Miracle-dz/UniFAR-FTUs huggingface dataset:
Tiny Transformers for Environmental Sound Classification at the Edge

With the growth of the Internet of Things and the rise of Big Data, data processing and machine learning applications are being moved to cheap and low size, weight, and power (SWaP) devices at the edge, often in the form of mobile phones, embedded systems, or microcontrollers. The field of Cyber-Physical Measurements and Signature Intelligence (MASINT) makes use of these devices to analyze and exploit data in ways not otherwise possible, which results in increased data quality, increased security, and decreased bandwidth. However, methods to train and deploy models at the edge are limited, and models with sufficient accuracy are often too large for the edge device. Therefore, there is a clear need for techniques to create efficient AI/ML at the edge. This work presents training techniques for audio models in the field of environmental sound classification at the edge. Specifically, we design and train Transformers to classify office sounds in audio clips. Results show that a BERT-based Transformer, trained on Mel spectrograms, can outperform a CNN using 99.85% fewer parameters. To achieve this result, we first tested several audio feature extraction techniques designed for Transformers, using ESC-50 for evaluation, along with various augmentations. Our final model outperforms the state-of-the-art MFCC-based CNN on the office sounds dataset, using just over 6,000 parameters -- small enough to run on a microcontroller.
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?

One of the most concerning threats for modern AI systems is data poisoning, where the attacker injects maliciously crafted training data to corrupt the system's behavior at test time. Availability poisoning is a particularly worrisome subset of poisoning attacks where the attacker aims to cause a Denial-of-Service (DoS) attack. However, the state-of-the-art algorithms are computationally expensive because they try to solve a complex bi-level optimization problem (the “hammer”). We observed that in particular conditions, namely, where the target model is linear (the “nut”), the usage of computationally costly procedures can be avoided. We propose a counter-intuitive but efficient heuristic that allows contaminating the training set such that the target system's performance is highly compromised. We further suggest a re-parameterization trick to decrease the number of variables to be optimized. Finally, we demonstrate that, under the considered settings, our framework achieves comparable, or even better, performances in terms of the attacker's objective while being significantly more computationally efficient.
Transformers Solve the Limited Receptive Field for Monocular Depth Prediction

While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation. Transformers, initially designed for natural language processing tasks, have emerged as alternative architectures with innate global self-attention mechanisms to capture long-range dependencies. In this paper, we propose TransDepth, an architecture which beneﬁts from both convolutional neural networks and transformers. To avoid the network to loose its ability to capture local-level details due to the adoption of transformers, we propose a novel decoder which employs on attention mechanisms based on gates. Notably, this is the ﬁrst paper which applies transformers into pixel-wise prediction problems involving continuous labels (i.e., monocular depth prediction and surface normal estimation). Extensive experiments demonstrate that the proposed TransDepth achieves state-of-the-art performance on three challenging datasets. The source code and trained models are available at https: //github.com/ygjwd12345/TransDepth .
Reactivation of the High Magnetic Field Pulsar PSR J1846–0258 with Magnetar-like Bursts

We report on the 2020 reactivation of the energetic high magnetic field pulsar PSR J1846–0258 and its pulsar wind nebula (PWN) after 14 yr of quiescence with new Chandra and Green Bank Telescope observations. The emission of short-duration bursts from J1846–0258 was accompanied by an enhancement of X-ray persistent flux and significant spectral softening, similar to those observed during its first bursting episode in 2006. The 2020 pulsar spectrum is described by a power-law model with a photon index Γ = 1.7 ± 0.3 in comparison to a Γ = 1.2 ± 0.1 before outburst, and shows evidence of an emerging thermal component with blackbody temperature kT = 0.7 ± 0.1 keV. The 0.5–10 keV unabsorbed flux increased from × 10−12 erg cm−2 s−1 in quiescence to × 10−11 erg cm−2 s−1 following the outburst. We did not detect any radio pulsations from the pulsar at 2 GHz, and we place an upper limit of 7.1 μJy and 55 mJy for the coherent pulsed emission and single pulses, respectively. The 2020 PWN spectrum, characterized by a photon index of 1.92 ± 0.04 and X-ray luminosity of (1.2 ± 0.1) × 1035 erg s−1 at a distance of 5.8 kpc, is consistent with those observed before the outburst. An analysis of regions closer to the pulsar shows small-scale time variabilities and brightness changes over the 20 yr period from 2000 to 2020, while the photon indices did not change. We conclude that the outburst in PSR J1846–0258 is a combination of crustal and magnetospheric effects, with no significant burst-induced variability in its PWN based on the current observations.
Tidal heating of black holes and exotic compact objects on the brane

During the early phase of in-spiral of a binary system, the tidal heating of a compact object due to its companion plays a significant role in the determination of the orbital evolution of the binary. The phenomenon depends crucially on the ‘hairs’, as well as on the nature of the compact object. It turns out that the presence of extra dimension affects both these properties, by incorporating an extra tidal charge for braneworld black holes and also by introducing quantum effects, leading to possible existence of exotic compact objects. It turns out that the phasing information from tidal heating in the gravitational wave waveform can constrain the tidal charge inherited from extra dimension to a value ∼ 10−6, the most stringent constraint, to date. Moreover, second order effects in tidal heating for exotic compact objects, also reveals an oscillatory behaviour with respect to spin, which has unique signatures. ∗sumantac.physics@gmail.com †skdatta@iucaa.in ‡subhadipsau2@gmail.com 1 ar X iv :2 10 3. 12 43 0v 1 [ gr -q c] 2 3 M ar 2 02 1
Approximately Counting Answers to Conjunctive Queries with Disequalities and Negations

We study the complexity of approximating the number of answers to a small query φ in a large database D. We establish an exhaustive classification into tractable and intractable cases if φ is a conjunctive query possibly including disequalities and negations: - If there is a constant bound on the arity of φ, and if the randomised Exponential Time Hypothesis (rETH) holds, then the problem has a fixed-parameter tractable approximation scheme (FPTRAS) if and only if the treewidth of φ is bounded. - If the arity is unbounded and φ does not have negations, then the problem has an FPTRAS if and only if the adaptive width of φ (a width measure strictly more general than treewidth) is bounded; the lower bound relies on the rETH as well. Additionally we show that our results cannot be strengthened to achieve a fully polynomial randomised approximation scheme (FPRAS): We observe that, unless NP=RP, there is no FPRAS even if the treewidth (and the adaptive width) is 1. However, if there are neither disequalities nor negations, we prove the existence of an FPRAS for queries of bounded fractional hypertreewidth, strictly generalising the recently established FPRAS for conjunctive queries with bounded hypertreewidth due to Arenas, Croquevielle, Jayaram and Riveros (STOC 2021).
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search

A myriad of recent breakthroughs in hand-crafted neural architectures for visual recognition have highlighted the urgent need to explore hybrid architectures consisting of diversified building blocks. Meanwhile, neural architecture search methods are surging with an expectation to reduce human efforts. However, whether NAS methods can efficiently and effectively handle diversified search spaces with disparate candidates (e.g. CNNs and transformers) is still an open question. In this work, we present Block-wisely Self-supervised Neural Architecture Search (BossNAS), an unsupervised NAS method that addresses the problem of in-accurate architecture rating caused by large weight-sharing space and biased supervision in previous methods. More specifically, we factorize the search space into blocks and utilize a novel self-supervised training scheme, named ensemble bootstrapping, to train each block separately before searching them as a whole towards the population center. Additionally, we present HyTra search space, a fabric-like hybrid CNN-transformer search space with searchable down-sampling positions. On this challenging search space, our searched model, BossNet-T, achieves up to 82.5% accuracy on ImageNet, surpassing EfficientNet by 2.4% with comparable compute time. Moreover, our method achieves superior architecture rating accuracy with 0.78 and 0.76 Spearman correlation on the canonical MBConv search space with ImageNet and on NATS-Bench size search space with CIFAR-100, respectively, surpassing state-of-the-art NAS methods. 1
Scalar product for the XXZ spin chain with general integrable boundaries * * Dedicated to the memory of Omar Foda.

We calculate the scalar product of Bethe states of the XXZ spin- 12 chain with general integrable boundary conditions. The off-shell equations satisfied by the transfer matrix and the off-shell Bethe vectors allow one to derive a linear system for the scalar product of off-shell and on-shell Bethe states. We show that this linear system can be solved in terms of a compact determinant formula that involves the Jacobian of the transfer matrix eigenvalue and certain q-Pochhammer polynomials of the boundary couplings.
Non-Euclidean Contraction Theory via Semi-Inner Products

In this paper, we study necessary and sufficient conditions for contraction and incremental stability of dynamical systems with respect to non-Euclidean norms. First, generalizing Lumer semi-inner products, we introduce weak semiinner products (WSIPs), and characterize their properties. We introduce and study the sign and max WSIPs for the `1 and `∞ norms, respectively. Using WSIPs, we then establish five equivalent characterizations for contraction, including one-sided Lipschitz continuity of the vector field as well as matrix measure and generalized Demidovich conditions for the corresponding Jacobian. Third, we extend our contraction framework in two directions: we prove equivalences for contraction of continuous vector fields and we formalize the weaker notion of equilibrium contraction, which ensures exponential convergence to a given equilibrium. Finally, as an application of the general theory, we provide (i) incremental input-to-state stability and finite input-state gain properties for contracting systems, and (ii) a hierarchical analysis approach to establish contraction properties for interconnected systems.
Multipopulation Minimal-Time Mean Field Games

In this paper, we consider a mean field game model inspired by crowd motion in which several interacting populations evolving in $\mathbb R^d$ aim at reaching given target sets in minimal time. The movement of each agent is described by a control system depending on their position, the distribution of other agents in the same population, and the distribution of agents on other populations. Thus, interactions between agents occur through their dynamics. We consider in this paper the existence of Lagrangian equilibria to this mean field game, their asymptotic behavior, and their characterization as solutions of a mean field game system, under few regularity assumptions on agents' dynamics. In particular, the mean field game system is established without relying on semiconcavity properties of the value function.
Can I Solve It? Identifying APIs Required to Complete OSS Tasks

Open Source Software projects add labels to open issues to help contributors choose tasks. However, manually labeling issues is time-consuming and error-prone. Current automatic approaches for creating labels are mostly limited to classifying issues as a bug/non-bug. In this paper, we investigate the feasibility and relevance of labeling issues with the domain of the APIs required to complete the tasks. We leverage the issues’ description and the project history to build prediction models, which resulted in precision up to 82% and recall up to 97.8%. We also ran a user study (n=74) to assess these labels’ relevancy to potential contributors. The results show that the labels were useful to participants in choosing tasks, and the API-domain labels were selected more often than the existing architecture-based labels. Our results can inspire the creation of tools to automatically label issues, helping developers to find tasks that better match their skills.
Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification

Reinforcement learning (RL) algorithms assume that users specify tasks by manually writing down a reward function. However, this process can be laborious and demands considerable technical expertise. Can we devise RL algorithms that instead enable users to specify tasks simply by providing examples of successful outcomes? In this paper, we derive a control algorithm that maximizes the future probability of these successful outcome examples. Prior work has approached similar problems with a two-stage process, first learning a reward function and then optimizing this reward function using another RL algorithm. In contrast, our method directly learns a value function from transitions and successful outcomes, without learning this intermediate reward function. Our method therefore requires fewer hyperparameters to tune and lines of code to debug. We show that our method satisfies a new data-driven Bellman equation, where examples take the place of the typical reward function term. Experiments show that our approach outperforms prior methods that learn explicit reward functions.
Challenges in systematic reviews and meta-analyses of mediation analyses

Systematic reviews and meta-analyses of mediation studies are increasingly being implemented in practice. Nonetheless, the methodology for conducting such review and analysis is still in a development phase, with much room for improvement. In this paper, we highlight and discuss challenges that investigators face in mediation systematic reviews and meta-analyses, then propose ways of accommodating these in practice.
Validation of a time-temperature-integrator for thermal processing of foods under pasteurization conditions

Summary In this work, a general theory for the development of a Time Temperature Integrator, TTI-system, is outlined. A z-value (or activation energy) of the TTI-system equal to that of target index is crucial for the usefulness of a system to monitor the impact of thermal processes on foods. The proposed TTI, namely the heat-stable fraction of peroxidase covalently immobilized on porous glass beads in dodecane, can be used to indicate the intensity of a delivered pasteurization process when a z-value of 10°C is used for calculating the lethality. Lethalities read from the bio-indicator showed excellent agreement with those integrated from the measured time-temperature data.
Leveraging background augmentations to encourage semantic focus in self-supervised contrastive learning

Unsupervised representation learning is an important challenge in computer vision, with self-supervised learning methods recently closing the gap to supervised representation learning. An important ingredient in high-performing self-supervised methods is the use of data augmentation by training models to place different augmented views of the same image nearby in embedding space. However, commonly used augmentation pipelines treat images holistically, disregarding the semantic relevance of parts of an image—e.g. a subject vs. a background—which can lead to the learning of spurious correlations. Our work addresses this problem by investigating a class of simple, yet highly effective “background augmentations”, which encourage models to focus on semantically-relevant content by discouraging them from focusing on image backgrounds. Background augmentations lead to substantial improvements (+1-2% on ImageNet-1k) in performance across a spectrum of state-of-the art self-supervised methods (MoCov2, BYOL, SwAV) on a variety of tasks, allowing us to reach within 0.3% of supervised performance. We also demonstrate that background augmentations improve robustness to a number of out of distribution settings, including natural adversarial examples, the backgrounds challenge, adversarial attacks, and ReaL ImageNet.
Unsupervised domain adaptation via coarse-to-fine feature alignment method using contrastive learning

Previous feature alignment methods in Unsupervised domain adaptation(UDA) mostly only align global features without considering the mismatch between class-wise features. In this work, we propose a new coarse-to-fine feature alignment method using contrastive learning called CFContra. It draws class-wise features closer than coarse feature alignment or class-wise feature alignment only, therefore improves the model's performance to a great extent. We build it upon one of the most effective methods of UDA called entropy minimization to further improve performance. In particular, to prevent excessive memory occupation when applying contrastive loss in semantic segmentation, we devise a new way to build and update the memory bank. In this way, we make the algorithm more efficient and viable with limited memory. Extensive experiments show the effectiveness of our method and model trained on the GTA5 to Cityscapes dataset has boost mIOU by 3.5 compared to the MinEnt algorithm. Our code will be publicly available.
Dark-bright excitons mixing in alloyed InGaAs self-assembled quantum dots

Quantum dots are arguably one of the best platforms for optically accessible spin based qubits. The paramount demand of extended qubit storage time can be met by using quantum-dot-confined dark exciton: a longlived electron-hole pair with parallel spins. Despite its name the dark exciton reveals weak luminescence that can be directly measured. The origins of this optical activity remain largely unexplored. In this work, using the atomistic tight-binding method combined with configuration-interaction approach, we demonstrate that atomic-scale randomness strongly affects oscillator strength of dark excitons confined in self-assembled cylindrical InGaAs quantum dots with no need for faceting or shape-elongation. We show that this process is mediated by two mechanisms: mixing dark and bright configurations by exchange interaction, and equally important appearance of non-vanishing optical transition matrix elements that otherwise correspond to nominally forbidden transitions in a non-alloyed case. The alloy randomness has essential impact on both bright and dark exciton states, including their energy, emission intensity, and polarization angle. We conclude that, due to the atomic-scale alloy randomness, finding dots with desired dark exciton properties may require exploration of a large ensemble, similarly to how dots with low bright exciton splitting are selected for entanglement generation.
A universal quantum gate set for transmon qubits with strong ZZ interactions

Junling Long, ∗ Tongyu Zhao, † Mustafa Bal, Ruichen Zhao, George S. Barron, Hsiang-sheng Ku, Joel A. Howard, Xian Wu, Corey Rae H. McRae, Xiu-Hao Deng, Guilhem J. Ribeill, Meenakshi Singh, Thomas A. Ohki, Edwin Barnes, Sophia E. Economou, and David P. Pappas Department of Physics, University of Colorado, Boulder, Colorado 80309, USA Department of Physics, Virginia Tech, Blacksburg, VA 24061, USA National Institute of Standards and Technology, Boulder, Colorado 80305, USA Department of Physics, Colorado School of Mines, Golden, Colorado 80401, USA Shenzhen Institute of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China Quantum Engineering and Computing, Raytheon BBN Technologies, Cambridge, MA 02138, USA. (Dated: March 24, 2021)
Dynamical dust traps in misaligned circumbinary discs: analytical theory and numerical simulations

Recent observations have shown that circumbinary discs can be misaligned with respect to the binary orbital plane.The lack of spherical symmetry, together with the non-planar geometry of these systems, causes differential precession which might induce the propagation of warps. While gas dynamics in such environments is well understood, little is known about dusty discs. In this work, we analytically study the problem of dust traps formation in misaligned circumbinary discs. We find that pile-ups may be induced not by pressure maxima, as the usual dust traps, but by a difference in precession rates between the gas and dust. Indeed, this difference makes the radial drift inefficient in two locations, leading to the formation of two dust rings whose position depends on the system parameters. This phenomenon is likely to occur to marginally coupled dust particles (St & 1) as both the effect of gravitational and drag force are considerable. We then perform a suite of three-dimensional SPH numerical simulations to compare the results with our theoretical predictions. We explore the parameter space, varying stellar mass ratio, disc thickness, radial extension, and we find a general agreement with the analytical expectations. Such dust pile-up prevents radial drift, fosters dust growth and may thus promote the planet formation in circumbinary discs.
Mittag-Leffler functions and their applications in network science

We describe a complete theory for walk-based centrality indices in complex networks defined in terms of Mittag-Leffler functions. This overarching theory includes as special cases well-known centrality measures like subgraph centrality and Katz centrality. The indices we introduce are parametrized by two numbers; by letting these vary, we show that Mittag-Leffler centralities interpolate between degree and eigenvector centrality, as well as between resolvent-based and exponential-based indices. We further discuss modeling and computational issues, and provide guidelines on parameter selection. The theory is then extended to the case of networks that evolve over time. Numerical experiments on synthetic and real-world networks are provided.
Asymptotically cylindrical steady K\"ahler-Ricci solitons

Let D be a compact Kähler manifold with trivial canonical bundle and Γ be a finite cyclical group of order m acting on C ×D by biholomorphisms, where the action on the first factor is generated by rotation of angle 2π/m. Furthermore, suppose that ΩD is a trivialisation of the canonical bundle such that Γ preserves the holomorphic form dz ∧ ΩD on C ×D, with z denoting the coordinate on C. The main result of this article is the construction of new examples of gradient steady Kähler-Ricci solitons on certain crepant resolutions of the orbifolds (C×D) /Γ. These new solitons converge exponentially to a Ricci-flat cylinder R× (S ×D)/Γ.