Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum error detection improves accuracy of chemical calculations on a quantum computer

The ultimate goal of quantum error correction is to achieve the fault-tolerance threshold beyond which quantum computers can be made arbitrarily accurate. This requires extraordinary resources and engineering eﬀorts. We show that even without achieving full fault-tolerance, quantum error detection is already useful on the current generation of quantum hardware. We demonstrate this by executing an end-to-end chemical calculation for the hydrogen molecule encoded in the [[4, 2, 2]] quantum error-detecting code. The encoded calculation with logical qubits signiﬁcantly improves the accuracy of the molecular ground-state energy.
Coarse-grained pressure dynamics in superfluid turbulence

Quantum mechanics places significant restrictions on the hydrodynamics of superfluid flows. Despite this it has been observed that turbulence in superfluids can, in a statistical sense, share many of the properties of its classical brethren; coherent bundles of superfluid vortices are often invoked as an important feature leading to this quasi-classical behavior. A recent experimental study [E. Rusaouen, B. Rousset, and P.-E. Roche, EPL, {\bf 118}, 1, 14005, (2017)] inferred the presence of these bundles through intermittency in the pressure field, however direct visualization of the quantized vortices to corroborate this finding was not possible. In this work, we performed detailed numerical simulations of superfluid turbulence at the level of individual quantized vortices through the vortex filament model. Through course-graining of the turbulent fields, we find compelling evidence supporting these conclusions at low temperature. Moreover, elementary simulations of an isolated bundle show that the number of vortices inside a bundle can be directly inferred from the magnitude of the pressure dip, with good theoretical agreement derived from the HVBK equations. Full simulations of superfluid turbulence show strong spatial correlations between course-grained vorticity and low pressure regions, with intermittent vortex bundles appearing as deviations from the underlying Maxwellian (vorticity) and Gaussian (pressure) distributions. Finally, simulations of a decaying random tangle in an ultra-quantum regime show a unique fingerprint in the evolution of the pressure distribution, which we argue can be fully understood using the HVBK framework.
Subarchitecture Ensemble Pruning in Neural Architecture Search

Neural architecture search (NAS) is gaining more and more attention in recent years because of its flexibility and remarkable capability to reduce the burden of neural network design. To achieve better performance, however, the searching process usually costs massive computations that might not be affordable for researchers and practitioners. Although recent attempts have employed ensemble learning methods to mitigate the enormous computational cost, however, they neglect a key property of ensemble methods, namely diversity, which leads to collecting more similar subarchitectures with potential redundancy in the final design. To tackle this problem, we propose a pruning method for NAS ensembles called “ subarchitecture ensemble pruning in neural architecture search (SAEP). ” It targets to leverage diversity and to achieve subensemble architectures at a smaller size with comparable performance to ensemble architectures that are not pruned. Three possible solutions are proposed to decide which subarchitectures to prune during the searching process. Experimental results exhibit the effectiveness of the proposed method by largely reducing the number of subarchitectures without degrading the performance.
Hidden Supersymmetries of Deformed Supersymmetric Mechanics

We consider quantum models corresponding to superymmetrizations of the two-dimensional harmonic oscillator based on worldline d = 1 realizations of the supergroup SU( N/2 |1), where the number of supersymmetries N is arbitrary even number. Constructed models possess the hidden supersymmetry SU( N/2 |2). Degeneracies of energy levels are spanned by representations of the hidden supersymmetry group.
How To Catch A Lion In The Desert - On The Solution Of The Coverage Directed Generation (CDG) Problem

The testing and verification of a complex hardware or software system, such as modern integrated circuits (ICs) found in everything from smartphones to servers, can be a difficult process. One of the most difficult and time-consuming tasks a verification team faces is reaching coverage closure, or hitting all events in the coverage space. Coverage-directed-generation (CDG), or the automatic generation of tests that can hit hard-to-hit coverage events, and thus provide coverage closure, holds the potential to save verification teams significant simulation resources and time. In this paper, we propose a new approach to the CDG problem by formulating the CDG problem as a noisy derivative free optimization (DFO) problem. However, this formulation is complicated by the fact that derivatives of the objective function are unavailable, and the objective function evaluations are corrupted by noise. We solve this noisy optimization problem by utilizing techniques from direct optimization coupled with a robust noise estimator, and by leveraging techniques from inverse problems to estimate the gradient of the noisy objective function. We demonstrate the efficiency and reliability of this new approach through numerical experiments with an abstract model of part of IBM's NorthStar processor, a superscalar in-order processor designed for servers.
Multilingual End-to-End Speech Translation

In this paper, we propose a simple yet effective framework for multilingual end-to-end speech translation (ST), in which speech utterances in source languages are directly translated to the desired target languages with a universal sequence-to-sequence architecture. While multilingual models have shown to be useful for automatic speech recognition (ASR) and machine translation (MT), this is the first time they are applied to the end-to-end ST problem. We show the effectiveness of multilingual end-to-end ST in two scenarios: one-to-many and many-to-many translations with publicly available data. We experimentally confirm that multilingual end-to-end ST models significantly outperform bilingual ones in both scenarios. The generalization of multilingual training is also evaluated in a transfer learning scenario to a very low-resource language pair. All of our codes and the database are publicly available to encourage further research in this emergent multilingual ST topic11Available at https://github.com/espnet/espnet.
Physical-Layer Security for Frequency Diverse Array-Based Directional Modulation in Fluctuating Two-Ray Fading Channels

The frequency diverse array (FDA)-based directional modulation (DM) technology plays an important role in the physical-layer security (PLS) transmission of 5G and beyond communications. In order to meet the tremendous increase in mobile data traffic, a new memory-efficient design for the FDA-DM-based PLS transmission is urgently demanded. In this article, an analytical symmetrical multi-carrier FDA model is proposed in three dimensions, namely, range, azimuth angle, and elevation angle, differing from the conventional analytical approach with only range and azimuth angle considered. Then, a single-point (SP) artificial noise (AN) aided FDA-DM scheme is proposed, which reduces the memory consumption significantly compared with the conventional zero-forcing (ZF) and singular value decomposition (SVD) approaches. Moreover, the PLS performance of the proposed FDA-DM scheme is analyzed in fluctuating two-ray (FTR) fading channels for the first time, including the average secrecy capacity (ASC) and the secrecy outage probability (SOP). More importantly, the closed-form expressions for the lower bound on ASC and the upper bound on SOP are derived, respectively. The effectiveness of the analytical expressions is verified by numerical simulations. This work opens a way to lower the memory requirements for DM-based PLS transmission of 5G and beyond communications.
Multinomial Concentration in Relative Entropy at the Ratio of Alphabet and Sample Sizes

We show that the moment generating function of the Kullback-Leibler divergence between the empirical distribution of $n$ independent samples from a distribution $P$ over a finite alphabet of size $k$ (e.g. a multinomial distribution) and $P$ itself is no more than that of a gamma distribution with shape $k - 1$ and rate $n$. The resulting exponential concentration inequality becomes meaningful (less than 1) when the divergence $\varepsilon$ is larger than $(k-1)/n$, whereas the standard method of types bound requires $\varepsilon > \frac{1}{n} \cdot \log{\binom{n+k-1}{k-1}} \geq (k-1)/n \cdot \log(1 + n/(k-1))$, thus saving a factor of order $\log(n/k)$ in the standard regime of parameters where $n\gg k$. Our proof proceeds via a simple reduction to the case $k = 2$ of a binary alphabet (e.g. a binomial distribution), and has the property that improvements in the case of $k = 2$ directly translate to improvements for general $k$.
Collective excitation of two-dimensional Bose–Einstein condensate in liquid phase with spin–orbit coupling

We have studied the collective excitation of Bose–Einstein condensate of short-range weak interacting atoms with spin–orbit coupling (SOC) in two dimensions in the liquid phase. In our study, we have included Rashba, Dresselhaus, as well as Raman SOC. The study of Bogoliubov excitation shows that in small interactions, only phonon modes are present, and for higher interactions, roton modes start to appear. Energy spectra contain two roton modes in relatively strong interacting atomic systems.
Interrogating the Explanatory Power of Attention in Neural Machine Translation

Attention models have become a crucial component in neural machine translation (NMT). They are often implicitly or explicitly used to justify the model’s decision in generating a specific token but it has not yet been rigorously established to what extent attention is a reliable source of information in NMT. To evaluate the explanatory power of attention for NMT, we examine the possibility of yielding the same prediction but with counterfactual attention models that modify crucial aspects of the trained attention model. Using these counterfactual attention mechanisms we assess the extent to which they still preserve the generation of function and content words in the translation process. Compared to a state of the art attention model, our counterfactual attention models produce 68% of function words and 21% of content words in our German-English dataset. Our experiments demonstrate that attention models by themselves cannot reliably explain the decisions made by a NMT model.
An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research

We present a new open-source torque-controlled legged robot system, with a low-cost and low-complexity actuator module at its core. It consists of a high-torque brushless DC motor and a low-gear-ratio transmission suitable for impedance and force control. We also present a novel foot contact sensor suitable for legged locomotion with hard impacts. A 2.2 kg quadruped robot with a large range of motion is assembled from eight identical actuator modules and four lower legs with foot contact sensors. Leveraging standard plastic 3D printing and off-the-shelf parts results in a lightweight and inexpensive robot, allowing for rapid distribution and duplication within the research community. We systematically characterize the achieved impedance at the foot in both static and dynamic scenarios, and measure a maximum dimensionless leg stiffness of 10.8 without active damping, which is comparable to the leg stiffness of a running human. Finally, to demonstrate the capabilities of the quadruped, we present a novel controller which combines feedforward contact forces computed from a kino-dynamic optimizer with impedance control of the center of mass and base orientation. The controller can regulate complex motions while being robust to environmental uncertainty.
Affordable Uplift: Supervised Randomization in Controlled Experiments

Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decision-making. Training and monitoring of uplift models require experimental data. However, the collection of data under randomized treatment assignment is costly, since random targeting deviates from an established targeting policy. To increase the cost-efficiency of experimentation and facilitate frequent data collection and model training, we introduce supervised randomization. It is a novel approach that integrates existing scoring models into randomized trials to target relevant customers, while ensuring consistent estimates of treatment effects through correction for active sample selection. An empirical Monte Carlo study shows that data collection under supervised randomization is cost-efficient, while downstream uplift models perform competitively.
Optimizing GPU Cache Policies for MI Workloads*

In recent years, machine intelligence (MI) applications have emerged as a major driver for the computing industry. Optimizing these workloads is important, but complicated. As memory demands grow and data movement overheads increasingly limit performance, determining the best GPU caching policy to use for a diverse range of MI workloads represents one important challenge. To study this, we evaluate 17 MI applications and characterize their behavior using a range of GPU caching strategies. In our evaluations, we find that the choice of caching policy in GPU caches involves multiple performance trade-offs and interactions, and there is no one-size-fits-all GPU caching policy for MI workloads. Based on detailed simulation results, we motivate and evaluate a set of cache optimizations that consistently match the performance of the best static GPU caching policies.
Flow: A Modular Learning Framework for Autonomy in Traffic.

The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility. However, due to numerous technical, political, and human factors challenges, new methodologies are needed to design vehicles and transportation systems for these positive outcomes. This article tackles important technical challenges arising from the partial adoption of autonomy (hence termed mixed autonomy, to involve both AVs and human-driven vehicles): partial control, partial observation, complex multi-vehicle interactions, and the sheer variety of traffic settings represented by real-world networks. To enable the study of the full diversity of traffic settings, we first propose to decompose traffic control tasks into modules, which may be configured and composed to create new control tasks of interest. These modules include salient aspects of traffic control tasks: networks, actors, control laws, metrics, initialization, and additional dynamics. Second, we study the potential of model-free deep Reinforcement Learning (RL) methods to address the complexity of traffic dynamics. The resulting modular learning framework is called Flow. Using Flow, we create and study a variety of mixed-autonomy settings, including single-lane, multi-lane, and intersection traffic. In all cases, the learned control law exceeds human driving performance (measured by system-level velocity) by at least 40% with only 5-10% adoption of AVs. In the case of partially-observed single-lane traffic, we show that a low-parameter neural network control law can eliminate commonly observed stop-and-go traffic. In particular, the control laws surpass all known model-based controllers, achieving near-optimal performance across a wide spectrum of vehicle densities (even with a memoryless control law) and generalizing to out-of-distribution vehicle densities.
A note on cycles of curves in a product of pairs

We discuss the role of subdivisions of tropical moduli spaces in logarithmic Gromov-Witten theory, and use them to study the virtual class of curves in a product of pairs. Our main result is that the cycle-valued logarithmic Gromov-Witten theory of $X\times Y$ decomposes into a product of pieces coming from $X$ and $Y$, but this decomposition must be considered in a blowup of the moduli space of curves. This blowup is specified by tropical moduli data. As an application, we show that the cycle of curves in a toric variety with fixed contact orders is a product of virtual strict transforms of double ramification cycles. The formalism we outline offers a unified viewpoint on a number of recent results in logarithmic Gromov-Witten theory, including works of Herr, Holmes-Pixton-Schmitt, and Nabijou and the author.
libGroomRL: Reinforcement Learning for Jets

In these proceedings, we present a library allowing for straightforward calls in C++ to jet grooming algorithms trained with deep reinforcement learning. The RL agent is trained with a reward function constructed to optimize the groomed jet properties, using both signal and background samples in a simultaneous multi-level training. We show that the grooming algorithm derived from the deep RL agent can match state-of-the-art techniques used at the Large Hadron Collider, resulting in improved mass resolution for boosted objects. Given a suitable reward function, the agent learns how to train a policy which optimally removes soft wide-angle radiation, allowing for a modular grooming technique that can be applied in a wide range of contexts. The neural network trained with GroomRL can be used in a FastJet analysis through the libGroomRL C++ library.
Impartial SWIPT-Assisted User Cooperation Schemes

In this paper, an impartial simultaneous wireless information and power transfer (SWIPT)-assisted cooperation mechanism is proposed for a non-orthogonal multiple access (NOMA) downlink scenario. The key idea of the proposed impartial user cooperation mechanism (IUCM) is that not only the cell-center user but also the cell-edge user applies the power-splitting architecture and utilizes the harvested energy to forward the other user’s information. Analytical results show that the proposed IUCM outperforms the traditional partial cooperation mechanism in terms of outage probability, diversity order and diversity-multiplexing trade-off (DMT). For comparison, the proposed IUCM is further incorporated into an orthogonal frequency-division multiple access (OFDMA) framework, which is shown to preserve the same diversity order, while has a worse but more flexible DMT performance compared with the IUCM in the NOMA framework. Furthermore, a classic non-linear energy harvesting model is introduced, based on which the outage probability, diversity order and DMT performance of the proposed IUCM and the conventional mechanism are analyzed. Finally, representative numerical results are given to validate our theoretical analysis.
Normalisation of Weights and Firing Rates in Spiking Neural Networks with Spike-Timing-Dependent Plasticity

Maintaining the ability to fire sparsely is crucial for information encoding in neural networks. Additionally, spiking homeostasis is vital for spiking neural networks with changing numbers of weights and neurons. We discuss a range of network stabilisation approaches, inspired by homeostatic synaptic plasticity mechanisms reported in the brain. These include weight scaling, and weight change as a function of the network's spiking activity. We tested normalisation of the sum of weights for all neurons, and by neuron type. We examined how this approach affects firing rate and performance on clustering of time-series data in the form of moving geometric shapes. We found that neuron type-specific normalisation is a promising approach for preventing weight drift in spiking neural networks, thus enabling longer training cycles. It can be adapted for networks with architectural plasticity.
Z′
 models with less-minimal flavor violation

We study the phenomenology of simplified ${Z}^{\ensuremath{'}}$ models with a global $U(2{)}^{3}$ flavor symmetry in the quark sector, broken solely by the Standard Model Yukawa couplings. This flavor symmetry, known as less-minimal flavor violation, protects $\mathrm{\ensuremath{\Delta}}F=2$ processes from dangerously large new physics (NP) effects and at the same time provides a free complex phase in $b\ensuremath{\rightarrow}s$ transitions, allowing for an explanation of the hints for additional direct $CP$ violation in kaon decays (${\ensuremath{\epsilon}}^{\ensuremath{'}}/\ensuremath{\epsilon}$) and in hadronic $B$-decays ($B\ensuremath{\rightarrow}K\ensuremath{\pi}$ puzzle). Furthermore, including the couplings of the ${Z}^{\ensuremath{'}}$ boson to the leptons, it is possible to address the intriguing hints for NP (above the $5\ensuremath{\sigma}$ level) in $b\ensuremath{\rightarrow}s{\ensuremath{\ell}}^{+}{\ensuremath{\ell}}^{\ensuremath{-}}$ transitions. Taking into account all flavor observables in a global fit, we find that our model can (for the first time) provide a common explanation for ${\ensuremath{\epsilon}}^{\ensuremath{'}}/\ensuremath{\epsilon}$, the $B\ensuremath{\rightarrow}K\ensuremath{\pi}$ puzzle and $b\ensuremath{\rightarrow}s{\ensuremath{\ell}}^{+}{\ensuremath{\ell}}^{\ensuremath{-}}$ data. Sizeable $CP$ violation in $b\ensuremath{\rightarrow}s{\ensuremath{\ell}}^{+}{\ensuremath{\ell}}^{\ensuremath{-}}$ observables, in particular ${A}_{8}$, is predicted, which can be tested in the near future, and an explanation of the $B\ensuremath{\rightarrow}K\ensuremath{\pi}$ and ${\ensuremath{\epsilon}}^{\ensuremath{'}}/\ensuremath{\epsilon}$ puzzles leads to effects in dijet tails at the LHC that are not far below the current limits. If we require that also $b\ensuremath{\rightarrow}s{\ensuremath{\ell}}^{+}{\ensuremath{\ell}}^{\ensuremath{-}}$ anomalies are explained, cancellations in dimuon tails (possibly by a second ${Z}^{\ensuremath{'}}$) are needed to satisfy LHC data.
Noise estimation and reduction in heart sounds using time frequency block thresholding method

In this paper a novel method of de-noising phonocardiogram by time-frequency overlapping group shrinkage method is described. In this method sigma, the standard deviation of the stationary noise present in a noisy phonocardiogram is found using activity detection. This noise is then cancelled by attenuating it in the time frequency domain. The accuracy of noise reduction is measured by SNR. Overlapping group shrinkage algorithm reduces the effect of noise by attenuating them using hard or soft thresholding. Performance of this method was found to be far better compared to other methods such as soft thresholding and block thresholding.
Running title : Reassessment of NOEs in an unfolded SH 3 domain Title : Reassessment of NOE data for the unfolded state of an SH 3 domain highlights residual β-structure

a Department of Biochemistry, University of Toronto, Toronto, Ontario, M5S 1A8, Canada b Structural Biology and Biochemistry Program, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, M5G 1X8, Canada c Department of Medical Genetics and Microbiology, University of Toronto, Toronto, Ontario, M5S 1A8, Canada d Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543