Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Constellation Rotation and Symbol-level Precoding Optimization in the Downlink of Multiuser MISO Channels

This paper tackles the problem of the simultaneous interference among the multiple users in the downlink of a wireless multiantenna system. In order to exploit the multiuser interference and transform it into useful power at the receiver side, symbol-level precoding is utilized through joint exploitation of the data from each individual user and the channel state information. This paper introduces a complexity reduction scheme that exploits the constellations symmetry to reduce the number of possible calculations and the number of constraints in the symbol-level optimization problem. This paper also proposes a new transmission technique that jointly optimizes the transmit symbol-level precoding and the constellation rotation of the data stream for each user. The purpose is to increase the probability of having constructive interference, and thus better performance. We focus on improving the energy efficiency by reducing the required power to satisfy certain quality of service constraints through solving a symbol-level precoding problem and finding the optimal phase by which each user constellation should be rotated. This problem is non-convex, therefore, we propose an efficient branch-and-bound algorithm combined with semidefinite relaxation to solve the problem and find an efficient solution. Numerical results are presented in a comparative fashion to show the effectiveness of the proposed technique, which outperforms the state-of-the-art symbol-level precoding schemes in terms of energy efficiency, and power consumption.
Direct formation of nitrogen-vacancy centers in nitrogen doped diamond along the trajectories of swift heavy ions

We report depth-resolved photoluminescence measurements of nitrogen-vacancy (NV$^-$) centers formed along the tracks of swift heavy ions in type Ib synthetic single crystal diamonds that had been doped with 100 ppm nitrogen during crystal growth. Analysis of the spectra shows that NV$^-$ centers are formed preferentially within regions where electronic stopping processes dominate and not at the end of the ion range where elastic collisions lead to formation of vacancies and defects. Thermal annealing further increases NV yields after irradiation with SHI preferentially in regions with high vacancy densities. NV centers formed along the tracks of single swift heavy ions can be isolated with lift-out techniques for explorations of color center qubits in quasi 1D registers with average qubit spacing of a few nm and of order 100 color centers per micron along 10 to 30 micron long percolation chains.
Keep the bursts and ditch the interleavers

To facilitate applications in IoT, 5G, and beyond, there is an engineering need to enable high-rate, low-latency communications. Errors in physical channels typically arrive in clumps, but most decoders are designed assuming that channels are memoryless. As a result, communication networks rely on interleaving over tens of thousands of bits so that channel conditions match decoder assumptions. Even for short high rate codes, awaiting sufficient data to interleave at the sender and de-interleave at the receiver is a significant source of unwanted latency. Using existing decoders with non-interleaved channels causes a degradation in block error rate performance owing to mismatch between the decoder’s channel model and true channel behaviour.Through further development of the recently proposed Guessing Random Additive Noise Decoding (GRAND) algorithm, which we call GRAND-MO for GRAND Markov Order, here we establish that by abandoning interleaving and embracing bursty noise, low-latency, short-code, high-rate communication is possible with block error rates that outperform their interleaved counterparts by a substantial margin. Moreover, while most decoders are twinned to a specific code-book structure, GRANDMO can decode any code. Using this property, we establish that certain well-known structured codes are ill-suited for use in bursty channels, but Random Linear Codes (RLCs) are robust to correlated noise. This work suggests that the use of RLCs with GRAND-MO is a good candidate for applications requiring high throughput with low latency.
Learning World Transition Model for Socially Aware Robot Navigation

Moving in dynamic pedestrian environments is one of the important requirements for autonomous mobile robots. We present a model-based reinforcement learning approach for robots to navigate through crowded environments. The navigation policy is trained with both real interaction data from multi-agent simulation and virtual data from a deep transition model that predicts the evolution of surrounding dynamics of mobile robots. A reward function considering social conventions is designed to guide the training of the policy. Specifically, the policy model takes laser scan sequence and robot’s own state as input and outputs steering command. The laser sequence is further transformed into stacked local obstacle maps disentangled from robot’s ego motion to separate the static and dynamic obstacles, simplifying the model training. We observe that the policy using our method can be trained with significantly less real interaction data in simulator but achieve similar level of success rate in social navigation tasks compared with other methods. Experiments are conducted in multiple social scenarios both in simulation and on real robots, the learned policy can guide the robots to the final targets successfully in a socially compliant manner. Code is available at https://github.com/YuxiangCui/model-based-social-navigation.
Geometry Perspective Of Estimating Learning Capability Of Neural Networks

The paper uses statistical and differential geometric motivation to acquire prior information about the learning capability of an artificial neural network on a given dataset. The paper considers a broad class of neural networks with generalized architecture performing simple least square regression with stochastic gradient descent (SGD). The system characteristics at two critical epochs in the learning trajectory are analyzed. During some epochs of the training phase, the system reaches equilibrium with the generalization capability attaining a maximum. The system can also be coherent with localized, non-equilibrium states, which is characterized by the stabilization of the Hessian matrix. The paper proves that neural networks with higher generalization capability will have a slower convergence rate. The relationship between the generalization capability with the stability of the neural network has also been discussed. By correlating the principles of high-energy physics with the learning theory of neural networks, the paper establishes a variant of the Complexity-Action conjecture from an artificial neural network perspective.
An Approach for the Identification of Information Leakage in Automotive Infotainment systems

The advancements in the digitization world has revolutionized the automotive industry. Today’s modern cars are equipped with internet, computers that can provide autonomous driving functionalities as well as infotainment systems that can run mobile operating systems, like Android Auto and Apple CarPlay. Android Automotive is Google’s android operating system tailored to run natively on vehicle’s infotainment systems, it allows third party apps to be installed and run on vehicle’s infotainment systems. Such apps may raise security concerns related to user’s safety, security and privacy. This paper investigates security concerns of in-vehicle apps, specifically, those related to inter component communication (ICC) among these apps. ICC allows apps to share information via inter or intra apps components through a messaging object called intent. In case of insecure communication, Intent can be hijacked or spoofed by malicious apps and user’s sensitive information can be leaked to hacker’s database. We investigate the attack surface and vulnerabilities in these apps and provide a static analysis approach and a tool to find data leakage vulnerabilities. The approach can also provide hints to mitigate these leaks. We evaluate our approach by analyzing a set of Android Auto apps downloaded from Google Play store, and we report our validated results on vulnerabilities identified on those apps.
Augmented Equivariant Attention Networks for Electron Microscopy Image Super-Resolution

Taking electron microscopy (EM) images in high-resolution is time-consuming and expensive and could be detrimental to the integrity of the samples under observation. Advances in deep learning enable us to perform super-resolution computationally, so as to obtain high-resolution images from low-resolution ones. When training super-resolution models on pairs of experimentally acquired EM images, prior models suffer from performance loss while using the pooled-training strategy due to their inability to capture inter-image dependencies and common features shared among images. Although there exist methods that take advantage of shared features among input instances in image classification tasks, they in the current form cannot be applied to super-resolution tasks because they fail to preserve an essential property in image-to-image transformation problems, which is the equivariance property to spatial permutations. To address these limitations, we propose the augmented equivariant attention networks (AEANets) with better capability to capture inter-image dependencies and shared features, while preserving the equivariance to spatial permutations. The proposed AEANets captures inter-image dependencies and common features shared among images via two augmentations on the attention mechanism; namely, the shared references and the batch-aware attention during training. We theoretically show the equivariance property of the proposed augmented attention model and experimentally show that AEANets consistently outperforms the baselines in both quantitative and visual results.
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra

We create classical (non-quantum) dynamic data structures supporting queries for recommender systems and least-squares regression that are comparable to their quantum analogues. De-quantizing such algorithms has received a flurry of attention in recent years; we obtain sharper bounds for these problems. More significantly, we achieve these improvements by arguing that the previous quantum-inspired algorithms for these problems are doing leverage or ridge-leverage score sampling in disguise. With this recognition, we are able to employ the large body of work in numerical linear algebra to obtain algorithms for these problems that are simpler and faster than existing approaches. We also consider static data structures for the above problems, and obtain close-to-optimal bounds for them. To do this, we introduce a new randomized transform, the Gaussian Randomized Hadamard Transform (GRHT). It was thought in the numerical linear algebra community that to obtain nearly-optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns, regression, low rank approximation, maximum matching on general graphs and linear matroid union, that one would need to resolve the main open question of Nelson and Nguyen (FOCS, 2013) regarding the logarithmic factors in existing oblivious subspace embeddings. We bypass this question, using GRHT, and obtain optimal or nearly-optimal bounds for these problems. For the fundamental problems of rank computation and finding a linearly independent subset of columns, our algorithms improve Cheung, Kwok, and Lau (JACM, 2013) and are optimal to within a constant factor and a $\log\log(n)$-factor, respectively. Further, for constant factor regression and low rank approximation we give the first optimal algorithms, for the current matrix multiplication exponent.
Engines of Parsimony: Part II; Performance Trade-offs for Communicating Reversible Computers

In Part I of this series, the limits on the sustained performance of large reversible computers were investigated and found to scale as $\sqrt{AV}$ where $A$ is the convex bounding surface area of the system and $V$ its internal volume, compared to $A$ for an irreversible computer. This analysis neglected to consider interactions between components of the system however, instead focussing on raw computational power. In this part we extend this analysis to consider synchronisation events such as communication between independent reversible processors subject to a limiting supply of free energy. It is found that, whilst asynchronous computation can proceed at a rate $b\lambda$, synchronisation events proceed at the much slower rate $\sim b^2\lambda$; in these rate expressions, $\lambda$ is the gross transition rate for each processor and $b\sim\sqrt{A/V}\ll1$ is the 'computational bias' measuring the net fraction of transitions which are successful. Whilst derived for Brownian reversible computers, this result applies to all forms of reversible computer, including Quantum computers. In fact this result is an upper bound, and one must choose the phase space geometry of the synchronisation events carefully to avoid even worse performance. In the limit of large computers, communication will therefore tend to freeze out as $b\to0$; if, however, one is willing to restrict the number of processors permitted to share state at any given time then this rate can be ameliorated and performance on par with asynchronous computation can be recovered.
Delay-Optimal Scheduling for IRS-Aided Mobile Edge Computing

In this letter, we consider an intelligent reflecting surface (IRS)-aided mobile edge computing (MEC) system, where an IRS is deployed to assist computation offloading from two users to an access point connected with an edge cloud. For the IRS-aided data transmission, in contrast to the conventional non-orthogonal multiple-access (NOMA) and time-division multiple-access (TDMA), we propose a new flexible time-sharing NOMA scheme that allows users to flexibly divide their data into two parts transmitted via NOMA and TDMA, respectively, thus encapsulating both conventional NOMA and TDMA as special cases. We formulate an optimization problem to minimize the sum delay of the two users by designing the IRS passive reflection and users’ computation-offloading scheduling under the IRS discrete-phase constraint. Although this problem is non-convex, we obtain its optimal solution for both the cases of infinite and finite cloud computing capacities. Furthermore, we show that NOMA and TDMA based transmissions are preferred in different scenarios, depending on the users’ cloud-computing time as well as the rate discrepancy between NOMA and TDMA with IRS.
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training

We study the effect of mini-batching on the loss landscape of deep neural networks using spiked, field-dependent random matrix theory. We demonstrate that the magnitude of the extremal values of the batch Hessian are larger than those of the empirical Hessian. We also derive similar results for the Generalised Gauss-Newton matrix approximation of the Hessian. As a consequence of our theorems we derive an analytical expressions for the maximal learning rates as a function of batch size, informing practical optimisation schemes for both stochastic gradient descent (linear scaling) and adaptive algorithms such as Adam (square root scaling). Whilst the linear scaling for stochastic gradient descent has been derived under more restrictive conditions, which we generalise, the square root scaling rule for adaptive optimisers is, to our knowledge, completely novel. We validate our claims on the VGG/WideResNet architectures on the CIFAR-100 and ImageNet datasets.
Image Clustering Using an Augmented Generative Adversarial Network and Information Maximization

Image clustering has recently attracted significant attention due to the increased availability of unlabeled datasets. The efficiency of traditional clustering algorithms heavily depends on the distance functions used and the dimensionality of the features. Therefore, performance degradation is often observed when tackling either unprocessed images or high-dimensional features extracted from processed images. To deal with these challenges, we propose a deep clustering framework consisting of a modified generative adversarial network (GAN) and an auxiliary classifier. The modification employs Sobel operations prior to the discriminator of the GAN to enhance the separability of the learned features. The discriminator is then leveraged to generate representations as to the input to an auxiliary classifier. An objective function is utilized to train the auxiliary classifier by maximizing the mutual information between the representations obtained via the discriminator model and the same representations perturbed via adversarial training. We further improve the robustness of the auxiliary classifier by introducing a penalty term into the objective function. This minimizes the divergence across multiple transformed representations generated by the discriminator model with a low dropout rate. The auxiliary classifier is implemented with a group of multiple cluster-heads, where a tolerance hyper-parameter is used to tackle imbalanced data. Our results indicate that the proposed method achieves competitive results compared with state-of-the-art clustering methods on a wide range of benchmark datasets including CIFAR-10, CIFAR-100/20, and STL10.
Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$, identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$.
Cosmic Reionization May Still Have Started Early and Ended Late: Confronting Early Onset with CMB Anisotropy and 21 cm Global Signals.

The global history of reionization was shaped by the relative amounts of starlight released by three halo mass groups: atomic-cooling halos (ACHs) with virial temperatures Tvir > 10^4 K, either (1) massive enough to form stars even after reionization (HMACHs, >~ 10^9 Msun) or (2) less-massive (LMACHs), subject to star formation suppression when overtaken by reionization, and (3) H2-cooling minihalos (MHs) with Tvir < 10^4 K, whose star formation is predominantly suppressed by the H2-dissociating Lyman-Werner (LW) background. Our previous work showed that including MHs caused two-stage reionization - early rise to x ~ 0.1, driven by MHs, followed by a rapid rise, late, to x ~ 1, driven by ACHs - with a signature in CMB polarization anisotropy predicted to be detectable by the Planck satellite. Motivated by this prediction, we model global reionization semi-analytically for comparison with Planck CMB data and the EDGES global 21cm absorption feature, for models with: (1) ACHs, no feedback; (2) ACHs, self-regulated; and (3) ACHs and MHs, self-regulated. Model (3) agrees well with Planck E-mode polarization data, even with a substantial tail of high-redshift ionization, beyond the limit proposed by the Planck Collaboration (2018). No model reproduces the EDGES feature. For model (3), |dTb| <~ 60 mK across the EDGES trough, an order of magnitude too shallow, and absorption starts at higher z but is spectrally featureless. Early onset reionization by Population III stars in MHs is compatible with current constraints, but only if the EDGES interpretation is discounted or else other processes we did not include account for it.
Description of Resonant Inelastic X-Ray Scattering in Correlated Metals

To fully capitalize on the potential and versatility of resonant inelastic x-ray scattering (RIXS), it is essential to develop the capability to interpret different RIXS contributions through calculations, including the dependence on momentum transfer, from first-principles for correlated materials. Toward that objective, we present new methodology for calculating the full RIXS response of a correlated metal in an unbiased fashion. Through comparison of measurements and calculations that tune the incident photon energy over a wide portion of the Fe L$_3$ absorption resonance of the example material BaFe$_2$As$_2$, we show that the RIXS response in BaFe$_2$As$_2$ is dominated by the direct channel contribution, including the Raman-like response below threshold, which we explain as a consequence of the finite core-hole lifetime broadening. Calculations are initially performed within the first-principles Bethe-Salpeter framework, which we then significantly improve by convolution with an effective spectral function for the intermediate-state excitation. We construct this spectral function, also from first-principles, by employing the cumulant expansion of the Green's function and performing a real-time time dependent density functional theory calculation of the response of the electronic system to the perturbation of the intermediate-state excitation. Importantly, this allows us to evaluate the indirect RIXS response from first-principles, accounting for the full periodicity of the crystal structure and with dependence on the momentum transfer.
The marine source of C 2 -C 6 aliphatic hydrocarbons

C~-C 6 Nonmethane hydrocarbon (NMHC) concentrations in the atmospheric boundary layer and in surface seawater were simultaneously measured during an oceanographic cruise in the intertropical Indian Ocean. NMHC were found to be mainly C~-C 4 alkenes and C2-C 3 alkanes. Their concentrations ranged from 1 to 30× 10 -9 1/1 in the seawater and 0.1 to 15 ppbv in the atmosphere. Seawater appeared to be a source because the C2-C 6 NMHC were supersaturated with respect to the atmosphere by 2 or 3 orders of magnitude. After a selection of the pure marine atmospheric samples, performed with the help of stable and radioactive continental tracers, we found an identical composition in NMHC of surface air and seawater. This observation enabled us to establish that the gas transfer between sea and air occurred according to nonsteady state processes, and that the fluxes cannot be deduced only from atmospheric measurements. An order of magnitude value of the oceanic source for the different NMHC is however derived from the comparison of their sea water concentrations to that of propane and an independent evaluation of the marine source of this last compound.