Miracle-dz/UniFAR-FTUs huggingface dataset:
A Discriminative Single-Shot Segmentation Network for Visual Object Tracking

Template-based discriminative trackers are currently the dominant tracking paradigm due to their robustness, but are restricted to bounding box tracking and a limited range of transformation models, which reduces their localization accuracy. We propose a discriminative single-shot segmentation tracker – D3S<inline-formula><tex-math notation="LaTeX">$_2$</tex-math><alternatives><mml:math><mml:msub><mml:mrow/><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="lukezic-ieq1-3137933.gif"/></alternatives></inline-formula>, which narrows the gap between visual object tracking and video object segmentation. A single-shot network applies two target models with complementary geometric properties, one invariant to a broad range of transformations, including non-rigid deformations, the other assuming a rigid object to simultaneously achieve robust online target segmentation. The overall tracking reliability is further increased by decoupling the object and feature scale estimation. Without per-dataset finetuning, and trained only for segmentation as the primary output, D3S<inline-formula><tex-math notation="LaTeX">$_2$</tex-math><alternatives><mml:math><mml:msub><mml:mrow/><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="lukezic-ieq2-3137933.gif"/></alternatives></inline-formula> outperforms all published trackers on the recent short-term tracking benchmark VOT2020 and performs very close to the state-of-the-art trackers on the GOT-10k, TrackingNet, OTB100 and LaSoT. D3S<inline-formula><tex-math notation="LaTeX">$_2$</tex-math><alternatives><mml:math><mml:msub><mml:mrow/><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="lukezic-ieq3-3137933.gif"/></alternatives></inline-formula> outperforms the leading segmentation tracker SiamMask on video object segmentation benchmarks and performs on par with top video object segmentation algorithms.
Genuine multipartite correlations in a boundary time crystal

In this work we study genuine multipartite correlations (GMC’s) in a boundary time crystal (BTC). Boundary time crystals are nonequilibrium quantum phases of matter in contact to an environment, for which a macroscopic fraction of the many-body system breaks the time-translation symmetry. We analyze both (i) the structure (orders) of GMC’s among the subsystems, as well as (ii) their build-up dynamics for an initially uncorrelated state. We ﬁnd that, in the thermodynamic limit (and only in such a limit), multipartite correlations of all orders grow indeﬁnitely in time in the BTC phase, further displaying a persistent oscillatory behavior around their mean growth. The orders of the correlations show a power-law decaying hierarchy among its k -partitions. Moreover, in the long-time limit the GMC’s are shown extensive with the system size, contrasting to the subextensive scaling in the non time-crystal (ferromagnetic) phase of the model. We also discuss the classical and quantum nature of these correlations with basis on multipartite entanglement witnesses, speciﬁcally, the analysis of the Quantum Fisher Information (QFI). Both GMC and QFI are able to capture and distinguish the diﬀerent phases of the model. Our work highlights the genuine many-body properties of these peculiar non-equilibrium phases of matter. 34 we ﬁnd a valley in the dynamics of GMC’s ( I k ), so that in the next time t = 7 . 26 a peak in I k is found. This alternating behavior is maintained until t = 14 . 48, which enable us to see the matrix approaching to the quasi-diagonal NESS.
Quantum enhancement in time-delays estimation through spectrally resolved two-photon interference

The quantum interference occurring between two indistinguishable photons impinging on the two input faces of a beam-splitter can be exploited for a range of applications, from quantum optical coherence tomography, to quantum metrology including time intervals measurements. In the latter, recent advances managed to reach a resolution in the estimation of the delay between the two photons of the order of attoseconds, i.e. in the nanometer scale. Unfortunately, these techniques are highly affected in the estimation precision by any experimental distinguishability between the photons at the detectors. Here, we perform an analysis of the precision achievable in the estimation of the delay between two independent photons interfering at a beam-splitter when frequency-resolved measurements are employed. Remarkably, we show that the observation of the spectra of the photons at the output ports when coincidence and bunching events are recorded, largely enhance the precision of the estimation for any degree of distinguishability between the photons at the detectors. In particular, we find that such scheme is effective also for temporal delays much larger than the coherence time of each photons, a regime in ∗danilo.triggiani@port.ac. uk †vincenzo.tamma@port.ac. uk which standard two-photon interferometers or spectral analyses at the single-photon level, do not provide any information. Furthermore, we show that by increasing the bandwidth of the photons it is possible to further increase quadratically the precision in the estimation, differently from non-resolved twophoton interference where the precision degrades for large bandwidth values. Therefore, such estimation scheme with frequency-resolving detectors allows to substantially enhance the precision of measurements of time delays, including the ones induced by one of the two photons going through a transparent materials or biological tissue with unknown thickness. Relevant applications can range from the characterization of two-dimensional nanomaterials to the analysis of biological samples, including DNA and cell membranes.
A Large-Scale Characterization of How Readers Browse Wikipedia

Despite the importance and pervasiveness of Wikipedia as one of the largest platforms for open knowledge, surprisingly little is known about how people navigate its content when seeking information. To bridge this gap, we present the first systematic large-scale analysis of how readers browse Wikipedia. Using billions of page requests from Wikipedia’s server logs, we measure how readers reach articles, how they transition between articles, and how these patterns combine into more complex navigation paths. We find that navigation behavior is characterized by highly diverse structures. Although most navigation paths are shallow, comprising a single pageload, there is much variety, and the depth and shape of paths vary systematically with topic, device type, and time of day. We show that Wikipedia navigation paths commonly mesh with external pages as part of a larger online ecosystem, and we describe how naturally occurring navigation paths are distinct from targeted navigation in lab-based settings. Our results further suggest that navigation is abandoned when readers reach low-quality pages. Taken together, these insights contribute to a more systematic understanding of readers’ information needs and allow for improving their experience on Wikipedia and the Web in general.
Compromised ACC vehicles can degrade current mixed-autonomy traffic performance while remaining stealthy against detection

We demonstrate that a supply-chain level compromise of the adaptive cruise control (ACC) capability on equipped vehicles can be used to significantly degrade system level performance of current day mixed-autonomy freeway networks. Via a simple threat model which causes random deceleration attacks (RDAs), compromised vehicles create congestion waves in the traffic that decrease average speed and network throughput. We use a detailed and realistic traffic simulation environment to quantify the impacts of the attack on a model of a real high-volume freeway in the United States. We find that the effect of the attack depends both on the level of underlying traffic congestion, and what percentage of ACC vehicles can be compromised. In moderate congestion regimes the attack can degrade mean commuter speed by over 7%. In high density regimes overall network throughput can be reduced by up to 3%. And, in moderate to high congestion regimes, it can cost commuters on the network over 300 USD/km hr. All of these results motivate that the proposed attack is able to significantly degrade performance of the traffic network. We also develop an anomaly detection technique that uses GPS traces on vehicles to identify malicious/compromised vehicles. We employ this technique on data from the simulation experiments and find that it is unable to identify compromised ACCs compared to benign/normal drivers. That is, these attacks are stealthy to detection. Stronger attacks can be accurately labeled as malicious, motivating that there is a limit to how impactful attacks can be before they are no longer stealthy. Finally, we experimentally execute the attack on a real and commercially available ACC vehicle, demonstrating the possible real world feasibility of an RDA.
Digital resonator fiber optic gyro based on a miniature laser source

A resonator fiber optic gyro (RFOG) based on a fiber-coupled semiconductor DFB-LD with an FPGA-based digital processor is set up. A bias stability of 23deg/h over one hour is successfully demonstrated. This is the best result in long time stability reported to date, to the best of our knowledge, for the RFOG based on a miniaturized laser source.
Self-Supervised Learning based Monaural Speech Enhancement with Multi-Task Pre-Training

In self-supervised learning, it is challenging to reduce the gap between the enhancement performance on the estimated and target speech signals with existed pre-tasks. In this paper, we propose a multi-task pre-training method to improve the speech enhancement performance with self-supervised learning. Within the pre-training autoencoder (PAE), only a limited set of clean speech signals are required to learn their latent representations. Meanwhile, to solve the limitation of single pre-task, the proposed masking module exploits the dereverberated mask and estimated ratio mask to denoise the mixture as the second pre-task. Different from the PAE, where the target speech signals are estimated, the downstream task autoencoder (DAE) utilizes a large number of unlabeled and unseen reverberant mixtures to generate the estimated mixtures. The trained DAE is shared by the learned representations and masks. Experimental results on a benchmark dataset demonstrate that the proposed method outperforms the state-of-the-art approaches.
Few-Shot Font Generation With Weakly Supervised Localized Representations

Automatic few-shot font generation aims to solve a well-defined, real-world problem because manual font designs are expensive and sensitive to the expertise of designers. Existing methods learn to disentangle style and content elements by developing a universal style representation for each font style. However, this approach limits the model in representing diverse local styles because it is unsuitable for complicated letter systems. For example, Chinese characters consist of a varying number of components (often called “radical”) with a highly complex structure. In this paper, we propose a novel font generation method that learns localized styles, namely component-wise style representations, instead of universal styles. The proposed style representations enable synthesizing complex local details in text designs. However, learning component-wise styles solely from a few reference glyphs is infeasible when a target script has a large number of components, for example, over 200 for Chinese. To reduce the number of required reference glyphs, we represent component-wise styles by a product of component and style factors inspired by low-rank matrix factorization. Owing to the combination of strong representation and a compact factorization strategy, our method shows remarkably better few-shot font generation results (with only eight reference glyphs) than other state-of-the-art methods. Moreover, strong locality supervision was not utilized, such as the location of each component, skeleton, or strokes. The source code is available at https://github.com/clovaai/lffont.
Self-Covering, finiteness, and fibering over a circle

A topological space is called self-covering if it is a nontrivial cover of itself. We prove that a closed self-covering manifold $M$ with free abelian fundamental group fibers over a circle under certain assumptions. In particular, we give a complete answer to the question whether a self-covering manifold with fundamental group $\mathbb Z$ is a fiber bundle over $S^1$, except for the $4$-dimensional smooth case. As an algebraic Hilfssatz, we develop a criterion for finite generation of modules over a commutative Noetherian ring. We also construct examples of self-covering manifolds with non-free abelian fundamental group, which are not fiber bundles over $S^1$
Neural-Network Quantum States for Periodic Systems in Continuous Space

Gabriel Pescia, Jiequn Han, Alessandro Lovato, 4, 5 Jianfeng Lu, and Giuseppe Carleo Ecole Polytechnique Fédérale de Lausanne (EPFL), Institute of Physics, CH-1015 Lausanne, Switzerland Center for Computational Mathematics, Flatiron Institute, New York, NY 10010, USA Physics Division, Argonne National Laboratory, Argonne, IL 60439 Computational Science Division, Argonne National Laboratory, Argonne, Illinois 60439, USA INFN-TIFPA Trento Institute of Fundamental Physics and Applications, 38123 Trento, Italy Departments of Mathematics, Physics, and Chemistry, Duke University, Durham, NC 27708, USA (Dated: December 23, 2021)
Few-Shot Object Detection: A Survey

—Humans are able to learn to recognize new objects even from a few examples. In contrast, training deep-learning-based object detectors requires huge amounts of annotated data. To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection aims to learn from few object instances of new categories in the target domain. In this survey, we provide an overview of the state of the art in few-shot object detection. We categorize approaches according to their training scheme and architectural layout. For each type of approaches, we describe the general realization as well as concepts to improve the performance on novel categories. Whenever appropriate, we give short takeaways regarding these concepts in order to highlight the best ideas. Eventually, we introduce commonly used datasets and their evaluation protocols and analyze reported benchmark results. As a result, we emphasize common challenges in evaluation and identify the most promising current trends in this emerging ﬁeld of few-shot object detection.
MECATS: Mixture-of-Experts for Quantile Forecasts of Aggregated Time Series

We introduce a mixture of heterogeneous experts framework called MECATS , which simultaneously forecasts the values of a set of time series that are related through an aggregation hierarchy. Different types of forecasting models can be employed as individual experts so that the form of each model can be tailored to the nature of the corresponding time series. MECATS learns hierarchical relationships during the training stage to help generalize better across all the time series being modeled and also mitigates coherency issues that arise due to constraints imposed by the hierarchy. We further build multiple quantile estimators on top of the point forecasts. The resulting probabilistic forecasts are nearly coherent, distribution-free, and independent of the choice of forecasting models. We conduct a comprehensive evaluation on both point and probabilistic forecasts and also formulate an extension for situations where change points exist in sequential data. In general, our method is robust, adaptive to datasets with different properties, and highly conﬁgurable and efﬁcient for large-scale forecasting pipelines.
Ghost-dil-NetVLAD: A Lightweight Neural Network for Visual Place Recognition

Visual place recognition (VPR) is a challenging task with the unbalance between enormous computational cost and high recognition performance. Thanks to the practical feature extraction ability of the lightweight convolution neural networks (CNNs) and the train-ability of the vector of locally aggregated descriptors (VLAD) layer, we propose a lightweight weakly supervised end-to-end neural network consisting of a front-ended perception model called GhostCNN and a learnable VLAD layer as a back-end. GhostCNN is based on Ghost modules that are lightweight CNN-based architectures. They can generate redundant feature maps using linear operations instead of the traditional convolution process, making a good trade-off between computation resources and recognition accuracy. To enhance our proposed lightweight model further, we add dilated convolutions to the Ghost module to get features containing more spatial semantic information, improving accuracy. Finally, rich experiments conducted on a commonly used public benchmark and our private dataset validate that the proposed neural network reduces the FLOPs and parameters of VGG16-NetVLAD by 99.04% and 80.16%, respectively. Besides, both models achieve similar accuracy.
Predicting treatment effects from observational studies using machine learning methods: A simulation study

Measuring treatment effects in observational studies is challenging because of confounding bias. Confounding occurs when a variable affects both the treatment and the outcome. Traditional methods such as propensity score matching estimate treatment effects by conditioning on the confounders. Recent literature has presented new methods that use machine learning to predict the counterfactuals in observational studies which then allow for estimating treatment effects. These studies however, have been applied to real world data where the true treatment effects have not been known. This study aimed to study the effectiveness of this counterfactual prediction method by simulating two main scenarios: with and without confounding. Each type also included linear and non-linear relationships between input and output data. The key item in the simulations was that we generated known true causal effects. Linear regression, lasso regression and random forest models were used to predict the counterfactuals and treatment effects. These were compared these with the true treatment effect as well as a naive treatment effect. The results show that the most important factor in whether this machine learning method performs well, is the degree of non-linearity in the data. Surprisingly, for both non-confounding \textit{and} confounding, the machine learning models all performed well on the linear dataset. However, when non-linearity was introduced, the models performed very poorly. Therefore under the conditions of this simulation study, the machine learning method performs well under conditions of linearity, even if confounding is present, but at this stage should not be trusted when non-linearity is introduced.
Use of overstrength and inelastic response in seismic design

Inelastic analysis procedures are more dependable means for predicting the actual seismic response compared with elastic analysis methods. Encouragement to use the former procedures is still limited and designers tend to favour elastic analysis procedures. The permissible reduction in response parameters obtained from elastic analysis unlike those from inelastic analysis procedures discourages the effective use of the latter analysis methods in design. This paper proposes a simple and theoretically-based approach that utilises inelastic structural response in design. To describe the approach, the correlation of seismic demands obtained from ineleastic and elastic analysis procedures carried out using a wide range of reinforced concrete buildings of different characteristics, ranging from 8 to 60 storeys, is investigated. Verified fibre element models and diverse input ground motions are employed in the inelastic simulations. The presented approach enables designers to arrive at a realistic and cost-effect.
Collision of Two Stellar Associations in the Nearby Gum Nebula

Based on Gaia DR2 data and new CHIRON radial velocities, we have discovered that two nearby stellar associations UPK 535 (318.08 ± 0.29 pc, 25+15 −10 Myr, 174 stars) and Yep 3 (339.54 ± 0.25 pc , 45 +55 −20 Myr, 297 stars) in the Gum Nebula have recently collided. We project stars’ current positions, motions, and measurement uncertainties backward and forward through time in a 10,000-trial Monte Carlo simulation. On average, the associations’ centres of mass come within 18.89 ± 0.73 pc of each other 0.84 ± 0.03 Myr ago. A mode of 54 ± 7 close (<1 pc) stellar encounters occur during the collision. We cannot predict specific star-star close encounters with our current ∼7.6 pc distance precision and 21.5-per-cent-complete radial velocity sample. Never the less, we find that two stars in UPK 535 and two stars in Yep 3 undergo a nonspecific close encounter in >70 per cent of trials and multiple close encounters in ∼30 per cent. On average, the closest approach of any two stars is 0.13 ± 0.06 pc, or 27,000 ± 12,000 au. With impulse-tracing values up to 2.7+3.1 −1.1 M⊙ pc −2 km−1 s, such close encounters could perturb stars’ Oort cloud comets (if present), cause heavy bombardment events for exoplanets (if present), and reshape solar system architectures. Finally, an expansion of our simulation suggests other associations in the region are also interacting. Association collisions may be commonplace, at least in the Gum Nebula straddling the Galactic plane, and may spur solar system evolution more than previously recognized.
SOLIS - The MLOps journey from data acquisition to actionable insights

Machine Learning operations is unarguably a very important and also one of the hottest topics in Artificial Intelligence lately. Being able to define very clear hypotheses for actual real-life problems that can be addressed by machine learning models, collecting and curating large amounts of data for model training and validation followed by model architecture search and actual optimization and finally presenting the results fits very well the scenario of Data Science experiments. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems. Automating live configuration mechanisms, on the fly adapting to live or offline data capture and consumption, serving multiple models in parallel either on edge or cloud architectures, addressing specific limitations of GPU memory or compute power, post-processing inference or prediction results and serving those either as APIs or with IoT based communication stacks in the same end-to-end pipeline are the real challenges that we try to address in this particular paper. In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all above requirements while using basic cross-platform tensor framework and script language engines.