Miracle-dz/UniFAR-FTUs huggingface dataset:
Content-Based Weak Supervision for Ad-Hoc Re-Ranking

One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training. In contrast with prior work, we examine the use of weak supervision sources for training that yield pseudo query-document pairs that already exhibit relevance (e.g., newswire headline-content pairs and encyclopedic heading-paragraph pairs). We also propose filtering techniques to eliminate training samples that are too far out of domain using two techniques: a heuristic-based approach and novel supervised filter that re-purposes a neural ranker. Using several leading neural ranking architectures and multiple weak supervision datasets, we show that these sources of training pairs are effective on their own (outperforming prior weak supervision techniques), and that filtering can further improve performance.
Towards a better balance in sinusoidal plus stochastic representation

Sound can be separated into a sinusoidal and a stochastic part, which is useful for transformation and compression. In such a sinusoidal plus stochastic representation, one of the important issues is to obtain a good separation. The paper presents a method to obtain this deterministic-stochastic balance. The method uses an iterative sinusoidal extraction method and a stopping criterion, which indicates when the (important) tonal components are extracted from the signal. Two measures are investigated, with and without psychoacoustic information, to be used in different applications. First, including psychoacoustic information, the stopping criterion is used in the sinusoidal analysis to construct a sinusoidal part that only contains psychoacoustically relevant tonal components. Second, deliberately not including psychoacoustic information, the stopping criterion is used, in a preprocessing stage before the noise analysis, to remove tonal components from the residual. An important advantage of this method is its simplicity and results are promising.
Large Scale Adversarial Representation Learning

Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation quality translates to substantially improved representation learning performance. Our approach, BigBiGAN, builds upon the state-of-the-art BigGAN model, extending it to representation learning by adding an encoder and modifying the discriminator. We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation. Pretrained BigBiGAN models -- including image generators and encoders -- are available on TensorFlow Hub (https://tfhub.dev/s? publisher=deepmind&q=bigbigan).
Interference Management in UAV-Assisted Integrated Access and Backhaul Cellular Networks

An integrated access and backhaul (IAB) network architecture can enable flexible and fast deployment of the next-generation cellular networks. However, mutual interference between access and backhaul links, small inter-site distance, and spatial dynamics of user distribution pose major challenges in the practical deployment of the IAB networks. To tackle these problems, we leverage the flying capabilities of unmanned aerial vehicles (UAVs) as hovering IAB-nodes and propose an interference management algorithm to maximize the overall sum rate of the IAB network. In particular, we jointly optimize the user and base station associations, the downlink power allocations for access and backhaul transmissions, and the spatial configurations of the UAVs. We consider two spatial configuration modes of the UAVs, distributed UAVs and drone antenna array (DAA), and show how they are intertwined with the spatial distribution of ground users. Our numerical results show that the proposed algorithm achieves an average of $2.9\times $ and $6.7\times $ gains in the received downlink signal-to-interference-plus-noise ratio (SINR) and overall network sum rate, respectively. Finally, the numerical results reveal that UAVs can not only be used for coverage improvement but also for capacity boosting in the IAB cellular networks.
Characterizations of Hardy spaces for Fourier integral operators

We prove several characterizations of the Hardy spaces for Fourier integral operators $\mathcal{H}^{p}_{FIO}(\mathbb{R}^{n})$, for $1<p<\infty$. First we characterize $\mathcal{H}^{p}_{FIO}(\mathbb{R}^{n})$ in terms of $L^{p}(\mathbb{R}^{n})$-norms of parabolic frequency localizations. As a corollary, any characterization of $L^{p}(\mathbb{R}^{n})$ yields a corresponding version for $\mathcal{H}^{p}_{FIO}(\mathbb{R}^{n})$. In particular, we obtain a maximal function characterization and a characterization in terms of vertical square functions.
On the applicability of quantum-optical concepts in strong-coupling nanophotonics

Rooted in quantum optics and benefiting from its well-established foundations, strong coupling in nanophotonics has experienced increasing popularity in recent years. With nanophotonics being an experiment-driven field, the absence of appropriate theoretical methods to describe ground-breaking advances has often emerged as an important issue. To address this problem, the temptation to directly transfer and extend concepts already available from quantum optics is strong, even if a rigorous justification is not always available. In this review we discuss situations where, in our view, this strategy has indeed overstepped its bounds. We focus on exciton–plasmon interactions, and particularly on the idea of calculating the number of excitons involved in the coupling. We analyse how, starting from an unfounded interpretation of the term N/V that appears in theoretical descriptions at different levels of complexity, one might be tempted to make independent assumptions for what the number N and the volume V are, and attempt to calculate them separately. Such an approach can lead to different, often contradictory results, depending on the initial assumptions (e.g. through different treatments of V as the—ambiguous in plasmonics—mode volume). We argue that the source of such contradictions is the question itself—How many excitons are coupled? , which disregards the true nature of the coupled components of the system, has no meaning and often not even any practical importance. If one is interested in validating the quantum nature of the system—which appears to be the motivation driving the pursuit of strong coupling with small N—one could instead focus on quantities such as the photon emission rate or the second-order correlation function. While many of the issues discussed here may appear straightforward to specialists, our target audience is predominantly newcomers to the field, either students or scientists specialised in different disciplines. We have thus tried to minimise the occurrence of proofs and overly-technical details, and instead provide a qualitative discussion of analyses that should be avoided, hoping to facilitate further growth of this promising area.
Asymptotic reduction, solution, and homogenisation of a thermo-electrochemical model for a lithium-ion battery.

We study two thermo-electrochemical models for lithium-ion batteries. The first is based on volume averaging the electrode microstructure whereas the second is based on the pseudo-two-dimensional (P2D) approach which treats the electrode as a collection of spherical particles. A scaling analysis is used to reduce the volume-averaged model and show that the electrochemical reactions are the dominant source of heat. Matched asymptotic expansions are used to compute solutions of the volume-averaged model for the cases of constant applied current, oscillating applied current, and constant cell potential. The asymptotic and numerical solutions of the volume-averaged model are in remarkable agreement with numerical solutions of the thermal P2D model for (dis)charge rates up to 2C, and reasonable agreement is found at 4C. Homogenisation is then used to derive a thermal model for a battery consisting of several connected lithium-ion cells. Despite accounting for the Arrhenius dependence of the reaction coefficients, we show that thermal runaway does not occur in the model. Instead, the cell potential is simply pushed closer to the open-circuit potential. We also show that in many cases, the homogenised battery model can be solved analytically, making it ideal for use in on-board thermal management systems.
Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG

Electroencephalography (EEG) is an important clinical tool for grading injury caused by lack of oxygen or blood to the brain during birth. Characteristics of low-voltage waveforms, known as inter-bursts, are related to different grades of injury. This study assesses the suitability of an existing inter-burst detection method, developed from preterm infants born <30 weeks of gestational age, to detect inter-bursts in term infants. Different features from the temporal organisation of the inter-bursts are combined using a multi-layer perceptron (MLP) machine learning algorithm to classify four grades of injury in the EEG. We find that the best performing feature, percentage of inter-bursts, has an accuracy of 59.3%. Combining this with the maximum duration of inter-bursts in the MLP produces a testing accuracy of 77.8%, with similar performance to existing multi-feature methods. These results validate the use of the preterm detection method in term EEG and show how simple measures of the inter-burst interval can be used to classify different grades of injury.
Scaling behavior of the stationary states arising from dissipation at continuous quantum transitions

We study the critical behavior of the nonequilibrium dynamics and of the steady states emerging from the competition between coherent and dissipative dynamics close to quantum phase transitions. The latter is induced by the coupling of the system with a Markovian bath, such that the evolution of the system's density matrix can be effectively described by a Lindblad master equation. We devise general scaling behaviors for the out-of-equilibrium evolution and the stationary states emerging in the large-time limit for generic initial conditions, in terms of the parameters of the Hamiltonian providing the coherent driving and those associated with the dissipative interactions with the environment. Our framework is supported by numerical results for the dynamics of a one-dimensional lattice fermion gas undergoing a quantum Ising transition, in the presence of dissipative mechanisms which include local pumping and decay of particles.
Water-Based Reconfigurable Frequency Selective Rasorber With Thermally Tunable Absorption Band

In this article, a novel water-based reconfigurable frequency selective rasorber (FSR) at microwave band is proposed, which has a thermally tunable absorption band above the transmission band. By breaking through the technical barriers of traditional FSR reconfigurable feature design, we have achieved water-based FSR for the first time in a public report. The water-based FSR consists of a bandpass frequency selective surface (FSS) and a 3-D-printed container. The water substrate is filled into the sealed space constructed by the above two structures. Numerical results show that the FSR can achieve absorption with high absorptivity from 8.3 to 15.2 GHz and can obtain a transmission band of 5.2–7.0 GHz. The minimum insertion loss of the transmission band reaches 0.72 dB at 6.14 GHz. In addition, the FSR has the reconfigurable characteristics of absorbing or reflecting electromagnetic waves by filling with water or not. Due to the Debye model of water, the absorption band can be adjusted by water temperature, while the passband remains stable. At last, the designed FSR based on water has been fabricated, and experimental results are presented to demonstrate the validity of the proposed structure.
A Soft-switched Fast Cell-to-Cell Voltage Equalizer for Electrochemical Energy Storage

Batteries are connected in series to meet the voltage requirement in many applications. A voltage equalizer circuit is necessary to ensure that none of the batteries is over-charged or over-discharged. A novel fast soft-switched cell-to-cell voltage equalizer topology is proposed in this work. This topology can transfer charge from multiple over-charged batteries to multiple under-charged batteries simultaneously avoiding any unnecessary charging or discharging of a battery to achieve fast voltage equalization. The proposed circuit topology and modulation method ensure zero voltage switching under all battery conditions. The circuit operation and soft-switching are analyzed and experimentally verified with a four battery voltage equalizer prototype. The prototype is tested with a battery bank and a hybrid ultra-capacitor bank, and a high conversion efficiency is verified.
On finite exchangeability and conditional independence

We study the independence structure of finitely exchangeable distributions over random vectors and random networks. In particular, we provide necessary and sufficient conditions for an exchangeable vector so that its elements are completely independent or completely dependent. We also provide a sufficient condition for an exchangeable vector so that its elements are marginally independent. We then generalize these results and conditions for exchangeable random networks. In this case, it is demonstrated that the situation is more complex. We show that the independence structure of exchangeable random networks lies in one of six regimes represented by undirected and bidirected independence graphs in graphical model sense. In addition, under certain additional assumptions, we provide necessary and sufficient conditions for the exchangeable network distributions to be faithful to each of these graphs.
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning

Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds. Permutation-equivariant networks have become a popular solution – they operate on individual data points with simple perceptrons and extract contextual information with global pooling. This can be achieved with a simple normalization of the feature maps, a global operation that is unaffected by the order. In this paper, we propose Attentive Context Normalization (ACN), a simple yet effective technique to build permutation-equivariant networks robust to outliers. Specifically, we show how to normalize the feature maps with weights that are estimated within the network, excluding outliers from this normalization. We use this mechanism to leverage two types of attention: local and global – by combining them, our method is able to find the essential data points in high-dimensional space in order to solve a given task. We demonstrate through extensive experiments that our approach, which we call Attentive Context Networks (ACNe), provides a significant leap in performance compared to the state-of-the-art on camera pose estimation, robust fitting, and point cloud classification under noise and outliers. Source code: https://github.com/vcg-uvic/acne.
On the Importance of demand Consolidation in Mobility on Demand

Mobility on Demand (MoD) services are revolutionizing the way people move in cities around the world and are often considered a convenient alternative to public transit. MoD is usually intended as a door-to-door service. However, there has been recent interest toward consolidating, e.g., aggregating, the travel demand by limiting the number of admitted stop locations. This implies users have to walk from/to their intended origin/destination.The contribution of this paper is a systematic study the impact of consolidation on the operator cost and on user QoS. We introduce a MoD system where pick-ups and drop-offs can only occur in a limited subset of admitted stop locations. The density of such locations is a system parameter: the less the density, the more the user demand is consolidated. We show that, by decreasing stop density, we can increase system capacity (number of passengers we are able to serve). On the contrary, increasing it, we can improve QoS. The system is tested in AMoDSim, an open-source simulator.This work is a first step toward flexible mobility services that are able to autonomously re-conFigure themselves, favoring capacity or QoS, depending on the amount of travel demand coming from users. In other words, the services we envisage in this work shift their operational mode to any intermediate point in the range from a taxi-like door-to-door service to a bus-like service, with few served stops and more passengers on-board.
Eigenvalues: the Rosetta Stone for Neutrino Oscillations in Matter

We present a new method of exactly calculating neutrino oscillation probabilities in matter. We leverage the "eigenvector-eigenvalue identity" to show that, given the eigenvalues, all mixing angles in matter follow surprisingly simply. The CP violating phase in matter can then be determined from the Toshev identity. Then, to avoid the cumbersome expressions for the exact eigenvalues, we have applied previously derived perturbative, approximate eigenvalues to this scheme and discovered them to be even more precise than previously realized. We also find that these eigenvalues converge at a rate of five orders of magnitude per perturbative order which is the square of the previously realized expectation. Finally, we provide an updated speed versus accuracy plot for oscillation probabilities in matter, to include the methods of this paper.
A Bi-Directional Transformer for Musical Chord Recognition

Chord recognition is an important task since chords are highly abstract and descriptive features of music. For effective chord recognition, it is essential to utilize relevant context in audio sequence. While various machine learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been employed for the task, most of them have limitations in capturing long-term dependency or require training of an additional model. In this work, we utilize a self-attention mechanism for chord recognition to focus on certain regions of chords. Training of the proposed bi-directional Transformer for chord recognition (BTC) consists of a single phase while showing competitive performance. Through an attention map analysis, we have visualized how attention was performed. It turns out that the model was able to divide segments of chords by utilizing adaptive receptive field of the attention mechanism. Furthermore, it was observed that the model was able to effectively capture long-term dependencies, making use of essential information regardless of distance.
Autonomous conversion of information to work in quantum dots

We consider an autonomous implementation of Maxwell's demon in a quantum dot architecture. As in the original thought experiment, only the second law of thermodynamics is seemingly violated when disregarding the demon. The autonomous architecture allows us to compare descriptions in terms of information to a more traditional, thermoelectric characterization. Our detailed investigation of information-to-work conversion is based on fluctuation relations and second law like inequalities in addition to the average heat and charge currents. By introducing a time-reversal on the level of individual electrons, we find a novel fluctuation relation that is not connected to any symmetry of the moment generating function of heat and particle flows. Furthermore, we show how an effective Markovian master equation with broken detailed balance for the system alone can emerge from a full description, allowing for an investigation of the entropic cost associated to breaking detailed balance. Interestingly, while the entropic cost of performing a perfect measurement diverges, the entropic cost of breaking detailed balance does not. Our results connect various approaches and idealized scenarios found in the literature and can be tested experimentally with present day technology.
Spatio-Temporal Reconstructions of Global CO2-Fluxes using Gaussian Markov Random Fields

Atmospheric inverse modelling is a method for reconstructing historical fluxes of green-house gas between land and atmosphere, using observed atmospheric concentrations and an atmospheric tracer transport model. The small number of observed atmospheric concentrations in relation to the number of unknown flux components makes the inverse problem ill-conditioned, and assumptions on the fluxes are needed to constrain the solution. A common practise is to model the fluxes using latent Gaussian fields with a mean structure based on estimated fluxes from combinations of process modelling (natural fluxes) and statistical bookkeeping (anthropogenic emissions). Here, we reconstruct global \CO flux fields by modelling fluxes using Gaussian Markov Random Fields (GMRF), resulting in a flexible and computational beneficial model with a Matern-like spatial covariance, and a temporal covariance defined through an auto-regressive model with seasonal dependence. In contrast to previous inversions, the flux is defined on a spatially continuous domain, and the traditionally discrete flux representation is replaced by integrated fluxes at the resolution specified by the transport model. This formulation removes aggregation errors in the flux covariance, due to the traditional representation of area integrals by fluxes at discrete points, and provides a model closer resembling real-life space-time continuous fluxes.
The origin of slow Alfvénic solar wind at solar minimum

Although the origins of slow solar wind are unclear, there is increasing evidence that at least some of it is released in a steady state on overexpanded coronal hole magnetic field lines. This type of slow wind has similar properties to the fast solar wind, including strongly Alfvénic fluctuations. In this study, a combination of proton, alpha particle, and electron measurements are used to investigate the kinetic properties of a single interval of slow Alfvénic wind at 0.35 au. It is shown that this slow Alfvénic interval is characterized by high alpha particle abundances, pronounced alpha–proton differential streaming, strong proton beams, and large alpha-to-proton temperature ratios. These are all features observed consistently in the fast solar wind, adding evidence that at least some Alfvénic slow solar wind also originates in coronal holes. Observed differences between speed, mass flux, and electron temperature between slow Alfvénic and fast winds are explained by differing magnetic field geometry in the lower corona.
Approximate Fictitious Play for Mean Field Games

The theory of Mean Field Games (MFG) allows characterizing the Nash equilibria of an infinite number of identical players, and provides a convenient and relevant mathematical framework for the study of games with a large number of agents in interaction. Until very recently, the literature only considered Nash equilibria between fully informed players. In this paper, we focus on the realistic setting where agents with no prior information on the game learn their best response policy through repeated experience. We study the convergence to a (possibly approximate) Nash equilibrium of a fictitious play iterative learning scheme where the best response is approximately computed, typically by a reinforcement learning (RL) algorithm. Notably, we show for the first time convergence of model free learning algorithms towards non-stationary MFG equilibria, relying only on classical assumptions on the MFG dynamics. We illustrate our theoretical results with a numerical experiment in continuous action-space setting, where the best response of the iterative fictitious play scheme is computed with a deep RL algorithm.