Miracle-dz/UniFAR-FTUs huggingface dataset:
SAIC_Cambridge-HuPBA-FBK Submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021

This report presents the technical details of our submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021. To participate in the challenge we deployed spatio-temporal feature extraction and aggregation models we have developed recently: GSF and XViT. GSF is an efficient spatio-temporal feature extracting module that can be plugged into 2D CNNs for video action recognition. XViT is a convolution free video feature extractor based on transformer architecture. We design an ensemble of GSF and XViT model families with different backbones and pretraining to generate the prediction scores. Our submission, visible on the public leaderboard, achieved a top-1 action recognition accuracy of 44.82%, using only RGB.
Autonomous aerial robotics for package delivery: A technical review

Small unmanned aerial vehicles (UAVs) have gained significant interest in the last decade. More specifically these vehicles have the capacity to impact package delivery logistics in a disruptive way. This paper reviews research problems and state‐of‐the‐art solutions that facilitate package delivery. Different aerial manipulators and grippers are listed along with control techniques to address stability issues. Landing on a platform is next discussed which encompasses static and dynamic platforms. Landing on a dynamic platform presents further challenges. This includes delayed control responses and poor precision of the relative motion between the platform and the aerial vehicle. Subsequently, risks such as weather conditions, state estimation, and collision avoidance to ensure safe transit is considered. Finally, delivery UAV routing is investigated which categorizes the topic into two areas: drone operations and drone–truck collaborative operations. Additionally, we compare the solutions against design, environmental, and legal constraints.
Unital Qubit Queue-channels: Classical Capacity and Product Decoding

Quantum queue-channels arise naturally in the context of buffering in quantum networks, wherein the noise suffered by the quantum states depends on the time spent waiting in the buffer. It has been shown that the upper-bound on the classical capacity of an additive queue-channel has a simple expression, and is achievable for the erasure and depolarizing channels [1]. In this paper, we characterise the classical capacity for the class of unital qubit queue-channels, and show that a simple product (non-entangled) decoding strategy is capacity-achieving. As an intermediate result, we derive an explicit capacity achieving product decoding strategy for any i.i. d. unital qubit channel, which could be of independent interest. As an important special case, we also derive the capacity and optimal decoding strategies for a symmetric generalized amplitude damping (GAD) queue-channel. Our results provide useful insights towards designing practical quantum communication networks, and highlight the need to explicitly model the impact of buffering.
Human-in-the-Loop Refinement of Word Embeddings

Word embeddings are a fixed, distributional representation of the context of words in a corpus learned from word co-occurrences. Despite their proven utility in machine learning tasks, word embedding models may capture uneven semantic and syntactic representations, and can inadvertently reflect various kinds of bias present within corpora upon which they were trained. It has been demonstrated that post-processing of word embeddings to apply information found in lexical dictionaries can improve the semantic associations, thus improving their quality. Building on this idea, we propose a system that incorporates an adaptation of word embedding post-processing, which we call"interactive refitting", to address some of the most daunting qualitative problems found in word embeddings. Our approach allows a human to identify and address potential quality issues with word embeddings interactively. This has the advantage of negating the question of who decides what constitutes bias or what other quality issues may affect downstream tasks. It allows each organization or entity to address concerns they may have at a fine grained level and to do so in an iterative and interactive fashion. It also allows for better insight into what effect word embeddings, and refinements to word embeddings, have on machine learning pipelines.
Meta Internal Learning

Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. Since these models are trained on a single image, they are limited in their scale and application. To overcome these issues, we propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively. In the presented meta-learning approach, a single-image GAN model is generated given an input image, via a convolutional feedforward hypernetwork $f$. This network is trained over a dataset of images, allowing for feature sharing among different models, and for interpolation in the space of generative models. The generated single-image model contains a hierarchy of multiple generators and discriminators. It is therefore required to train the meta-learner in an adversarial manner, which requires careful design choices that we justify by a theoretical analysis. Our results show that the models obtained are as suitable as single-image GANs for many common image applications, significantly reduce the training time per image without loss in performance, and introduce novel capabilities, such as interpolation and feedforward modeling of novel images.
UGV-Assisted Wireless Powered Backscatter Communications for Large-Scale IoT Networks

Wireless powered backscatter communications (WPBC) is capable of implementing ultra-low-power communication, thus promising in the Internet of Things (IoT) networks. In practice, however, it is challenging to apply WPBC in large-scale IoT networks because of its short communication range. To address this challenge, this paper exploits an unmanned ground vehicle (UGV) to assist WPBC in large-scale IoT networks. In particular, we investigate the joint design of network planning and dynamic resource allocation of the access point (AP), tag reader, and UGV to minimize the total energy consumption. Also, the AP can operate in either half-duplex (HD) or full-duplex (FD) multiplexing mode. Under HD mode, the optimal cell radius is derived and the optimal power allocation and transmit/receive beamforming are obtained in closed form. Under FD mode, the optimal resource allocation, as well as two suboptimal ones with low computational complexity, is developed. Simulation results disclose that dynamic power allocation at the tag reader rather than at the AP dominates the network energy efficiency while the AP operating in FD mode outperforms that in HD mode concerning energy efficiency.
The Variability of Model Specification

It's regarded as an axiom that a good model is one that compromises between bias and variance. The bias is measured in training cost, while the variance of a (say, regression) model is measure by the cost associated with a validation set. If reducing bias is the goal, one will strive to fetch as complex a model as necessary, but complexity is invariably coupled with variance: greater complexity implies greater variance. In practice, driving training cost to near zero does not pose a fundamental problem; in fact, a sufficiently complex decision tree is perfectly capable of driving training cost to zero; however, the problem is often with controlling the model's variance. We investigate various regression model frameworks, including generalized linear models, Cox proportional hazard models, ARMA, and illustrate how misspecifying a model affects the variance.
A structure preserving shift-invert infinite Arnoldi algorithm for a class of delay eigenvalue problems with Hamiltonian symmetry

In this work we consider a class of delay eigenvalue problems that admit a spectrum similar to that of a Hamiltonian matrix, in the sense that the spectrum is symmetric with respect to both the real and imaginary axis. More precisely, we present a method to iteratively approximate the eigenvalues of such delay eigenvalue problems closest to a given purely real or imaginary shift, while preserving the symmetries of the spectrum. To this end the presented method exploits the equivalence between the considered delay eigenvalue problem and the eigenvalue problem associated with a linear but infinite-dimensional operator. To compute the eigenvalues closest to the given shift, we apply a specifically chosen shift-invert transformation to this linear operator and compute the eigenvalues with the largest modulus of the new shifted and inverted operator using an (infinite) Arnoldi procedure. The advantage of the chosen shift-invert transformation is that the spectrum of the transformed operator has a"real skew-Hamiltonian"-like structure. Furthermore, it is proven that the Krylov space constructed by applying this operator, satisfies an orthogonality property in terms of a specifically chosen bilinear form. By taking this property into account during the orthogonalization process, it is ensured that even in the presence of rounding errors, the obtained approximation for, e.g., a simple, purely imaginary eigenvalue is simple and purely imaginary. The presented work can thus be seen as an extension of [V. Mehrmann and D. Watkins,"Structure-Preserving Methods for Computing Eigenpairs of Large Sparse Skew-Hamiltonian/Hamiltonian Pencils", SIAM J. Sci. Comput. (22.6), 2001], to the considered class of delay eigenvalue problems. Although the presented method is initially defined on function spaces, it can be implemented using finite dimensional linear algebra operations.
Cavity-enhanced absorption sensing with robust sideband locking

We present a simple, continuous, cavity-enhanced optical absorption measurement technique based on high-bandwidth Pound-Drever-Hall (PDH) sideband locking. The technique provides a resonant amplitude quadrature readout that can be mapped onto the cavity's internal loss rate, and is naturally compatible with weak probe beams. With a proof-of-concept 5-cm-long Fabry-Perot cavity, we measure an absorption sensitivity of $6.6 \times 10^{-11} ~\text{cm}^{-1}/\sqrt{\text{Hz}}$ at 100 kHz (roughly the cavity bandwidth) and $\sim 10^{-10}~\text{cm}^{-1}/\sqrt{\text{Hz}}$ from 30 kHz to 1 MHz, with $38~\mu$W collected from the cavity's circulating power.
Modeling clothing as a separate layer for an animatable human avatar

We have recently seen great progress in building photorealistic animatable full-body codec avatars, but generating high-fidelity animation of clothing is still difficult. To address these difficulties, we propose a method to build an animatable clothed body avatar with an explicit representation of the clothing on the upper body from multi-view captured videos. We use a two-layer mesh representation to register each 3D scan separately with the body and clothing templates. In order to improve the photometric correspondence across different frames, texture alignment is then performed through inverse rendering of the clothing geometry and texture predicted by a variational autoencoder. We then train a new two-layer codec avatar with separate modeling of the upper clothing and the inner body layer. To learn the interaction between the body dynamics and clothing states, we use a temporal convolution network to predict the clothing latent code based on a sequence of input skeletal poses. We show photorealistic animation output for three different actors, and demonstrate the advantage of our clothed-body avatars over the single-layer avatars used in previous work. We also show the benefit of an explicit clothing model that allows the clothing texture to be edited in the animation output.
On the transcendence of special values of Goss $L$-functions attached to Drinfeld modules

Let Fq be the finite field with q elements and consider the rational function field K := Fq(θ). For a Drinfeld module φ defined over K, we study the transcendence of special values of the Goss L-function attached to the abelian t-motive Mφ of φ. Moreover, when φ is a Drinfeld module of rank r ≥ 2 defined over K which has everywhere good reduction, we prove that the value of the Goss L-function attached to the (r− 1)-st exterior power of Mφ at any positive integer is transcendental over K. As an application of our methods, we also prove the transcendence of Taelman L-values of an explicit family of abelian t-modules.
Fundamental parameters and abundance analysis of the components in the SB2 system HD 60803

We performed a detailed spectroscopic study of the SB2 system HD 60803 based on high-resolution spectra obtained with the different spectrographs. The analysis was done with two independent methods: a) the direct modelling of the observed binary spectrum by a sum of synthetic spectra varying a set of free parameters and minimizing a difference between the observed and theoretical spectra; b) spectrum disentangling and an independent modelling of the individual components. Being applied to binary spectra from different spectrographs both methods converge to a consistent solution for the fundamental parameters of the HD 60803 components: )eff=6 055±70 K, log 6=4.08±0.12, bt=1.45±0.18 km s−1, [M/H]=0.03±0.06 (primary), and )eff=6 069±70 K, log 6=4.14±0.09, bt=1.48±0.18 km s−1, [M/H]=0.03±0.06 (secondary). Differential abundance analysis of the components did not reveal any significant difference in their chemical composition. Besides Li both components have solar atmospheric abundances. Li abundance exceeds the solar one by ∼2 dex, but it agrees with Li abundance in main-sequence late F-stars. Relative-to-solar abundances in both components slightly correlate with the condensation temperature the same way as was found in the solar analogs with/without detected giant planets. The estimated age of the system is 5.5±0.5 Gyr.
SeanNet: Semantic Understanding Network for Localization Under Object Dynamics

We aim for domestic robots to perform long-term indoor service. Under the object-level scene dynamics induced by daily human activities, a robot needs to robustly localize itself in the environment subject to scene uncertainties. Previous works have addressed visual-based localization in static environments, yet the object-level scene dynamics challenge existing methods for the long-term deployment of the robot. This paper proposes a SEmantic understANding Network (SeanNet) architecture that enables an effective learning process with coupled visual and semantic inputs. With a dataset that contains object dynamics, we propose a cascaded contrastive learning scheme to train the SeanNet for learning a vector scene embedding. Subsequently, we can measure the similarity between the current observed scene and the target scene, whereby enables robust localization under object-level dynamics. In our experiments, we benchmark SeanNet against state-of-the-art image-encoding networks (baselines) on scene similarity measures. The SeanNet architecture with the proposed training method can achieve an 85.02\% accuracy which is higher than baselines. We further integrate the SeanNet and the other networks as the localizers into a visual navigation application. We demonstrate that SeanNet achieves higher success rates compared to the baselines.
A study protocol for a comparative effectiveness evaluation of antiandrogenic medications against Standard of Care

This paper presents a study protocol for an effectiveness evaluation of antiandrogenic (NAM) medications (i.e. abiraterone acetate and enzalutamide) against standard of care in patients suffering from metastatic castrate resistant prostate cancer. The study protocol takes stock in the Rubin Causal Model for observational data in using historical comparisons for the analysis on three outcomes: mortality, pain and skeleton related events. We restrict the evaluation to 1,285 NAM patients with a prostate cancer diagnosis after June 2012 and find the comparisons in the group of men that had a prostate cancer diagnosis between June 2008 and June 2010. The rich registry data and the lack of NAMs as an option for the comparisons provide support for the validity of the design. However, while the design yields balance on the observed covariates, one cannot discard the possibility that unobserved confounders are not balanced. Thus, sensitivity analyses are suggested.
Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling

Many supervised machine learning tasks, such as future state prediction in dynamical systems, require precise modeling of a forecast’s uncertainty. The Multiple Hypotheses Prediction (MHP) approach addresses this problem by providing several hypotheses that represent possible outcomes. Unfortunately, with the common l2 loss function, these hypotheses do not preserve the data distribution’s characteristics. We propose an alternative loss for distribution preserving MHP and review relevant theorems supporting our claims. Furthermore, we empirically show that our approach yields more representative hypotheses on a synthetic and a real-world motion prediction data set. The outputs of the proposed method can directly be used in sampling-based Monte-Carlo methods.
Calibration of Surface Brightness Fluctuations for Dwarf Galaxies in the Hyper Suprime-Cam gi Filter System

Surface brightness fluctuation (SBF) magnitudes are a powerful standard candle to measure distances to semiresolved galaxies in the local universe, a majority of which are dwarf galaxies that often have bluer colors than bright early-type galaxies. We present an empirical i-band SBF calibration in a blue regime, 0.2 ≲ (g − i)0 ≲ 0.8 in the Hyper Suprime-Cam (HSC) magnitude system. We measure SBF magnitudes for 12 nearby dwarf galaxies of various morphological types with archival HSC imaging data, and use their tip of the red giant branch distances to derive fluctuation–color relations. In order to subtract contributions of fluctuations due to young stellar populations, we use five different g-band magnitude masking thresholds, M g,thres = −3.5, −4.0, −4.5, −5.0, and −5.5 mag. We find that the rms scatter of the linear fit to the relation is the smallest (rms = 0.16 mag) in the case of M g,thres = −4.0 mag, M¯i = (−2.65 ± 0.13) + (1.28 ± 0.24) × (g − i)0. This scatter is much smaller than those in the previous studies (rms = 0.26 mag), and is closer to the value for bright red galaxies (rms = 0.12 mag). This calibration is consistent with predictions from metal-poor simple stellar population models.
Spin-resolved thermal signatures of Majorana-Kondo interplay in double quantum dots

Piotr Majek, ∗ Krzysztof P. Wójcik, 3 and Ireneusz Weymann Institute of Spintronics and Quantum Information, Faculty of Physics, Adam Mickiewicz University in Poznań, ul. Uniwersytetu Poznańskiego 2, 61-614 Poznań, Poland Institute of Molecular Physics, Polish Academy of Sciences, ul. Smoluchowskiego 17, 60-179 Poznań, Poland Physikalisches Institut, Universität Bonn, Nussallee 12, D-53115 Bonn, Germany (Dated: February 4, 2022)
Semi-relaxed Gromov Wasserstein divergence with applications on graphs

Comparing structured objects such as graphs is a fundamental operation involved in many learning tasks. To this end, the Gromov-Wasserstein (GW) distance, based on Optimal Transport (OT), has proven to be successful in handling the specific nature of the associated objects. More specifically, through the nodes connectivity relations, GW operates on graphs, seen as probability measures over specific spaces. At the core of OT is the idea of conservation of mass, which imposes a coupling between all the nodes from the two considered graphs. We argue in this paper that this property can be detrimental for tasks such as graph dictionary or partition learning, and we relax it by proposing a new semi-relaxed Gromov-Wasserstein divergence. Aside from immediate computational benefits, we discuss its properties, and show that it can lead to an efficient graph dictionary learning algorithm. We empirically demonstrate its relevance for complex tasks on graphs such as partitioning, clustering and completion.
Co-training an Unsupervised Constituency Parser with Weak Supervision

We introduce a method for unsupervised parsing that relies on bootstrapping classifiers to identify if a node dominates a specific span in a sentence. There are two types of classifiers, an inside classifier that acts on a span, and an outside classifier that acts on everything outside of a given span. Through self-training and co-training with the two classifiers, we show that the interplay between them helps improve the accuracy of both, and as a result, effectively parse. A seed bootstrapping technique prepares the data to train these classifiers. Our analyses further validate that such an approach in conjunction with weak supervision using prior branching knowledge of a known language (left/right-branching) and minimal heuristics injects strong inductive bias into the parser, achieving 63.1 F_1 on the English (PTB) test set. In addition, we show the effectiveness of our architecture by evaluating on treebanks for Chinese (CTB) and Japanese (KTB) and achieve new state-of-the-art results.
Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction

Reflective and textureless surfaces such as windows, mirrors, and walls can be a challenge for object and scene reconstruction. These surfaces are often poorly reconstructed and filled with depth discontinuities and holes, making it difficult to cohesively reconstruct scenes that contain these planar discontinuities. We propose Echoreconstruction, an audio-visual method that uses the reflections of sound to aid in geometry and audio reconstruction for virtual conferencing, teleimmersion, and other AR/VR experience. The mobile phone prototype emits pulsed audio, while recording video for RGB-based 3D reconstruction and audio-visual classification. Reflected sound and images from the video are input into our audio (EchoCNN-A) and audio-visual (EchoCNN-AV) convolutional neural networks for surface and sound source detection, depth estimation, and material classification. The inferences from these classifications enhance scene 3D reconstructions containing open spaces and reflective surfaces by depth filtering, inpainting, and placement of unmixed sound sources in the scene. Our prototype, VR demo, and experimental results from real-world and virtual scenes with challenging surfaces and sound indicate high success rates on classification of material, depth estimation, and closed/open surfaces, leading to considerable visual and audio improvement in 3D scenes (see Figure 1).
Scaling up instance annotation via label propagation

Manually annotating object segmentation masks is very time-consuming. While interactive segmentation methods offer a more efficient alternative, they become unaffordable at a large scale because the cost grows linearly with the number of annotated masks. In this paper, we propose a highly efficient annotation scheme for building large datasets with object segmentation masks. At a large scale, images contain many object instances with similar appearance. We exploit these similarities by using hierarchical clustering on mask predictions made by a segmentation model. We propose a scheme that efficiently searches through the hierarchy of clusters and selects which clusters to annotate. Humans manually verify only a few masks per cluster, and the labels are propagated to the whole cluster. Through a large-scale experiment to populate 1M unlabeled images with object segmentation masks for 80 object classes, we show that (1) we obtain 1M object segmentation masks with an total annotation time of only 290 hours; (2) we reduce annotation time by 76× compared to manual annotation; (3) the segmentation quality of our masks is on par with those from manually annotated datasets. Code, data, and models are available online1.