Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Learning Ensemble Framework for Off-Nadir Geocentric Pose Prediction

Computational methods to accelerate natural disaster response include change detection, map alignment, and vision-aided navigation. Current software functions optimally only on near-nadir images, though off-nadir images are often the first sources of information following a natural disaster. The use of off-nadir images for the aforementioned tasks requires the computation of geocentric pose, which is an aerial vehicle’s spatial orientation with respect to gravity. This study proposes a deep learning ensemble framework to predict geocentric pose using 5,923 near-nadir and off-nadir RGB satellite images of cities worldwide. First, a U-Net Fully Convolutional Neural Network predicts the pixel-wise above-ground elevation mask of the RGB images. Then, the elevation masks are concatenated with the RGB images to form four-channel inputs fed into a second convolutional model, which predicts orientation angle and magnification scale. A performance accuracy of R2 = 0.917 significantly outperforms previous methodologies. In addition, outlier removal is performed through supervised interpolation, and a sensitivity analysis of elevation masks is conducted to gauge the usefulness of data features, motivating future avenues of feature engineering. The high-accuracy software built in this study contributes to mapping and navigation procedures for effective disaster response to save lives.
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization

Federated recommender system (FRS), which enables many local devices to train a shared model jointly without transmitting local raw data, has become a prevalent recommendation paradigm with privacy-preserving advantages. However, previous work on FRS performs similarity search via inner product in continuous embedding space, which causes an efficiency bottleneck when the scale of items is extremely large. We argue that such a scheme in federated settings ignores the limited capacities in resource-constrained user devices (i.e., storage space, computational overhead, and communication bandwidth), and makes it harder to be deployed in large-scale recommender systems. Besides, it has been shown that transmitting local gradients in real-valued form between server and clients may leak users’ private information. To this end, we propose a lightweight federated recommendation framework with privacy-preserving matrix factorization, LightFR, that is able to generate high-quality binary codes by exploiting learning to hash technique under federated settings, and thus enjoys both fast online inference and economic memory consumption. Moreover, we devise an efficient federated discrete optimization algorithm to collaboratively train model parameters between the server and clients, which can effectively prevent real-valued gradient attacks from malicious parties. Through extensive experiments on four real-world datasets, we show that our LightFR model outperforms several state-of-the-art FRS methods in terms of recommendation accuracy, inference efficiency, and data privacy.
EmuNoC: Hybrid Emulation for Fast and Flexible Network-on-Chip Prototyping on FPGAs

Networks-on-Chips (NoCs) recently became widely used, from multi-core CPUs to edge-AI accelerators. Emulation on FPGAs promises to accelerate their RTL modeling compared to slow simulations. However, realistic test stimuli are challenging to generate in hardware for diverse applications. In other words, both a fast and flexible design framework is required. The most promising solution is hybrid emulation, in which parts of the design are simulated in software, and the other parts are emulated in hardware. This paper proposes a novel hybrid emulation framework called EmuNoC. We introduce a clock-synchronization method and software-only packet generation that improves the emulation speed by 36.3 × to 79.3 × over state-of-the-art frameworks while retaining the flexibility of a pure-software interface for stimuli simulation. We also increased the area efficiency to model up to an NoC with 169 routers on a single FPGA, while previous frameworks only achieved 64 routers.
Walk the Random Walk: Learning to Discover and Reach Goals Without Supervision

Learning a diverse set of skills by interacting with an environment without any external supervision is an important challenge. In particular, obtaining a goal-conditioned agent that can reach any given state is useful in many applications. We propose a novel method for training such a goal-conditioned agent without any external rewards or any domain knowledge. We use random walk to train a reachability network that predicts the similarity between two states. This reachability network is then used in building goal memory containing past observations that are diverse and well-balanced. Finally, we train a goal-conditioned policy network with goals sampled from the goal memory and reward it by the reachability network and the goal memory. All the components are kept updated throughout training as the agent discovers and learns new goals. We apply our method to a continuous control navigation and robotic manipulation tasks.
On Classifying Images using Quantum Image Representation

Quantum Image Representation is researched from last few years, and more active in the recent past. Set to examine how these representations would be useful for Image Processing in a quantum way, we considered the Quantum Machine Learning problem of image classification in this paper. Encouraging results have been provided on classifying benchmark datasets of grayscale and colour images using two different classifiers and their combination. Multiclass classification performance has also been tested.
Scalable Multiple Network Inference with the Joint Graphical Horseshoe

Network models are useful tools for modelling complex associations. If a Gaussian graphical model is assumed, conditional independence is determined by the non-zero entries of the inverse covariance (precision) matrix of the data. The Bayesian graphical horseshoe estimator provides a robust and flexible framework for precision matrix inference, as it introduces local, edge-specific parameters which prevent over-shrinkage of non-zero off-diagonal elements. However, for many applications such as statistical omics, the current implementation based on Gibbs sampling becomes computationally inefficient or even unfeasible in high dimensions. Moreover, the graphical horseshoe has only been formulated for a single network, whereas interest has grown in the network analysis of multiple data sets that might share common structures. We propose (i) a scalable expectation conditional maximisation (ECM) algorithm for obtaining the posterior mode of the precision matrix in the graphical horseshoe, and (ii) a novel joint graphical horseshoe estimator, which borrows information across multiple related networks to improve estimation. We show, on both simulated and real omics data, that our single-network ECM approach is more scalable than the existing graphical horseshoe Gibbs implementation, while achieving the same level of accuracy. We also show that our joint-network proposal successfully leverages shared edge-specific information between networks while still retaining differences, outperforming state-of-the-art methods at any level of network similarity.
Algorithms for 2-connected network design and flexible Steiner trees with a constant number of terminals

The $k$-Steiner-2NCS problem is as follows: Given a constant $k$, and an undirected connected graph $G = (V,E)$, non-negative costs $c$ on $E$, and a partition $(T, V-T)$ of $V$ into a set of terminals, $T$, and a set of non-terminals (or, Steiner nodes), where $|T|=k$, find a minimum-cost two-node connected subgraph that contains the terminals. We present a randomized polynomial-time algorithm for the unweighted problem, and a randomized PTAS for the weighted problem. We obtain similar results for the $k$-Steiner-2ECS problem, where the input is the same, and the algorithmic goal is to find a minimum-cost two-edge connected subgraph that contains the terminals. Our methods build on results by Bj\"orklund, Husfeldt, and Taslaman (ACM-SIAM SODA 2012) that give a randomized polynomial-time algorithm for the unweighted $k$-Steiner-cycle problem; this problem has the same inputs as the unweighted $k$-Steiner-2NCS problem, and the algorithmic goal is to find a minimum-size simple cycle $C$ that contains the terminals ($C$ may contain any number of Steiner nodes).
Determination of elastic parameters of lipid membranes from simulation under varied external pressure.

Many cellular processes such as endocytosis, exocytosis, and vesicle trafficking involve membrane deformations, which can be analyzed in the framework of the elastic theories of lipid membranes. These models operate with phenomenological elastic parameters. A connection between these parameters and the internal structure of lipid membranes can be provided by three-dimensional (3D) elastic theories. Considering a membrane as a 3D layer, Campelo et al. [F. Campelo et al., Adv. Colloid Interface Sci. 208, 25 (2014)10.1016/j.cis. 2014.01. 018] developed a theoretical basis for the calculation of elastic parameters. In this work we generalize and improve this approach by considering a more general condition of global incompressibility instead of local incompressibility. Crucially, we find an important correction to the theory of Campelo et al., which if not taken into account leads to a significant miscalculation of elastic parameters. With the total volume conservation taken into account, we derive an expression for the local Poisson's ratio, which determines how the local volume changes upon stretching and permits a more precise determination of elastic parameters. Also, we substantially simplify the procedure by calculating the derivatives of the moments of the local tension with respect to stretching instead of calculating the local stretching modulus. We obtain a relation between the Gaussian curvature modulus as a function of stretching and the bending modulus, showing that these two elastic parameters are not independent, as was previously assumed. The proposed algorithm is applied to membranes composed of pure dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylcholine (DOPC), and their mixture. The following elastic parameters of these systems are obtained: the monolayer bending and stretching moduli, spontaneous curvature, neutral surface position, and local Poisson's ratio. It is shown that the bending modulus of the DPPC/DOPC mixture follows a more complex trend than predicted by the classical Reuss averaging, which is often employed in theoretical frameworks.
VRKitchen2.0-IndoorKit: A Tutorial for Augmented Indoor Scene Building in Omniverse

lacks a platform that can easily serve both indoor scene synthesis and benchmarking with various algorithms. Meanwhile, computer need a toolkit for implementing advanced synthesizing techniques. To facilitate the study of indoor scene building methods and their potential robotics applications, we introduce NDOOR IT : a built-in toolkit for N VIDIA that provides ﬂexible pipelines for indoor scene building, scene randomizing, and animation controls. Be-sides, combining Python coding in the animation software I NDOOR K IT assists researchers in creating real-time training and controlling for avatars and robotics. The source code for this toolkit is available at https://github.com/realvcla/VRKitchen2.0-Tutorial , and the tutorial along with the toolkit is available at https://vrkitchen20-tutorial.readthedocs. io/en/latest/ .
Online Event Selection for Mu3e using GPUs

In the search for physics beyond the Standard Model the Mu3e experiment tries to observe the lepton flavor violating decay μ+ → e+e–e+. By observing the decay products of 1 • 108μ/s it aims to either observe the process, or set a new upper limit on its estimated branching ratio. The high muon rates result in high data rates of 80 Gbps, dominated by data produced through background processes. We present the Online Event Selection, a three step algorithm running on the graphics processing units (GPU) of the 12 Mu3e filter farm computers.By using simple and fast geometric selection criteria, the algorithm first reduces the amount of possible event candidates to below 5% of the initial set. These candidates are then used to reconstruct full particle tracks, correctly reconstructing over 97% of signal tracks. Finally a possible decay vertex is reconstructed using simple geometric considerations instead of a full reconstruction, correctly identifying over 94% of signal events.We also present a full implementation of the algorithm, fulfilling all performance requirements at the targeted muon rate and successfully reducing the data rate by a factor of 200.
Homomorphic Sortition - Secret Leader Election for Blockchain

In a secret single leader election protocol (SSLE), one of the system members is chosen as a leader and no other member can know who the leader is, unless she decides to reveal herself. Leader-election oracles are known to be instrumental in designing efficient consensus protocols, and one can expect that secret leader election may help in developing long-lived blockchain systems that are secure with respect to the adaptive adversary. In this paper, we introduce secret leader sortition (SLS) that we believe to perfectly match progress and security expectations of proof-of-stake (PoS) blockchains. An SLS protocol produces an unpredictable permutation of system members that can be fed to an accompanying blockchain protocol in order to ensure deterministic finality . We describe Homomorphic Sortition, an SLS protocol based on Threshold Fully Homomorphic Encryption (ThFHE). An interesting novelty of our protocol is a cryptographic hash function evaluation under ThFHE and, somewhat surprisingly, we show that all ThFHE stimuli required for our protocol can be efficiently and proactively constructed in a parallelizable fashion. In contrast to existing secret leader election protocols, our SLS solution is purely asynchronous and it fairly leverages the stake distribution among the system members: the probability distribution for each position in the permutation is proportional to the stakes of remaining candidates. As we demonstrate, our SLS protocol seamlessly partners with a large family of PoS blockchain implementations.
ReuseKNN: Neighborhood Reuse for Privacy-Aware Recommendations

User-based KNN recommender systems ( UserKNN ) utilize the rating data of a target user’s 𝑘 nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors since their rating data might be exposed to other users or malicious parties. To reduce this risk, existing work applies differential privacy by adding randomness to the neighbors’ ratings, which reduces the accuracy of UserKNN . In this work, we introduce ReuseKNN , a novel privacy-aware recommender system. The main idea is to identify small but highly reusable neighborhoods so that (i) only a minimal set of users requires protection with differential privacy, and (ii) most users do not need to be protected with differential privacy, since they are only rarely exploited as neighbors. In our experiments on five diverse datasets, we make two key observations: Firstly, ReuseKNN requires significantly smaller neighborhoods, and thus, fewer neighbors need to be protected with differential privacy compared to traditional UserKNN . Secondly, despite the small neighborhoods, ReuseKNN outperforms UserKNN and a fully differentially private approach in terms of accuracy. Overall, ReuseKNN ’s recommendation process leads to significantly less privacy risk for users than in the case of UserKNN .
Cosmic microwave background anisotropies generated by cosmic strings with small-scale structure

We study the impact of kinks on the cosmic microwave background (CMB) anisotropies generated by cosmic string networks. To do so, we extend the Unconnected Segment Model to describe the stress-energy tensor of a network of cosmic strings with kinks and implement this extension in CMBACT to compute the CMB anisotropies generated by these wiggly string networks. Our results show that the inclusion of kinks leads, in general, to an enhancement of the temperature and polarization angular power spectra, when compared to those generated by cosmic string networks without small-scale structure with the same energy density, on scales corresponding to the distance between kinks. This enhancement, that is more prominent in the case of the temperature anisotropies, is essentially caused by a significant increase of the vector-mode anisotropies, since kinks, due to their shape, generate vortical motions of matter — a phenomenon that is not taken into account when resorting to an effective description of wiggly cosmic strings. These results, although derived using a simplified string configuration in which the interkink distance and their sharpness remain fixed, seem to indicate that the explicit inclusion of kinks may be essential to perform more accurate predictions of the CMB anisotropies generated by cosmic strings on small scales.
Real-Time Online Skeleton Extraction and Gesture Recognition on Pepper

: We present a multi-stage pipeline for simple gesture recognition. The novelty of our approach is the association of different technologies, resulting in the ﬁrst real-time system as of now to conjointly extract skeletons and recognise gesture on a Pepper robot. For this task, Pepper has been augmented with an embedded GPU for running deep CNNs and a ﬁsh-eye camera to capture whole scene interaction. We show in this article that real-case scenarios are challenging, and the state-of-the-art approaches hardly deal with unknown human gestures. We present here a way to handle such cases.
A Geometric Method for Improved Uncertainty Estimation in Real-time

Machine learning classifiers are probabilistic in nature, and thus inevitably involve uncertainty. Predicting the probability of a specific input to be correct is called uncertainty (or confidence) estimation and is crucial for risk management. Post-hoc model calibrations can improve models' uncertainty estimations without the need for retraining, and without changing the model. Our work puts forward a geometric-based approach for uncertainty estimation. Roughly speaking, we use the geometric distance of the current input from the existing training inputs as a signal for estimating uncertainty and then calibrate that signal (instead of the model's estimation) using standard post-hoc calibration techniques. We show that our method yields better uncertainty estimations than recently proposed approaches by extensively evaluating multiple datasets and models. In addition, we also demonstrate the possibility of performing our approach in near real-time applications. Our code is available at our Github https://github.com/NoSleepDeveloper/Geometric-Calibrator.
Unseen Object 6D Pose Estimation: A Benchmark and Baselines

Estimating the 6D pose for unseen objects is in great demand for many real-world applications. However, current state-of-the-art pose estimation methods can only handle objects that are previously trained. In this paper, we propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing. We collect a dataset with both real and synthetic images and up to 48 unseen objects in the test set. In the mean while, we propose a new metric named Infimum ADD (IADD) which is an invariant measurement for objects with different types of pose ambiguity. A two-stage baseline solution for this task is also provided. By training an end-to-end 3D correspondences network, our method finds corresponding points between an unseen object and a partial view RGBD image accurately and efficiently. It then calculates the 6D pose from the correspondences using an algorithm robust to object symmetry. Extensive experiments show that our method outperforms several intuitive baselines and thus verify its effectiveness. All the data, code and models will be made publicly available. Project page: www.graspnet. net/unseen6d
Zipper Entanglement Renormalization for Free Fermions

Entanglement renormalization refers to a sequence of real-space coarse-graining transformations in which short-range entanglement on successively longer length scales are distilled out. In this work, we introduce a state-based approach, “zipper entanglement renormalization” (ZER), for free-fermion systems. The name derives from a unitary we construct at every renormalization step, dubbed the zipper, which unzips the state into an approximate tensor product between a short-range entangled state and a renormalized one carrying the longer-range entanglement. By successively performing ZER on the renormalized states, we obtain a unitary transformation of the input state into a state that is approximately factorized over the emergent renormalization spacetime. As a demonstration, we apply ZER to one-dimensional models and show that it eﬃciently disentangles the ground states of the Su-Schrieﬀer-Heeger model, a scale-invariant critical state, as well as a more general gapless state with two sets of Fermi points.
Analysis of account behaviors in Ethereum during an economic impact event

One of the main events that involve the world economy in 2022 is the conﬂict between Russia and Ukraine. This event offers a rare opportunity to analyze how events of this magnitude can reﬂect the use of cryptocurrencies. This work aims to investigate the behavior of accounts and their transactions on the Ethereum cryptocurrency during this event. To this end, we collected all transactions that occurred two weeks before and two weeks after the beginning of the conﬂict, organized into two groups: the collection of the accounts involved in these transactions and the subset of these ones that interacted with a service in Ethereum, called Flashbots Auction. We modeled temporal graphs where each node represents an account, and each edge represents a transaction between two accounts. Then, we analyzed the behavior of these accounts with graph metrics for both groups during each observed week. The results showed changes in the behavior and activity of users and their accounts, as well as variations in the daily volume of transactions.