Miracle-dz/UniFAR-FTUs huggingface dataset:
Atomic and entanglement dynamics in the mixed squeezed coherent state version of the Jaynes-Cummings interaction

Abstract. The main objective of this work is to study the mixed state version of the JaynesCummings model in the context of two-level atom interacting with a mixed field state of a squeezed vacuum and a coherent state. Here, the pure squeezed coherent state (PSCS) and the mixed squeezed coherent state (MSCS) have been used as the states of the radiation field. The photon-counting distribution (PCD), the atomic inversion and the entanglement dynamics of atom-field interaction for both the radiation fields are investigated and compared with each other. We observe that depending on the state of the field, squeezing has very different effects on coherent photons. Mild squeezing on the coherent photons localizes the PCD for PSCS; however, for MSCS there is no such localization observed instead squeezing manifests for MSCS as oscillations in the PCD. The effects of squeezing on the atomic inversion and the entanglement dynamics for PSCS are very different as compared with the corresponding quantities associated with MSCS; in fact, they are contrasting. It is well known in the literature that for PSCS, increasing the squeezing increases the well-known ringing revivals in the atomic inversion, and also increases irregularity in the entanglement dynamics. However, increasing the squeezing in MSCS very significantly alters the collapse-revival pattern in the atomic inversion and the entanglement dynamics of the Jaynes-Cummings model. For MSCS, the effect of squeezing on the quadrature variables and Mandel’s Q parameter are also presented.
Improving Cell-Free Massive MIMO Detection Performance via Expectation Propagation

Cell-free (CF) massive multiple-input multiple-output (M-MIMO) technology plays a prominent role in the beyond fifth-generation (5G) networks. However, designing a high performance CF M-MIMO detector is a challenging task due to the presence of pilot contamination which appears when the number of pilot sequences is smaller than the number of users. This work proposes a CF M-MIMO detector referred to as CF expectation propagation (CF-EP) that incorporates the pilot contamination when calculating the posterior belief. The simulation results show that the proposed detector achieves significant improvements in terms of the bit-error rate and sum spectral efficiency performances as compared to the ones of the state-of-the-art CF detectors.
Freeze-in dark matter through forbidden channel in U(1) B-L

We examine a scenario for freeze-in production of dark matter, which occurs due to the large thermal correction to the mass of a decaying mediator particle present in the thermal bath of the early Universe. We show that the decays, which are kinematically forbidden otherwise, can open up at very high temperatures and dominate the dark matter production. We explore such forbidden production of dark matter in the minimal U(1) B-L model, comparing dark matter phenomenology in the context of forbidden frozen-in with the standard picture.
Towards a test of quantum gravity with a levitated nanodiamond containing a spin

There is significant interest in potential experimental tests of macroscopic quantum effects, both to test potential modifications to quantum theory and to probe the quantum nature of gravity. A proposed platform with which to generate the required macroscopic quantum spatial superposition is a nanodiamond containing a negatively charged nitrogen vacancy (NV-) centre. In this review, methods to fabricate nanodiamonds containing NVsuitable for these quantum applications are discussed. The proposed probes of the macroscopic limits of quantum theory are presented along with the spin physics of the NV- centre relevant to those tests.
Thermal keV neutrino dark matter in minimal gauged B-L model with cosmic inflation

We investigate the possibility of keV scale thermal dark matter in a minimal gauged B − L extension of the standard model with three right-handed neutrinos in the context of cosmic inflation. The complex singlet scalar field responsible for the spontaneous breaking of B−L gauge symmetry is non-minimally coupled to gravity and serves the role of inflaton. The lightest right-handed neutrino N1 can be a dark matter candidate if its couplings to leptons are sufficiently suppressed or forbidden. While keV scale N1 gives rise to the possibility of warm dark matter, its thermal production leads to over-abundance. The subsequent entropy dilution due to heavier right-handed neutrino decay can bring the DM abundance within the observed limit. We constrain the model parameters from the requirement of producing sufficient entropy dilution, inflationary observables along with other phenomenological constraints. While these requirements cannot satisfy light neutrino data, suitable extension of the minimal model can accommodate it.
Failure-Averse Active Learning for Physics-Constrained Systems

Active learning is a subfield of machine learning that is devised for the design and modeling of systems with highly expensive sampling costs. Industrial and engineering systems are generally subject to physics constraints that may induce fatal failures when they are violated, while such constraints are frequently underestimated in active learning. In this paper, we develop a novel active learning method that avoids failures considering implicit physics constraints that govern the system. The proposed approach is driven by two tasks: safe variance reduction explores the safe region to reduce the variance of the target model, and safe region expansion aims to extend the explorable region. The integrated acquisition function is devised to conflate two tasks and judiciously optimize them. The proposed method is applied to the composite fuselage assembly process with consideration of material failure using the Tsai-Wu criterion, and it is able to achieve zero failure without the knowledge of explicit failure regions. Note to Practitioners—This paper is motivated by engineering systems with implicit physics constraints related to system failures. Implicit physics constraints refer to failure processes in which explicit analytic forms do not exist, so demanding numerical simulations or real experiments are required to check one’s safety. The main objective of this paper is to develop an active learning strategy that safely learns the target process in the system by minimizing failures without preliminary reliability analysis. The proposed method mainly targets real systems whose failure conditions are not thoroughly investigated or uncertain. We applied the proposed method to the predictive modeling of composite fuselage deformation in the aircraft manufacturing process, and it achieved zero failure in sampling by considering the composite failure criterion.
Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks

We consider the problem of dynamic spectrum access (DSA) in cognitive wireless networks, consisting of primary users (PUs) and secondary users (SUs), where only partial observations are available at the SUs due to narrowband sensing and transmissions. The network operates in a time-slotted regime, where the traffic patterns of the PUs are modeled as finite-memory Markov chains, that are unknown to the SUs. Since observations are partial, then both channel sensing and access actions affect the throughput. Focusing on the case in which there is a single SU, our objective is to maximize the SU’s long-term throughput. To that aim, we develop a novel algorithm that learns both access and sensing policies via deep Q-learning, dubbed Double Deep Q-network for Sensing and Access (DDQSA). To the best of our knowledge, this is the first work that jointly optimizes both sensing and access policies for DSA via deep Q-learning. Next, we consider wireless networks with access policy which implements a fixed channel hopping dynamics, for which we analytically determine the optimal SU sensing and access policy and its associated throughput. Then, we demonstrate that indeed, the proposed DDQSA algorithm can achieve near-optimal performance for the considered network. Our results show that the proposed DDQSA algorithm learns a policy that implements both sensing and channel access, which significantly outperforms existing approaches, and can achieve the optimal performance in certain scenarios.
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge

Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory-Economic Sparse Training (MEST) framework targeting for accurate and fast execution on edge devices. The proposed MEST framework consists of enhancements by Elastic Mutation (EM) and Soft Memory Bound (&S) that ensure superior accuracy at high sparsity ratios. Different from the existing works for sparse training, this current work reveals the importance of sparsity schemes on the performance of sparse training in terms of accuracy as well as training speed on real edge devices. On top of that, the paper proposes to employ data efficiency for further acceleration of sparse training. Our results suggest that unforgettable examples can be identified in-situ even during the dynamic exploration of sparsity masks in the sparse training process, and therefore can be removed for further training speedup on edge devices. Comparing with state-of-the-art (SOTA) works on accuracy, our MEST increases Top-1 accuracy significantly on ImageNet when using the same unstructured sparsity scheme. Systematical evaluation on accuracy, training speed, and memory footprint are conducted, where the proposed MEST framework consistently outperforms representative SOTA works. A reviewer strongly against our work based on his false assumptions and misunderstandings. On top of the previous submission, we employ data efficiency for further acceleration of sparse training. And we explore the impact of model sparsity, sparsity schemes, and sparse training algorithms on the number of removable training examples. Our codes are publicly available at: https://github.com/boone891214/MEST.
Hamilton-Jacobi Equations for Controlled Magnetic Hamiltonian Systems

In this paper, we first define a kind of controlled magnetic Hamiltonian (CMH) system, and give a good expression of the dynamical vector field of the CMH system, such that we can describe the magnetic vanishing condition and the CMH-equivalence, and derive precisely the geometric constraint conditions of the magnetic symplectic form for the dynamical vector field of the CMH system, which are called the Type I and Type II of Hamilton-Jacobi equation. Next, we prove that the CMH-equivalence for the CMH systems leaves the solutions of the associated Hamilton-Jacobi equations invariant, if the associated magnetic Hamiltonian systems are equivalent. These research works reveal the deeply internal relationships of the magnetic symplectic forms, the dynamical vector fields and controls of the CMH systems.
Scheduling jobs with stochastic holding costs

We study a single-server scheduling problem for the objective of minimizing the expected cumulative holding cost incurred by jobs, where parameters defining stochastic job holding costs are unknown to the scheduler. We consider a general setting allowing for different job classes, where jobs of the same class have statistically identical holding costs and service times, with an arbitrary number of jobs across classes. In each time step, the server can process a job and observes random holding costs of the jobs that are yet to be completed. We consider a learning-based $c\mu$ rule scheduling which starts with a preemption period of fixed duration, serving as a learning phase, and having gathered data about jobs, it switches to nonpreemptive scheduling. Our algorithms are designed to handle instances with large and small gaps in mean job holding costs and achieve near-optimal performance guarantees. The performance of algorithms is evaluated by regret, where the benchmark is the minimum possible total holding cost attained by the $c\mu$ rule scheduling policy when the parameters of jobs are known. We show regret lower bounds and algorithms that achieve nearly matching regret upper bounds. Our numerical results demonstrate the efficacy of our algorithms and show that our regret analysis is nearly tight.
Charon: Load-Aware Load-Balancing in P4

Load-Balancers play an important role in data centers as they distribute network flows across application servers and guarantee per-connection consistency. It is hard however to make fair load balancing decisions so that all resources are efficiently occupied yet not overloaded. Tracking connection states allows to infer server load states and make informed decisions, but at the cost of additional memory space consumption. This makes it hard to implement on programmable hardware, which has constrained memory but offers line-rate performance. This paper presents Charon, a stateless load-aware load balancer that has line-rate performance implemented in P4-NetFPGA. Charon passively collects load states from application servers and employs the power-of-2-choices scheme to make data-driven load balancing decisions and improve resource utilization. Per-connection consistency is preserved statelessly by encoding server ID in a covert channel. The prototype design and implementation details are described in this paper. Simulation results show performance gains in terms of load distribution fairness, quality of service, throughput and processing latency.
Nonnegative Tucker Decomposition with Beta-divergence for Music Structure Analysis of audio signals

Résumé – La décomposition en Tucker nonnégatif (NTD), un modèle de décomposition tensorielle, permet d’extraire des motifs pertinents de manière non-supervisée, notamment dans le cadre de signaux audios. Néanmoins, les algorithmes actuels pour calculer la NTD sont souvent conçus pour optimiser la norme euclidienne. Ce travail propose un algorithme à mises à jour multiplicatives pour optimiser la NTD par rapport à la β -divergence, souvent considérée comme plus pertinente pour traiter des signaux audios. En particulier, cet article montre comment implémenter ces mises à jour de manière eﬃcace en utilisant l’algèbre tensoriel. Finalement, des résultats expérimentaux sur la tâche d’estimation de la structure musicale montrent que la NTD optimisée par rapport à la β -divergence améliore les précédents résultats obtenus par rapport à la norme euclidienne. Abstract – Nonnegative Tucker decomposition (NTD), a tensor decomposition model, has received increased interest in the recent years because of its ability to blindly extract meaningful patterns, in particular in music information retrieval. Nevertheless, existing algorithms to compute NTD are mostly designed for the Euclidean loss. This work proposes a multiplicative updates algorithm to compute NTD with the β -divergence loss, often considered a better loss for audio processing. We notably show how to implement eﬃciently the multiplicative rules using tensor algebra. Finally, we show on a music structure analysis task that unsupervised NTD ﬁtted with β -divergence loss outperforms earlier results obtained with the Euclidean loss.
Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions

The deep learning based time-domain models, e.g. Conv-TasNet, have shown great potential in both single-channel and multi-channel speech enhancement. However, many experiments on the time-domain speech enhancement model are done in simulated conditions, and it is not well studied whether the good performance can generalize to real-world scenarios. In this paper, we aim to provide an insightful investigation of applying multi-channel Conv-TasNet based speech enhancement to both simulation and real data. Our preliminary experiments show a large performance gap between the two conditions in terms of the ASR performance. Several approaches are applied to close this gap, including the integration of multi-channel Conv-TasNet into the beamforming model with various strategies, and the joint training of speech enhancement and speech recognition models. Our experiments on the CHiME-4 corpus show that our proposed approaches can greatly reduce the speech recognition performance discrepancy between simulation and real data, while preserving the strong speech enhancement capability in the frontend.
Finding the Best Partitioning Policy for Efficient Verification of Autonomous Systems at Runtime

The autonomous systems need to decide how to react to the changes at runtime efficiently. The ability to rigorously analyze the environment and the system together is theoretically possible by the model-driven approaches; however, the model size and timing limitations are two significant obstacles against such an autonomous decision-making process. To tackle this issue, the incremental approximation technique can be used to partition the model and only verify a partition if it is affected by the change. This paper proposes a policy-based analysis approach that finds the best partitioning policy among a set of available policies based on two proposed metrics, namely Balancing and Variation. The metrics quantitatively evaluate the generated components from the incremental approximation scheme according to their size and frequency. We investigate the validity of the approach both theoretically and experimentally via a case study on energy harvesting systems. The results confirm the effectiveness of the proposed approach.
Can't Fool Me: Adversarially Robust Transformer for Video Understanding

Deep neural networks have been shown to perform poorly on adversarial examples. To address this, several techniques have been proposed to increase robustness of a model for image classification tasks. However, in video understanding tasks, developing adversarially robust models is still unexplored. In this paper, we aim to bridge this gap. We first show that simple extensions of image based adversarially robust models slightly improve the worst-case performance. Further, we propose a temporal attention regularization scheme in Transformer to improve the robustness of attention modules to adversarial examples. We illustrate using a large-scale video data set YouTube-8M that the final model (A-ART) achieves close to non-adversarial performance on its adversarial example set. We achieve 91% GAP on adversarial examples, whereas baseline Transformer and simple adversarial extensions achieve 72.9% and 82% respectively, showing significant improvement in robustness over the state-of-the-art.
Symmetry results for Serrin-type problems in ring-shaped domains

<abstract><p>In this work, we employ the technique developed in <sup>[<xref ref-type="bibr" rid="b2">2</xref>]</sup> to prove rotational symmetry for a class of Serrin-type problems for the standard Laplacian. We also discuss in some length how our strategy compares with the classical moving plane method. </p></abstract>
New mechanism for primordial black hole formation during reheating

The Reheating process at the end of inflation is often modeled by an oscillating scalar field which shows a background dust-like behaviour, prompting the analysis of gravitational collapse and black hole formation in this era to be approached by the spherical collapse of standard structure formation. In the scalar field dark matter structure formation process virialized halos halt the direct collapse, resulting in halos with condensed central cores at the de Broglie scale of the dominant scalar field. We show that a similar process can take place during reheating, leading to the formation of primordial black holes (PBHs). We study the formation of PBHs through the gravitational further collapse of structures virialized during reheating, looking at the collapse of either the whole structure, or that of the central core within these configurations. We compute the threshold amplitude for the density contrast to undergo this process, for both free and self-interacting scalar fields. We discuss the relevance of our results for the abundance of PBHs at the lower end of the mass spectrum.
BioGrad: Biologically Plausible Gradient-Based Learning for Spiking Neural Networks

Spiking neural networks (SNN) are delivering energy-efficient, massively parallel, and low-latency solutions to AI problems, facilitated by the emerging neuromorphic chips. To harness these computational benefits, SNN need to be trained by learning algorithms that adhere to brain-inspired neuromorphic principles, namely event-based, local, and online computations. Yet, the state-of-the-art SNN training algorithms are based on backprop that does not follow the above principles. Due to its limited biological plausibility, the application of backprop to SNN requires non-local feedback pathways for transmitting continuous-valued errors, and relies on gradients from future timesteps. The introduction of biologically plausible modifications to backprop has helped overcome several of its limitations, but limits the degree to which backprop is approximated, which hinders its performance. We propose a biologically plausible gradient-based learning algorithm for SNN that is functionally equivalent to backprop, while adhering to all three neuromorphic principles. We introduced multi-compartment spiking neurons with local eligibility traces to compute the gradients required for learning, and a periodic"sleep"phase to further improve the approximation to backprop during which a local Hebbian rule aligns the feedback and feedforward weights. Our method achieved the same level of performance as backprop with multi-layer fully connected SNN on MNIST (98.13%) and the event-based N-MNIST (97.59%) datasets. We deployed our learning algorithm on Intel's Loihi to train a 1-hidden-layer network for MNIST, and obtained 93.32% test accuracy while consuming 400 times less energy per training sample than BioGrad on GPU. Our work shows that optimal learning is feasible in neuromorphic computing, and further pursuing its biological plausibility can better capture the benefits of this emerging computing paradigm.
Conflict-Averse Gradient Descent for Multi-task Learning

The goal of multi-task learning is to enable more efficient learning than single task learning by sharing model structures for a diverse set of tasks. A standard multi-task learning objective is to minimize the average loss across all tasks. While straightforward, using this objective often results in much worse final performance for each task than learning them independently. A major challenge in optimizing a multi-task model is the conflicting gradients, where gradients of different task objectives are not well aligned so that following the average gradient direction can be detrimental to specific tasks' performance. Previous work has proposed several heuristics to manipulate the task gradients for mitigating this problem. But most of them lack convergence guarantee and/or could converge to any Pareto-stationary point. In this paper, we introduce Conflict-Averse Gradient descent (CAGrad) which minimizes the average loss function, while leveraging the worst local improvement of individual tasks to regularize the algorithm trajectory. CAGrad balances the objectives automatically and still provably converges to a minimum over the average loss. It includes the regular gradient descent (GD) and the multiple gradient descent algorithm (MGDA) in the multi-objective optimization (MOO) literature as special cases. On a series of challenging multi-task supervised learning and reinforcement learning tasks, CAGrad achieves improved performance over prior state-of-the-art multi-objective gradient manipulation methods.