD) GOLD database. Participants 1 699 709 314 consultation events from 12 330 545 patients, in 845 general practices (1 April 2000 to 31 March 2019). Methods Consultation information was aggregated by financial year into: all consultations/all staff; all consultations/general practitioners (GPs); face-to-face consultations/all staff; face-to-face consultations/GPs. Patients with a number of consultations above the 90th centile, within each year, were classified as frequent attenders. Negative binomial regressions examined the association between available practice characteristics and consultation distribution. Results Among frequent attenders, all consultations by GPs increased from a median (25th and 75th centile) of 13 (10 and 16) to 21 (18 and 25) and all consultations by all staff increased from 27 (23–30) to 60 (51–69) over the study period. Approximately four out of ten consultations of any type concerned frequent attenders and the proportion of consultations attributed to them increased over time, particularly for face-to-face consultations with GPs, from a median of 38.0% (35.9%–40.3%) in 2000–2001 to 43.0% (40.6%–46.4%) in 2018–2019. Regression analyses indicated decreasing trends over time for face-to-face consultations and increasing trends for all consultation types, for both GPs and all staff. Frequent attenders consulted approximately five times more than the rest of the practice population, on average, with adjusted incidence rate ratios ranging between 4.992 (95% CI 4.917 to 5.068) for face-to-face consultations with all staff and 5.603 (95% CI 5.560 to 5.647) for all consultations with GPs. Conclusions Frequent attenders progressively contributed to increased workload in general practices across the UK from 2000 to 2019. Important knowledge gaps remain in terms of the demographic, social and health characteristics of frequent attenders and how UK general practices can be prepared to meet the needs of these patients.