Miracle-dz/UniFAR-FTUs huggingface dataset:
MCMix: Anonymous Messaging via Secure Multiparty Computation

We present MCMix, an anonymous messaging system that completely hides communication metadata and can scale in the order of hundreds of thousands of users. Our approach is to isolate two suitable functionalities, called dialing and conversation, that when used in succession, realize anonymous messaging. With this as a starting point, we apply secure multiparty computation (“MC” or MPC) and proceed to realize them. We then present an implementation using Sharemind, a prevalent MPC system. Our implementation is competitive in terms of latency with previous messaging systems that only offer weaker privacy guarantees. Our solution can be instantiated in a variety of different ways with different MPC implementations, overall illustrating how MPC is a viable and competitive alternative to mix-nets and DC-nets for anonymous communication.
VideoStory Embeddings Recognize Events when Examples are Scarce

This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire representation from freely available web videos and their descriptions using an embedding between video features and term vectors. In our proposed embedding, which we call VideoStory, the correlations between the terms are utilized to learn a more effective representation by optimizing a joint objective balancing descriptiveness and predictability.We show how learning the VideoStory using a multimodal predictability loss, including appearance, motion and audio features, results in a better predictable representation. We also propose a variant of VideoStory to recognize an event in video from just the important terms in a text query by introducing a term sensitive descriptiveness loss. Our experiments on three challenging collections of web videos from the NIST TRECVID Multimedia Event Detection and Columbia Consumer Videos datasets demonstrate: i) the advantages of VideoStory over representations using attributes or alternative embeddings, ii) the benefit of fusing video modalities by an embedding over common strategies, iii) the complementarity of term sensitive descriptiveness and multimodal predictability for event recognition without examples. By it abilities to improve predictability upon any underlying video feature while at the same time maximizing semantic descriptiveness, VideoStory leads to state-of-the-art accuracy for both few- and zero-example recognition of events in video.
Jacquard: A Large Scale Dataset for Robotic Grasp Detection

Grasping skill is a major ability that a wide number of real-life applications require for robotisation. State-of-the-art robotic grasping methods perform prediction of object grasp locations based on deep neural networks. However, such networks require huge amount of labeled data for training making this approach often impracticable in robotics. In this paper, we propose a method to generate a large scale synthetic dataset with ground truth, which we refer to as the Jacquard grasping dataset. Jacquard is built on a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D images and annotations of successful grasping positions based on grasp attempts performed in a simulated environment. We carried out experiments using an off-the-shelf CNN, with three different evaluation metrics, including real grasping robot trials. The results show that Jacquard enables much better generalization skills than a human labeled dataset thanks to its diversity of objects and grasping positions. For the purpose of reproducible research in robotics, we are releasing along with the Jacquard dataset a web interface for researchers to evaluate the successfulness of their grasping position detections using our dataset.
General Models for Rational Cameras and the Case of Two-Slit Projections

The rational camera model recently introduced in [18] provides a general methodology for studying abstract nonlinear imaging systems and their multi-view geometry. This paper builds on this framework to study physical realizations of rational cameras. More precisely, we give an explicit account of the mapping between between physical visual rays and image points (missing in the original description), which allows us to give simple analytical expressions for direct and inverse projections. We also consider primitive camera models, that are orbits under the action of various projective transformations, and lead to a general notion of intrinsic parameters. The methodology is general, but it is illustrated concretely by an in-depth study of two-slit cameras, that we model using pairs of linear projections. This simple analytical form allows us to describe models for the corresponding primitive cameras, to introduce intrinsic parameters with a clear geometric meaning, and to define an epipolar tensor characterizing two-view correspondences. In turn, this leads to new algorithms for structure from motion and self-calibration.
Mitigation of faults in grid-connected wind-driven single machine brushless double-fed induction generator

The effect of three-phase grid fault on the performance of a wind-driven single machine-brushless double fed induction generator (SM-BDFIG) is investigated. The fault-ride-through (FRT) of the grid-connected SM-BDFIG is then studied when installing a Static Synchronous Compensator (STATCOM) between the grid and the generator. Recovery from the grid fault before installing the STATCOM is studied and compared to the generation system recovery with installed STATCOM. The performances of the stator and rotor currents, stator and rotor voltages, electric torque, active power, reactive power, and battery pack voltage and current are presented for both cases. The total harmonic distortion (THD) of stator and rotor voltages and currents are also presented and compared. Results proved the faster recovery from grid faults, the continuity of currents and voltages, and the continuity of active power supplied to the grid when installing the STATCOM. However, slightly higher THD took place in the stator and rotor voltages and currents due to the switching pattern of the STATCOM.
Feathers dataset for fine-grained visual categorization

This paper introduces a novel dataset FeatherV1, containing 28,272 images of feathers categorized by 595 bird species. It was created to perform taxonomic identification of bird species by a single feather, which can be applied in amateur and professional ornithology. FeatherV1 is the first publicly available bird's plumage dataset for machine learning, and it can raise interest for a new task in fine-grained visual recognition domain. The latest version of the dataset can be downloaded at https://github.com/feathers-dataset/feathersv1-dataset. We also present feathers classification task results. We selected several deep learning architectures (DenseNet based) for categorical crossentropy values comparison on the provided dataset.
Indoor Millimeter-Wave Systems: Design and Performance Evaluation

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. Although we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. This article introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts and then moves on to experimentally validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.
The One-Way Velocity of Light Is Measurable Without Synchronized Clocks and Two Clocks Are Synchronizable Without One-Way Light Signals

The one way velocity of light is measurable with only one clock and this measurability invalidates the Conventionality of Synchronization thesis. Systematic measurements of the isotropy in optical barrier penetration and of the mean decay lengths of unstable particles can determine the one way velocities of light and of particles. Presently, everyday experience, standard laboratory practices, and experiments with synchronization independent results in these two areas indicate that the one way velocity of light is the same as its round trip velocity. Also, the Special Relativity Postulate allows the synchronization of two isochronous stationary clocks by using a single moving isochronous clock without knowing the one way velocity of light. Thus, the Conventionality of Synchronization thesis is invalid on several grounds.
Short-cadence K2 observations of an accretion-state transition in the polar Tau 4

The Kepler spacecraft observed a total of only four AM Herculis cataclysmic variable stars during its lifetime. We analyze the short-cadence K2 light curve of one of those systems, Tau 4 (RX J0502.8+1624), which underwent a serendipitous jump from a low-accretion state into a high state during the final days of the observation. Apart from one brief flare, there was no evidence of accretion during the 70 d of observations of the low state. As Tau 4 transitioned into a high state, the resumption of accretion was very gradual, taking approximately six days (~90 binary orbits). We supplement Tau 4's K2 light curve with time-resolved spectroscopy obtained in both high and low states of accretion. High-excitation lines, such as He II 468.6 nm, were extraordinarily weak, even when the system was actively accreting. This strongly suggests the absence of an accretion shock, placing Tau 4 in the bombardment regime predicted for AM Herculis systems with low accretion rates. In both the high-state and low-state spectra, Zeeman absorption features from the white dwarf's photosphere are present and reveal a surface-averaged field strength of $15\pm2$ MG. Remarkably, the high-state spectra also show Zeeman-split emission lines produced in a region with a field strength of $12\pm1$ MG. Zeeman emission has not been previously reported in an AM Herculis system, and we propose that the phenomenon is caused by a temperature inversion in the WD's atmosphere near the accretion region.
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings

Word alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT. However, most approaches require parallel training data and quality decreases as less training data is available. We propose word alignment methods that require no parallel data. The key idea is to leverage multilingual word embeddings – both static and contextualized – for word alignment. Our multilingual embeddings are created from monolingual data only without relying on any parallel data or dictionaries. We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners – even with abundant parallel data; e.g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical aligner, trained on 100k parallel sentences.
OGLE-2017-BLG-0406: Spitzer Microlens Parallax Reveals Saturn-mass Planet Orbiting M-dwarf Host in the Inner Galactic Disk

We report the discovery and analysis of the planetary microlensing event OGLE-2017-BLG-0406, which was observed both from the ground and by the Spitzer satellite in a solar orbit. At high magnification, the anomaly in the light curve was densely observed by ground-based-survey and follow-up groups, and it was found to be explained by a planetary lens with a planet/host mass ratio of from the light-curve modeling. The ground-only and Spitzer-“only” data each provide very strong one-dimensional (1D) constraints on the 2D microlens parallax vector . When combined, these yield a precise measurement of and of the masses of the host and planet Mplanet = 0.41 ± 0.05 MJup. The system lies at a distance DL = 5.2 ± 0.5 kpc from the Sun toward the Galactic bulge, and the host is more likely to be a disk population star according to the kinematics of the lens. The projected separation of the planet from the host is (i.e., just over twice the snow line). The Galactic-disk kinematics are established in part from a precise measurement of the source proper motion based on OGLE-IV data. By contrast, the Gaia proper-motion measurement of the source suffers from a catastrophic 10σ error.
Non-Blocking Simultaneous Multithreading: Embracing the Resiliency of Deep Neural Networks

Deep neural networks (DNNs) are known for their inability to utilize underlying hardware resources due to hard-ware susceptibility to sparse activations and weights. Even in finer granularities, many of the non-zero values hold a portion of zero-valued bits that may cause inefficiencies when executed on hard-ware. Inspired by conventional CPU simultaneous multithreading (SMT) that increases computer resource utilization by sharing them across several threads, we propose non-blocking SMT (NB-SMT) designated for DNN accelerators. Like conventional SMT, NB-SMT shares hardware resources among several execution flows. Yet, unlike SMT, NB-SMT is non-blocking, as it handles structural hazards by exploiting the algorithmic resiliency of DNNs. Instead of opportunistically dispatching instructions while they wait in a reservation station for available hardware, NB-SMT temporarily reduces the computation precision to accommodate all threads at once, enabling a non-blocking operation. We demonstrate NB-SMT applicability using SySMT, an NB-SMT-enabled output-stationary systolic array (OS-SA). Compared with a conventional OS-SA, a 2-threaded SySMT consumes 1.4× the area and delivers 2× speedup with 33% energy savings and less than 1% accuracy degradation of state-of-the-art CNNs with ImageNet. A 4-threaded SySMT consumes 2.5× the area and delivers, for example, 3.4× speedup and 39%×energy savings with 1% accuracy degradation of 40%-pruned ResNet-18.
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity

Unsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications. These models, however, retain some of the limitations of traditional static word embeddings. In particular, they encode only the distributional knowledge available in raw text corpora, incorporated through language modeling objectives. In this work, we complement such distributional knowledge with external lexical knowledge, that is, we integrate the discrete knowledge on word-level semantic similarity into pretraining. To this end, we generalize the standard BERT model to a multi-task learning setting where we couple BERT’s masked language modeling and next sentence prediction objectives with an auxiliary task of binary word relation classification. Our experiments suggest that our “Lexically Informed” BERT (LIBERT), specialized for the word-level semantic similarity, yields better performance than the lexically blind “vanilla” BERT on several language understanding tasks. Concretely, LIBERT outperforms BERT in 9 out of 10 tasks of the GLUE benchmark and is on a par with BERT in the remaining one. Moreover, we show consistent gains on 3 benchmarks for lexical simplification, a task where knowledge about word-level semantic similarity is paramount, as well as large gains on lexical reasoning probes.
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder

Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. However, in real scenarios, factors with semantics are not necessarily independent. Instead, there might be an underlying causal structure which renders these factors dependent. We thus propose a new VAE based framework named CausalVAE, which includes a Causal Layer to transform independent exogenous factors into causal endogenous ones that correspond to causally related concepts in data. We further analyze the model identifiabitily, showing that the proposed model learned from observations recovers the true one up to a certain degree by providing supervision signals (e.g. feature labels). Experiments are conducted on various datasets, including synthetic and real word benchmark CelebA. Results show that the causal representations learned by CausalVAE are semantically interpretable, and their causal relationship as a Directed Acyclic Graph (DAG) is identified with good accuracy. Furthermore, we demonstrate that the proposed CausalVAE model is able to generate counterfactual data through"do-operation"to the causal factors.
Younger workers’ attitudes and perceptions towards older colleagues

Purpose The purpose of this paper is to investigate younger workers’ perceptions of older colleagues, including whether there is evidence of ageism. Design/methodology/approach Convenience sampling was used to recruit ten individuals who were both below the age of 35 and employed at a multigenerational workplace in England. The study is qualitative, involving semi-structured interviews that were audio-recorded, transcribed and analysed using thematic analysis. Findings This study found that whilst some younger employees valued working with older colleagues as they believe that their differing characteristics are complementary, others felt that it leads to intergenerational conflict due to contrasting approaches towards work. Positive perceptions of older workers included their increased knowledge and experience, reliability and better social skills; however, ageism was also prevalent, such as the perception of older workers as resistant to change, slower at using technology and lacking the drive to progress. This study also provided evidence for the socioemotional selectivity and social identity theories. Research limitations/implications This study has a small sample size and participants were only recruited from London. Practical implications In order to create working environments that are conducive to the well-being of employees of all ages, organisations should place an emphasis on reducing intergenerational tension. This could be achieved by team building sessions that provide an opportunity for individuals to understand generational differences. Originality/value There is minimal evidence from the UK focussing on the perceptions of specifically younger workers towards older colleagues and the basis of their attitudes. Only by gaining an insight into their attitudes and the reasoning behind them, can efforts be made to decrease ageism.
Deep Unfolded Multicast Beamforming

Multicast beamforming is a promising technique for multicast communication. Providing an efficient and powerful beamforming design algorithm is a crucial issue because multicast beamforming problems such as a max-min-fair problem are NP-hard in general. Recently, deep learning-based approaches have been proposed for beamforming design. Although these approaches using deep neural networks exhibit reasonable performance gain compared with conventional optimization-based algorithms, their scalability is an emerging problem for large systems in which beamforming design becomes a more demanding task. In this paper, we propose a novel deep unfolded trainable beamforming design with high scalability and efficiency. The algorithm is designed by expanding the recursive structure of an existing algorithm based on projections onto convex sets and embedding a constant number of trainable parameters to the expanded network, which leads to a scalable and stable training process. Numerical results show that the proposed algorithm can accelerate its convergence speed by using unsupervised learning, which is a challenging training process for deep unfolding.
Nielsen equivalence in closed 2-orbifold groups

We prove that any generating tuple of the fundamental group of a sufficiently large 2-dimensional orbifold is represented by an almost orbifold covering. As a corollary we obtain a generalization of Louder's Theorem which asserts that any two generating tuples of the fundamental group of a closed surface are Nielsen equivalent.
Predicting Online Item-choice Behavior: A Shape-restricted Regression Perspective

This paper examines the relationship between user pageview (PV) histories and their item-choice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user--item pair. We propose a shape-restricted optimization model that accurately estimates item-choice probabilities for all possible PV sequences. This model imposes monotonicity constraints on item-choice probabilities by exploiting partial orders for PV sequences, according to the recency and frequency of a user's previous PVs. To improve the computational efficiency of our optimization model, we devise efficient algorithms for eliminating all redundant constraints according to the transitivity of the partial orders. Experimental results using real-world clickstream data demonstrate that our method achieves higher prediction performance than that of a state-of-the-art optimization model and common machine learning methods.
The Magnetic Sensitivity of the Resonance and Subordinate Lines of Mg II in the Solar Chromosphere

We carry out a theoretical study of the polarization of the solar Mg ii h–k doublet (including its extended wings) and the subordinate ultraviolet (UV) triplet around 280 nm. These lines are of great diagnostic interest, as they encode information on the physical properties of the solar atmosphere from the upper photosphere to the chromosphere–corona transition region. We base our study on radiative transfer calculations of spectral line polarization in one-dimensional models of quiet and plage regions of the solar atmosphere. Our calculations take into account the combined action of atomic polarization, quantum level interference, frequency redistribution, and magnetic fields of arbitrary strength. In particular, we study the sensitivity of the emergent Stokes profiles to changes in the magnetic field through the Zeeman and Hanle effects. We also study the impact of the chromospheric plasma dynamics on the emergent Stokes profiles, taking into account the angle-dependent frequency redistribution in the h–k resonance transitions. The results presented here are of interest for the interpretation of spectropolarimetric observations in this important region of the solar UV spectrum.
Roundtrip: A Deep Generative Neural Density Estimator

Density estimation is a fundamental problem in both statistics and machine learning. In this study, we proposed Roundtrip as a general-purpose neural density estimator based on deep generative models. Roundtrip retains the generative power of generative adversarial networks (GANs) but also provides estimates of density values. Unlike previous neural density estimators that put stringent conditions on the transformation from the latent space to the data space, Roundtrip enables the use of much more general mappings. In a series of experiments, Roundtrip achieves state-of-the-art performance in a diverse range of density estimation tasks.