Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms

The performance of recommendation algorithms is closely tied to key characteristics of the data sets they use, such as sparsity, popularity bias, and preference distributions. In this paper, we conduct a comprehensive explanatory analysis to shed light on the impact of a broad range of data characteristics within the point-of-interest (POI) recommendation domain. To accomplish this, we extend prior methodologies used to characterize traditional recommendation problems by introducing new explanatory variables specifically relevant to POI recommendation. We subdivide a POI recommendation data set on New York City into domain-driven subsamples to measure the effect of varying these characteristics on different state-of-the-art POI recommendation algorithms in terms of accuracy, novelty, and item exposure. Our findings, obtained through the application of an explanatory framework employing multiple-regression models, reveal that the relevant independent variables encompass all categories of data characteristics and account for as much as $R^2 = $ 85-90\% of the accuracy and item exposure achieved by the algorithms. Our study reaffirms the pivotal role of prominent data characteristics, such as density, popularity bias, and the distribution of check-ins in POI recommendation. Additionally, we unveil novel factors, such as the proximity of user activity to the city center and the duration of user activity. In summary, our work reveals why certain POI recommendation algorithms excel in specific recommendation problems and, conversely, offers practical insights into which data characteristics should be modified (or explicitly recognized) to achieve better performance.
Large deviation principles for graphon sampling

We investigate possible large deviation principles (LDPs) for the $n$-vertex sampling from a given graphon with various speeds $s(n)$ and resolve all the cases except when the speed $s(n)$ is of order $n^2$. For quadratic speed $s=(c+o(1))n^2$, we establish an LDP for an arbitrary $k$-step graphon, which extends a result of Chatterjee and Varadhan [Europ. J. Combin. , 32 (2011) 1000-1017] who did this for $k=1$ (that is, for the homogeneous binomial random graphs). This is done by reducing the problem to the LDP for stochastic $k$-block models established recently by Borgs, Chayes, Gaudio, Petti and Sen ["A large deviation principle for block models", arxiv:2007.14508, 2020]. Also, we improve some results by Borgs et al.
CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings

Video Quality Assessment (VQA) aims to simulate the process of perceiving video quality by the human visual system (HVS). The judgments made by HVS are always influenced by human subjective feelings. However, most of the current VQA research focuses on capturing various distortions in the spatial and temporal domains of videos, while ignoring the impact of human feelings. In this paper, we propose CLiF-VQA, which considers both features related to human feelings and spatial features of videos. In order to effectively extract features related to human feelings from videos, we explore the consistency between CLIP and human feelings in video perception for the first time. Specifically, we design multiple objective and subjective descriptions closely related to human feelings as prompts. Further we propose a novel CLIP-based semantic feature extractor (SFE) which extracts features related to human feelings by sliding over multiple regions of the video frame. In addition, we further capture the low-level-aware features of the video through a spatial feature extraction module. The two different features are then aggregated thereby obtaining the quality score of the video. Extensive experiments show that the proposed CLiF-VQA exhibits excellent performance on several VQA datasets.
A Linear Parameter-Varying Approach to Data Predictive Control

By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-feedback and state-feedback-based LPV-DPC methods with terminal ingredients, which guarantee exponential stability and recursive feasibility. We provide methods for the data-based computation of these terminal ingredients. Furthermore, an in-depth analysis of the properties and implementation aspects of the LPV-DPC schemes is given, including alternative recursive formulations, application for nonlinear systems and handling noise-disturbed data. We demonstrate the performance of the proposed methods on a simulation example involving a nonlinear unbalanced disc system.
Uncovering the multifractality of Lagrangian pair dispersion in shock-dominated turbulence

Lagrangian pair dispersion provides insights into mixing in turbulent flows. By direct numerical simulations (DNS) we show that the statistics of pair dispersion in the randomly forced two-dimensional Burgers equation, which is a typical model of shock-dominated turbulence, is very different from its incompressible counterpart because Lagrangian particles get trapped in shocks. We develop a heuristic theoretical framework that accounts for this -- a generalization of the multifractal model -- whose prediction of the scaling of Lagrangian exit times agrees well with our DNS.
Pruning random resistive memory for optimizing analogue AI

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and environmental sustainability. One promising solution is to revisit analogue computing, a technique that predates digital computing and exploits emerging analogue electronic devices, such as resistive memory, which features in-memory computing, high scalability, and nonvolatility. However, analogue computing still faces the same challenges as before: programming nonidealities and expensive programming due to the underlying devices physics. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network. Software-wise, the topology of a randomly weighted neural network is optimized by pruning connections rather than precisely tuning resistive memory weights. Hardware-wise, we reveal the physical origin of the programming stochasticity using transmission electron microscopy, which is leveraged for large-scale and low-cost implementation of an overparameterized random neural network containing high-performance sub-networks. We implemented the co-design on a 40nm 256K resistive memory macro, observing 17.3% and 19.9% accuracy improvements in image and audio classification on FashionMNIST and Spoken digits datasets, as well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE datasets, respectively. This is accompanied by 82.1%, 51.2%, and 99.8% improvement in energy efficiency thanks to analogue in-memory computing. By embracing the intrinsic stochasticity and in-memory computing, this work may solve the biggest obstacle of analogue computing systems and thus unleash their immense potential for next-generation AI hardware.
A Spectral Technique for Estimating Turbulent Stress, Scalar Flux Magnitude, and Eddy Viscosity in the Ocean Boundary Layer under Pack Ice

Abstract The hypothesis is tested that, for the planetary boundary layer, turbulent vertical velocity (w) spectral density, normalized by u2∗/k (u2∗ is Reynolds stress magnitude and k is wavenumber: 2π times frequency divided by mean flow speed), is a “universal” function of nondimensional wavenumber k/kmax, where kmax is the wavenumber at the peak in the area-preserving log–log w spectrum. Data from clusters of turbulence-measuring instruments deployed through the ocean boundary layer beneath pack ice during the yearlong Surface Heat Budget of the Arctic (SHEBA) project were analyzed by averaging spectra in 3-h bins, then nondimensionalizing weighted w spectral density by directly measured Reynolds stress magnitude and wavenumber by kmax. In the outer boundary layer, normalized spectra were remarkably uniform, suggesting that (i) the fundamental turbulence scale is inversely proportional to kmax and (ii) the w wavenumber spectrum by itself may be used to estimate local stress magnitude and eddy viscosity.
Synchronization and oscillation quenching in interacting metronomes on a movable platform: simple model and bifurcation analysis

Various oscillatory phenomena occur in the world. Because some oscillations are related to abnormal states (e.g., particular diseases), establishing state-transition methods from an oscillatory to a resting state is important. In this study, we construct a simple metronome model and analyze the oscillation-quenching phenomenon of metronomes on a platform as an example of such state transitions. Although numerous studies were conducted on the metronome dynamics, most of them focused on the synchronization, and few studies treated the oscillation quenching because of the difficulty in analysis. To facilitate the analysis, we model a metronome as a linear spring pendulum with an impulsive force (escapement mechanism) described by a fifth-order polynomial. By performing an averaging approximation, we obtain a phase diagram for the in-phase synchronization, anti-phase synchronization, and oscillation quenching. We also numerically integrate the equation of motion and confirm the agreement between the analytical and numerical results. Despite the simplicity, our model successfully reproduces essential phenomena in interacting mechanical clocks, such as the bistability of in-phase and anti-phase synchrony and oscillation quenching occurring for a large mass ratio between the oscillator and the platform. We believe that our simple model will contribute to future analyses of other dynamics observed in metronomes.
OR Residual Connection Achieving Comparable Accuracy to ADD Residual Connection in Deep Residual Spiking Neural Networks

Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing for their biological fidelity and the capacity to execute energy-efficient spike-driven operations. As the demand for heightened performance in SNNs surges, the trend towards training deeper networks becomes imperative, while residual learning stands as a pivotal method for training deep neural networks. In our investigation, we identified that the SEW-ResNet, a prominent representative of deep residual spiking neural networks, incorporates non-event-driven operations. To rectify this, we introduce the OR Residual connection (ORRC) to the architecture. Additionally, we propose the Synergistic Attention (SynA) module, an amalgamation of the Inhibitory Attention (IA) module and the Multi-dimensional Attention (MA) module, to offset energy loss stemming from high quantization. When integrating SynA into the network, we observed the phenomenon of"natural pruning", where after training, some or all of the shortcuts in the network naturally drop out without affecting the model's classification accuracy. This significantly reduces computational overhead and makes it more suitable for deployment on edge devices. Experimental results on various public datasets confirmed that the SynA enhanced OR-Spiking ResNet achieved single-sample classification with as little as 0.8 spikes per neuron. Moreover, when compared to other spike residual models, it exhibited higher accuracy and lower power consumption. Codes are available at https://github.com/Ym-Shan/ORRC-SynA-natural-pruning.
Explain-then-Translate: An Analysis on Improving Program Translation with Self-generated Explanations

This work explores the use of self-generated natural language explanations as an intermediate step for code-to-code translation with language models. Across three types of explanations and 19 programming languages constructed from the MultiPL-E dataset, we find the explanations to be particularly effective in the zero-shot case, improving performance by 12% on average. Improvements with natural language explanations are particularly pronounced on difficult programs. We release our dataset, code, and canonical solutions in all 19 languages.
Comparative Multi-View Language Grounding

In this work, we consider the task of resolving object referents when given a comparative language description. We present a Multi-view Approach to Grounding in Context (MAGiC) that leverages transformers to pragmatically reason over both objects given multiple image views and a language description. In contrast to past efforts that attempt to connect vision and language for this task without fully considering the resulting referential context, MAGiC makes use of the comparative information by jointly reasoning over multiple views of both object referent candidates and the referring language expression. We present an analysis demonstrating that comparative reasoning contributes to SOTA performance on the SNARE object reference task.
The Relationships between Experienced Landscape Types and People's Visiting Forms on a Trail in a Forest

京都大学芦生演習林を訪れた48グループの一般利用者を対象として写真投影法による調査を行った。 得られたデータから, 現実の森林レクリエーション行動下において体験され, 評価される森林景観を, 視対象, 視点, 視距離地形, 構図などの複合的な要素からなる景観型として整理し, それらの操作可能性を論じるとともに, 景観型と来訪者の利用形態との関係を検討した。 その結果, 林内散策行動の条件下で景観体験となりやすい景観のパターンが抽出された。 また, レクリエーション利用者の環境に対する態度は, 異なる複数のタイプとしてとらえられ, 多様な景観体験を確保したレクリエーション地域計画の必要性が示唆された。
How do Minimum-Norm Shallow Denoisers Look in Function Space?

Neural network (NN) denoisers are an essential building block in many common tasks, ranging from image reconstruction to image generation. However, the success of these models is not well understood from a theoretical perspective. In this paper, we aim to characterize the functions realized by shallow ReLU NN denoisers -- in the common theoretical setting of interpolation (i.e., zero training loss) with a minimal representation cost (i.e., minimal $\ell^2$ norm weights). First, for univariate data, we derive a closed form for the NN denoiser function, find it is contractive toward the clean data points, and prove it generalizes better than the empirical MMSE estimator at a low noise level. Next, for multivariate data, we find the NN denoiser functions in a closed form under various geometric assumptions on the training data: data contained in a low-dimensional subspace, data contained in a union of one-sided rays, or several types of simplexes. These functions decompose into a sum of simple rank-one piecewise linear interpolations aligned with edges and/or faces connecting training samples. We empirically verify this alignment phenomenon on synthetic data and real images.
MacMahon's sums-of-divisors and allied $q$-series

Here we investigate the $q$-series \begin{align*} \mathcal{U}_a(q)&=\sum_{n=0}^{\infty} MO(a;n)q^n&:=\sum_{0<k_1<k_2<\cdots<k_a} \frac{q^{k_1+k_2+\cdots+k_a}}{(1-q^{k_1})^2(1-q^{k_2})^2\cdots(1-q^{k_a})^2},\\ \mathcal{U}_a^{\star}(q)&=\sum_{n=0}^{\infty}M(a;n)q^n&:=\sum_{1\leq k_1\leq k_2\leq\cdots\leq k_a} \frac{q^{k_1+k_2+\cdots+k_a}}{(1-q^{k_1})^2(1-q^{k_2})^2\cdots(1-q^{k_a})^2}. \end{align*} MacMahon introduced the $\mathcal{U}_a(q)$ in his seminal work on partitions and divisor functions. Recent works show that these series are sums of quasimodular forms with weights $\leq 2a. $ We make this explicit by describing them in terms of Eisenstein series. We use these formulas to obtain explicit and general congruences for the coefficients $MO(a;n)$ and $M(a;n). $ Notably, we prove the conjecture of Amdeberhan-Andrews-Tauraso as the $m=0$ special case of the infinite family of congruences $$ MO(11m+10; 11n+7)\equiv 0\pmod{11}, $$ and we prove that $$ MO(17m+16; 17n+15)\equiv 0\pmod{17}. $$ We obtain further formulae using the limiting behavior of these series. For $n\leq a+\binom{a+1}2,$ we obtain a ``hook length'' formulae for $MO(a;n)$, and for $n\leq 2a$, we find that $M(a;n)=\binom{a+n-1}{n-a}+\binom{a+n-2}{n-a-1}. $
Observing supermassive black holes with deci-Hz gravitational-wave detectors

The most massive black holes in our Universe form binaries at the centre of merging galaxies. The recent evidence for a gravitational-wave (GW) background from pulsar timing may constitute the first observation that these supermassive black hole binaries (SMBHBs) merge. Yet, the most massive SMBHBs are out of reach of interferometric detectors and are exceedingly difficult to resolve individually with pulsar timing. These limitations call for unexplored strategies to detect individual SMBHBs in the uncharted frequency band $\lesssim10^{-5}\,\rm Hz$ in order to establish their abundance and decipher the coevolution with their host galaxies. Here we show that SMBHBs imprint detectable long-term modulations on GWs from stellar-mass binaries residing in the same galaxy. We determine that proposed deci-Hz GW interferometers sensitive to numerous stellar-mass binaries will uncover modulations from $\sim\mathcal{O}(10^{-1}$ - $10^4)$ SMBHBs with masses $\sim\mathcal{O}(10^7$ - $10^9)\,\rm M_\odot$ out to redshift $z\sim3.5$. This offers a unique opportunity to map the population of SMBHBs through cosmic time, which might remain inaccessible otherwise.
NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion

Over the past few years, monocular depth estimation and completion have been paid more and more attention from the computer vision community because of their widespread applications. In this paper, we introduce novel physics (geometry)-driven deep learning frameworks for these two tasks by assuming that 3D scenes are constituted with piece-wise planes. Instead of directly estimating the depth map or completing the sparse depth map, we propose to estimate the surface normal and plane-to-origin distance maps or complete the sparse surface normal and distance maps as intermediate outputs. To this end, we develop a normal-distance head that outputs pixel-level surface normal and distance. Meanwhile, the surface normal and distance maps are regularized by a developed plane-aware consistency constraint, which are then transformed into depth maps. Furthermore, we integrate an additional depth head to strengthen the robustness of the proposed frameworks. Extensive experiments on the NYU-Depth-v2, KITTI and SUN RGB-D datasets demonstrate that our method exceeds in performance prior state-of-the-art monocular depth estimation and completion competitors. The source code will be available at https://github.com/ShuweiShao/NDDepth.
Liouville theorems for conformally invariant fully nonlinear equations. I

A fundamental theorem of Liouville asserts that positive entire harmonic functions in Euclidean spaces must be constant. A remarkable Liouville-type theorem of Caffarelli-Gidas-Spruck states that positive entire solutions of $-\Delta u=u^{ \frac {n+2}{n-2} }$, $n\ge 3$, are unique modulo M\"obius transformations. Far-reaching extensions were established for general fully nonlinear conformally invariant equations through the works of Chang-Gursky-Yang, Li-Li, Li, and Viaclovsky. In this paper, we derive necessary and sufficient conditions for the validity of such Liouville-type theorems. This leads to necessary and sufficient conditions for local gradient estimates of solutions to hold, assuming a one-sided bound on the solutions, for a wide class of fully nonlinear elliptic equations involving Schouten tensors. A pivotal advancement in proving these Liouville-type theorems is our enhanced understanding of solutions to such equations near isolated singularities. In particular, we utilize earlier results of Caffarelli-Li-Nirenberg on lower- and upper-conical singularities. For general conformally invariant fully nonlinear elliptic equations, we prove that a viscosity super- (sub-)solution can be extended across an isolated singularity if and only if it is a lower- (upper-)conical singularity. We also provide necessary and sufficient conditions for lower- (upper-)conical behavior of a function near isolated singularities in terms of its conformal Hessian.
COMPOSITION AND MODELING OF RIPARIAN VEGETATION IN THE WEST FORK OF THE GALLATIN RIVER WATERSHED

Riparian areas contribute to the health of watersheds through their influence on hydrologic, biogeochemical, physical, and ecological processes. Limited research has focused on riparian systems of small mountain watersheds in the western U.S., which are increasingly under pressure from development activities. Watershed managers would benefit from an increased understanding of environment-riparian relationships in mountainous watersheds, for the purpose of assessing habitat and potential available nutrient buffering. This study assessed vegetation-environment relationships using digitally-derived terrain variables and wetland indicator scores, and used these relationships to assess the composition and model the cross-valley extent of riparian vegetation within the West Fork of the Gallatin River watershed in southwest Montana. Digital terrain analysis was used to extract the following terrain predictors: elevation above and distance from streams, plot gradient, valley width, and a topographic wetness index, which integrates the upslope area that contributes flow to a plot, and the plot gradient, thus serving as a measure of site wetness. Species abundance was used to assign weighted plot wetland indicator scores in order to focus on cross-valley gradients, with plots below a threshold score (mesic plots) designated as riparian plots. Linear regression, and two types of generalized regression (additive and linear) were used to assess relationships between terrain predictors and (a) individual species, (b) species associations based on clustering of plots, (c) plot wetland indicator scores and (d) riparian plots as defined by the threshold wetland indicator score. Elevation above the stream and plot gradient were the strongest predictors, followed by the topographic wetness index. While generalized additive models often resulted in higher D values (e.g. r), they were often overfit when using plot wetland indicator scores as a response variable. Therefore generalized linear models were the strongest models for predicting the cross-valley extent of riparian vegetation. Lastly, prediction of the shrub strata produced stronger models than the herb strata. This study demonstrated that digitally-derived terrain variables and wetland indicator scores provide a useful method of assessing riparian vegetationenvironment relationships, which can be used to aid researchers and managers in better understanding riparian systems within mountainous watersheds of this region.
Spatial and temporal patterns of surface soil moisture over India estimated using surface wetness index from SSM/I microwave radiometer

Results from an approach to infer surface soil moisture from time series analysis of surface wetness index derived using the Special Sensor Microwave/Imager (SSM/I) are presented. Soil moisture quantification was based on the study of temporal changes in surface wetness index and its scaling to maximum and air‐dry limits of soil in each grid cell (0.33°). The estimated soil moisture of Illinois, USA was compared with field measured soil moisture (0–10 cm) obtained from the Global Soil Moisture Data Bank. A root mean square error of 7.18% was found between estimated and measured volumetric soil moisture. A consistency in soil moisture and rainfall pattern was found in the un‐irrigated areas of northern India (Jodhpur, Varanasi) and southern India (Madurai), influenced by southwest and northeast monsoons, respectively. Soil moisture of more than 0.30 m3m−3 was observed in the absence of rainfall due to the irrigation of rice crop in (Punjab) during the pre‐southwest monsoon period (May).