Miracle-dz/UniFAR-FTUs huggingface dataset:
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce

Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. In this paper, we propose K-PLUG, a knowledge-injected pre-trained language model based on the encoder-decoder transformer that can be transferred to both natural language understanding and generation tasks. We verify our method in a diverse range of e-commerce scenarios that require domain-specific knowledge. Specifically, we propose five knowledge-aware self-supervised pre-training objectives to formulate the learning of domain-specific knowledge, including e-commerce domain-specific knowledge-bases, aspects of product entities, categories of product entities, and unique selling propositions of product entities. K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks.
Unsupervised Learning in Next-Generation Networks: Real-Time Performance Self-Diagnosis

This letter demonstrates the use of unsupervised machine learning to enable performance self-diagnosis of next-generation cellular networks. We propose two simplified applications of unsupervised learning that can enable real-time performance self-diagnosis on edge nodes such as the radio access network intelligent controller (RIC). The first application detects anomalous performance and finds its root cause of faults, configuration, or network procedure failures. The second application uses clustering to learn the relationship between two performance measures. Our proposed applications run in near-constant time complexity, making them, combined with subject-matter expertise validation, suitable real-time RIC applications for network diagnosis.
Optimal Downlink Training Sequence for Massive MIMO Secret-Key Generation

In this paper, the secret-key capacity is max-imized by optimizing the downlink training sequence in a time division duplexing (TDD) massive multiple-input-multiple-output (MIMO) scenario. Both single-user and multiple user cases are considered. As opposed to previous works, the optimal training sequence and the related secret-key capacity is characterized in closed-form in the single-user case and the large antenna multiple-user case. Designs taking into account a constraint on the maximal number of pilots are also proposed. In the multiple-user case, both the max-min and the sum capacity criteria are considered, including potential user priorities. In the end, it is shown that massive MIMO boosts the secret-key capacity by leveraging: i) spatial dimensionality gain and ii) array gain. Moreover, in the large antenna case, the multiple-user capacity is obtained with no extra pilot overhead as compared to the single-user case.
Muon g−2 Discrepancy Within D‐brane String Compactifications

Very recently, the Muon g − 2 experiment at Fermilab has confirmed the E821 Brookhaven result, which hinted at a deviation of the muon anomalous magnetic moment from the Standard Model (SM) expectation. The combined results from Brookhaven and Fermilab show a difference with the SM prediction δaμ = (251 ± 59) × 10−11 at a significance of 4. 2σ, strongly indicating the presence of new physics. Motivated by this new result we reexamine the contributions to δaμ from both: (i) the ubiquitous U(1) gauge bosons of D‐brane string theory constructions and (ii) the Regge excitations of the string. We show that, for a string scale O(PeV) , the contribution from anomalous U(1) gauge bosons which couple to hadrons could help to reduce (though not fully eliminate) the discrepancy reported by the Muon g − 2 Collaboration. Consistency with null results from LHC searches of new heavy vector bosons imparts the dominant constraint. We demonstrate that the contribution from Regge excitations is strongly suppressed as it was previously conjectured. We also comment on contributions from Kaluza‐Klein (KK) modes, which could help resolve the δaμ discrepancy. In particular, we argue that for 4‐stack intersecting D‐brane models, the KK excitations of the U(1) boson living on the lepton brane would not couple to hadrons and therefore can evade the LHC bounds while fully bridging the δaμ gap observed at Brookhaven and Fermilab.
Heating Process Characteristics and Kinetics of Biomass at Different Oxygen Concentrations

Abstract Mass transfer is very important for the combustion of biomass fuels, especially pellets. The oxygen diffusion, which results in a nonuniform distribution of oxygen in the pellets, should be considered in the modeling of pellet combustion. In this study, the effect of oxygen on thermal degradation of biomass is studied by thermogravimetric (TG) experiments and then a kinetic model considering oxygen effect is developed. The fuels investigated are spruce, sophora, wheat straw, and peanut straw. TG curves are taken under different oxygen concentrations, ranging from 0 to 20 %. Oxygen concentration has little effect on the devolatilization but is critical for the behavior of char combustion. Reaction mechanism is proposed based on the observation of decomposition at different reaction conditions. The kinetic model consists of three devolatilization reactions (for cellulose, hemicellulose, and lignin) and one char combustion reaction. In the devolatilization steps, char and gas are formed as products and oxygen is not involved in the reactions. A power law dependence of oxygen is assumed for the char combustion stage. The model parameters are fitted by the experimental TG data and good agreement is observed between the experimental and modeled data at various oxygen concentrations and for different biomass fuels. It is expected that the developed kinetic model could be applied for the modeling of pellets combustion considering oxygen diffusion process.
Revisiting the Onsets and Frames Model with Additive Attention

Recent advances in automatic music transcription (AMT) have achieved highly accurate polyphonic piano transcription results by incorporating onset and offset detection. The existing literature, however, focuses mainly on the leverage of deep and complex models to achieve state-of-the-art (SOTA) accuracy, without understanding model behaviour. In this paper, we conduct a comprehensive examination of the Onsets-and-Frames AMT model, and pinpoint the essential components contributing to a strong AMT performance. This is achieved through exploitation of a modified additive attention mechanism. The experimental results suggest that the attention mechanism beyond a moderate temporal context does not benefit the model, and that rule-based post-processing is largely responsible for the SOTA performance. We also demonstrate that the onsets are the most significant attentive feature regardless of model complexity. The findings encourage AMT research to weigh more on both a robust onset detector and an effective post-processor.
Human-in-the-Loop Fault Localisation Using Efficient Test Prioritisation of Generated Tests

Many existing fault localisation techniques become less effective or even inapplicable when not adequately supported by a rich test suite. To overcome this challenge, we present a human-in-the-loop fault localisation technique, QFiD, that works with only a small number of initial failing test cases. We augment the failing test cases with automatically generated test data and elicit oracles from a human developer to label the test cases. A new result-aware test prioritisation metric allows us to significantly reduce the labelling effort by prioritising the test cases to achieve maximum localisation accuracy. An evaluation with EvoSuite and our test prioritisation metric shows that QFiD can significantly increase the localisation accuracy. After only ten human labellings, QFiD can localise 27% and 66% of real-world faults in Defects4J at the top and within the top ten, respectively. This is a 13 and 2 times higher performance than when using the initial test cases. QFiD is also resilient to human errors, retaining 80% of its acc@1 performance on average when we introduce a 30% error rate to the simulated human oracle.
Defects in crystals of soft colloidal particles

In this paper we use computer simulations to examine point defects in systems of “soft” colloidal particles including Hertzian spheres, and star polymers. We use Monte Carlo simulations to determine the deformation of the different crystals associated with vacancies and interstitials and use thermodynamic integration to predict the equilibrium concentrations of such defects. We find that the nature of the lattice distortion is mainly determined by the crystal structure and not by the specifics of the interaction potential. We can distinguish one-, two-, and three-dimensional lattice distortions and find that the range of the distortion generally depends on the dimensionality. We find that in both model systems the deformation of the body-centered cubic (BCC) crystal caused by an interstitial is one-dimensional and we show that its structure is well described as a crowdion. Similarly, we show that the one-dimensional deformation of the hexagonal (H) crystal of Hertzian spheres caused by a vacancy can be characterized as a voidion. Interestingly, with the exception of the FCC crystal in the Hertzian sphere model, in all cases we find that the interstitial concentration is higher than the vacancy concentration. Most noteworthy, the concentration of interstitials in the BCC crystals can reach up to 1%.
A First Look at Scams on YouTube

YouTube has become the second most popular website according to Alexa, and it represents an enticing platform for scammers to attract victims. Because of the computational difficulty of classifying multimedia, identifying scams on YouTube is more difficult than text-based media. As a consequence, the research community to-date has provided little insight into the prevalence, lifetime, and operational patterns of scammers on YouTube. In this short paper, we present a preliminary exploration of scam videos on YouTube. We begin by identifying 74 search queries likely to lead to scam videos based on the authors' experience seeing scams during routine browsing. We then manually review and characterize the results to identify 668 scams in 3,700 videos. In a detailed analysis of our classifications and metadata, we find that these scam videos have a median lifetime of nearly nine months, and many rely on external websites for monetization. We also explore the potential of detecting scams from metadata alone, finding that metadata does not have enough predictive power to distinguish scams from legitimate videos. Our work demonstrates that scams are a real problem for YouTube users, motivating future work on this topic.
Improving Optimal Power Flow Relaxations Using 3-Cycle Second-Order Cone Constraints

This paper develops a novel second order cone relaxation of the semidefinite programming formulation of optimal power flow, that does not imply the `angle relaxation'. We build on a technique developed by Kim et al., extend it for complex matrices, and apply it to 3x3 positive semidefinite matrices to generate novel second-order cone constraints that augment upon the well-known 2x2 principal-minor based second-order cone constraints. Finally, we apply it to optimal power flow in meshed networks and provide numerical illustrations.
Measurement of the longitudinal diffusion of ionization electrons in the MicroBooNE detector

Accurate knowledge of electron transport properties is vital to understanding the information provided by liquid argon time projection chambers (LArTPCs). Ionization electron drift-lifetime, local electric field distortions caused by positive ion accumulation, and electron diffusion can all significantly impact the measured signal waveforms. This paper presents a measurement of the effective longitudinal electron diffusion coefficient, DL, in MicroBooNE at the nominal electric field strength of 273.9 V/cm. Historically, this measurement has been made in LArTPC prototype detectors. This represents the first measurement in a large-scale (85 tonne active volume) LArTPC operating in a neutrino beam. This is the largest dataset ever used for this measurement. Using a sample of ∼70,000 through-going cosmic ray muon tracks tagged with MicroBooNE's cosmic ray tagger system, we measure DL = 3.74+0.28 -0.29 cm2/s.
Quantum Walks, Feynman Propagators and Graph Topology on an IBM Quantum Computer

Topological data analysis is a rapidly developing area of data science where one tries to discover topological patterns in data sets to generate insight and knowledge discovery. In this project we use quantum walk algorithms to discover features of a data graph on which the walk takes place. This can be done faster on quantum computers where all paths can be explored using superposition. We begin with simple walks on a polygon and move up to graphs described by higher dimensional meshes. We use insight from the physics description of quantum walks defined in terms of probability amplitudes to go from one site on a graph to another distant site and show how this relates to the Feynman propagator or Kernel in the physics terminology. Our results from quantum computation using IBM's Qiskit quantum computing software were in good agreement with those obtained using classical computing methods.
Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches

Concept-based explanations have emerged as a popular way of extracting human-interpretable representations from deep discriminative models. At the same time, the disentanglement learning literature has focused on extracting similar representations in an unsupervised or weakly-supervised way, using deep generative models. Despite the overlapping goals and potential synergies, to our knowledge, there has not yet been a systematic comparison of the limitations and trade-offs between concept-based explanations and disentanglement approaches. In this paper, we give an overview of these fields, comparing and contrasting their properties and behaviours on a diverse set of tasks, and highlighting their potential strengths and limitations. In particular, we demonstrate that state-of-the-art approaches from both classes can be data inefficient, sensitive to the specific nature of the classification/regression task, or sensitive to the employed concept representation.
Quantum Representation of Affine Weyl Groups and Associated Quantum Curves

We study a quantum (non-commutative) representation of the affine Weyl group mainly of type $E_8^{(1)}$, where the representation is given by birational actions on two variables $x$, $y$ with $q$-commutation relations. Using the tau variables, we also construct quantum"fundamental"polynomials $F(x,y)$ which completely control the Weyl group actions. The geometric properties of the polynomials $F(x,y)$ for the commutative case is lifted distinctively in the quantum case to certain singularity structures as the $q$-difference operators. This property is further utilized as the characterization of the quantum polynomials $F(x,y)$. As an application, the quantum curve associated with topological strings proposed recently by the first named author is rederived by the Weyl group symmetry. The cases of type $D_5^{(1)}$, $E_6^{(1)}$, $E_7^{(1)}$ are also discussed.
Don't Give Up on Large Optimization Problems; POP Them!

Resource allocation problems in many computer systems can be formulated as mathematical optimization problems. However, finding exact solutions to these problems using off-the-shelf solvers in an online setting is often intractable for"hyper-scale"system sizes with tight SLAs, leading system designers to rely on cheap, heuristic algorithms. In this work, we explore an alternative approach that reuses the original optimization problem formulation. By splitting the original problem into smaller, more tractable problems for subsets of the system and then coalescing resulting sub-allocations into a global solution, we achieve empirically quasi-optimal (within 1.5%) performance for multiple domains with several orders-of-magnitude improvement in runtime. Deciding how to split a large problem into smaller sub-problems, and how to coalesce split allocations into a unified allocation, needs to be performed carefully in a domain-aware way. We show common principles for splitting problems effectively across a variety of tasks, including cluster scheduling, traffic engineering, and load balancing.
Aspects of Lorentz-Poincar\'e-symmetry violating physics in a supersymmetric scenario

We study a Lorentz invariance violating extension for the pure photonic sector of the standard model in a supersymmetric scenario. We identify a number of independent background fermion condensates. An effective photonic action is proposed which is induced by the SUSY background fermion condensates. The physical consequences leading to direct measurable effects over the screening and confining properties are considered. In the specific case of the Carroll-Field-Jackiw, we pay special attention in analyzing the dispersion relations to derive the photon and photino masses in terms of the supersymmetric background parameters. In connection with astrophysical aspects of Lorentz-symmetry violation, we discuss the time delay between electromagnetic waves of different frequencies as a consequence of the appearance of the massive photon. We also point out that, in the scenario we propose to accommodate Lorentz-symmetry violation in presence of a supersymmetric background, there is room for a sort of fermionic Primakoff effect in which a photino-photon conversion may be induced by the fermionic sector of the supersymmetric background.
Meaningful Adversarial Stickers for Face Recognition in Physical World

Face recognition (FR) systems have been widely applied in safety-critical ﬁelds with the introduction of deep learning. However, the existence of adversarial examples brings potential security risks to FR systems. To identify their vulnerability and help improve their robustness, in this paper, we propose Meaningful Adversarial Stickers, a physically feasible and easily implemented attack method by using meaningful real stickers existing in our life, where the at-tackers manipulate the pasting parameters of stickers on the face, instead of designing perturbation patterns and then printing them like most existing works. We conduct attacks in the black-box setting with limited information which is more challenging and practical. To effectively solve the pasting position, rotation angle, and other parameters of the stickers, we design Region based Heuristic Differential Algorithm, which utilizes the inbreeding strategy based on regional aggregation of effective solutions and the adaptive adjustment strategy of evaluation criteria. Extensive experiments are conducted on two public datasets including LFW and CelebA with respective to three representative FR models like FaceNet, SphereFace, and CosFace, achieving attack success rates of 81.78%, 72.93%, and 79.26% respectively with only hundreds of queries. The results in the physical world conﬁrm the effectiveness of our method in complex physical conditions. When continuously changing the face posture of testers, the method can still perform successful attacks up to 98.46%, 91.30% and 86.96% in the time series.
Leptophilic composite asymmetric dark matter and its detection

We propose a model which explains the baryon asymmetry of the universe and dark matter relic density at the same time. In this model, dark matter candidate is the dark baryon composed by dark quarks. A scalar mediator, which couples to the standard model leptons and dark quarks, is introduced to generate the asymmetry of baryon and dark baryon simultaneously. Direct detection and collider detection of this model are studied. We find that current underground direct detection experiments and LHC can hardly detect this model. But future lepton colliders, such as CEPC, have great potential to detect a large portion of the model parameter space by “displaced lepton jet” signal. ∗ mczhang@jnu.edu. cn 1 ar X iv :2 10 4. 06 98 8v 1 [ he pph ] 1 4 A pr 2 02 1
A Gaussian fixed point random walk

In this note, we design a discrete random walk on the real line which takes steps $0, \pm 1$ (and one with steps in $\{\pm 1, 2\}$) where at least $96\%$ of the signs are $\pm 1$ in expectation, and which has $\mathcal{N}(0,1)$ as a stationary distribution. As an immediate corollary, we obtain an online version of Banaszczyk's discrepancy result for partial colorings and $\pm 1, 2$ signings. Additionally, we recover linear time algorithms for logarithmic bounds for the Koml\'{o}s conjecture in an oblivious online setting.
Distributed Word Representation in Tsetlin Machine

—Tsetlin Machine (TM) is an interpretable pattern recognition algorithm based on propositional logic. The algorithm has demonstrated competitive performance in many Natural Language Processing (NLP) tasks, including sentiment analysis, text classiﬁcation, and Word Sense Disambiguation (WSD). To obtain human-level interpretability, legacy TM employs Boolean input features such as bag-of-words (BOW). However, the BOW representation makes it difﬁcult to use any pre-trained information, for instance, word2vec and GloVe word representations. This restriction has constrained the performance of TM compared to deep neural networks (DNNs) in NLP. To reduce the performance gap, in this paper, we propose a novel way of using pre-trained word representations for TM. The approach signiﬁcantly enhances the TM performance and maintains interpretability at the same time. We achieve this by extracting semantically related words from pre-trained word representations as input features to the TM. Our experiments show that the accuracy of the proposed approach is signiﬁcantly higher than the previous BOW-based TM, reaching the level of DNN-based models.
Efficient conformer-based speech recognition with linear attention

Recently, conformer-based end-to-end automatic speech recognition, which outperforms recurrent neural network based ones, has received much attention. Although the parallel computing of conformer is more efficient than recurrent neural networks, the computational complexity of its dot-product self-attention is quadratic with respect to the length of the input feature. To reduce the computational complexity of the self-attention layer, we propose multi-head linear self-attention for the self-attention layer, which reduces its computational complexity to linear order. In addition, we propose to factorize the feed for-ward module of the conformer by low-rank matrix factorization, which successfully reduces the number of the parameters by ap-proximate 50 % with little performance loss. The proposed model, named linear attention based conformer (LAC), can be trained and inferenced jointly with the connectionist temporal classification objective, which further improves the performance of LAC. To evaluate the effectiveness of LAC, we conduct experiments on the AISHELL-l and LibriSpeech corpora. Results show that the proposed LAC achieves better performance than 7 recently proposed speech recognition models, and is competitive with the state-of-the-art conformer. Meanwhile, the proposed LAC has a number of parameters of only 50 % over the conformer with faster training speed than the latter.