Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Efficient Learning With Low Resolution Stochastic Domain Wall Synapse for Deep Neural Networks

We demonstrate extremely low resolution quantized (nominally 5-state) synapses with large stochastic variations in synaptic weights can be energy efficient and achieve reasonably high testing accuracies compared to Deep Neural Networks (DNNs) of similar sizes using floating-point precision synaptic weights. Specifically, voltage-controlled domain wall (DW) devices demonstrate stochastic behavior and can only encode limited states; however, they are extremely energy efficient during both training and inference. In this study, we propose both in-situ and ex-situ training algorithms, based on modification of the algorithm proposed by Hubara et al., 2017 which works well with quantization of synaptic weights, and train several 5-layer DNNs on MNIST dataset using 2-, 3- and 5-state DW devices as a synapse. For in-situ training, a separate high precision memory unit preserves and accumulates the weight gradients which prevents accuracy loss due to weight quantization. For ex-situ training, a precursor DNN is first trained based on weight quantization and DW device model. Moreover, a noise tolerance margin is included in both of the training methods to account for the intrinsic device noise. The highest inference accuracies we obtain after in-situ and ex-situ training are ~ 96.67% and ~96.63%, respectively, which is very close to the baseline accuracy of ~97.1% obtained from a similar topology DNN having floating-point precision weights with no stochasticity. Large inter-state intervals due to quantized weights and noise tolerance margin enables in-situ training with significantly lower number of programming attempts. Our proposed approach demonstrates a possibility of at least two orders of magnitude energy savings compared to the floating-point approach implemented in CMOS. This approach is specifically attractive for low power intelligent edge devices where the ex-situ learning can be utilized for energy efficient non-adaptive tasks and the in-situ learning can provide the opportunity to adapt and learn in a dynamically evolving environment.
The impact of one-way streets on the asymmetry of the shortest commuting routes

On a daily commute, the shortest route from home to work rarely overlaps completely the shortest way back. We analyze this asymmetry for several cities and show that it exists even without traffic, due to a non-negligible fraction of one-way streets. For different pairs of origin-destination ($\rm OD$), we compute the log-ratio $r=\ln(\ell_{\rm D}/\ell_{\rm O})$, where $\ell_{\rm O}$ and $\ell_{\rm D}$ are the lengths of the shortest routes from $\rm O$ to $\rm D$ and from $\rm D$ to $\rm O$, respectively. While its average is zero, the amplitude of the fluctuations decays as a power law of the $\rm OD$ shortest path length, $r\sim \ell_{\rm O}^{-\beta}$. Similarly, the fraction of one-way streets in a shortest route also decays as $\ell_{\rm O}^{-\alpha}$. Based on semi-analytic arguments, we show that $\beta=(1+\alpha)/2$. Thus, the value of the exponent $\beta$ is related to correlations in the structure of the underlying street network.
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities

Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the growth of IoT devices, vast quantities of data that may contain users' private information will be generated. The high communication and storage costs, mixed with privacy concerns, will increasingly challenge the traditional ecosystem of centralized over-the-cloud learning and processing for IoT platforms. Federated Learning (FL) has emerged as the most promising alternative approach to this problem. In FL, training data-driven machine learning models is an act of collaboration between multiple clients without requiring the data to be brought to a central point, hence alleviating communication and storage costs and providing a great degree of user-level privacy. However, there are still some challenges existing in the real FL system implementation on IoT networks. In this paper, we will discuss the opportunities and challenges of FL in IoT platforms, as well as how it can enable diverse IoT applications. In particular, we identify and discuss seven critical challenges of FL in IoT platforms and highlight some recent promising approaches towards addressing them.
Retrieval of Aerosol Properties from in Situ, Multi-Angle Light Scattering Measurements Using Invertible Neural Networks

Atmospheric aerosols have a major influence on the earths climate and public health. Hence, studying their properties and recovering them from light scattering measurements is of great importance. State of the art retrieval methods such as pre-computed look-up tables and iterative, physics-based algorithms can suffer from either accuracy or speed limitations. These limitations are becoming increasingly restrictive as instrumentation technology advances and measurement complexity increases. Machine learning algorithms offer new opportunities to overcome these problems, by being quick and precise. In this work we present a method, using invertible neural networks to retrieve aerosol properties from in situ light scattering measurements. In addition, the algorithm is capable of simulating the forward direction, from aerosol properties to measurement data. The applicability and performance of the algorithm are demonstrated with simulated measurement data, mimicking in situ laboratory and field measurements. With a retrieval time in the millisecond range and a weighted mean absolute percentage error of less than 1.5%, the algorithm turned out to be fast and accurate. By introducing Gaussian noise to the data, we further demonstrate that the method is robust with respect to measurement errors. In addition, realistic case studies are performed to demonstrate that the algorithm performs well even with missing measurement data.
The Eye As A Detector

When our eyes follow an object that moves in space, a number of very complex control systems operate in order to maintain a sharp image at a specific retinal location while keeping the overall retinal illumination more or less constant. In the eye movement control system, it is the distance between retinal image (or area of interest in this image) and the fovea that is minimized. Therefore, the object we fixate with our eyes is always imaged onto that part of the retina with the highest visual acuity. In the pupillary control system, it is the average light level that is measured and kept within comfortable limits by changing the pupil diameter . Finally, in the accommodation control system, certain image errors are evaluated (presently, we do not know exactly which ones) and reduce(' by changing the lens power. In all these control systems, the retina plays a crucial role as the first stage of the error detector. However, the retina is a far more complex structure than the familiar photo-sensitive surfaces in physical light detectors. There are relay stations (bipolar cells, ganglion cells) in each signal channel between a photoreceptor and optic nerve fiber, and abundant anatomical and physiological evidence exists for interactions between signal channels at various levels in the retina (horizontal cells, amacrine cells). These facts have to be kept in mind whenever detector properties of the eye are discussed. Another complication arises from the fact that we can never define the output of the system exactly. In a photocell it would be a single variable, for example, the cathode photocurrent. In the human eye, it is an already coded signal that is transmitted over approximately 10 optic nerve fibers to higher centers of the central nervous system. It is impossible at the pre-sent time to monitor simultapeously signals transmitted over large numbers of optic nerve fibers.
Practical Scheduling for Real-World Serverless Computing

Serverless computing has seen rapid growth due to the ease-of-use and cost-efficiency it provides. However, function scheduling, a critical component of serverless systems, has been overlooked. In this paper, we take a first-principles approach toward designing a scheduler that caters to the unique characteristics of serverless functions as seen in real-world deployments. We first create a taxonomy of scheduling policies along three dimensions. Next, we use simulation to explore the scheduling policy space for the function characteristics in a 14-day trace of Azure functions and conclude that frequently used features such as late binding and random load balancing are sub-optimal for common execution time distributions and load ranges. We use these insights to design Hermes, a scheduler for serverless functions with three key characteristics. First, to avoid head-of-line blocking due to high function execution time variability, Hermes uses a combination of early binding and processor sharing for scheduling at individual worker machines. Second, Hermes uses a hybrid load balancing approach that improves consolidation at low load while employing least-loaded balancing at high load to retain high performance. Third, Hermes is both load and locality-aware, reducing the number of cold starts compared to pure load-based policies. We implement Hermes for Apache OpenWhisk and demonstrate that, for the case of the function patterns observed both in the Azure and in other real-world traces, it achieves up to 85% lower function slowdown and 60% higher throughput compared to existing policies.
A feast for trolls - Engagement analysis of counternarratives against online toxicity

This report provides an engagement analysis of counternarratives against online toxicity. Between February 2020 and July 2021, we observed over 15 million toxic messages on social media identified by our fine-grained, multilingual detection AI. Over 1,000 dashboard users responded to toxic messages with combinations of visual memes, text, or AI-generated text, or they reported content. This leads to new, real-life insights on self-regulatory approaches for the mitigation of online hate.
Statistical Measurements of Dispersion Measure Fluctuations in Fast Radio Bursts

Extragalactic fast radio bursts (FRBs) have large dispersion measures (DMs) and are unique probes of intergalactic electron density fluctuations. By using the recently released First CHIME/FRB Catalog, we reexamined the structure function (SF) of DM fluctuations. It shows a large DM fluctuation similar to that previously reported in Xu & Zhang, but no clear correlation hinting toward large-scale turbulence is reproduced with this larger sample. To suppress the distortion effect from FRB distances and their host DMs, we focus on a subset of CHIME catalog with DM < 500 pc cm−3. A trend of nonconstant SF and nonzero correlation function (CF) at angular separations θ less than 10° is seen, but with large statistical uncertainties. The difference found between SF and that derived from CF at θ ≲ 10° can be ascribed to the large statistical uncertainties or the density inhomogeneities on scales on the order of 100 Mpc. The possible correlation of electron density fluctuations and inhomogeneities of density distribution should be tested when several thousands of FRBs are available.
RR Lyrae-based Distances for 39 Nearby Dwarf Galaxies Calibrated to Gaia eDR3

We provide uniform RR Lyrae-based distances to 39 dwarf galaxies in and around the Local Group. We determine distances based on a Bayesian hierarchical model that uses periods and magnitudes of published RR Lyrae in dwarf galaxies and is anchored to well-studied Milky Way (MW) RR Lyrae with spectroscopic metallicities and Gaia eDR3 parallaxes. Gaia eDR3 parallaxes for the anchor sample are a factor of 2, on average, more precise than DR2 parallaxes, and allow for a much better constrained period–luminosity–metallicity relation. While ∼75% of our distances are within 1σ of recent RR Lyrae distances in the literature, our distances are also ∼2–3 times more precise than distances in the literature, on average. On average, our distances are ∼0.05 mag closer than distances in the literature, as well as ∼0.06 mag closer than distances derived using a theoretical period–luminosity–metallicity relation. These discrepancies are largely due to our eDR3 parallax anchor. We show that the Hipparcos-anchored RR Lyrae distance scale of Carretta et al. overpredicts distances to MW RR Lyrae by ∼0.05 mag. The largest uncertainties in our distances are (i) the lack of direct metallicity measurements for RR Lyrae and (ii) the heterogeneity of published RR Lyrae photometry. We provide simple formulae to place new dwarf galaxies with RR Lyrae on a common distance scale with this work. We provide a public code that can easily incorporate additional galaxies and data from future surveys, providing a versatile framework for the cartography of the local universe with RR Lyrae.
Data Augmentation for Speech Recognition in Maltese: A Low-Resource Perspective

Developing speech technologies is a challenge for low-resource languages for which both annotated and raw speech data is sparse. Maltese is one such language. Recent years have seen an increased interest in the computational processing of Maltese, including speech technologies, but resources for the latter remain sparse. In this paper, we consider data augmentation techniques for improving speech recognition for such languages, focusing on Maltese as a test case. We consider three diﬀerent types of data augmentation: unsupervised training, multilingual training and the use of synthesized speech as training data. The goal is to determine which of these techniques, or combination of them, is the most eﬀective to improve speech recognition for languages where the starting point is a small corpus of approximately 7 hours of transcribed speech. Our results show that combining the three data augmentation techniques studied here lead us to an absolute WER improvement of 15% without the use of a language model.
Structural instability and charge modulations in the kagome superconductor 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>A</mml:mi><mml:msub><mml:mi mathvariant="normal">V</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">Sb</mml:mi><mml:mn>5</mml:mn></m

Recently, both charge density wave (CDW) and superconductivity have been observed in Kagome compounds $A$V$_3$Sb$_5$. However, the nature of CDW that results in many novel charge modulations is still under hot debate. By means of the first-principles calculations, we discover two kinds of CDW states, the trimerized and hexamerized 2$\times$2 phase and dimerized 4$\times$1 phase existing in $A$V$_3$Sb$_5$. Our phonon excitation spectrum and electronic Lindhard function calculations reveal that the most intensive structural instability in $A$V$_3$Sb$_5$ originates from a combined in-plane vibration mode of V atoms through the electron-phonon coupling, rather than the Fermi surface nesting effect. Crucially, a metastable 4$\times$1 phase with V-V dimer pattern and twofold symmetric bowtie shaped charge modulation is revealed in CsV$_3$Sb$_5$, implying that both dimerization and trimerization exist in the V Kagome layers. These results provide essential understanding of CDW instability and new thoughts for the novel charge modulation patterns.
Robust Preparation of Wigner-Negative States with Optimized SNAP-Displacement Sequences

Hosting nonclassical states of light in three-dimensional microwave cavities has emerged as a promising paradigm for continuous-variable quantum information processing. Here we experimentally demonstrate high-ﬁdelity generation of a range of Wigner-negative states useful for quantum computation, such as Schrödinger-cat states, binomial states, Gottesman-Kitaev-Preskill states, as well as cubic phase states. The latter states have been long sought after in quantum optics and have never been achieved experimentally before. We use a sequence of interleaved selective number-dependent arbitrary phase (SNAP) gates and displacements. We optimize the state preparation in two steps. First we use a gradient-descent algorithm to optimize the parameters of the SNAP and displacement gates. Then we optimize the envelope of the pulses implementing the SNAP gates. Our results show that this way of creating highly nonclassical states in a harmonic oscillator is robust to ﬂuctuations of the system parameters such as the qubit frequency and the dispersive shift.
Improving Experience Replay through Modeling of Similar Transitions' Sets

In this work, we propose and evaluate a new reinforcement learning method, COMPact Experience Replay (COMPER), which uses temporal difference learning with predicted target values based on recurrence over sets of similar transitions, and a new approach for experience replay based on two transitions memories. Our objective is to reduce the required number of experiences to agent training regarding the total accumulated rewarding in the long run. Its relevance to reinforcement learning is related to the small number of observations that it needs to achieve results similar to that obtained by relevant methods in the literature, that generally demand millions of video frames to train an agent on the Atari 2600 games. We report detailed results from five training trials of COMPER for just 100,000 frames and about 25,000 iterations with a small experiences memory on eight challenging games of Arcade Learning Environment (ALE). We also present results for a DQN agent with the same experimental protocol on the same games set as the baseline. To verify the performance of COMPER on approximating a good policy from a smaller number of observations, we also compare its results with that obtained from millions of frames presented on the benchmark of ALE.
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning

Multi-agent formation as well as obstacle avoid-ance is one of the most actively studied topics in the field of multi-agent systems. Although some classic controllers like model predictive control (MPC) and fuzzy control achieve a certain measure of success, most of them require precise global information which is not accessible in harsh environments. On the other hand, some reinforcement learning (RL) based approaches adopt the leader-follower structure to organize different agents' behaviors, which sacrifices the collaboration between agents thus suffering from bottlenecks in maneuver-ability and robustness. In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL). Agents in our system only utilize local and relative information to make decisions and control themselves distributively, and will reorganize themselves into a new topology quickly in case that any of them is dis-connected. Our method achieves better performance regarding formation error, formation convergence rate and on-par success rate of obstacle avoidance compared with baselines (both classic control methods and another RL-based method). The feasibility of our method is verified by both simulation and hardware implementation with Ackermann-steering vehicles.
Data privacy protection in microscopic image analysis for material data mining

Recent progress in material data mining has been driven by high-capacity models trained on large datasets. However, collecting experimental data has been extremely costly owing to the amount of human effort and expertise required. Therefore, material researchers are often reluctant to easily disclose their private data, which leads to the problem of data island, and it is difficult to collect a large amount of data to train high-quality models. In this study, a material microstructure image feature extraction algorithm FedTransfer based on data privacy protection is proposed. The core contributions are as follows: 1) the federated learning algorithm is introduced into the polycrystalline microstructure image segmentation task to make full use of different user data to carry out machine learning, break the data island and improve the model generalization ability under the condition of ensuring the privacy and security of user data; 2) A data sharing strategy based on style transfer is proposed. By sharing style information of image that is not urgent for user confidentiality, it can reduce the performance penalty caused by the distribution difference of data among different users.
BROTOCs and Quantum Information Scrambling at Finite Temperature

Out-of-time-ordered correlators (OTOCs) have been extensively studied in recent years as a diagnostic of quantum information scrambling. In this paper, we study quantum information-theoretic aspects of the regularized finite-temperature OTOC. We introduce analytical results for the bipartite regularized OTOC (BROTOC): the regularized OTOC averaged over random unitaries supported over a bipartition. We show that the BROTOC has several interesting properties, for example, it quantifies the purity of the associated thermofield double state and the operator purity of the analytically continued time-evolution operator. At infinite-temperature, it reduces to one minus the operator entanglement of the time-evolution operator. In the zero-temperature limit and for nondegenerate Hamiltonians, the BROTOC probes the groundstate entanglement. By computing long-time averages, we show that the equilibration value of the BROTOC is intimately related to eigenstate entanglement. Finally, we numerically study the equilibration value of the BROTOC for various physically relevant Hamiltonian models and comment on its ability to distinguish integrable and chaotic dynamics.
Observational Limits on the Early-time Dust Mass in SN 1987A

In recent years, dust masses of a few tenths of a solar mass have been found in the expanding ejecta of a number of core-collapse supernovae. How dust forms in such quantities remains poorly understood; theories of dust formation predict lower total masses and much faster formation rates than observations imply. One suggestion to reconcile observations and theory was made by Dwek et al., who proposed that the dust forms very rapidly, and because of its optical depth, is not initially observationally detectable, only being gradually revealed as the ejecta expand. Observational dust masses at early times would then only be lower limits. Using a large grid of radiative transfer models covering dust masses from 10−4 to 1 M ⊙ to calculate both the spectral energy distribution and the emission line profiles from clumpy dust shells, we show that this cannot be the case. Some clump distributions allow dust masses of ∼0.01 M ⊙ to be concealed in clumps and still predict an SED consistent with the observations. However, these geometries predict emission line profiles that are inconsistent with the observations. Similarly, clump geometries that reproduce the observed emission line profiles with dust masses >0.01 M ⊙ do not reproduce the SED. However, models with ∼10−3 M ⊙ of amorphous carbon can reproduce both the SED and the emission line profiles. We conclude that no large masses of dust can be hidden from view in the ejecta of SN 1987A at early epochs, and that the majority of dust must thus have formed at epochs >1000 days.