Miracle-dz/UniFAR-FTUs huggingface dataset:
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement

Attention mechanisms, such as local and non-local attention, play a fundamental role in recent deep learning based speech enhancement (SE) systems. However, a natural speech contains many fast-changing and relatively briefly acoustic events, therefore, capturing the most informative speech features by indiscriminately using local and non-local attention is challenged. We observe that the noise type and speech feature vary within a sequence of speech and the local and non-local can respectively process different types of corrupted speech regions. To leverage this, we propose Selector-Enhancer, a dual-attention based convolution neural network (CNN) with a feature-filter that can dynamically select regions from low-resolution speech features and feed them to local or non-local attention operations. In particular, the proposed feature-filter is trained by using reinforcement learning (RL) with a developed difficulty-regulated reward that related to network performance, model complexity and “the difficulty of the SE task”. The results show that our method achieves comparable or superior performance to existing approaches. In particular, Selector-Enhancer is effective for real-world denoising, where the number and types of noise are varies on a single noisy mixture.
AsyInst: Asymmetric Affinity with DepthGrad and Color for Box-Supervised Instance Segmentation

The weakly supervised instance segmentation is a challenging task. The existing methods typically use bounding boxes as supervision and optimize the network with a regularization loss term such as pairwise color afﬁnity loss for instance segmentation. Through systematic analysis, we found that the commonly used pairwise afﬁnity loss has two limitations: (1) it works with color afﬁnity but leads to inferior performance with other modalities such as depth gradient, (2)the original afﬁnity loss does not prevent trivial predictions as intended but actually accelerates this process due to the afﬁnity loss term being symmetric. To over-come these two limitations, in this paper, we propose a novel asymmetric afﬁnity loss which provides the penalty against the trivial prediction and generalizes well with afﬁnity loss from different modalities. With the proposed asymmetric afﬁnity loss, our method outperforms the state-of-the-art methods on the Cityscapes dataset and outperforms our baseline method by 3.5% in mask AP.
Text Embeddings by Weakly-Supervised Contrastive Pre-training

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks. The model is trained in a contrastive manner with weak supervision signals from our curated large-scale text pair dataset (called CCPairs). E5 can be readily used as a general-purpose embedding model for any tasks requiring a single-vector representation of texts such as retrieval, clustering, and classification, achieving strong performance in both zero-shot and fine-tuned settings. We conduct extensive evaluations on 56 datasets from the BEIR and MTEB benchmarks. For zero-shot settings, E5 is the first model that outperforms the strong BM25 baseline on the BEIR retrieval benchmark without using any labeled data. When fine-tuned, E5 obtains the best results on the MTEB benchmark, beating existing embedding models with 40x more parameters.
Assessing and Analyzing the Resilience of Graph Neural Networks Against Hardware Faults

—Graph neural networks (GNNs) have recently emerged as a promising learning paradigm in learning graph-structured data and have demonstrated wide success across various domains such as recommendation systems, social networks, and electronic design automation (EDA). Like other deep learning (DL) methods, GNNs are being deployed in sophisticated modern hardware systems, as well as dedicated accelerators. However, despite the popularity of GNNs and the recent efforts of bringing GNNs to hardware, the fault tolerance and resilience of GNNs has generally been overlooked. Inspired by the inherent algorithmic resilience of DL methods, this paper conducts, for the ﬁrst time, a large-scale and empirical study of GNN resilience, aiming to understand the relationship between hardware faults and GNN accuracy. By developing a customized fault injection tool on top of PyTorch, we perform extensive fault injection experiments to various GNN models and application datasets. We observe that the error resilience of GNN models varies by orders of magnitude with respect to different models and application datasets. Further, we explore a low-cost error mitigation mechanism for GNN to enhance its resilience. This GNN resilience study aims to open up new directions and opportunities for future GNN accelerator design and architectural optimization.
The Effect of Interior Heat Flux on the Atmospheric Circulation of Hot and Ultra-hot Jupiters

Many hot and ultra-hot Jupiters have inflated radii, implying that their interiors retain significant entropy from formation. These hot interiors lead to an enhanced internal heat flux that impinges upon the atmosphere from below. In this work, we study the effect of this hot interior on the atmospheric circulation and thermal structure of hot and ultra-hot Jupiters. To do so, we incorporate the population-level predictions from evolutionary models of hot and ultra-hot Jupiters as input for a suite of general circulation models (GCMs) of their atmospheric circulation with varying semimajor axis and surface gravity. We conduct simulations with and without a hot interior, and find that there are significant local differences in temperature of up to hundreds of Kelvin and in wind speeds of hundreds of meters per second or more across the observable atmosphere. These differences persist throughout the parameter regime studied, and are dependent on surface gravity through the impact on photosphere pressure. These results imply that the internal evolution and atmospheric thermal structure and dynamics of hot and ultra-hot Jupiters are coupled. As a result, a joint approach including both evolutionary models and GCMs may be required to make robust predictions for the atmospheric circulation of hot and ultra-hot Jupiters.
A recipe for orbital eccentricity damping in the type-I regime for low-viscosity 2D discs

It is known that gap opening depends on the disc's viscosity; however, eccentricity damping formulas have only been derived at high viscosities, ignoring partial gap opening. We aim at obtaining a simple formula to model $e$-damping of the type-I regime in low viscosity discs, where even small planets may start opening partial. We perform high resolution 2D locally isothermal hydrodynamical simulations of planets with varying masses on fixed orbits in discs with varying aspect ratios and viscosities. We determine the torque and power felt by the planet to derive migration and eccentricity damping timescales. We first find a lower limit to the gap depths below which vortices appear; this happens roughly at the transition between type-I and type-II regimes. For the simulations that remain stable, we obtain a fit to the observed gap depth in the limit of vanishing eccentricities that is similar to the one currently used in the literature but is accurate down to $\alpha=3.16\times 10^{-5}$. We record the $e$-damping efficiency as a function of the observed gap depth and $e$: when the planet has opened a deep enough gap, a linear trend is observed independently of $e$; at shallower gaps this linear trend is preserved at low $e$, while it deviates to more efficient damping when $e$ is comparable to the disc's scale height. Both trends can be understood on theoretical grounds and are reproduced by a simple fitting formula. Our combined fits yield a simple recipe to implement type-I $e$-damping in $N$-body for partial gap opening planets that is consistent with high-resolution 2D hydro-simulations. The typical error of the fit is of the order of a few percent, and lower than the error of type-I torque formulas widely used in the literature. This will allow a more self-consistent treatment of planet-disc interactions of the type-I regime for population synthesis models at low viscosities.
LSVL: Large-scale season-invariant visual localization for UAVs

Localization of autonomous unmanned aerial vehicles (UAVs) relies heavily on Global Navigation Satellite Systems (GNSS), which are susceptible to interference. Especially in security applications, robust localization algorithms independent of GNSS are needed to provide dependable operations of autonomous UAVs also in interfered conditions. Typical non-GNSS visual localization approaches rely on known starting pose, work only on a small-sized map, or require known flight paths before a mission starts. We consider the problem of localization with no information on initial pose or planned flight path. We propose a solution for global visual localization on a map at scale up to 100 km2, based on matching orthoprojected UAV images to satellite imagery using learned season-invariant descriptors. We show that the method is able to determine heading, latitude and longitude of the UAV at 12.6-18.7 m lateral translation error in as few as 23.2-44.4 updates from an uninformed initialization, also in situations of significant seasonal appearance difference (winter-summer) between the UAV image and the map. We evaluate the characteristics of multiple neural network architectures for generating the descriptors, and likelihood estimation methods that are able to provide fast convergence and low localization error. We also evaluate the operation of the algorithm using real UAV data and evaluate running time on a real-time embedded platform. We believe this is the first work that is able to recover the pose of an UAV at this scale and rate of convergence, while allowing significant seasonal difference between camera observations and map.
Reconciling a Centroid-Hypothesis Conflict in Source-Free Domain Adaptation

Source-free domain adaptation (SFDA) aims to transfer knowledge learned from a source domain to an unlabeled target domain, where the source data is unavailable during adaptation. Existing approaches for SFDA focus on self-training usually including well-established entropy minimization techniques. One of the main challenges in SFDA is to reduce accumulation of errors caused by domain misalignment. A recent strategy successfully managed to reduce error accumulation by pseudo-labeling the target samples based on class-wise prototypes (centroids) generated by their clustering in the representation space. However, this strategy also creates cases for which the cross-entropy of a pseudo-label and the minimum entropy have a conflict in their objectives. We call this conflict the centroid-hypothesis conflict. We propose to reconcile this conflict by aligning the entropy minimization objective with that of the pseudo labels' cross entropy. We demonstrate the effectiveness of aligning the two loss objectives on three domain adaptation datasets. In addition, we provide state-of-the-art results using up-to-date architectures also showing the consistency of our method across these architectures.
Multilayer Graphene as an Endoreversible Otto Engine

We examine the performance of a finite-time, endoreversible Otto heat engine with a working medium of monolayer or multilayered graphene subjected to an external magnetic field. As the energy spectrum of multilayer graphene under an external magnetic field depends strongly on the number of layers, so too does its thermodynamic behavior. We show that this leads to a simple relationship between the engine efficiency and the number of layers of graphene in the working medium. Furthermore, we find that the efficiency at maximum power for bilayer and trilayer working mediums can exceed that of a classical endoreversible Otto cycle. Conversely, a working medium of monolayer graphene displays identical efficiency at maximum power to a classical working medium. These results demonstrate that layered graphene can be a useful material for the construction of efficient thermal machines for diverse quantum device applications.
The Type Ibn Supernova 2019kbj: Indications for Diversity in Type Ibn Supernova Progenitors

Type Ibn supernovae (SNe) are a rare class of stellar explosions whose progenitor systems are not yet well determined. We present and analyze observations of the Type Ibn SN 2019kbj, and model its light curve in order to constrain its progenitor and explosion parameters. SN 2019kbj shows roughly constant temperature during the first month after peak, indicating a power source (likely circumstellar material interaction) that keeps the continuum emission hot at ∼15,000 K. Indeed, we find that the radioactive decay of 56Ni is disfavored as the sole power source of the bolometric light curve. A radioactive decay + circumstellar material (CSM) interaction model, on the other hand, does reproduce the bolometric emission well. The fits prefer a uniform-density CSM shell rather than CSM due to a steady mass-loss wind, similar to what is seen in other Type Ibn SNe. The uniform-density CSM shell model requires ∼0.1 M ⊙ of 56Ni and ∼1 M ⊙ total ejecta mass to reproduce the light curve. SN 2019kbj differs in this manner from another Type Ibn SN with derived physical parameters, SN 2019uo, for which an order of magnitude lower 56Ni mass and larger ejecta mass were derived. This points toward a possible diversity in SN Ibn progenitor systems and explosions.
Quantum advantage in temporally flat measurement-based quantum computation

Several classes of quantum circuits have been shown to provide a quantum computational advantage under certain assumptions. The study of ever more restricted classes of quantum circuits capable of quantum advantage is motivated by possible simplifications in experimental demonstrations. In this paper we study the efficiency of measurement-based quantum computation with a completely flat temporal ordering of measurements. We propose new constructions for the deterministic computation of arbitrary Boolean functions, drawing on correlations present in multi-qubit Greenberger, Horne, and Zeilinger (GHZ) states. We characterize the necessary measurement complexity using the Clifford hierarchy, and also generally decrease the number of qubits needed with respect to previous constructions. In particular, we identify a family of Boolean functions for which deterministic evaluation using non-adaptive MBQC is possible, featuring quantum advantage in width and number of gates with respect to classical circuits.
Improving Deep Localized Level Analysis: How Game Logs Can Help

Player modelling is the ﬁeld of study associated with under- standing players. One pursuit in this ﬁeld is affect prediction: the ability to predict how a game will make a player feel. We present novel improvements to affect prediction by using a deep convolutional neural network (CNN) to predict player experience trained on game event logs in tandem with localized level structure information. We test our approach on levels based on Super Mario Bros. ( Inﬁnite Mario Bros. ) and Super Mario Bros. : The Lost Levels ( Gwario ), as well as original Super Mario Bros. levels. We outperform prior work, and demonstrate the utility of training on player logs, even when lacking them at test time for cross-domain player modelling.
Not Your Average App: A Large-scale Privacy Analysis of Android Browsers

The transparency and privacy behavior of mobile browsers has remained widely unexplored by the research community. In fact, as opposed to regular Android apps, mobile browsers may present contradicting privacy behaviors. On the one end, they can have access to (and can expose) a unique combination of sensitive user data, from users’ browsing history to permission-protected personally identifiable information (PII) such as unique identifiers and geolocation. However, on the other end, they also are in a unique position to protect users’ privacy by limiting data sharing with other parties by implementing ad-blocking features. In this paper, we perform a comparative and empirical analysis on how hundreds of Android web browsers protect or expose user data during browsing sessions. To this end, we collect the largest dataset of Android browsers to date, from the Google Play Store and four Chinese app stores. Then, we developed a novel analysis pipeline that combines static and dynamic analysis methods to find a wide range of privacy-enhancing (e.g., ad-blocking) and privacy-harming behaviors (e.g., sending browsing histories to third parties, not validating TLS certificates, and exposing PII---including non-resettable identifiers---to third parties) across browsers. We find that various popular apps on both Google Play and Chinese stores have these privacy-harming behaviors, including apps that claim to be privacy-enhancing in their descriptions. Overall, our study not only provides new insights into important yet overlooked considerations for browsers’ adoption and transparency, but also that automatic app analysis systems (e.g., sandboxes) need context-specific analysis to reveal such privacy behaviors.
A comparison of measured spiral arm properties with model predictions

We present measurements of the morphological properties of the spiral arms in a sample of 17 galaxies for which density wave and stochastic theory predictions are available. For both models crude correlations of predicted and observed arm properties are apparent along the Hubble sequence. In detail, however, the agreement is poor for both models. Arm pitch and arm widths are at least as well correlated with the rotational velocities of the galaxies, independent of the model. This sugggests that the trends in spiral structure observed along the Hubble sequence may be largely kinematic in origin.
Phototransistor with nanocrystalline Si/amorphous Si bilayer channel

We report a field-effect phototransistor with a channel comprising a thin nanocrystalline silicon transport layer and a thicker hydrogenated amorphous silicon absorption layer. The semiconductor and dielectric layers were deposited by radio-frequency plasma enhanced chemical vapor deposition. The phototransistor with channel length of 24 microns and photosensitive area of 1.4 mm2 shows an off-current of about 1 pA, and high photoconductive gain in the subthreshold region. Measurements of the quantum efficiency at different incident light intensities and biasing conditions, along with spectral-response characteristics, and threshold voltage stability characterization demonstrate the feasibility of the phototransistor for low light level detection.
The Chemical Evolution of the Globular Cluster ω Centauri (NGC 5139)

We present abundances for 22 chemical elements in 10 red giant members of the massive Galactic globular cluster ω Centauri. The spectra are of relatively high spectral resolution and signal-to-noise. Using these abundances plus published literature values, abundance trends are defined as a function of the standard metallicity indicator iron. The lowest metallicity stars in ω Cen have [Fe/H] ∼ -1.8, and the initial abundance distribution in the cluster is established at this metallicity. The stars in the cluster span a range of [Fe/H] ∼ -1.8 to -0.8. At the lowest metallicity, the heavy-element abundance is found to be well characterized by a scaled solar system r-process distribution, as found in other stellar populations at this metallicity. As iron increases, the s-process heavy-element abundances increase dramatically. Comparisons of the s-process increases with recent stellar models finds that s-process nucleosynthesis in 1.5–3 M⊙ asymptotic giant branch stars (AGB) fits well the heavy-element abundance distributions. In these low-mass AGB stars, the dominant neutron source is 13C(α, n)16O. A comparison of the Rb/Zr abundance ratios in ω Cen finds that these ratios are consistent with the 13C source. The reason ω Cen displays such a large s-process component is possibly due to the fact that in such a relatively low-mass stellar system, AGB ejecta, because of their low velocity winds, are more efficiently retained in the cluster relative to the much faster moving Type II supernova ejecta. Significant s-process enrichment relative to Fe, from the lower mass AGB stars, would require that the cluster was active in star formation for quite a long interval of time, of the order of 2–3 Gyr. The AGB ejecta were mixed with the retained fraction of Type II supernova ejecta and with the residual gas of initial composition. The analysis of α-rich elements shows that no significant amounts of Type Ia supernova debris were retained by the cluster. In this context, interpretation of the low and constant observed [Cu/Fe] ∼ -0.6 (derived here for the first time in this cluster) finds a plausible interpretation.
Graph Summarization with Graph Neural Networks

The goal of graph summarization is to represent large graphs in a structured and compact way. A graph summary based on equivalence classes preserves pre-defined features of a graph's vertex within a $k$-hop neighborhood such as the vertex labels and edge labels. Based on these neighborhood characteristics, the vertex is assigned to an equivalence class. The calculation of the assigned equivalence class must be a permutation invariant operation on the pre-defined features. This is achieved by sorting on the feature values, e. g. , the edge labels, which is computationally expensive, and subsequently hashing the result. Graph Neural Networks (GNN) fulfill the permutation invariance requirement. We formulate the problem of graph summarization as a subgraph classification task on the root vertex of the $k$-hop neighborhood. We adapt different GNN architectures, both based on the popular message-passing protocol and alternative approaches, to perform the structural graph summarization task. We compare different GNNs with a standard multi-layer perceptron (MLP) and Bloom filter as non-neural method. For our experiments, we consider four popular graph summary models on a large web graph. This resembles challenging multi-class vertex classification tasks with the numbers of classes ranging from $576$ to multiple hundreds of thousands. Our results show that the performance of GNNs are close to each other. In three out of four experiments, the non-message-passing GraphMLP model outperforms the other GNNs. The performance of the standard MLP is extraordinary good, especially in the presence of many classes. Finally, the Bloom filter outperforms all neural architectures by a large margin, except for the dataset with the fewest number of $576$ classes.
Effect of adhesion on dynamic and static friction in surface micromachining

We measure friction of monolayer-lubricated microelectromechanical systems surfaces under both static and dynamic conditions while continuously controlling the applied normal load at positive or negative values (i.e., compression or tension). The dynamic friction experiment methodology we have devised enables fitting to the complete one-dimensional equation of motion. We observe friction at zero applied load, and quantitatively attribute this to interfacial adhesion. Within error, the adhesive force is the same under static and dynamic conditions.