Miracle-dz/UniFAR-FTUs huggingface dataset:
Super-resolution in magnetic resonance imaging: A review

For the last 15 years, super-resolution (SR) algorithms have successfully been applied to magnetic resonance imaging (MRI) data to increase the spatial resolution of scans after acquisition has been performed, thus facilitating the doctors' diagnosis. The variety of application and techniques has grown ever since, especially in the MRI modality, showing the interest of the community to such postacquisition processing. This article presents a review of the general principle of SR as well as how this principle has been adapted to MRI data. The main algorithms and the principal acquisition protocols are detailed for both static and moving subjects. The presented strategies are discussed and compared according to the data specificities. Later, different ways of measuring the resolution enhancement and quantify the benefit of SR are detailed. Finally, unexplored perspectives on the application of SR to MRI data are discussed. © 2012 Wiley Periodicals, Inc. Concepts Magn Reson Part A 40A: 306–325, 2012.
Layer-by-Layer Assembled Solid Polymer Electrolyte for Electrochromic Devices

A new solid polymer electrolyte film fabricated by layer-by-layer (LbL) assembly is presented. The electrolyte film consists of four interbonding layers per deposition cycle, which combines electrostatic and hydrogen bonding in the same structure. Linear poly(ethylene imine) (LPEI) and poly(ethylene oxide) (PEO) are used to enhance the dissolution of lithium salt and the ionic transport through segmental motions of polymer chains. Characterization of film structure and growth shows good incorporation of electrostatic and hydrogen bonding, because of the versatile control of the ionization of poly(acrylic acid) (PAA), which serves as a bridging molecule. Ionic conductivity values, as described by the Vogel−Fulcher−Tammann equation, are found to be above 10−5 S/cm for the dried electrolyte at room temperature and moderate humidity (52%RH). Thermal analysis reveals two competing processes, namely, cross-linking of LPEI and PAA to form an amide compound and segregation of PEO crystalline phase, which results .
Loop currents in quantum matter

In many quantum materials, strong electron correlations lead to the emergence of new states of matter. In particular, the study in the last decades of the complex phase diagram of high temperature superconducting cuprates highlighted intra-unit-cell electronic instabilities breaking discrete Ising-like symmetries, while preserving the lattice translation invariance. Polarized neutron diffraction experiments have provided compelling evidences supporting a new form of intra-unit-cell magnetism, emerging concomitantly with the so-called pseudogap state of these materials. This observation is currently interpreted as the magnetic hallmark of an intra-unit-cell loop current order, breaking both parity and time-reversal symmetries. More generally, this magneto-electric state is likely to exist in a wider class of quantum materials beyond superconducting cuprates. For instance, it has been already observed in hole-doped Mott insulating iridates or in the spin liquid state of hole-doped 2-leg ladder cuprates.
DRO: Deep Recurrent Optimizer for Structure-from-Motion

There are increasing interests of studying the structure-from-motion (SfM) problem with machine learning techniques. While earlier methods directly learn a mapping from images to depth maps and camera poses, more recent works enforce multi-view geometry through optimization embed in the learning framework. This paper presents a novel optimization method based on recurrent neural networks to further exploit the potential of neural networks in SfM. Our neural optimizer alternatively updates the depth and camera poses through iterations to minimize a feature-metric cost. Two gated recurrent units are designed to trace the historical information during the iterations. Our network works as a zeroth-order optimizer, where the computation and memory expensive cost volume or gradients are avoided. Experiments demonstrate that our recurrent optimizer effectively reduces the feature-metric cost while reﬁn-ing the depth and poses. Our method outperforms previous methods and is more efﬁcient in computation and memory consumption than cost-volume-based methods. The code of our method will be made public.
Temporal Context Aggregation Network for Temporal Action Proposal Refinement

Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field. The proposals generated by current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval owing to the lack of efficient temporal modeling and effective boundary context utilization. In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement. Specifically, we first design a Local-Global Temporal Encoder (LGTE), which adopts the channel grouping strategy to efficiently encode both "local and global" temporal inter-dependencies. Furthermore, both the boundary and internal context of proposals are adopted for frame-level and segment-level boundary regressions, respectively. Temporal Boundary Regressor (TBR) is designed to combine these two regression granularities in an end-to-end fashion, which achieves the precise boundaries and reliable confidence of proposals through progressive refinement. Extensive experiments are conducted on three challenging datasets: HACS, ActivityNet-v1.3, and THUMOS-14, where TCANet can generate proposals with high precision and recall. By combining with the existing action classifier, TCANet can obtain remarkable temporal action detection performance compared with other methods. Not surprisingly, the proposed TCANet won the 1st place in the CVPR 2020 - HACS challenge leaderboard on temporal action localization task.
Transitive double Lie algebroids via core diagrams

The core diagram of a double Lie algebroid consists of the core of the double Lie algebroid, together with the two core-anchor maps to the sides of the double Lie algebroid. If these two core-anchors are surjective, then the double Lie algebroid and its core diagram are called transitive. This paper establishes an equivalence between transitive double Lie algebroids, and transitive core diagrams over a fixed base manifold. In other words, it proves that a transitive double Lie algebroid is completely determined by its core diagram.The comma double Lie algebroid associated to a morphism of Lie algebroids is defined. If the latter morphism is one of the core-anchors of a transitive core diagram, then the comma double algebroid can be quotiented out by the second core-anchor, yielding a transitive double Lie algebroid, which is the one that is equivalent to the transitive core diagram.Brown's and Mackenzie's equivalence of transitive core diagrams (of Lie groupoids) with transitive double Lie groupoids is then used in order to show that a transitive double Lie algebroid with integrable sides and core is automatically integrable to a transitive double Lie groupoid.
TeCoMiner: Topic Discovery Through Term Community Detection

This note is a short description of TeCoMiner, an interactive tool for exploring the topic content of text collections. Unlike other topic modeling tools, TeCoMiner is not based on some generative probabilistic model but on topological considerations about co-occurrence networks of terms. We outline the methods used for identifying topics, describe the features of the tool, and sketch an application, using a corpus of policy related scientific news on environmental issues published by the European Commission over the last decade.
Note on the offspring distribution for group testing in the linear regime

ABSTRACT. The group testing problem is concerned with identifying a small set of k infected individuals in a large population of n people. At our disposal is a testing scheme that can test groups of individuals. A test comes back positive if and only if at least one individual is infected. In this note, we lay groundwork for analysing belief propagation for group testing when k scales linearly in n. To this end, we derive the offspring distribution for different types of individuals. With these distributions at hand, one can employ the population dynamics algorithm to simulate the posterior marginal distribution resulting from belief propagation.
Finetuning Pretrained Transformers into RNNs

Transformers have outperformed recurrent neural networks (RNNs) in natural language generation. But this comes with a signifi- cant computational cost, as the attention mechanism’s complexity scales quadratically with sequence length. Efficient transformer variants have received increasing interest in recent works. Among them, a linear-complexity recurrent variant has proven well suited for autoregressive generation. It approximates the softmax attention with randomized or heuristic feature maps, but can be difficult to train and may yield suboptimal accuracy. This work aims to convert a pretrained transformer into its efficient recurrent counterpart, improving efficiency while maintaining accuracy. Specifically, we propose a swap-then-finetune procedure: in an off-the-shelf pretrained transformer, we replace the softmax attention with its linear-complexity recurrent alternative and then finetune. With a learned feature map, our approach provides an improved tradeoff between efficiency and accuracy over the standard transformer and other recurrent variants. We also show that the finetuning process has lower training cost relative to training these recurrent variants from scratch. As many models for natural language tasks are increasingly dependent on large-scale pretrained transformers, this work presents a viable approach to improving inference efficiency without repeating the expensive pretraining process.
Horizontaler Transfer von Antibiotikaresistenzgenen — Diskussion und Erkenntnisse

Mit Sorge beobachtet man die Entwicklung, das immer mehr infektiose Keime gegen den Einsatz von Antibiotika mit Resistenzentwicklung reagieren. Schon besiegt geglaubte Volkskrankheiten wie Tuberkulose sind plotzlich wieder auf dem Vormarsch. Urn die Resistenzentwicklung bei infektiosen Keimen zu limitieren, wird deshalb von Medizinern empfohlen, den Einsatz von Antibiotika sorgfaltig zu kontrollieren. Antibiotika sollten nur bei schwerwiegenden, bakteriellen Infektionen verordnet werden und ihre Auswahl sich an den Resistenzspektren der infektiosen Keime orientieren. Der Patient sollte die Antibiotikatherapie strong nach den Anweisungen des Arztes durchfuhren und sie nicht bei ersten Anzeichen einer Besserung absetzen. Diese Regeln sind in der Offentlichkeit gut bekannt und werden deshalb meist auch befolgt.
Mostly electric assisted airplanes (MEAP) for regional aviation: A South Asian perspective

Aircraft manufacturing relies on pre-order bookings. The configuration of the to be assembled aircraft is fixed by the design assisted market surveys. The sensitivity of the supply chain to the market conditions, makes, the relationship between the product (aircraft) and the associated service (aviation), precarious. Traditional model to mitigate this risk to profitability rely on increasing the scales of operations. However, the emergence of new standards of air quality monitoring and insistence on the implementation, demands additional corrective measures. In the quest for a solution, this research commentary establishes a link, between the airport taxes and the nature of the transporting unit. It warns, that merely, increasing the number of mid haulage range aircrafts (MHA) in the fleet, may not be enough, to overcome this challenge. In a two-pronged approach, the communication proposes, the use of mostly electric assisted air planes, and small sized airports as the key to solving this complex problem. As a side-note the appropriateness of South Asian region, as a test-bed for MEAP based aircrafts is also investigated. The success of this the idea can be potentially extended, to any other aviation friendly region of the world.
Covariant phase observables

Covariant phase observables constitute a simple solution of the quantum phase problem of a single‐mode optical field. They share three important properties: their range of values is the phase interval [0,2π), they are covariant under shifts generated by the number operator (which is necessary for coherent state phase measurements), and they have the uniform phase distribution in number states. Moreover, some phase observables have been measured (e.g. the phase observable associated to the Q‐function). The canonical phase observable has some additional properties which distinguish it from other covariant phase observables: it generates number shifts, it is uniquely associated to the polar decomposition of the lowering operator (Dirac's idea [1]), it has a projection valued covariant dilation (Newton's extension [2]), and it has a projection valued discretization (the Pegg‐Barnett formalism [3]). The single‐mode covariant phase theory can easily be extended to the two‐mode theory of covariant phase difference observables. Finally, most of all phase theories have connections to the covariant phase theory.
Pattern recognition by optically induced surface currents in a magnetic neural network

The search for energy-efficient alternatives for computer data processing urges us to look at the neural architecture of our brain, as it indeed uses much less energy than a computer. While working on one such alternative, we built a neural network in which the magnetization of a multilayered CoPt sample as an artificial synapse adapts to ultrashort optical pulses. As an underlying mechanism behind such magnetization adaptation, we exposed a new phenomenon in the form of surface currents stem out from the interactions of these helicity-dependent circular pulses with the sample. To supervise this network for a pattern recognition functionality, 90% of patterns from a data set of twenty-seven, constituting each pattern by nine binary inputs are utilized1. In the network training, a consistent number of picosecond pump pulses, in their normal incidence to the sample, sequentially adjust each synaptic weight assigned with every input of such patterns. These, instead Gaussian, flat-top shaped pulses are used for the first time in this research field and here they resolve the sample inhomogeneity and the efficiency issues of the network. For the same efficiency reason, weight readout was also accomplished by never used picosecond probe pulses in the magneto-optic imaging. Further, on the training, the network keeps adjusting the weights until all the weight-dependent patterns are categorized into one of the twofold classes around a pre-set threshold. Convincingly, this supervised network identifies 10% rest of the untrained patterns from the given data set and thus motivates us to develop other complexes yet energy-efficient magneto-optical networks with more advanced functionalities.
Pole-like Objects Mapping and Long-Term Robot Localization in Dynamic Urban Scenarios

Localization on 3D data is a challenging task for unmanned vehicles, especially in long-term dynamic urban scenarios. Due to the generality and long-term stability, the pole-like objects are very suitable as landmarks for unmanned vehicle localization in time-varying scenarios. In this paper, a long-term LiDAR-only localization algorithm based on semantic cluster map is proposed. At first, the Convolutional Neural Network(CNN) is used to infer the semantics of LiDAR point clouds. Combined with the point cloud segmentation, the static objects pole/trunk are extracted and registered into global semantic cluster map. When the unmanned vehicle re-enters the environment again, the relocalization is completed by matching the clusters of current scan with the clusters of the global map. Furthermore, the matching between the local and global maps stably outputs the global pose at 2Hz to correct the drift of the 3D LiDAR odometry. The experimental results on our campus dataset demonstrate that the proposed approach performs better in localization accuracy compared with the current state-of-the-art methods. The source of this paper is available at: http://www.github. com/HITSZ-NRSL/long-term-localization.
First comprehensive report on the composition of the leaf volatile terpenoids of Pinus arizonica Engelm. and P. ponderosa var. brachyptera (Engelm.) Lemmon

The first comprehensive report on the composition of the volatile leaf terpenoids of Pinus arizonica and P. ponderosa var. brachyptera from Arizona is presented. The leaf oil of P. arizonica contains major amounts of α-pinene (19.4%), germacrene D (19.0%), (E)-caryphyllene (10.1%) and βphellandrene (9.0%) with moderate amounts of β-pinene (5.0%) and limonene (4.4%). The leaf oil of P. p. var. brachyptera has considerable α-pinene (15.2%), germacrene D (13.6%), β-pinene (11.2%), (E)caryphyllene (9.7%) and δ-3-carene (9.2%), with modest amounts of β-phellandrene (2.4%), limonene (2.3%),terpinolene (2.1%), camphene(2.1%), myrcene(2.1%), bornyl acetate(2.0%) and (Z)-βocimene(1.8%). The oil of P. arizonica contains seven unique compounds: longifolene, unknown sesquiterpene (KI 1494), ethyl dodecanoate, ethyl tetradecanoate, iso-abienol, abienol and unknown diterpene (KI 2341). The oil of P. p. var. brachyptera has eight unique compounds: δ-3-carene, 2undecanone, β-longipinene, β-himachalene, α-cuprenene, unknown sesquiterpene aldehyde (KI 1756), putative (2E)-tridecenol, and phenyl ethyl octanoate. Published on-line www.phytologia. org Phytologia 97(1): 45-50 (Jan 2, 2015). ISSN 030319430.
Unsupervised collaborative learning using privileged information

In the collaborative clustering framework, the hope is that by combining several clustering solutions, each one with its own bias and imperfections, one will get a better overall solution. The goal is that each local computation, quite possibly applied to distinct data sets, benefits from the work done by the other collaborators. This article is dedicated to collaborative clustering based on the Learning Using Privileged Information paradigm. Local algorithms weight incoming information at the level of each observation, depending on the confidence level of the classification of that observation. A comparison between our algorithm and state of the art implementations shows improvement of the collaboration process using the proposed approach.
Optical amplification for astronomical imaging at higher resolution

Heisenberg’s uncertainty principle tells us that it is impossible to determine simultaneously the position of a photon crossing a telescope’s aperture and its momentum. Super-resolution imaging techniques rely on modification of the observed sample, or on entangling photons. In astronomy we have no access to the object, but resolution may be improved by optical amplification. Unfortunately, spontaneous emission contributes noise and negates the possible gain from stimulated emissions. We show that it is possible to increase the weight of the stimulated photons by considering photon statistics, and observe an improvement in resolution. Most importantly, we demonstrate a method which can apply for all imaging purposes.
AQEyes: Visual Analytics for Anomaly Detection and Examination of Air Quality Data

Anomaly detection plays a key role in air quality analysis by enhancing situational awareness and alerting users to potential hazards. However, existing anomaly detection approaches for air quality analysis have their own limitations regarding parameter selection (e.g., need for extensive domain knowledge), computational expense, general applicability (e.g., require labeled data), interpretability, and the efficiency of analysis. Furthermore, the poor quality of collected air quality data (inconsistently formatted and sometimes missing) also increases the difficulty of analysis substantially. In this paper, we systematically formulate design requirements for a system that can solve these limitations and then propose AQEyes, an integrated visual analytics system for efficiently monitoring, detecting, and examining anomalies in air quality data. In particular, we propose a unified end-to-end tunable machine learning pipeline that includes several data pre-processors and featurizers to deal with data quality issues. The pipeline integrates an efficient unsupervised anomaly detection method that works without the use of labeled data and overcomes the limitations of existing approaches. Further, we develop an interactive visualization system to visualize the outputs from the pipeline. The system incorporates a set of novel visualization and interaction designs, allowing analysts to visually examine air quality dynamics and anomalous events in multiple scales and from multiple facets. We demonstrate the performance of this pipeline through a quantitative evaluation and show the effectiveness of the visualization system using qualitative case studies on real-world datasets.
Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition

Temporal context is key to the recognition of expressions of emotion. Existing methods, that rely on recurrent or self-attention models to enforce temporal consistency, work on the feature level, ignoring the task-specific temporal dependencies, and fail to model context uncertainty. To alleviate these issues, we build upon the framework of Neural Processes to propose a method for apparent emotion recognition with three key novel components: (a) probabilistic contextual representation with a global latent variable model; (b) temporal context modelling using task-specific predictions in addition to features; and (c) smart temporal context selection. We validate our approach on four databases, two for Valence and Arousal estimation (SEWA and AffWild2), and two for Action Unit intensity estimation (DISFA and BP4D). Results show a consistent improvement over a series of strong baselines as well as over state-of-the-art methods.
A literature review

Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory tract infection worldwide, resulting in approximately sixty thousand annual hospitalizations of < 5-year-olds in the United States alone and three million annual hospitalizations globally. The development of over 40 vaccines and immunoprophylactic interventions targeting RSV has the potential to significantly reduce the disease burden from RSV infection in the near future. In the context of RSV, a highly contagious pathogen, dynamic transmission models (DTMs) are valuable tools in the evaluation and comparison of the effectiveness of different interventions. This review, the first of its kind for RSV DTMs, provides a valuable foundation for future modelling efforts and highlights important gaps in our understanding of RSV epidemics. Specifically, we have searched the literature using Web of Science, Scopus, Embase, and PubMed to identify all published manuscripts reporting the development of DTMs focused on the population transmission of RSV. We reviewed the resulting studies and summarized the structure, parameterization, and results of the models developed therein. We anticipate that future RSV DTMs, combined with cost-effectiveness evaluations, will play a significant role in shaping decision making in the development and implementation of intervention programs.