Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a statistical physics of dating apps

Over the last ten years, a sharp rise in the number of dating apps has broadened the spectrum of how one can get in contact with new acquaintances. A common feature of such apps is a swipe, enabling a user to decide whether to like or dislike another user. As is the case in real life, a user may be more or less popular, which implies that the distribution of likes among different users is broad. In this paper, we show how likes are distributed across users, based on different decision-making strategies, app settings and their feedback. We apply theoretical methods originally developed in non-equilibrium statistical physics to investigate the dynamics of dating app networks. More specifically, we show that whenever a dating app differentially displays users with respect to their popularity, users are split into two categories: a first category including users who have received the most likes and a second category, referred to as a condensate, which in long-term will be reduced to a small fraction of likes or to no likes at all. Finally, we explore realist models based on a rating system of the users, known as Elo. These models will turn out to exhibit behaviour typical of gelating systems, characterized by a bimodal distribution of likes among the users with broad tails. Altogether, we provide a minimal theoretical framework to infer statistical observables in social networks governed by coupled internal states.
Achievable Regions and Precoder Designs for the Multiple Access Wiretap Channels With Confidential and Open Messages

This paper investigates the secrecy achievable region of multiple access wiretap (MAC-WT) channels where, besides confidential messages, the users have also open messages to transmit. All these messages are intended for the legitimate receiver (or Bob for brevity) but only the confidential messages need to be protected from the eavesdropper (Eve). We first consider a discrete memoryless (DM) MAC-WT channel where both Bob and Eve jointly decode their interested messages. By using random coding, we find an achievable rate region, within which perfect secrecy can be realized, i.e., all users can communicate with Bob with arbitrarily small probability of error, while the confidential information leaked to Eve tends to zero. Due to the high implementation complexity of joint decoding, we also consider the DM MAC-WT channel where Bob simply decodes messages independently while Eve still applies joint decoding. We then extend the results in the DM case to a Gaussian vector (GV) MAC-WT channel. Based on the information theoretic results, we further maximize the sum secrecy rate of the GV MAC-WT system by designing precoders for all users. Since the problems are non-convex, we provide iterative algorithms to obtain suboptimal solutions. Simulation results show that compared with existing schemes, secure communication can be greatly enhanced by the proposed algorithms, and in contrast to the works which only focus on the network secrecy performance, the system spectrum efficiency can be effectively improved since open messages can be simultaneously transmitted.
Fast measurement of angular velocity in air-driven flat rotors with periodical features

We present a method for resolving 2D angular movement in a set of particles provided with periodical features. The main advantage of the base algorithm in our method lies in its low computational complexity while displaying a relatively high accuracy. This is specially useful in situations where real-time angular measurement is crucial, such as industrial applications. For the sake of illustration, we apply our technique to a system of active spinners thermalized with an upflow air current. We show that the proposed method allows for accurate measurement of fast dynamics features in the systems, such as rapid velocity autorrelation oscillations and particle spin average field.
Personalized Trajectory Prediction via Distribution Discrimination

Trajectory prediction is confronted with the dilemma to capture the multi-modal nature of future dynamics with both diversity and accuracy. In this paper, we present a distribution discrimination (DisDis) method to predict personalized motion patterns by distinguishing the potential distributions. Motivated by that the motion pattern of each person is personalized due to his/her habit, our DisDis learns the latent distribution to represent different motion patterns and optimize it by the contrastive discrimination. This distribution discrimination encourages latent distributions to be more discriminative. Our method can be integrated with existing multi-modal stochastic predictive models as a plug-and-play module to learn the more discriminative latent distribution. To evaluate the latent distribution, we further propose a new metric, probability cumulative minimum distance (PCMD) curve, which cumulatively calculates the minimum distance on the sorted probabilities. Experimental results on the ETH and UCY datasets show the effectiveness of our method. 1
ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback

High-quality computer science education is limited by the difficulty of providing instructor feedback to students at scale. While this feedback could in principle be automated, supervised approaches to predicting the correct feedback are bottlenecked by the intractability of annotating large quantities of student code. In this paper, we instead frame the problem of providing feedback as few-shot classification, where a meta-learner adapts to give feedback to student code on a new programming question from just a few examples annotated by instructors. Because data for meta-training is limited, we propose a number of amendments to the typical few-shot learning framework, including task augmentation to create synthetic tasks, and additional side information to build stronger priors about each task. These additions are combined with a transformer architecture to embed discrete sequences (e.g. code) to a prototypical representation of a feedback class label. On a suite of few-shot natural language processing tasks, we match or outperform state-of-the-art performance. Then, on a collection of student solutions to exam questions from an introductory university course, we show that our approach reaches an average precision of 88% on unseen questions, surpassing the 82% precision of teaching assistants. Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university. This is, to the best of our knowledge, the first successful deployment of a machine learning based feedback to open-ended student code.
High order finite difference WENO methods with unequal-sized sub-stencils for the Degasperis-Procesi type equations

In this paper, we develop two ﬁnite difference weighted essentially non-oscillatory (WENO) schemes with unequal-sized sub-stencils for solving the Degasperis-Procesi (DP) and µ -Degasperis-Procesi ( µ DP) equations, which contain nonlinear high order derivatives, and possibly peakon solutions or shock waves. By introducing auxiliary variable(s), we rewrite the DP equation as a hyperbolic-elliptic system, and the µ DP equation as a ﬁrst order system. Then we choose a linear ﬁnite difference scheme with suitable order of accuracy for the auxiliary variable(s), and two ﬁnite difference WENO schemes with unequal-sized sub-stencils for the primal variable. One WENO scheme uses one large stencil and several smaller stencils, and the other WENO scheme is based on the multi-resolution framework which uses a se-ries of unequal-sized hierarchical central stencils. Comparing with the classical WENO scheme which uses several small stencils of the same size to make up a big stencil, both WENO schemes with unequal-sized sub-stencils are simple in the choice of the stencil and enjoy the freedom of arbitrary positive linear weights. Another advantage is that the ﬁnal reconstructed polynomial on the target cell is a polynomial of the same de-gree as the polynomial over the big stencil, while the classical ﬁnite difference WENO reconstruction can only be obtained for speciﬁc points inside the target interval. Numerical tests are provided to demonstrate the high order accuracy and non-oscillatory properties of the proposed schemes.
Geometry of canonical genus four curves

We apply Bridgeland stability conditions machinery to describe the geometry of some classical moduli spaces associated with canonical genus four curves in $\mathbb{P}^3$ via an effective control over its wall-crossing. These moduli spaces with several irreducible components include the moduli space of Pandharipande-Thomas stable pairs, and the Hilbert scheme, which are not previously studied. We give a full list of irreducible components of the space of stable pairs, along with a birational description of each component, and a partial list for the Hilbert scheme.
REPRODUCING PROPERTIES OF LATTICE ORBITS OF NILPOTENT LIE GROUPS AND STRICT COMPARISON OF PROJECTIONS

This paper provides sufficient density conditions for the existence of smooth vectors generating a frame or Riesz sequence in the lattice orbit of a square-integrable projective representation of a nilpotent Lie group. The conditions involve the product of lattice co-volume and formal dimension, and complement Balian–Low type theorems for the nonexistence of smooth frames and Riesz sequences at the critical density. The proof hinges on a connection between smooth lattice orbits and generators for an explicitly constructed finitely generated Hilbert C-module. An important ingredient in the approach is that twisted group C -algebras associated to finitely generated nilpotent groups have finite decomposition rank, hence finite nuclear dimension, which allows us to deduce that any matrix algebra over such a simple C-algebra has strict comparison of projections.
SIPM PHOTON EMISSION IN THE DARK

In this paper, we report on the photon emission of Silicon Photomultipliers (SiPMs) from dark noise charge avalanches, with the main objective of better understanding the associated systematics for next-generation, large area, SiPM-based physics experiments. A new apparatus for spectral and imaging analysis was developed at TRIUMF and used to measure the light produced by two SiPMs considered as photo-sensor candidates for the nEXO neutrinoless double-beta decay experiment: one Fondazione Bruno Kessler (FBK) VUV-HD Low Field (LF) Low After Pulse (Low AP) (VUV-HD3) SiPM, and one Hamamatsu Photonics K.K. (HPK) VUV4 Multi-Pixel Photon Counter (MPPC). Spectral measurements of their light emission were taken with varying over-voltage in the wavelength range of 450–1020 nm. For the FBK VUV-HD3, at an over-voltage of 12.1± 1.0 V, we measure a photon yield (number of photons (γ) emitted per charge carrier (e−)) of (4.04± 0.02)× 10−6 γ/e−. The emission spectrum of the FBK VUV-HD3 contains an interference pattern consistent with thin-film interference. Additionally, images of the FBK VUV-HD3 show a small number of highly localized regions with increased light intensity (hotspots) randomly distributed over the SiPM surface area. For the HPK VUV4 MPPC, at an over-voltage of 10.7± 1.0 V, we measure a photon yield of (8.71± 0.04)× 10−6 γ/e−. In contrast to the FBK VUV-HD3, the emission spectra of the HPK VUV4 don’t show an interference pattern—most likely due to a thinner surface coating. Images of the HPK VUV4 also reveal a larger number of hotspots compared to the FBK VUV-HD3, especially in one of the corners of the device. If the occurrence of hotspots in the SiPMs is caused by an increase of crystal defects, generally due to fabrication processes, we could conclude that the FBK production is cleaner than that of HPK MPPCs.
Characterizing current structures in 3D hybrid-kinetic simulations of plasma turbulence

In space and astrophysical plasmas turbulence leads to the development of coherent structures characterized by a strong current density and important magnetic shears. Using hybrid-kinetic simulations of turbulence (3D with different energy injection scales) we investigate the development of these coherent structures and characterize their shape. First, we present different methods to estimate the overall shape of the 3D structure using local measurements, foreseeing application on satellite data. Then we study the local magnetic configuration inside and outside current peak regions, comparing the statistics in the two cases. Last, we compare the statistical properties of the local configuration obtained in simulations with the ones obtained analyzing an MMS dataset having similar plasma parameters. Thanks to our analysis, 1) we validate the possibility to study the overall shape of 3D structures using local methods, 2) we provide an overview of local magnetic configuration emerging in different turbulent regimes, 3) we show that our 3D-3V simulations can reproduce the structures that emerge in MMS data for the periods studied by Phan et al. (2018), Stawarz et al. (2019).
Profile to Frontal Face Recognition in the Wild Using Coupled Conditional GAN

In recent years, with the advent of deep-learning, face recognition has achieved exceptional success. However, many of these deep face recognition models perform much better in handling frontal faces compared to profile faces. The major reason for poor performance in handling of profile faces is that it is inherently difficult to learn pose-invariant deep representations that are useful for profile face recognition. In this paper, we hypothesize that the profile face domain possesses a latent connection with the frontal face domain in a latent feature subspace. We look to exploit this latent connection by projecting the profile faces and frontal faces into a common latent subspace and perform verification or retrieval in the latent domain. We leverage a coupled conditional generative adversarial network (cpGAN) structure to find the hidden relationship between the profile and frontal images in a latent common embedding subspace. Specifically, the cpGAN framework consists of two conditional GAN-based sub-networks, one dedicated to the frontal domain and the other dedicated to the profile domain. Each sub-network tends to find a projection that maximizes the pair-wise correlation between the two feature domains in a common embedding feature subspace. The efficacy of our approach compared with the state-of-the-art is demonstrated using the CFP, CMU Multi-PIE, IJB-A, and IJB-C datasets. Additionally, we have also implemented a coupled convolutional neural network (cpCNN) and an adversarial discriminative domain adaptation network (ADDA) for profile to frontal face recognition. We have evaluated the performance of cpCNN and ADDA and compared it with the proposed cpGAN. Finally, we have also evaluated our cpGAN for reconstruction of frontal faces from input profile faces contained in the VGGFace2 dataset.
s-Recollements and its localization

We introduce a new concept of s-recollements of extriangulated categories, which generalizes recollements of abelian categories, recollements of triangulated categories, as well as recollements of extriangulated categories. Moreover, some basic properties of s-recollements are presented. We also discuss the behavior of the localization theory on the adjoint pair of exact functors. Finally, we provide a method to obtain a recollement of triangulated categories from s-recollements of extriangulated categories via the localization theory.
Learning more skills through optimistic exploration

Unsupervised skill learning objectives (Gregor et al., 2016, Eysenbach et al., 2018) allow agents to learn rich repertoires of behavior in the absence of extrinsic rewards. They work by simultaneously training a policy to produce distinguishable latent-conditioned trajectories, and a discriminator to evaluate distinguishability by trying to infer latents from trajectories. The hope is for the agent to explore and master the environment by encouraging each skill (latent) to reliably reach different states. However, an inherent exploration problem lingers: when a novel state is actually encountered, the discriminator will necessarily not have seen enough training data to produce accurate and confident skill classifications, leading to low intrinsic reward for the agent and effective penalization of the sort of exploration needed to actually maximize the objective. To combat this inherent pessimism towards exploration, we derive an information gain auxiliary objective that involves training an ensemble of discriminators and rewarding the policy for their disagreement. Our objective directly estimates the epistemic uncertainty that comes from the discriminator not having seen enough training examples, thus providing an intrinsic reward more tailored to the true objective compared to pseudocount-based methods (Burda et al., 2019). We call this exploration bonus discriminator disagreement intrinsic reward, or DISDAIN. We demonstrate empirically that DISDAIN improves skill learning both in a tabular grid world (Four Rooms) and the 57 games of the Atari Suite (from pixels). Thus, we encourage researchers to treat pessimism with DISDAIN.
Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality

Recommender systems (RSs) employ user-item feedback, e.g., ratings, to match customers to personalized lists of products. Approaches to top-k recommendation mainly rely on Learning-To-Rank algorithms and, among them, the most widely adopted is Bayesian Personalized Ranking (BPR), which bases on a pair-wise optimization approach. Recently, BPR has been found vulnerable against adversarial perturbations of its model parameters. Adversarial Personalized Ranking (APR) mitigates this issue by robustifying BPR via an adversarial training procedure. The empirical improvements of APR's accuracy performance on BPR have led to its wide use in several recommender models. However, a key overlooked aspect has been the beyond-accuracy performance of APR, i.e., novelty, coverage, and amplification of popularity bias, considering that recent results suggest that BPR, the building block of APR, is sensitive to the intensification of biases and reduction of recommendation novelty. In this work, we model the learning characteristics of the BPR and APR optimization frameworks to give mathematical evidence that, when the feedback data have a tailed distribution, APR amplifies the popularity bias more than BPR due to an unbalanced number of received positive updates from short-head items. Using matrix factorization (MF), we empirically validate the theoretical results by performing preliminary experiments on two public datasets to compare BPR-MF and APR-MF performance on accuracy and beyond-accuracy metrics. The experimental results consistently show the degradation of novelty and coverage measures and a worrying amplification of bias.
Geometry Uncertainty Projection Network for Monocular 3D Object Detection

Geometry Projection is a powerful depth estimation method in monocular 3D object detection. It estimates depth dependent on heights, which introduces mathematical priors into the deep model. But projection process also introduces the error amplification problem, in which the error of the estimated height will be amplified and reflected greatly at the output depth. This property leads to uncontrollable depth inferences and also damages the training efficiency. In this paper, we propose a Geometry Uncertainty Projection Network (GUP Net) to tackle the error amplification problem at both inference and training stages. Specifically, a GUP module is proposed to obtains the geometry-guided uncertainty of the inferred depth, which not only provides high reliable confidence for each depth but also benefits depth learning. Furthermore, at the training stage, we propose a Hierarchical Task Learning strategy to reduce the instability caused by error amplification. This learning algorithm monitors the learning situation of each task by a proposed indicator and adaptively assigns the proper loss weights for different tasks according to their pre-tasks situation. Based on that, each task starts learning only when its pre-tasks are learned well, which can significantly improve the stability and efficiency of the training process. Extensive experiments demonstrate the effectiveness of the proposed method. The overall model can infer more reliable object depth than existing methods and outperforms the state-of-the-art image-based monocular 3D detectors by 3.74% and 4.7% AP40 of the car and pedestrian categories on the KITTI benchmark. The code and model will be released at https://github.com/SuperMHP/GUPNet.
Onset of wake meandering for a floating offshore wind turbine under side-to-side motion

Abstract Wind turbine wakes, being convectively unstable, may behave as an amplifier of upstream perturbations and make downstream turbines experience strong inflow fluctuations. In this work, we investigate the effects of the side-to-side motion of a floating offshore wind turbine (FOWT) on wake dynamics using large-eddy simulation and linear stability analysis (LSA). When the inflow turbulence intensity is low, simulation results reveal that the turbine motion with certain Strouhal numbers $St = fD/U_\infty \in (0.2,0.6)$ (where $f$ is the motion frequency, $D$ is the rotor diameter, and $U_\infty$ is the incoming wind speed), which overlap with the Strouhal numbers of wake meandering induced by the shear layer instability, can lead to wake meandering with amplitudes being one order of magnitude larger than the FOWT motion for the most unstable frequency. For high inflow turbulence intensity, on the other hand, the onset of wake meandering is dominated by the inflow turbulence. The probability density function of the spanwise instantaneous wake centres is observed being non-Gaussian and closely related to that of the side-to-side motion. This complements the existing wake meandering mechanisms, that the side-to-side motion of an FOWT can be a novel origin for the onset of wake meandering. It is also found that LSA can predict the least stable frequencies and the amplification factor with acceptable accuracy for motion amplitude $0.01D$. Effects of nonlinearity are observed when motion amplitude increases to $0.04D$, for which the most unstable turbine oscillations shift slightly to lower frequencies and the amplification factor decreases.
PIPS, an advanced platform for period detection in time series – I. Fourier-likelihood periodogram and application to RR Lyrae stars

We describe the Period detection and Identification Pipeline Suite (PIPS) — a new, fast, and statistically robust platform for period detection and analysis of astrophysical time-series data. PIPSis an open-source Python package that provides various pre-implemented methods and a customisable framework for automated, robust period measurements with principled uncertainties and statistical significance calculations. In addition to detailing the general algorithm that underlies PIPS, this paper discusses one of PIPS’ central and novel features, the Fourier-likelihood periodogram, and compares its performance to existing methods. The resulting improved performance implies that one can construct deeper, larger, and more reliable sets of derived properties from various observations, including all-sky surveys. We present a comprehensive validation of PIPSagainst artificially generated data, which demonstrates the reliable performance of our algorithm for a class of periodic variable stars (RR Lyrae stars).