Miracle-dz/UniFAR-FTUs huggingface dataset:
Basil: A Fast and Byzantine-Resilient Approach for Decentralized Training

Decentralized (i.e., serverless) training across edge nodes can suffer substantially from potential Byzantine nodes that can degrade the training performance. However, detection and mitigation of Byzantine behaviors in a decentralized learning setting is a daunting task, especially when the data distribution at the users is heterogeneous. As our main contribution, we propose Basil, a fast and computationally efficient Byzantine-robust algorithm for decentralized training systems, which leverages a novel sequential, memory-assisted and performance-based criteria for training over a logical ring while filtering the Byzantine users. In the IID dataset setting, we provide the theoretical convergence guarantees of Basil, demonstrating its linear convergence rate. Furthermore, for the IID setting, we experimentally demonstrate that Basil is robust to various Byzantine attacks, including the strong Hidden attack, while providing up to absolute ~16% higher test accuracy over the state-of-the-art Byzantine-resilient decentralized learning approach. Additionally, we generalize Basil to the non-IID setting by proposing Anonymous Cyclic Data Sharing (ACDS), a technique that allows each node to anonymously share a random fraction of its local non-sensitive dataset (e.g., landmarks images) with all other nodes. Finally, to reduce the overall latency of Basil resulting from its sequential implementation over the logical ring, we propose Basil+ that enables Byzantine-robust parallel training across groups of logical rings, and at the same time, it retains the performance gains of Basil due to sequential training within each group. Furthermore, we experimentally demonstrate the scalability gains of Basil+ through different sets of experiments.
Secure Transmission for Hierarchical Information Accessibility in Downlink MU-MIMO

Physical layer security is a useful tool to prevent illegal wiretapping to confidential information. In this paper, we consider a generalized model of conventional physical layer security, referred as hierarchical information accessibility (HIA). A main feature of the HIA model is that a network has a hierarchy in information access, wherein decoding feasibility is determined by each user’s priority. Under this HIA model, we formulate a sum secrecy rate maximization problem with regard to precoding vectors. This problem is challenging since multiple non-smooth functions are involved into the secrecy rate to fulfill the HIA conditions and also the problem is non-convex. To address the challenges, we approximate the minimum function by using the LogSumExp technique, thereafter obtain the first-order optimality condition. One key observation is that the derived condition is cast as a functional eigenvalue problem, where the eigenvalue is equivalent to the approximated objective function of the formulated problem. Accordingly, we show that finding a principal eigenvector is equivalent to finding a local optimal solution. To this end, we develop a novel method called generalized power iteration for HIA (GPI-HIA). Simulations demonstrate that the GPI-HIA significantly outperforms other baseline methods in terms of the secrecy rate.
Path Loss in Urban LoRa Networks: A Large-Scale Measurement Study

Urban LoRa networks promise to provide a cost-efficient and scalable communication backbone for smart cities. One core challenge in rolling out and operating these networks is radio network planning, i.e., precise predictions about possible new locations and their impact on network coverage. Path loss models aid in this task, but evaluating and comparing different models requires a sufficiently large set of high-quality received packet power samples. In this paper, we report on a corresponding large-scale measurement study covering an urban area of 200 km2 over a period of 230 days using sensors deployed on garbage trucks, resulting in more than 112 thousand high-quality samples for received packet power. Using this data, we compare eleven previously proposed path loss models and additionally provide new coefficients for the Log-distance model. Our results reveal that the Log-distance model and other well-known empirical models such as Okumura or Winner+ provide reasonable estimations in an urban environment, and terrain based models such as ITM or ITWOM have no advantages. In addition, we derive estimations for the needed sample size in similar measurement campaigns. To stimulate further research in this direction, we make all our data publicly available.
Blockchain for Trust and Reputation Management in Cyber-physical Systems

The salient features of blockchain, such as decentralisation and transparency, have allowed the development of Decentralised Trust and Reputation Management Systems (DTRMS), which mainly aim to quantitatively assess the trustworthiness of the network participants and help to protect the network from adversaries. In the literature, proposals of DTRMS have been applied to various Cyber-physical Systems (CPS) applications, including supply chains, smart cities and distributed energy trading. In this chapter, we outline the building blocks of a generic DTRMS and discuss how it can benefit from blockchain. To highlight the significance of DTRMS, we present the state-of-the-art of DTRMS in various field of CPS applications. In addition, we also outline challenges and future directions in developing DTRMS for CPS.
Four-Dimensional Generalized Ricci Flows with Nilpotent Symmetry

We study solutions to generalized Ricci flow on four-manifolds with a nilpotent, codimension 1 symmetry. We show that all such flows are immortal, and satisfy type III curvature and diameter estimates. Using a new kind of monotone energy adapted to this setting, we show that blowdown limits lie in a canonical finite-dimensional family of solutions. The results are new for Ricci flow.
An ALMA study of hub-filament systems I. On the clump mass concentration within the most massive cores

The physical processes behind the transfer of mass from parsec-scale clumps to massive-star-forming cores remain elusive. We investigate the relation between the clump morphology and the mass fraction that ends up in its most massive core (MMC) as a function of infrared brightness, i.e. a clump evolutionary tracer. Using ALMA 12 m and ACA we surveyed 6 infrared-dark hubs in 2.9 mm continuum at ∼3″ resolution. To put our sample into context, we also re-analysed published ALMA data from a sample of 29 high mass-surface density ATLASGAL sources. We characterise the size, mass, morphology, and infrared brightness of the clumps using Herschel and Spitzer data. Within the 6 newly observed hubs, we identify 67 cores, and find that the MMCs have masses between 15–911 M⊙ within a radius of 0.018–0.156 pc. The MMC of each hub contains 3–24 per cent of the clump mass (fMMC), becoming 5–36 per cent once core masses are normalised to the median core radius. Across the 35 clumps, we find no significant difference in the median fMMC values of hub and non-hub systems, likely the consequence of a sample bias. However, we find that fMMC is ∼7.9 times larger for infrared-dark clumps compared to infrared-bright ones. This factor increases up to ∼14.5 when comparing our sample of 6 infrared-dark hubs to infrared-bright clumps. We speculate that hub-filament systems efficiently concentrate mass within their MMC early on during its evolution. As clumps evolve, they grow in mass, but such growth does not lead to the formation of more massive MMCs.
Education and Food Consumption Patterns: Quasi-Experimental Evidence from Indonesia

How does food consumption improve educational outcomes is an important policy issue for developing countries. Applying the Indonesian Family Life Survey (IFLS) 2014, we estimate the returns of food consumption to education and investigate if more educated individuals tend to consume healthier bundles than less-educated individuals do. We implement the Expected Outcome Methodology, which is similar to Average Treatment on The Treated (ATT) conceptualized by Angrist and Pischke (2009). We find that education tends to tilt consumption towards healthier foods. Specifically, individuals with upper secondary or higher levels of education, on average, consume 31.5% more healthy foods than those with lower secondary education or lower levels of education. With respect to unhealthy food consumption, more highly-educated individuals, on average, consume 22.8% less unhealthy food than less-educated individuals. This suggests that education can increase the inequality in the consumption of healthy food bundles. Our study suggests that it is important to design policies to expand education for all for at least up to higher secondary level in the context of Indonesia. Our finding also speaks to the link between food-health gradient and human capital formation for a developing country such as Indonesia.
Neutrinoless Double Beta Decay with Germanium Detectors: 1026 yr and Beyond

In the global landscape of neutrinoless double beta (0νββ) decay search, the use of semiconductor germanium detectors provides many advantages. The excellent energy resolution, the negligible intrinsic radioactive contamination, the possibility of enriching the crystals up to 88% in the 76Ge isotope as well as the high detection efficiency, are all key ingredients for highly sensitive 0νββ decay search. The Majorana and Gerda experiments successfully implemented the use of germanium (Ge) semiconductor detectors, reaching an energy resolution of 2.53 ± 0.08 keV at the Qββ and an unprecedented low background level of 5.2×10−4 cts/(keV·kg·yr), respectively. In this paper, we will review the path of 0νββ decay search with Ge detectors from the original idea of E. Fiorini et al. in 1967, to the final recent results of the Gerda experiment setting a limit on the half-life of 76Ge 0νββ decay at T1/2>1.8×1026 yr (90% C.L.). We will then present the LEGEND project designed to reach a sensitivity to the half-life up to 1028 yr and beyond, opening the way to the exploration of the normal ordering region.
Efficient Path-Sensitive Data-Dependence Analysis

This paper presents a scalable path- and context-sensitive data-dependence analysis. The key is to address the aliasing-path-explosion problem via a sparse, demand-driven, and fused approach that piggybacks the computation of pointer information with the resolution of data dependence. Specifically, our approach decomposes the computational efforts of disjunctive reasoning into 1) a context- and semi-path-sensitive analysis that concisely summarizes data dependence as the symbolic and storeless value-flow graphs, and 2) a demand-driven phase that resolves transitive data dependence over the graphs. We have applied the approach to two clients, namely thin slicing and value flow analysis. Using a suite of 16 programs ranging from 13 KLoC to 8 MLoC, we compare our techniques against a diverse group of state-of-the-art analyses, illustrating significant precision and scalability advantages of our approach.
Graph Fourier Transform Based Audio Zero-Watermarking

The frequent exchange of multimedia information in thepresent era projects an increasing demand for copyright protection. In this work, we propose a novel audio zero-watermarking technology based on graph Fourier transform for enhancing the robustness with respect to copyright protection. In this approach, the combined shift operator is used to construct the graph signal, upon which the graph Fourier analysis is performed. The selected maximum absolute graph Fourier coefficients representing the characteristics of the audio segment are then encoded into a feature binary sequence using K-means algorithm. Finally, the resultant feature binary sequence is XOR-ed with the watermark binary sequence to realize the embedding of the zero-watermarking. The experimental studies show that the proposed approach performs more effectively in resisting common or synchronization attacks than the existing state-of-the-art methods.
Model-Driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information

Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.
Learning from Peers: Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G Network Slicing

—Radio access network (RAN) slicing is an important part of network slicing in 5G. The evolving network architecture requires the orchestration of multiple network resources such as radio and cache resources. In recent years, machine learning (ML) techniques have been widely applied for network slicing. However, most existing works do not take advantage of the knowledge transfer capability in ML. In this paper, we propose a transfer reinforcement learning (TRL) scheme for joint radio and cache resources allocation to serve 5G RAN slicing. We ﬁrst deﬁne a hierarchical architecture for the joint resources allocation. Then we propose two TRL algorithms: Q-value transfer reinforcement learning (QTRL) and action selection transfer reinforcement learning (ASTRL). In the proposed schemes, learner agents utilize the expert agents’ knowledge to improve their performance on target tasks. The proposed algorithms are compared with both the model-free Q-learning and the model-based priority proportional fairness and time-to-live (PPF-TTL) algorithms. Compared with Q-learning, QTRL and ASTRL present 23.9% lower delay for Ultra Reliable Low Latency Communications slice and 41.6% higher throughput for enhanced Mobile Broad Band slice, while achieving signiﬁcantly faster convergence than Q-learning. Moreover, 40.3% lower URLLC delay and almost twice eMBB throughput are observed with respect to PPF-TTL.
Generating Dataset For Large-scale 3D Facial Emotion Recognition

The tremendous development in deep learning has led facial expression recognition (FER) to receive much attention in the past few years. Although 3D FER has an inherent edge over its 2D counterpart, work on 2D images has dominated the field. The main reason for the slow development of 3D FER is the unavailability of large training and large test datasets. Recognition accuracies have already saturated on existing 3D emotion recognition datasets due to their small gallery sizes. Unlike 2D photographs, 3D facial scans are not easy to collect, causing a bottleneck in the development of deep 3D FER networks and datasets. In this work, we propose a method for generating a large dataset of 3D faces with labeled emotions. We also develop a deep convolutional neural network(CNN) for 3D FER trained on 624,000 3D facial scans. The test data comprises 208,000 3D facial scans.
Unsupervised topological learning for atomic structures identification

We propose an unsupervised learning methodology build on a Gaussian mixture model computed on topological descriptors from persistent homology, for the structural analysis of materials at the atomic scale. Based only on atomic positions and without a priori knowledge, our method automatically identifies relevant local atomic structures in a system of interest. Along with a complete description of the procedure, we provide a concrete example of application by analysing large-scale molecular dynamics simulations of bulks crystal and liquid as well as homogeneous nucleation events of elemental Zr at the nose of the time-temperature-transformation curve. This method opens the way to deeper and autonomous studies of structural dependent phenomena occurring at the atomic scale in materials. 1 ar X iv :2 10 9. 08 12 6v 1 [ co nd -m at . d is -n n] 1 6 Se p 20 21
Predicting the outcome of team movements - Player time series analysis using fuzzy and deep methods for representation learning

We extract and use player position time-series data, tagged along with the action types, to build a competent model for representing team tactics behavioral patterns and use this representation to predict the outcome of arbitrary movements. We provide a framework for the useful encoding of short tactics and space occupations in a more extended sequence of movements or tactical plans. We investigate game segments during a match in which the team in possession of the ball regularly attempts to reach a position where they can take a shot at goal for a single game. A carefully designed and efficient kernel is employed using a triangular fuzzy membership function to create multiple time series for players' potential of presence at different court regions. Unsupervised learning is then used for time series using triplet loss and deep neural networks with exponentially dilated causal convolutions for the derived multivariate time series. This works key contribution lies in its approach to model how short scenes contribute to other longer ones and how players occupies and creates new spaces in-game court. We discuss the effectiveness of the proposed approach for prediction and recognition tasks on the professional basketball SportVU dataset for the 2015-16 half-season. The proposed system demonstrates descent functionality even with relatively small data.
Rubber tori in the boundary of expanded stable maps

We investigate torus actions on logarithmic expansions in the context of enumerative geometry. Our main result is an intrinsic and coordinate‐free description of the higher rank rubber torus appearing in the boundary of the space of expanded stable maps. The rubber torus is constructed canonically from the tropical moduli space, and its action on each stratum of the expanded target is encoded in a linear tropical position map. The presence of 2‐morphisms in the universal target forces expanded stable maps differing by the rubber action to be identified. This provides the first step towards a recursive description of the boundary of the expanded moduli space, with future applications including localisation and rubber calculus.
Study of inelastic channels by positron impact on simple molecules

The present work concentrates on the investigation of various inelastic channels such as positronium (Ps) formation, direct ionization, and total ionization by the positron impact for simple molecules in an extensive energy range (∼Ps formation threshold to 5 keV). The modified form of the well-known spherical complex optical potential formalism and the complex scattering potential-ionization contribution method are used for the calculations. Although only limited literature was available for the molecules studied in the present endeavor, in general, reasonable agreement is found with the existing data. A phenomenological technique developed recently is used in the present work to calculate the Ps formation cross section efficiently.
Marginal regeneration-induced drainage of surface bubbles

The prediction of the lifetime of surface bubbles necessitates a better understanding of the thinning dynamics of the bubble cap. In 1959, Mysel et al. [1], proposed that marginal regeneration i.e. the rise of patches, thinner than the film should be taken into account to describe the film drainage. Nevertheless, an accurate description of these buoyant patches and of their dynamics as well as a quantification of their contribution to the thinning dynamics is still lacking. In this paper, we visualize the patches, and show that their rising velocities and sizes are in good agreement with models respectively based on the balance of gravitational and surface viscous forces and on a Rayleigh-Taylor like instability [2, 3]. Our results suggest that, in an environment saturated in humidity, the drainage induced by their dynamics correctly describes the film drainage at the apex of the bubble within the experimental error bars. We conclude that the film thinning of soap bubbles is indeed controlled, to a large extent, by marginal regeneration in the absence of evaporation.
Molecule–molecule and atom–molecule collisions with ultracold RbCs molecules

Understanding ultracold collisions involving molecules is of fundamental importance for current experiments, where inelastic collisions typically limit the lifetime of molecular ensembles in optical traps. Here we present a broad study of optically trapped ultracold RbCs molecules in collisions with one another, in reactive collisions with Rb atoms, and in nonreactive collisions with Cs atoms. For experiments with RbCs alone, we show that by modulating the intensity of the optical trap, such that the molecules spend 75% of each modulation cycle in the dark, we partially suppress collisional loss of the molecules. This is evidence for optical excitation of molecule pairs mediated via sticky collisions. We find that the suppression is less effective for molecules not prepared in the spin-stretched hyperfine ground state. This may be due either to longer lifetimes for complexes in the dark or to laser-free decay pathways. For atom–molecule mixtures, RbCs + Rb and RbCs + Cs, we demonstrate that the rate of collisional loss of molecules scales linearly with the density of atoms. This indicates that, in both cases, the loss of molecules is rate-limited by two-body atom–molecule processes. For both mixtures, we measure loss rates that are below the thermally averaged universal limit.
BOSS: A Blockchain Off-State Sharing System

Blockchain has been applied to data sharing to ensure the integrity of data and chain of custody. Sharing big data such as large biomedical data files is a challenge to blockchain systems since the ledger is not designed to maintain big files, access control is an issue, and users may be dishonest. We call big data such as big files stored outside of a ledger that includes the blockchain and world state at a blockchain node as"off-state"and propose an off-state sharing protocol for a blockchain system to share big data between pairs of nodes. In our protocol, only encrypted files are transferred. The cryptographic key is stored in the world state in a secure way and can be accessed only by authorized parties. A receiver has to request the corresponding cryptographic key from the sender to decrypt such encrypted files. All requests are run through transactions to establish reliable chain of custody. We design and implement a prototypical blockchain off-state sharing system, BOSS, with Hyperledger Fabric. Extensive experiments were performed to validate the feasibility and performance of BOSS.