Miracle-dz/UniFAR-FTUs huggingface dataset:
Nuclear level densities and γ-ray strength functions of Kr

The γ-ray strength function (γSF) and nuclear level density (NLD) have been extracted for the first time from inverse kinematic reactions with the Oslo Method. This novel technique allows measurements of these properties across a wide range of previously inaccessible nuclei. Proton-γ coincidence events from the d(Kr, pγ)Kr reaction were measured at iThemba LABS and the γSF and NLD in Kr obtained. The low-energy region of the γSF is compared to Shell Model calculations which suggest this region to be dominated by M1 strength. The γSF and NLD are used as input parameters to Hauser-Feshbach calculations to constrain (n, γ) cross sections of nuclei using the TALYS reaction code. These results are compared to Kr(n, γ) data from direct measurements. PACS. 21.10. Ma Level density – 25.45. Hi Transfer reactions – 21.60. Cs Shell model – 21.10. Pc Singleparticle levels and strength functions – 24.60. Dr Statistical compound-nucleus reactions
The Equivariant Tamagawa Number Conjecture for modular motives with coefficients in the Hecke algebra

We propose a formulation of the Equivariant Tamagawa Number Conjecture for modular motives with coefficients in universal deformation rings and Hecke algebras; something which seems to have been heretofore missing because the complexes of Galois cohomology required were not known to be perfect. We show that the fundamental line of this conjecture satisfies the expected compatibility property at geometric points (more precisely at the points satisfying the Weight-Monodromy conjecture) and is compatible with level-lowering and level-raising. Combining these properties with the methods of Euler and Taylor-Wiles systems, we prove a significant part of the ETNC with coefficients in Hecke algebras for motives attached to modular forms and the full conjecture when the main conjecture of Iwasawa theory for modular forms is known to be true.
A Tale of Two Trees: One Writes, and Other Reads. {Optimized Oblivious Accesses to Large-Scale Blockchains}

The Bitcoin network has offered a new way of securely performing financial transactions over the insecure network. Nevertheless, this ability comes with the cost of storing a large (distributed) ledger, which has become unsuitable for personal devices of any kind. Although the simplified payment verification (SPV) clients can address this storage issue, a Bitcoin SPV client has to rely on other Bitcoin nodes to obtain its transaction history and the current approaches offer no privacy guarantees to the SPV clients. This work presents $T^3$, a trusted hardware-secured Bitcoin full client that supports efficient oblivious search/update for Bitcoin SPV clients without sacrificing the privacy of the clients. In this design, we leverage the trusted execution and attestation capabilities of a trusted execution environment (TEE) and the ability to hide access patterns of oblivious random access memory (ORAM) to protect SPV clients' requests from a potentially malicious server. The key novelty of $T^3$ lies in the optimizations introduced to conventional ORAM, tailored for expected SPV client usages. In particular, by making a natural assumption about the access patterns of SPV clients, we are able to propose a two-tree ORAM construction that overcomes the concurrency limitation associated with traditional ORAMs. We have implemented and tested our system using the current Bitcoin Unspent Transaction Output database. Our experiment shows that the system is feasible to be deployed in practice while providing strong privacy and security guarantees to Bitcoin SPV clients.
A Tale of Two Trees: One Writes, and Other Reads. Optimized Oblivious Accesses to Large-Scale Blockchains

The Bitcoin network has offered a new way of securely performing financial transactions over the insecure network. Nevertheless, this ability comes with the cost of storing a large (distributed) ledger, which has become unsuitable for personal devices of any kind. Although the simplified payment verification (SPV) clients can address this storage issue, a Bitcoin SPV client has to rely on other Bitcoin nodes to obtain its transaction history and the current approaches offer no privacy guarantees to the SPV clients. This work presents $T^3$, a trusted hardware-secured Bitcoin full client that supports efficient oblivious search/update for Bitcoin SPV clients without sacrificing the privacy of the clients. In this design, we leverage the trusted execution and attestation capabilities of a trusted execution environment (TEE) and the ability to hide access patterns of oblivious random access memory (ORAM) to protect SPV clients' requests from a potentially malicious server. The key novelty of $T^3$ lies in the optimizations introduced to conventional ORAM, tailored for expected SPV client usages. In particular, by making a natural assumption about the access patterns of SPV clients, we are able to propose a two-tree ORAM construction that overcomes the concurrency limitation associated with traditional ORAMs. We have implemented and tested our system using the current Bitcoin Unspent Transaction Output database. Our experiment shows that the system is feasible to be deployed in practice while providing strong privacy and security guarantees to Bitcoin SPV clients.
Evict Queue BTC Daemon Bitcoin Network SPV Clients 1 2 3 5 6 1 2 3 5 8 Secure Channel Unsecure Channel # # Update Steps Read Steps

The Bitcoin network has offered a new way of securely performing financial transactions over the insecure network. Nevertheless, this ability comes with the cost of storing a large (distributed) ledger, which has become unsuitable for personal devices of any kind. Although the simplified payment verification (SPV) clients can address this storage issue, a Bitcoin SPV client has to rely on other Bitcoin nodes to obtain its transaction history and the current approaches offer no privacy guarantees to the SPV clients. This work presents T , a trusted hardware-secured Bitcoin full client that supports efficient and scalable oblivious search/update for Bitcoin SPV clients without sacrificing the privacy of the clients. In this design, we leverage the trusted execution and attestation capabilities of a trusted execution environment (TEE) and the ability to hide access patterns of oblivious random access memory (ORAM) to protect SPV clients’ requests from a potentially malicious server. The key novelty of T 3 lies in the optimizations introduced to conventional oblivious random access memory (ORAM), tailored for expected SPV client usages. In particular, by making a natural assumption about the access patterns of SPV clients, we are able to propose a two-tree ORAM construction that overcomes the concurrency limitation associated with traditional ORAMs. We have implemented and tested our system using the current Bitcoin Unspent Transaction Output database. Our experiment shows that the system is highly efficient in practice while providing strong privacy and security guarantees to Bitcoin SPV clients.
Transfer Reinforcement Learning for 5G-NR mm-Wave Networks

In this paper, we aim at interference mitigation in 5G millimeter-Wave (mm-Wave) communications by employing beamforming and Non-Orthogonal Multiple Access (NOMA) techniques with the aim of improving network's aggregate rate. Despite the potential capacity gains of mm-Wave and NOMA, many technical challenges might hinder that performance gain. In particular, the performance of Successive Interference Cancellation (SIC) diminishes rapidly as the number of users increases per beam, which leads to higher intra-beam interference. Furthermore, intersection regions between adjacent cells give rise to inter-beam inter-cell interference. To mitigate both interference levels, optimal selection of the number of beams in addition to best allocation of users to those beams is essential. In this paper, we address the problem of joint user-cell association and selection of number of beams for the purpose of maximizing the aggregate network capacity. We propose three machine learning-based algorithms; transfer Q-learning (TQL), Q-learning, and Best SINR association with Density-based Spatial Clustering of Applications with Noise (BSDC) algorithms and compare their performance under different scenarios. Under mobility, TQL and Q-learning demonstrate 12% rate improvement over BSDC at the highest offered traffic load. For stationary scenarios, Q-learning and BSDC outperform TQL, however TQL achieves about 29% convergence speedup compared to Q-learning.
Performance comparison of optical flow and block matching methods in shearing and rotating models

Accurate estimation of myocardial motion based on ultrasound imaging is of great value for evaluation of cardiac function. Typically, myocardium undergoes complex motion and deformation including shear deformation and rotation. Thus a compression model is insufficient for investigating the performance of different algorithms. In this study, simulated shearing and rotating models are used to study the performance of optical flow (OF) and block matching (BM) methods based on ultrasound radio-frequency (RF) data. A deforming model was simulated with applied axial shear strains of 2- 6%, respectively. In addition, a rotating model was simulated with rotation angles of 0.5°-4°, respectively. Axial strains of 0%, 1% and 2% were also applied to these two models to study the influence of applied strain on the estimation of axial shear strain and rotation. To quantify the estimation performance, the root mean square error (RMSE) was used as the evaluation criterion. The results show that OF has lower RMSEs of the estimated displacement, strain and rotation angle than BM, especially at large axial shear strains and rotation angles. For the shearing model, the RMSEs of axial strains, lateral strains, and axial shear strains are reduced by up to 95.5%, 70.3% and 90.0%, respectively. For the rotating model, the RMSEs of axial strains, lateral strains, and rotation angles are reduced by up to 96.9%, 93.4% and 89.7%, respectively. OF is proved to outperform BM and thus is recommended to be used for shear strain and rotation estimation. The validations of phantom and in-vivo experiments are still required.
Scaling parameters selection principle for the scaled unscented Kalman filter

The paper deals with the state estimation of the widely used scaled unscented Kalman filter (UKF). In particular, the stress is laid on the scaling parameters selection principle for the scaled UKF. Several problems caused by recommended constant scaling parameters are highlighted. On the basis of the analyses, an effective scaled UKF is proposed with self-adaptive scaling parameters, which is easy to understand and implement in engineering. Two typical strong nonlinear examples are given and their simulation results show the effectiveness of the proposed principle and algorithm.
Mini-Batch Metropolis–Hastings With Reversible SGLD Proposal

Abstract Traditional Markov chain Monte Carlo (MCMC) algorithms are computationally intensive and do not scale well to large data. In particular, the Metropolis–Hastings (MH) algorithm requires passing over the entire dataset to evaluate the likelihood ratio in each iteration. We propose a general framework for performing MH-MCMC using mini-batches of the whole dataset and show that this gives rise to approximately a tempered stationary distribution. We prove that the algorithm preserves the modes of the original target distribution and derive an error bound on the approximation with mild assumptions on the likelihood. To further extend the utility of the algorithm to high-dimensional settings, we construct a proposal with forward and reverse moves using stochastic gradient and show that the construction leads to reasonable acceptance probabilities. We demonstrate the performance of our algorithm in both low dimensional models and high dimensional neural network applications. Particularly in the latter case, compared to popular optimization methods, our method is more robust to the choice of learning rate and improves testing accuracy. Supplementary materials for this article are available online.
REAL-X—Robot Open-Ended Autonomous Learning Architecture: Building Truly End-to-End Sensorimotor Autonomous Learning Systems

Open-ended learning is a core research field of developmental robotics and AI aiming to build learning machines and robots that can autonomously acquire knowledge and skills incrementally as infants. The first contribution of this work is to highlight the challenges posed by the previously proposed benchmark “REAL competition” fostering the development of truly open-ended learning robots. The benchmark involves a simulated camera-arm robot that: 1) in a first “intrinsic phase” acquires sensorimotor competence by autonomously interacting with objects and 2) in a second “extrinsic phase” is tested with tasks, unknown in the intrinsic phase, to measure the quality of previously acquired knowledge. The benchmark requires the solution of multiple challenges usually tackled in isolation, in particular exploration, sparse-rewards, object learning, generalization, task/goal self-generation, and autonomous skill learning. As a second contribution, the work presents a “REAL-X” architecture. Different systems implementing the architecture can solve different versions of the benchmark progressively releasing initial simplifications. The REAL-X systems are based on a planning approach that dynamically increases abstraction and on intrinsic motivations to foster exploration. Some systems achieves a good performance level in very demanding conditions. Overall, the REAL benchmark is shown to represent a valuable tool for studying open-ended learning in its hardest form.
Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion

Despite the widespread use of supervised deep learning methods for affect recognition from speech, they are severely limited by the lack of a sufficient amount of labelled speech data. Considering the abundant availability of unlabelled data, this paper proposed a semi-supervised model that can effectively utilise the unlabelled data in multi-task learning way in order to improve the performance of speech emotion recognition. The proposed model adversarialy learns a shared representation for two auxiliary tasks along with emotion identification as the main task. We consider speaker and gender identification as auxiliary tasks in order to operate the model on any large audio corpus. We demonstrate that in a scenario with limited labelled training samples, one can significantly improve the performance of a supervised classification task by simultaneously training with additional auxiliary tasks having an availability of large amount of data. The proposed model is rigorously evaluated for both categorical and dimensional emotion classification tasks. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on two publicly available datasets.
An evanescent field absorption gas sensor at mid-IR 3.39 μm wavelength

Abstract Trace gases such as H2O, CO, CO2, NO, N2O, NO2 and CH4 strongly absorb in the mid-IR (>2.5 μm) spectral region due to their fundamental rotational and vibrational transitions. CH4 gas is relatively non-toxic, however, it is extremely explosive when mixed with other chemicals in levels as low as 5% and it can cause death by asphyxiation. In this work, we propose a silicon strip waveguide at 3.39 μm for CH4 gas sensing based on the evanescent field absorption. These waveguides can provide the highest evanescent field ratio (EFR)>55% with adequate dimensions. Moreover, EFR and sensitivity of the sensor are highly dependent on the length of the waveguide up to a certain limit. Therefore, it is always a compromise between the length of the waveguide and EFR in order to obtain greater sensitivity.
HINDSIGHT TRUST REGION POLICY OPTIMIZATION

As reinforcement learning continues to drive machine intelligence beyond its conventional boundary, unsubstantial practices in sparse reward environment severely limit further applications in a broader range of advanced fields. Motivated by the demand for an effective deep reinforcement learning algorithm that accommodates sparse reward environment, this paper presents Hindsight Trust Region Policy Optimization (HTRPO), a method that efficiently utilizes interactions in sparse reward conditions to optimize policies within trust region and, in the meantime, maintains learning stability. Firstly, we theoretically adapt the TRPO objective function, in the form of the expected return of the policy, to the distribution of hindsight data generated from the alternative goals. Then, we apply Monte Carlo with importance sampling to estimate KL-divergence between two policies, taking the hindsight data as input. Under the condition that the distributions are sufficiently close, the KL-divergence is approximated by another f -divergence. Such approximation results in the decrease of variance and alleviates the instability during policy update. Experimental results on both discrete and continuous benchmark tasks demonstrate that HTRPO converges significantly faster than previous policy gradient methods. It achieves effective performances and high data-efficiency for training policies in sparse reward environments.
Distributed Compressive Sensing

Abstract : Compressive sensing is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery. In this paper we introduce a new theory for distributed compressive sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intra- and inter-signal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. Our theoretical contribution is to characterize the fundamental performance limits of DCS recovery for jointly sparse signal ensembles in the noiseless measurement setting; our result connects single-signal, joint, and distributed (multi-encoder) compressive sensing. To demonstrate the efficacy of our framework and to show that additional challenges such as computational tractability can be addressed, we study in detail three example models for jointly sparse signals. For these models, we develop practical algorithms for joint recovery of multiple signals from incoherent projections. In two of our three models, the results are asymptotically best-possible, meaning that both the upper and lower bounds match the performance of our practical algorithms. Moreover, simulations indicate that the asymptotics take effect with just a moderate number of signals. DCS is immediately applicable to a range of problems in sensor arrays and networks.
Understanding the dynamics of biological and neural oscillator networks through mean-field reductions: a review

Many biological and neural systems can be seen as networks of interacting periodic processes. Importantly, the function of these networks depends on the collective dynamics: Synchrony of oscillations is probably amongst the most prominent examples of collective behavior and has been associated both with function and dysfunction. Understanding how network structure and interactions, as well as the microscopic properties of individual units, shape the emergent collective dynamics is critical to find factors that lead to malfunction. However, many biological systems such as the brain consist of a large number of dynamical units. Hence, their analysis has either primarily relied on simplified heuristic models on a coarse scale, or the analysis comes at a huge computational cost. Here we review recently introduced approaches - commonly known as the Ott-Antonsen and Watanabe-Strogatz reductions - that allow to simplify the analysis by bridging small and large scales: To obtain reduced model equations, a subpopulation in an oscillator network is replaced by a single variable that describes its collective state exactly. The resulting equations are next-generation models: Rather than being heuristic, they capture microscopic properties of the underlying system. At the same time, they are sufficiently simple to analyze without great computational effort. In the last decade, these reduction methods have become instrumental to understand how network structure and interaction shapes the collective dynamics and the emergence of synchrony. We review this progress based on concrete examples and outline possible limitations. Finally, we discuss how linking the reduced models with experimental data can guide the way towards the development of new treatment approaches, for example, for neural disease.
2019 IEEE Conference on Games (CoG)

Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics. This simultaneous focus on quality and diversity with explicit metrics sets QD algorithms apart from standard single- and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivity. In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; we discuss these and propose challenges for future work.
Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)

In this article, we describe the design and implementation of a publicly accessible dermatology image analysis benchmark challenge. The goal of the challenge is to sup- port research and development of algorithms for automated diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images. The challenge was divided into sub-challenges for each task involved in image analysis, including lesion segmentation, dermoscopic feature detection within a lesion, and classification of melanoma. Training data included 900 images. A separate test dataset of 379 images was provided to measure resultant performance of systems developed with the training data. Ground truth for both training and test sets was generated by a panel of dermoscopic experts. In total, there were 79 submissions from a group of 38 participants, making this the largest standardized and comparative study for melanoma diagnosis in dermoscopic images to date. While the official challenge duration and ranking of participants has concluded, the datasets remain available for further research and development.
Learned Initializations for Optimizing Coordinate-Based Neural Representations: Supplemental Material

We found that modifying the weight initialization for these coordinate-based networks drastically changed their convergence behavior during test-time optimization. As a result, we tuned the optimization method and hyperparameters for each part of each experiment (using held-out validation sets) in order to provide the fairest possible comparison and to not bias the results against the non-metalearned initializations. For example, we often found that SGD outperformed Adam when doing test-time optimization using meta-learned initializations, but that Adam was significantly better than SGD with a standard random initialization. All experiments are implemented in JAX [1]. Each experiment is trained on either a single NVIDIA V100, 2080 Ti, or 3080 Ti. In all cases where the Adam optimizer [5] is used, we keep the standard parameter choices for β1 = 0.9, β2 = 0.999, = 10−8.