Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperbolic–parabolic normal form and local classical solutions for cross-diffusion systems with incomplete diffusion

Abstract We investigate degenerate cross-diffusion equations, with a rank-deficient diffusion-matrix, modelling multispecies population dynamics driven by partial pressure gradients. These equations have recently been found to arise in a mean-field limit of interacting stochastic particle systems. To date, their analysis in multiple space dimensions has been confined to the purely convective case with equal mobility coefficients. In this article, we introduce a normal form for an entropic class of such equations which reveals their structure of a symmetric hyperbolic–parabolic system. Due to the state-dependence of the range and kernel of the singular diffusive matrix, our way of rewriting the equations is different from that classically used for symmetric second-order systems with a nullspace invariance property. By means of this change of variables, we solve the Cauchy problem for short times and positive initial data in for
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change

Recent research has revealed that neural language models at scale suffer from poor temporal generalization capability, i.e., language model pre-trained on static data from past years performs worse over time on emerging data. Existing methods mainly perform continual training to mitigate such a misalignment. While effective to some extent but is far from being addressed on both the language modeling and downstream tasks. In this paper, we empirically observe that temporal generalization is closely affiliated with lexical semantic change, which is one of the essential phenomena of natural languages. Based on this observation, we propose a simple yet effective lexical-level masking strategy to post-train a converged language model. Experiments on two pre-trained language models, two different classification tasks, and four benchmark datasets demonstrate the effectiveness of our proposed method over existing temporal adaptation methods, i.e., continual training with new data. Our code is available at https://github.com/zhaochen0110/LMLM.
Visualising Generative Spaces Using Convolutional Neural Network Embeddings

As academic interest in procedural content generation (PCG) for games has increased, so has the need for methodologies for comparing and contrasting the output spaces of alternative PCG systems. In this paper we introduce and evaluate a novel approach for visualising the generative spaces of level generation systems, using embeddings extracted from a trained convolutional neural network. We evaluate the approach in terms of its ability to produce 2D visualisations of encoded game levels that correlate with their behavioural characteristics. The results across two alternative game domains, Super Mario and Boxoban, indicate that this approach is powerful in certain settings and that it has the potential to supersede alternative methods for visually comparing generative spaces. However its performance was also inconsistent across the domains investigated in this work, as well as it being susceptible to intermittent failure. We conclude that this method is worthy of further evaluation, but that future implementations of it would benefit from significant refinement.
The sl2(R) coalgebra symmetry and the superintegrable discrete-time systems

In this paper, we classify all the variational discrete-time systems in quasi-standard form in N degrees of freedom admitting coalgebra symmetry with respect to the generic realisation of the Lie–Poisson algebra sl2(R) . This approach naturally yields several quasi-maximally and maximally superintegrable discrete-time systems, both known and new. We conjecture that this exhausts the (super)integrable cases associated with this algebraic construction.
Probing the Plasma Tail of Interstellar Comet 2I/Borisov

We present an occultation study of compact radio sources by the plasma tail of interstellar comet 2I/Borisov (C/2019 Q4) both pre- and near-perihelion using the Arecibo and Green Bank radio telescopes. The interplanetary scintillation technique was used to probe the plasma tail at the P band (302–352 MHz), 820 MHz, and the L band (1120–1730 MHz). The presence and absence of scintillation at different perpendicular distances from the central axis of the plasma tail suggests a narrow tail of less than 6′ at a distance of ∼10′ (∼106 km) from the comet nucleus. Data recorded during the occultation of B1019+083 on 2019 October 31 with the Arecibo Telescope covered the width of the plasma tail from its outer region to the central axis. The systematic increase in scintillation during the occultation provides the plasma properties associated with the tail when the comet was at its pre-perihelion phase. The excess level of L-band scintillation indicates a plasma density enhancement of ∼15–20 times that of the background solar wind. The evolving shape of the observed scintillation power spectra across the tail from its edge to the central axis suggests a density spectrum flatter than Kolmogorov and that the plasma density irregularity scales present in the tail range between 10 and 700 km. The discovery of a high-frequency spectral excess corresponding to irregularity scales much smaller than the Fresnel scale suggests the presence of small-scale density structures in the plasma tail, likely caused by interaction between the solar wind and the plasma environment formed by the comet.
LILA: A Unified Benchmark for Mathematical Reasoning

Mathematical reasoning skills are essential for general-purpose intelligentsystems to perform tasks from grocery shopping to climate modeling.Towards evaluating and improving AI systems in this domain, we proposeLILA, a unified mathematical reasoning benchmark consisting of 23 diversetasks along four dimensions:(i) mathematical abilities e.g., arithmetic, calculus (ii) language format e.g., question-answering, fill-in-the-blanks (iii) language diversity e.g., no language, simple language (iv) external knowledge e.g., commonsense, physics. We construct our benchmark by extending 20 datasets benchmark by collecting task instructions and solutions in the form of Python programs,thereby obtaining explainable solutions in addition to the correct answer.We additionally introduce two evaluation datasets to measure out-of-distribution performance and robustness to language perturbation.Finally, we introduce BHASKARA,a general-purpose mathematical reasoning model trained on LILA. Importantly, we find that multi-tasking leads to significant improvements (average relative improvement of 21.83% F1 score vs. single-task models),while the best performing model only obtains 60.40%,indicating the room for improvement in general mathematical reasoning and understanding.
A Multi-modal Deformable Land-air Robot for Complex Environments

Single locomotion robots often struggle to adapt in highly variable or uncertain environments, especially in emergencies. In this paper, a multi-modal deformable robot is introduced that can both fly and drive. Compatibility issues with multi-modal locomotive fusion for this hybrid land-air robot are solved using proposed design conceptions, including power settings, energy selection, and designs of deformable structure. The robot can also automatically transform between land and air modes during 3D planning and tracking. Meanwhile, we proposed a algorithms for evaluation the performance of land-air robots. A series of comparisons and experiments were conducted to demonstrate the robustness and reliability of the proposed structure in complex field environments.
Turbulence and added drag over acoustic liners

Abstract We present pore-resolved compressible direct numerical simulations of turbulent flows grazing over perforated plates, that closely resemble the acoustic liners found on aircraft engines. Our direct numerical simulations explore a large parameter space including the effects of porosity, thickness and viscous-scaled diameter of the perforated plates, at friction Reynolds numbers $\textit {Re}_\tau = 500\unicode{x2013}2000$, which allows us to develop a robust theory for estimating the added drag induced by acoustic liners. We find that acoustic liners can be regarded as porous surfaces with a wall-normal permeability and that the relevant length scale characterizing their added drag is the inverse of the wall-normal Forchheimer coefficient. Unlike other types of porous surfaces featuring Darcian velocities inside the pores, the flow inside the orifices of acoustic liners is fully turbulent, with a magnitude of the wall-normal velocity fluctuations comparable to the peak in the near-wall cycle. We provide clear evidence of a fully rough regime for acoustic liners, also confirmed by the increasing relevance of pressure drag. Once the fully rough asymptote is reached, canonical acoustic liners provide an added drag comparable to that of sand-grain roughness with viscous-scaled height matching the inverse of the viscous-scaled Forchheimer permeability of the plate.
QuaLA-MiniLM: a Quantized Length Adaptive MiniLM

Limited computational budgets often prevent transformers from being used in production and from having their high accuracy utilized. A knowledge distillation approach addresses the computational efficiency by self-distilling BERT into a smaller transformer representation having fewer layers and smaller internal embedding. However, the performance of these models drops as we reduce the number of layers, notably in advanced NLP tasks such as span question answering. In addition, a separate model must be trained for each inference scenario with its distinct computational budget. Dynamic-TinyBERT tackles both limitations by partially implementing the Length Adaptive Transformer (LAT) technique onto TinyBERT, achieving x3 speedup over BERT-base with minimal accuracy loss. In this work, we expand the Dynamic-TinyBERT approach to generate a much more highly efficient model. We use MiniLM distillation jointly with the LAT method, and we further enhance the efficiency by applying low-bit quantization. Our quantized length-adaptive MiniLM model (QuaLA-MiniLM) is trained only once, dynamically fits any inference scenario, and achieves an accuracy-efficiency trade-off superior to any other efficient approaches per any computational budget on the SQuAD1.1 dataset (up to x8.8 speedup with<1% accuracy loss). The code to reproduce this work is publicly available on Github.
<inline-formula><tex-math notation="LaTeX">$kt$</tex-math></inline-formula>-Safety: Graph Release via <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-Anonymity and <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula>-Closeness

In a wide spectrum of real-world applications, it is very important to analyze and mine graph data such as social networks, communication networks, citation networks, and so on. However, the release of such graph data often raises privacy issue, and the graph privacy preservation has recently drawn much attention from the database community. While prior works on graph privacy preservation mainly focused on protecting the privacy of either the graph structure only or vertex attributes only, in this paper, we propose a novel mechanism for graph privacy preservation by considering attacks from both graph structures and vertex attributes, which transforms the original graph to a so-called <inline-formula><tex-math notation="LaTeX">$kt$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>k</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="lian-ieq4-3221333.gif"/></alternatives></inline-formula>-safe graph, via <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="lian-ieq5-3221333.gif"/></alternatives></inline-formula>-anonymity and <inline-formula><tex-math notation="LaTeX">$t$</tex-math><alternatives><mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="lian-ieq6-3221333.gif"/></alternatives></inline-formula>-closeness. We prove that the generation of a <inline-formula><tex-math notation="LaTeX">$kt$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>k</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="lian-ieq7-3221333.gif"/></alternatives></inline-formula>-safe graph is NP-hard, therefore, we propose a feasible framework for effectively and efficiently anonymizing a graph with low anonymization cost. In particular, we design a cost-model-based graph partitioning approach to enable our proposed divide-and-conquer strategy for the graph anonymization, and propose effective optimization techniques such as pruning method and a tree synopsis to improve the anonymization efficiency over large-scale graphs. Extensive experiments have been conducted to verify the efficiency and effectiveness of our proposed <inline-formula><tex-math notation="LaTeX">$kt$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>k</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="lian-ieq8-3221333.gif"/></alternatives></inline-formula>-safe graph generation approach on both real and synthetic data sets.
The fluid/gravity correspondence: Lectures notes from the 2008 Summer School on Particles, Fields, and Strings

This is a paper compiled by students of the 2008 Summer School on Particles, Fields, and Strings held at the University of British Columbia on lectures given by Veronika Hubeny as understood and interpreted by the authors. We start with an introduction to the AdS/CFT duality. More specifically, we discuss the correspondence between relativistic, conformal hydrodynamics and Einstein's theory of gravity. Within our framework the Einstein equations are an effective description for the string theory in the bulk of AdS_5 spacetime and the hydrodynamic fluid equations represent the conformal field theory near thermal equilibrium on the boundary. In particular we present a new technique for calculating properties in fluid dynamics using the stress-energy tensor induced on the boundary, by the gravitational field in the bulk, and comparing it with the form of the stress-energy tensor from hydrodynamics. A detailed treatment can be found in [JHEP 02 (2008) 045] and [arXiv:0803.2526].
A mini-supernova model for optical afterglows of GRB

An energy deposition of ∼ 10 50 ergs into the exterior 10 − 3 M ⊙ layers of a red giant is calculated to produce an optical phenomenon similar to afterglows of gamma-ray bursts (GRB) recently observed. This mechanism can be realized if a GRB is generated by some mechanism in a close binary system. In contrast to a “hypernova” scenario for GRB recently proposed by Paczy´nski (1997), this model does not require a huge kinetic energy of the expanding shell to explain optical afterglows of GRB. we huge kinetic of the a optical afterglow. We show that deposition of the energy of gamma-ray burst into the exterior envelope of a red supergiant in a close binary system be suﬃcient to explain optical afterglow decaying with time in a way similar to that in GRB 970228.
Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers

In this paper, we focus on singing techniques within the scope of music information retrieval research. We investigate how singers use singing techniques using real-world recordings of famous solo singers in Japanese popular music songs (J-POP). First, we built a new dataset of singing techniques. The dataset consists of 168 commercial J-POP songs, and each song is annotated using various singing techniques with timestamps and vocal pitch contours. We also present descriptive statistics of singing techniques on the dataset to clarify what and how often singing techniques appear. We further explored the difficulty of the automatic detection of singing techniques using previously proposed machine learning techniques. In the detection, we also investigate the effectiveness of auxiliary information (i.e., pitch and distribution of label duration), not only providing the baseline. The best result achieves 40.4% at macro-average F-measure on nine-way multi-class detection. We provide the annotation of the dataset and its detail on the appendix website 0 .
Relative Attention-based One-Class Adversarial Autoencoder for Continuous Authentication of Smartphone Users

Behavioral biometrics-based continuous authentication is a promising authentication scheme, which uses behavioral biometrics recorded by built-in sensors to authenticate smartphone users throughout the session. However, current continuous authentication methods suffer some limitations: 1) behavioral biometrics from impostors are needed to train continuous authentication models. Since the distribution of negative samples from diverse attackers are unknown, it is a difficult problem to solve in real-world scenarios; 2) most deep learning-based continuous authentication methods need to train two models to improve authentication performance. A deep learning model for deep feature extraction, and a machine learning-based classifier for classification; 3) weak capability of capturing users' behavioral patterns leads to poor authentication performance. To solve these issues, we propose a relative attention-based one-class adversarial autoencoder for continuous authentication of smartphone users. First, we propose a one-class adversarial autoencoder to learn latent representations of legitimate users' behavioral patterns, which is trained only with legitimate smartphone users' behavioral biometrics. Second, we present the relative attention layer to capture richer contextual semantic representation of users' behavioral patterns, which modifies the standard self-attention mechanism using convolution projection instead of linear projection to perform the attention maps. Experimental results demonstrate that we can achieve superior performance of 1.05% EER, 1.09% EER, and 1.08% EER with a high authentication frequency (0.7s) on three public datasets.
Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition

Traditionally, in paralinguistic analysis for emotion detection from speech, emotions have been identified with discrete or dimensional (continuous-valued) labels. Accordingly, models that have been proposed for emotion detection use one or the other of these label types. However, psychologists like Russell and Plutchik have proposed theories and models that unite these views, maintaining that these representations have shared and complementary information. This paper is an attempt to validate these viewpoints computationally. To this end, we propose a model to jointly predict continuous and discrete emotional attributes and show how the relationship between these can be utilized to improve the robustness and performance of emotion recognition tasks. Our approach comprises multi-task and hierarchical multi-task learning frameworks that jointly model the relationships between continuous-valued and discrete emotion labels. Experimental results on two widely used datasets (IEMOCAP and MSPPodcast) for speech-based emotion recognition show that our model results in statistically significant improvements in performance over strong baselines with non-unified approaches. We also demonstrate that using one type of label (discrete or continuous-valued) for training improves recognition performance in tasks that use the other type of label. Experimental results and reasoning for this approach (called the mismatched training approach) are also presented.
IRS-Assisted RF-Powered IoT Networks: System Modeling and Performance Analysis

Emerged as a promising solution for future wireless communication systems, intelligent reflecting surface (IRS) is capable of reconfiguring the wireless propagation environment by adjusting the phase-shift of a large number of reflecting elements. To quantify the gain achieved by IRSs in the radio frequency (RF) powered Internet of Things (IoT) networks, in this work, we consider an IRS-assisted cellular-based RF-powered IoT network, where the cellular base stations (BSs) broadcast energy signal to IoT devices for energy harvesting (EH) in the charging stage, which is utilized to support the uplink (UL) transmissions in the subsequent UL stage. With tools from stochastic geometry, we first derive the distributions of the average signal power and interference power which are then used to obtain the energy coverage probability, UL coverage probability, overall coverage probability, spatial throughput and power efficiency, respectively. With the proposed analytical framework, we finally evaluate the effect on network performance of key system parameters, such as IRS density, IRS reflecting element number, charging stage ratio, etc. Compared with the conventional RF-powered IoT network, IRS passive beamforming brings the same level of enhancement in both energy coverage and UL coverage, leading to the unchanged optimal charging stage ratio when maximizing spatial throughput.
A Case Study of Chinese Sentiment Analysis on Social Media Reviews Based on LSTM

Network public opinion analysis is obtained through a combination of natural language processing (NLP) and public opinion supervision, and is crucial for monitoring public mood and trends. Therefore, network public opinion analysis can identify and solve potential and budding social problems. This study aims to realize an analysis of Chinese sentiment in social media reviews using a long short-term memory network (LSTM) model. A dataset was obtained from Sina Weibo using a web crawler and cleaned using Pandas. First, Chinese comments regarding the legal sentencing in of Tangshan attack and Jiang Ge Case were segmented and vectorized. Thereafter, a binary LSTM model was trained and tested. Finally, sentiment analysis results were obtained by analyzing the comments with the LSTM model. The accuracy of the proposed model has reached approximately 92%.
Symmetric Saliency-Based Adversarial Attack to Speaker Identification

Adversarial attack approaches to speaker identification either need high computational cost or are not very effective, to our knowledge. To address this issue, in this letter, we propose a novel generation-network-based approach, called symmetric saliency-based encoder-decoder (SSED), to generate adversarial voice examples to speaker identification. It contains two novel components. First, it uses a novel saliency map decoder to learn the importance of speech samples to the decision of a targeted speaker identification system, so as to make the attacker focus on generating artificial noise to the important samples. It also proposes an angular loss function to push the speaker embedding far away from the source speaker. Our experimental results demonstrate that the proposed SSED yields the state-of-the-art performance, i.e. over 97% targeted attack success rate and a signal-to-noise level of over 39 dB on both the open-set and close-set speaker identification tasks, with a low computational cost.