Miracle-dz/UniFAR-FTUs huggingface dataset:
Density-Ratio Based Personalised Ranking from Implicit Feedback

Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones. Most conventional methods cope with this issue by adopting a pairwise ranking approach with negative sampling. However, the pairwise ranking approach has a severe disadvantage in the convergence time owing to the quadratically increasing computational cost with respect to the sample size; it is problematic, particularly for large-scale datasets and complex models such as neural networks. By contrast, a pointwise approach does not directly solve a ranking problem, and is therefore inferior to a pairwise counterpart in top-K ranking tasks; however, it is generally advantageous in regards to the convergence time. This study aims to establish an approach to learn personalised ranking from implicit feedback, which reconciles the training efficiency of the pointwise approach and ranking effectiveness of the pairwise counterpart. The key idea is to estimate the ranking of items in a pointwise manner; we first reformulate the conventional pointwise approach based on density ratio estimation and then incorporate the essence of ranking-oriented approaches (e.g. the pairwise approach) into our formulation. Through experiments on three real-world datasets, we demonstrate that our approach dramatically reduces the convergence time (one to two orders of magnitude faster) and significantly improves the ranking performance.
Deep Reinforcement Learning for Producing Furniture Layout in Indoor Scenes

In the industrial interior design process, professional designers plan the size and position of furniture in a room to achieve a satisfactory design for selling. In this paper, we explore the interior scene design task as a Markov decision process (MDP), which is solved by deep reinforcement learning. The goal is to produce an accurate position and size of the furniture simultaneously for the indoor layout task. In particular, we first formulate the furniture layout task as a MDP problem by defining the state, action, and reward function. We then design the simulated environment and train reinforcement learning agents to produce the optimal layout for the MDP formulation. We conduct our experiments on a large-scale real-world interior layout dataset that contains industrial designs from professional designers. Our numerical results demonstrate that the proposed model yields higher-quality layouts as compared with the state-of-art model. The developed simulator and codes are available at \url{https://github.com/CODE-SUBMIT/simulator1}.
Collaboration among Image and Object Level Features for Image Colourisation

Image colourisation is an ill-posed problem, with multiple correct solutions which depend on the context and object instances present in the input datum. Previous approaches attacked the problem either by requiring intense user interactions or by exploiting the ability of convolutional neural networks (CNNs) in learning image level (context) features. However, obtaining human hints is not always feasible and CNNs alone are not able to learn object level semantics, unless multiple models pretrained with supervision are considered. In this work, we propose a single network, named UCapsNet, that disentangles image level features obtained through convolutions and object-level features captured by means of capsules. Then, by skip connections over different layers, we enforce collaboration between such disentangling factors to produce a high quality and plausible image colourisation. We pose the problem as a classification task that can be addressed by a fully self-supervised approach, thus requires no human effort. Experimental results on three benchmark datasets show that our approach outperforms existing methods on standard quality metrics and achieves state of the art performances on image colourisation. A large scale user study shows that our method is preferred over existing solutions.
An Improvement of Object Detection Performance using Multi-step Machine Learnings

Connecting multiple machine learning models into a pipeline is effective for handling complex problems. By breaking down the problem into steps, each tackled by a specific component model of the pipeline, the overall solution can be made accurate and explainable. This paper describes an enhancement of object detection based on this multi-step concept, where a post-processing step called the calibration model is introduced. The calibration model consists of a convolutional neural network, and utilizes rich contextual information based on the domain knowledge of the input. Improvements of object detection performance by 0.8-1.9 in average precision metric over existing object detectors have been observed using the new model.
A Sampling Algorithm to Compute the Set of Feasible Solutions for NonNegative Matrix Factorization with an Arbitrary Rank

Non-negative Matrix Factorization (NMF) is a useful method to extract features from multivariate data, but an important and sometimes neglected concern is that NMF can result in non-unique solutions. Often, there exist a Set of Feasible Solutions (SFS), which makes it more difficult to interpret the factorization. This problem is especially ignored in cancer genomics, where NMF is used to infer information about the mutational processes present in the evolution of cancer. In this paper the extent of non-uniqueness is investigated for two mutational counts data, and a new sampling algorithm, that can find the SFS, is introduced. Our sampling algorithm is easy to implement and applies to an arbitrary rank of NMF. This is in contrast to state of the art, where the NMF rank must be smaller than or equal to four. For lower ranks we show that our algorithm performs similarly to the polygon inflation algorithm that is developed in relations to chemometrics. Furthermore, we show how the size of the SFS can have a high influence on the appearing variability of a solution. Our sampling algorithm is implemented in an R package SFS (https://github. com/ragnhildlaursen/SFS).
Testing Simultaneous Diagonalizability

This paper proposes novel methods to test for simultaneous diagonalization of possibly asymmetric matrices. Motivated by various applications, a two-sample test as well as a generalization for multiple matrices are proposed. A partial version of the test is also studied to check whether a partial set of eigenvectors is shared across samples. Additionally, a novel algorithm for the considered testing methods is introduced. Simulation studies demonstrate favorable performance for all designs. Finally, the theoretical results are utilized to decouple vector autoregression models into multiple univariate time series, and to test for the same stationary distribution in recurrent Markov chains. These applications are demonstrated using macroeconomic indices of 8 countries and streamflow data, respectively.
Minimax Off-Policy Evaluation for Multi-Armed Bandits

We study the problem of off-policy evaluation in the multi-armed bandit model with bounded rewards, and develop minimax rate-optimal procedures under three settings. First, when the behavior policy is known, we show that the Switch estimator, a method that alternates between the plug-in and importance sampling estimators, is minimax rate-optimal for all sample sizes. Second, when the behavior policy is unknown, we analyze performance in terms of the competitive ratio, thereby revealing a fundamental gap between the settings of known and unknown behavior policies. When the behavior policy is unknown, any estimator must have mean-squared error larger—relative to the oracle estimator equipped with the knowledge of the behavior policy— by a multiplicative factor proportional to the support size of the target policy. Moreover, we demonstrate that the plug-in approach achieves this worst-case competitive ratio up to a logarithmic factor. Third, we initiate the study of the partial knowledge setting in which it is assumed that the minimum probability taken by the behavior policy is known. We show that the plug-in estimator is optimal for relatively large values of the minimum probability, but is sub-optimal when the minimum probability is low. In order to remedy this gap, we propose a new estimator based on approximation by Chebyshev polynomials that provably achieves the optimal estimation error. Numerical experiments on both simulated and real data corroborate our theoretical findings.
Automated Verification and Synthesis of Stochastic Hybrid Systems: A Survey

Stochastic hybrid systems have received significant attentions as a relevant modelling framework describing many systems, from engineering to the life sciences: they enable the study of numerous applications, including transportation networks, biological systems and chemical reaction networks, smart energy and power grids, and beyond. Automated verification and policy synthesis for stochastic hybrid systems can be inherently challenging: this is due to the heterogeneity of their dynamics (presence of continuous and discrete components), the presence of uncertainty, and in some applications the large dimension of state and input sets. Over the past few years, a few hundred articles have investigated these models, and developed diverse and powerful approaches to mitigate difficulties encountered in the analysis and synthesis of such complex stochastic systems. In this survey, we overview the most recent results in the literature and discuss different approaches, including (in)finite abstractions, verification and synthesis for temporal logic specifications, stochastic similarity relations, (control) barrier certificates, compositional techniques, and a selection of results on continuous-time stochastic systems; we finally survey recently developed software tools that implement the discussed approaches. Throughout the manuscript we discuss a few open topics to be considered as potential future research directions: we hope that this survey will guide younger researchers through a comprehensive understanding of the various challenges, tools, and solutions in this enticing and rich scientific area.
A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction

Sparse-view computed tomography (CT) is known as a widely used approach to reduce radiation dose while accelerating imaging through lowered projection views and correlated calculations. However, its severe imaging noise and streaking artifacts turn out to be a major issue in the low dose protocol. In this paper, we propose a dual-domain deep learning-based method that breaks through the limitations of currently prevailing algorithms that merely process single image slices. Since the scanned object usually contains a high degree of spatial continuity, the obtained consecutive imaging slices embody rich information that is largely unexplored. Therefore, we establish a cascade model named LS-AAE which aims to tackle the above problem. In addition, in order to adapt to the social trend of lightweight medical care, our model adopts the inverted residual with linear bottleneck in the module design to make it mobile and lightweight (reduce model parameters to one-eighth of its original) without sacrificing its performance. In our experiments, sparse sampling is conducted at intervals of 4{\deg}, 8{\deg} and 16{\deg}, which appears to be a challenging sparsity that few scholars have attempted before. Nevertheless, our method still exhibits its robustness and achieves the state-of-the-art performance by reaching the PSNR of 40.305 and the SSIM of 0.948, while ensuring high model mobility. Particularly, it still exceeds other current methods when the sampling rate is one-fourth of them, thereby demonstrating its remarkable superiority.
Using the Starlight Polarization Efficiency Integral to Constrain Shapes and Porosities of Interstellar Grains

We have developed a new method for using the observed starlight polarization and polarized submillimeter emission to constrain the shapes and porosities of interstellar grains. We first present the modified picket-fence approximation and verify that it is sufficiently accurate for modeling starlight polarization. We then introduce the observed starlight polarization integral Πobs as a measure of the overall strength of the observed polarization of starlight, and the starlight polarization efficiency integral Φ to characterize the effectiveness of different grain types for producing polarization of starlight. The ratio Πobs/Φ determines the mass-weighted alignment 〈f align〉 of the grains. Approximating the aligned grains in the ISM as spheroids, we use Πobs/Φ to show that the observed starlight polarization constrains the grains to have a minimum degree of asphericity. For porosity =0 , the minimum axial ratio is ∼1.4 for oblate spheroids, or ∼1.8 for prolate spheroids. If the grains are porous, more extreme axial ratios are required. The same grains that produce the starlight polarization are able to provide the observed polarized emission at submillimeter wavelengths but with further limits on shape and porosity. Porosities ≳0.75 are ruled out. If interstellar grains can be approximated by “astrodust” spheroids, we predict the ratio of 10 μm polarization to starlight polarization p V : p(10 μm)/p V = 0.219 ± 0.029. For Cyg OB2-12, we predict p(10 μm) = (2.1 ± 0.3)%, which should be observable.
Practical Distributed Control for VTOL UAVs to Pass a Tunnel

—Unmanned Aerial Vehicles (UAVs) are now becoming increasingly accessible to amateur and commercial users alike. An air trafﬁc management (ATM) system is needed to help ensure that this newest entrant into the skies does not collide with others. In an ATM, airspace can be composed of airways, intersections and nodes. In this paper, for simplicity, distributed coordinating the motions of Vertical TakeOff and Landing (VTOL) UAVs to pass an airway is focused. This is formulated as a tunnel passing problem , which includes passing a tunnel, inter-agent collision avoidance and keeping within the tunnel. Lyapunov-like functions are designed elaborately, and formal analysis based on invariant set theorem is made to show that all UAVs can pass the tunnel without getting trapped, avoid collision and keep within the tunnel. What is more, by the proposed distributed control, a VTOL UAV can keep away from another VTOL UAV or return back to the tunnel as soon as possible, once it enters into the safety area of another or has a collision with the tunnel during it is passing the tunnel. Simulations and experiments are carried out to show the effectiveness of the proposed method and the comparison with other methods.
Mahler measure of $P_d$ polynomials

This article investigates the Mahler measure of a family of 2-variate polynomials, denoted by Pd, d ≥ 1, unbounded in both degree and genus. By using a closed formula for the Mahler measure [9], we are able to compute m(Pd), for arbitrary d, as a sum of the values of dilogarithm at special roots of unity. We prove that m(Pd) converges and the limit is proportional to ζ(3), where ζ is the Riemann zeta function.
From soft to hard: the generation of functional and complex colloidal monolayers for nanolithography

Take polymer colloids—simple to make and ubiquitously available—let them self-assemble into a monolayer and you have, readily engineered, a sophisticated mask to create highly symmetric surface patterns with nanometre precision. Thirty years after its invention, this process, commonly referred to as colloidal lithography, is far from being old news and an ever-increasing range of scientists continues to use their creativity to come up with increasingly more complex surface patterns. As intriguing this technique is, the devil is in the details and it is far from trivial to achieve high order in a colloidal monolayer. This article reviews available crystallization techniques and discusses their scope and limitations in order to provide just the right method for your next experiment.
SynCron: Efficient Synchronization Support for Near-Data-Processing Architectures

Near-Data-Processing (NDP) architectures present a promising way to alleviate data movement costs and can provide significant performance and energy benefits to parallel applications. Typically, NDP architectures support several NDP units, each including multiple simple cores placed close to memory. To fully leverage the benefits of NDP and achieve high performance for parallel workloads, efficient synchronization among the NDP cores of a system is necessary. However, supporting synchronization in many NDP systems is challenging because they lack shared caches and hardware cache coherence support, which are commonly used for synchronization in multicore systems, and communication across different NDP units can be expensive.This paper comprehensively examines the synchronization problem in NDP systems, and proposes SynCron, an end-to-end synchronization solution for NDP systems. SynCron adds low-cost hardware support near memory for synchronization acceleration, and avoids the need for hardware cache coherence support. SynCron has three components: 1) a specialized cache memory structure to avoid memory accesses for synchronization and minimize latency overheads, 2) a hierarchical message-passing communication protocol to minimize expensive communication across NDP units of the system, and 3) a hardware-only overflow management scheme to avoid performance degradation when hardware resources for synchronization tracking are exceeded.We evaluate SynCron using a variety of parallel workloads, covering various contention scenarios. SynCron improves performance by $1.27\times$ on average (up to $1.78\times$) under high-contention scenarios, and by $ 1.35\times$ on average (up to $2.29\times)$ under low-contention real applications, compared to state-of-the-art approaches. SynCron reduces system energy consumption by $ 2.08\times$ on average (up to $4.25\times$).
Strong multipolar transition enhancement with graphene nanoislands

During the past half century, a major approximation was natural in the field of light-matter interaction: the point-dipole model. It was assumed that the wavelength is much larger than the size of the emitting atom or molecule, so that the emitter can be described as a single or a collection of elementary dipoles. As it is legitimate for visible light, the approximation does no longer hold near plasmonic nanostructures, where the effective wavelength can drop below 10 nm. In that case deviations arise from the approximate model. First, the emitter spatial extent influences the far-field spectrum. Second, high-order transitions beyond the dipolar ones are not forbidden anymore. Going beyond the approximation requires intensive numerical efforts to compute the photonic response over the spatial extent of the emitter, since the complete Green's function is required. Here, we develop a general model that computes the multipolar transition rates of a quantum emitter in a photonic environment, by computing the Green's function through an eigenpermittivity modal expansion. We apply the method on graphene nanoislands, and we demonstrate a local breakdown of the selection rules, with quadrupolar transition rates becoming 100 times larger than dipolar ones.
A note on the g and h control charts

In this note, we revisit the $g$ and $h$ control charts that are commonly used for monitoring the number of conforming cases between the two consecutive appearances of nonconformities. It is known that the process parameter of these charts is usually unknown and estimated by using the maximum likelihood estimator and the minimum variance unbiased estimator. However, the minimum variance unbiased estimator in the control charts has been inappropriately used in the quality engineering literature. This observation motivates us to provide the correct minimum variance unbiased estimator and investigate theoretical and empirical biases of these estimators under consideration. Given that these charts are developed based on the underlying assumption that samples from the process should be balanced, which is often not satisfied in many practical applications, we propose a method for constructing these charts with unbalanced samples.
Intelligent Frame Selection as a Privacy-Friendlier Alternative to Face Recognition

The widespread deployment of surveillance cameras for facial recognition gives rise to many privacy concerns. This study proposes a privacy-friendly alternative to large scale facial recognition. While there are multiple techniques to preserve privacy, our work is based on the minimization principle which implies minimizing the amount of collected personal data. Instead of running facial recognition software on all video data, we propose to automatically extract a high quality snapshot of each detected person without revealing his or her identity. This snapshot is then encrypted and access is only granted after legal authorization. We introduce a novel unsupervised face image quality assessment method which is used to select the high quality snapshots. For this, we train a variational autoencoder on high quality face images from a publicly available dataset and use the reconstruction probability as a metric to estimate the quality of each face crop. We experimentally confirm that the reconstruction probability can be used as biometric quality predictor. Unlike most previous studies, we do not rely on a manually defined face quality metric as everything is learned from data. Our face quality assessment method outperforms supervised, unsupervised and general image quality assessment methods on the task of improving face verification performance by rejecting low quality images. The effectiveness of the whole system is validated qualitatively on still images and videos.
On an uncountable family of graphs whose spectrum is a Cantor set

For each $p\geq 1$, the star automaton group $\mathcal{G}_{S_p}$ is an automaton group which can be defined starting from a star graph on $p+1$ vertices. We study Schreier graphs associated with the action of the group $\mathcal{G}_{S_p}$ on the regular rooted tree $T_{p+1}$ of degree $p+1$ and on its boundary $\partial T_{p+1}$. With the transitive action on the $n$-th level of $T_{p+1}$ is associated a finite Schreier graph $\Gamma^p_n$, whereas there exist uncountably many orbits of the action on the boundary, represented by infinite Schreier graphs which are obtained as limits of the sequence $\{\Gamma_n^p\}_{n\geq 1}$ in the Gromov-Hausdorff topology. We obtain an explicit description of the spectrum of the graphs $\{\Gamma_n^p\}_{n\geq 1}$. Then, by using amenability of $\mathcal{G}_{S_p}$, we prove that the spectrum of each infinite Schreier graph is the union of a Cantor set of zero Lebesgue measure, which is the Julia set of the quadratic map $f_p(z) = z^2-2(p-1)z -2p$, and a countable collection of isolated points supporting the KNS spectral measure. We also give a complete classification of the infinite Schreier graphs up to isomorphism of unrooted graphs, showing that they may have $1$, $2$ or $2p$ ends, and that the case of $1$ end is generic with respect to the uniform measure on $\partial T_{p+1}$.