Miracle-dz/UniFAR-FTUs huggingface dataset:
ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing

To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL). In this paper, we propose a novel LfD+RL framework, namely Adaptive Conditional Neural Movement Primitives (ACNMP), that allows efficient policy improvement in novel environments and effective skill transfer between different agents. This is achieved through exploiting the latent representation learned by the underlying Conditional Neural Process (CNP) model, and simultaneous training of the model with supervised learning (SL) for acquiring the demonstrated trajectories and via RL for new trajectory discovery. Through simulation experiments, we show that (i) ACNMP enables the system to extrapolate to situations where pure LfD fails; (ii) Simultaneous training of the system through SL and RL preserves the shape of demonstrations while adapting to novel situations due to the shared representations used by both learners; (iii) ACNMP enables order-of-magnitude sample-efficient RL in extrapolation of reaching tasks compared to the existing approaches; (iv) ACNMPs can be used to implement skill transfer between robots having different morphology, with competitive learning speeds and importantly with less number of assumptions compared to the state-of-the-art approaches. Finally, we show the real-world suitability of ACNMPs through real robot experiments that involve obstacle avoidance, pick and place and pouring actions.
Guided Source Separation

State-of-the-art separation of desired signal components from a mixture is achieved using time-frequency masks or filters estimated by a deep neural network (DNN). The desired components, thereby, are typically defined at the time of training. Recent approaches allow determining the desired components during inference via auxiliary information. Auxiliary information is, thereby, extracted from a reference snippet of the desired components by a second DNN, which estimates a set of adaptive weights (AW) of the first DNN. However, the AW methods require the reference snippet and the desired signal to exhibit time-invariant signal characteristics (SCs) and have only been applied for speaker separation. We show that these AW methods can be used for universal source separation and propose an AW method to extract time-variant auxiliary information from the reference signal. That way, the SCs are allowed to vary across time in the reference and mixture. Applications where the reference and desired signal cannot be assigned to a specific class and vary over time require a time-dependency. An example is acoustic echo cancellation, where the reference is the loudspeaker signal. To avoid strong scaling between the estimate and the mixture, we propose the dual scale-invariant signal-to-distortion ratio in a TASNET inspired DNN as the training objective. We evaluate the proposed AW systems using a wide range of different acoustic conditions and show the scenario dependent advantages of time-variant over time-invariant AW.
Faster object tracking pipeline for real time tracking

Multi-object tracking (MOT) is a challenging practical problem for vision based applications. Most recent approaches for MOT use precomputed detections from models such as Faster RCNN, performing fine-tuning of bounding boxes and association in subsequent phases. However, this is not suitable for actual industrial applications due to unavailability of detections upfront. In their recent work, Wang et al. proposed a tracking pipeline that uses a Joint detection and embedding model and performs target localization and association in realtime. Upon investigating the tracking by detection paradigm, we find that the tracking pipeline can be made faster by performing localization and association tasks parallely with model prediction. This, and other computational optimizations such as using mixed precision model and performing batchwise detection result in a speed-up of the tracking pipeline by 57.8\% (19 FPS to 30 FPS) on FullHD resolution. Moreover, the speed is independent of the object density in image sequence. The main contribution of this paper is showcasing a generic pipeline which can be used to speed up detection based object tracking methods. We also reviewed different batch sizes for optimal performance, taking into consideration GPU memory usage and speed.
FlowCaps: Optical Flow Estimation with Capsule Networks For Action Recognition

Capsule networks (CapsNets) have recently shown promise to excel in most computer vision tasks, especially pertaining to scene understanding. In this paper, we explore CapsNet’s capabilities in optical flow estimation, a task at which convolutional neural networks (CNNs) have already outperformed other approaches. We propose a CapsNet-based architecture, termed FlowCaps, which attempts to a) achieve better correspondence matching via finer-grained, motion-specific, and more-interpretable encoding crucial for optical flow estimation, b) perform better-generalizable optical flow estimation, c) utilize lesser ground truth data, and d) significantly reduce the computational complexity in achieving good performance, in comparison to its CNN-counterparts.
MENP: an open-source MATLAB implementation of multipole expansion for nanophotonics

In modern nanophotonics, multipolar interference plays an indispensable role to realize novel optical devices represented by metasurfaces with unprecedented functionalities. Not only to engineer sub-wavelength structures that constitute such devices but also to realize and interpret unnatural phenomena in nanophotonics, a program that efficiently carries out multipole expansion is highly demanded. MENP is a MATLAB program for computation of multipole contributions to light scattering from current density distributions induced in nanophotonic resonators. The main purpose of MENP is to carry out post-processing of a rigid multipole expansion for full-field simulations which in principle provide the information of all near- and far-field interactions (e.g. as a total scattering cross section). MENP decomposes total scattering cross sections into partial ones due to electric and magnetic dipoles and higher-order terms based on recently developed exact multipole expansion formulas. We validate the program by comparing results for ideal and realistic nanospheres with those obtained with the Mie theory. We also demonstrate the potential of MENP for analysis of anapole states by calculating the multipole expansion under the long-wavelength approximation which enables us to introduce toroidal dipole moments.
Binary Matrix Factorisation via Column Generation

Identifying discrete patterns in binary data is an important dimensionality reduction tool in machine learning and data mining. In this paper, we consider the problem of low-rank binary matrix factorisation (BMF) under Boolean arithmetic. Due to the hardness of this problem, most previous attempts rely on heuristic techniques. We formulate the problem as a mixed integer linear program and use a large scale optimisation technique of column generation to solve it without the need of heuristic pattern mining. Our approach focuses on accuracy and on the provision of optimality guarantees. Experimental results on real world datasets demonstrate that our proposed method is effective at producing highly accurate factorisations and improves on the previously available best known results for 15 out of 24 problem instances.
Exterior-point Operator Splitting for Nonconvex Learning

In this paper, we present nonconvex exterior-point operator splitting algorithm (NExOS), a novel linearly convergent first-order algorithm tailored for constrained nonconvex learning problems. We consider the problem of minimizing a convex cost function over a nonconvex constraint set, where projection onto the constraint set is single-valued around points of interest. A wide range of nonconvex learning problems have this structure including (but not limited to) sparse and low-rank optimization problems. By exploiting the underlying geometry of the constraint set, NExOS finds a locally optimal point by solving a sequence of penalized problems with strictly decreasing penalty parameters. NExOS solves each penalized problem by applying an outer iteration operator splitting algorithm, which converges linearly to a local minimum of the corresponding penalized formulation under regularity conditions. Furthermore, the local minima of the penalized problems converge to a local minimum of the original problem as the penalty parameter goes to zero. We implement NExOS in the open-source Julia package NExOS.jl, which has been extensively tested on many instances from a wide variety of learning problems. We study several examples of well-known nonconvex learning problems, and we show that in spite of being general purpose, NExOS is able to compute high quality solutions very quickly and is competitive with specialized algorithms.
COVID-19 Patient Detection from Telephone Quality Speech Data

In this paper, we try to investigate the presence of cues about the COVID-19 disease in the speech data. We use an approach that is similar to speaker recognition. Each sentence is represented as super vectors of short term Mel filter bank features for each phoneme. These features are used to learn a two-class classifier to separate the COVID-19 speech from normal. Experiments on a small dataset collected from YouTube videos show that an SVM classifier on this dataset is able to achieve an accuracy of 88.6% and an F1-Score of 92.7%. Further investigation reveals that some phone classes, such as nasals, stops, and mid vowels can distinguish the two classes better than the others.
Accelerated design of arbitrary meta-optics using a computational graph.

Ultrathin meta-optics offer unmatched, multifunctional control of light. Next-generation optical technologies, however, demand unprecedented performance. This will likely require design algorithms surpassing the capability of human intuition. For the workhorse continuous adjoint method, this requires explicitly deriving gradients--sometimes infeasible for complex photonics problems. Existing techniques also comprise a patchwork of application-specific algorithms, each constrained in scope and scatterer type. We instead leverage algorithmic differentiation as used in artificial neural networks, treating photonic design parameters as trainable weights, optical sources as inputs, and encapsulating device performance in the loss function. By solving a complex, degenerate eigenproblem and formulating rigorous coupled-wave analysis as a computational graph, we support both arbitrary, parameterized scatterers and topology optimization. With iteration times below the minimum achievable with the continuous adjoint method, we generate multilayer, multifunctional, and aperiodic meta-optics. As an open-source platform adaptable to other algorithms and problems, we enable fast and flexible meta-optical design.
Deep Reinforcement Learning for RAN Optimization and Control

Due to the high variability of the traffic in the radio access network (RAN), fixed network configurations are not flexible enough to achieve optimal performance. Our vendors provide several settings of the eNodeB to optimize the RAN performance, such as media access control scheduler, loading balance, etc. But the detailed mechanisms of the eNodeB configurations are usually very complicated and not disclosed, not to mention the large key performance indicators (KPIs) space needed to be considered. These make constructing a simulator, offline tuning, or rule-based solutions difficult. We aim to build an intelligent controller without strong assumption or domain knowledge about the RAN and can run 24/7 without supervision. To achieve this goal, we first build a closed-loop control testbed RAN in a lab environment with one eNodeB provided by one of the largest wireless vendors and four smartphones. Next, we build a double Q network agent trained with the live feedback of the key performance indicators from the RAN. Our work proved the effectiveness of applying deep reinforcement learning to improve network performance in a real RAN network environment.
Time Synchronized State Estimation for Incompletely Observed Distribution Systems Using Deep Learning Considering Realistic Measurement Noise

Time-synchronized state estimation is a challenge for distribution systems because of limited real-time observability. This paper addresses this challenge by formulating a deep learning (DL)-based approach to perform unbalanced three-phase distribution system state estimation (DSSE). Initially, a data-driven approach for judicious measurement selection to facilitate reliable state estimation is provided. Then, a deep neural network (DNN) is trained to perform DSSE for systems that are incompletely observed by synchrophasor measurement devices (SMDs). Robustness of the proposed methodology is demonstrated by considering realistic measurement error models for SMDs. A comparative study of the DNN-based DSSE with classical linear state estimation indicates that the DL-based approach gives better accuracy with a significantly smaller number of SMDs.
Effects of changes in isotopic baselines on the evaluation of food web structure using isotopic functional indices

Background This study aimed to assess whether ecological inferences from isotopic functional indices (IFIs) are impacted by changes in isotopic baselines in aquatic food webs. We used sudden CO2-outgassing and associated shifts in DIC-δ13C brought by waterfalls as an excellent natural experimental set-up to quantify impacts of changes in algal isotopic baselines on ecological inferences from IFIs. Methods Carbon (δ13C) and nitrogen (δ15N) stable isotopic ratios of invertebrate communities sharing similar structure were measured at above- and below-waterfall sampling sites from five rivers and streams in Southern Quebec (Canada). For each sampled invertebrate community, the six Laymans IFIs were then calculated in the δ -space (δ13C vs. δ15N). Results As expected, isotopic functional richness indices, measuring the overall extent of community trophic space, were strongly sensitive to changes in isotopic baselines unlike other IFIs. Indeed, other IFIs were calculated based on the distribution of species within δ-space and were not strongly impacted by changes in the vertical or horizontal distribution of specimens in the δ-space. Our results highlighted that IFIs exhibited different sensitivities to changes in isotopic baselines, leading to potential misinterpretations of IFIs in river studies where isotopic baselines generally show high temporal and spatial variabilities. The identification of isotopic baselines and their associated variability, and the use of independent trophic tracers to identify the actual energy pathways through food webs must be a prerequisite to IFIs-based studies to strengthen the reliability of ecological inferences of food web structural properties.
State distinguishability under weak measurement and postselection: A unified system and device perspective

We quantify the disturbance of a quantum state undergoing a sequence of observations, and particularly focus on a weak measurement followed by post-selection and compare these results to the projective counterpart. Taking into account the distinguishability of both, the system and the device, we obtain the exact trade-off between the system state disturbance and the change of the device pointer state. We show that for particular post-selection procedures the coupling strength between the system and the device can be significantly reduced without loosing measurement sensitivity, which is directly transferred to a reduced state disturbance of the system. We observe that a weak measurement alone does not provide this advantage but only in combination with post-selection a significant improvement in terms of increased measurement sensitivity and reduced state disturbance is found. We further show that under realistic experimental conditions this state disturbance is small, whereas the exact post-selection probability is considerably larger than the approximate value given by the overlap of the initial and final state when neglecting the system state disturbance.
Detecting Outliers with Foreign Patch Interpolation

In medical imaging, outliers can contain hypo/hyper-intensities, minor deformations, or completely altered anatomy. To detect these irregularities it is helpful to learn the features present in both normal and abnormal images. However this is difficult because of the wide range of possible abnormalities and also the number of ways that normal anatomy can vary naturally. As such, we leverage the natural variations in normal anatomy to create a range of synthetic abnormalities. Specifically, the same patch region is extracted from two independent samples and replaced with an interpolation between both patches. The interpolation factor, patch size, and patch location are randomly sampled from uniform distributions. A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor. This encourages the network to learn what features to expect normally and to identify where foreign patterns have been introduced. The estimate of the interpolation factor lends itself nicely to the derivation of an outlier score. Meanwhile the pixel-wise output allows for pixel- and subject- level predictions using the same model.Our code is available at https://github.com/jemtan/FPI
Musical analysis of Stravinski's "The Rite of Spring" based on computational methods

Stravinski's "The Rite of Spring" is one of the most well-known pieces from the classical contemporary music repertoire. However, its analysis has aroused different opinions within its construction and compositional foundations. In this sense, I here proposed my own manual analysis and a computational approach which aims to find a similar analysis, giving the opportunity of discovering new possible points of view and supplying the current deficiencies of the Musi"c Computing common analysis systems.
Graph cuts always find a global optimum (with a catch)

We prove that the alpha-expansion algorithm for MAP inference always returns a globally optimal assignment for Markov Random Fields with Potts pairwise potentials, with a catch: the returned assignment is only guaranteed to be optimal in a small perturbation of the original problem instance. In other words, all local minima with respect to expansion moves are global minima to slightly perturbed versions of the problem. On "real-world" instances, MAP assignments of small perturbations of the problem should be very similar to the MAP assignment(s) of the original problem instance. We design an algorithm that can certify whether this is the case in practice. On several MAP inference problem instances from computer vision, this algorithm certifies that MAP solutions to all of these perturbations are very close to solutions of the original instance. These results taken together give a cohesive explanation for the good performance of "graph cuts" algorithms in practice. Every local expansion minimum is a global minimum in a small perturbation of the problem, and all of these global minima are close to the original solution.
Near-Optimal Sparsity-Constrained Group Testing: Improved Bounds and Algorithms

Recent advances in noiseless non-adaptive group testing have led to a precise asymptotic characterization of the number of tests required for high-probability recovery in the sublinear regime <inline-formula> <tex-math notation="LaTeX">$k = n^{\theta }$ </tex-math></inline-formula> (with <inline-formula> <tex-math notation="LaTeX">$\theta \in (0,1)$ </tex-math></inline-formula>), with <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> individuals among which <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> are infected. However, the required number of tests may increase substantially under real-world practical constraints, notably including bounds on the maximum number <inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> of tests an individual can be placed in, or the maximum number <inline-formula> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula> of individuals in a given test. While previous works have given recovery guarantees for these settings, significant gaps remain between the achievability and converse bounds. In this paper, we substantially or completely close several of the most prominent gaps. In the case of <inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>-divisible items, we show that the definite defectives (DD) algorithm coupled with a random regular design is asymptotically optimal in dense scaling regimes, and optimal to within a factor of e more generally; we establish this by strengthening both the best known achievability and converse bounds. In the case of <inline-formula> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula>-sized tests, we provide a comprehensive analysis of the regime <inline-formula> <tex-math notation="LaTeX">$\Gamma = \Theta (1)$ </tex-math></inline-formula>, and again establish a precise threshold proving the asymptotic optimality of SCOMP (a slight refinement of DD) equipped with a tailored pooling scheme. Finally, for each of these two settings, we provide near-optimal adaptive algorithms based on sequential splitting, and provably demonstrate gaps between the performance of optimal adaptive and non-adaptive algorithms.
The Budapest Amyloid Predictor and Its Applications

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.
An Alfvenic reconnecting plasmoid thruster

A new concept for the generation of thrust for space propulsion is introduced. Energetic thrust is generated in the form of plasmoids (confined plasma in closed magnetic loops) when magnetic helicity (linked magnetic field lines) is injected into an annular channel. Using a novel configuration of static electric and magnetic fields, the concept utilizes a current-sheet instability to spontaneously and continuously create plasmoids via magnetic reconnection. The generated low-temperature plasma is simulated in a global annular geometry using the extended magnetohydrodynamic model. Because the system-size plasmoid is an Alfvenic outflow from the reconnection site, its thrust is proportional to the square of the magnetic field strength and does not ideally depend on the mass of the ion species of the plasma. Exhaust velocities in the range of 20 to $500\ \textrm {km}\ \textrm {s}^{-1}$, controllable by the coil currents, are observed in the simulations.