Miracle-dz/UniFAR-FTUs huggingface dataset:
CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems

We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements. Unlike traditional DL methods that learn a mapping from the measurements to the desired image, CoIL trains a multilayer perceptron (MLP) to encode the complete measurement field by mapping the coordinates of the measurements to their responses. CoIL is a self-supervised method that requires no training examples besides the measurements of the test object itself. Once the MLP is trained, CoIL generates new measurements that can be used within a majority of image reconstruction methods. We validate CoIL on sparse-view computed tomography using several widely-used reconstruction methods, including purely model-based methods and those based on DL. Our results demonstrate the ability of CoIL to consistently improve the performance of all the considered methods by providing high-fidelity measurement fields.
Characterizing the Online Learning Landscape

Hundreds of millions of people learn something new online every day. Simultaneously, the study of online education has blossomed within the human computer interaction community, with new systems, experiments, and observations creating and exploring previously undiscovered online learning environments. In this study we endeavor to characterize this entire landscape of online learning experiences using a national survey of 2260 US adults who are balanced to match the demographics of the U.S. We examine the online learning resources that they consult, and we analyze the subjects that they pursue using those resources. Furthermore, we compare both formal and informal online learning experiences on a larger scale than has ever been done before, to our knowledge, to better understand which subjects people are seeking for intensive study. We find that there is a core set of online learning experiences that are central to other experiences and these are shared among the majority of people who learn online. We conclude by showing how looking outside of these core online learning experiences can reveal opportunities for innovation in online education.
Enhancing Reading Strategies by Exploring A Theme-based Approach to Literature Surveys

Searching large digital repositories can be extremely frustrating, as common list-based formats encourage users to adopt a convenience-sampling approach that favours chance discovery and random search, over meaningful exploration. We have designed a methodology that allows users to visually and thematically explore corpora, while developing personalised holistic reading strategies. We describe the results of a three-phase qualitative study, in which experienced researchers used our interactive visualisation approach to analyse a set of publications and select relevant themes and papers. Using in-depth semi-structured interviews and stimulated recall, we found that users: (i) selected papers that they otherwise would not have read, (ii) developed a more coherent reading strategy, and (iii) understood the thematic structure and relationships between papers more effectively. Finally, we make six design recommendations to enhance current digital repositories that we have shown encourage users to adopt a more holistic and thematic research approach.
Local and Global Uniform Convexity Conditions

We review various characterizations of uniform convexity and smoothness on norm balls in finitedimensional spaces and connect results stemming from the geometry of Banach spaces with scaling inequalities used in analysing the convergence of optimization methods. In particular, we establish local versions of these conditions to provide sharper insights on a recent body of complexity results in learning theory, online learning, or offline optimization, which rely on the strong convexity of the feasible set. While they have a significant impact on complexity, these strong convexity or uniform convexity properties of feasible sets are not exploited as thoroughly as their functional counterparts, and this work is an effort to correct this imbalance. We conclude with some practical examples in optimization and machine learning where leveraging these conditions and localized assumptions lead to new complexity results.
A neural network classifier for electron identification on the DAMPE experiment

The Dark Matter Particle Explorer (DAMPE) is a space-borne particle detector and cosmic ray observatory in operation since 2015, designed to probe electrons and gamma rays from a few GeV to 10 TeV in energy, as well as cosmic protons and nuclei up to 100 TeV. Among the main scientific objectives is the precise measurement of the cosmic electron + positron flux, which, due to the very large proton background in orbit, requires a powerful particle identification method. In the past decade, the field of machine learning has provided us the needed tools. This paper presents a neural network based approach to cosmic electron identification and proton rejection and showcases its performance based on simulated Monte Carlo data. The neural network reaches significantly lower background than the classical, cut-based method for the same detection efficiency, especially at the highest energies probed by the detector. Good agreement between simulation and real data is demonstrated.
The Multiplicative Version of Azuma's Inequality, with an Application to Contention Analysis

Azuma's inequality is a tool for proving concentration bounds on random variables. The inequality can be thought of as a natural generalization of additive Chernoff bounds. On the other hand, the analogous generalization of multiplicative Chernoff bounds has, to our knowledge, never been explicitly formulated. We formulate a multiplicative-error version of Azuma's inequality. We then show how to apply this new inequality in order to greatly simplify (and correct) the analysis of contention delays in multithreaded systems managed by randomized work stealing.
A Bonus-Malus Framework for Cyber Risk Insurance and Optimal Cybersecurity Provisioning

The cyber risk insurance market is at a nascent stage of its development, even as the magnitude of cyber losses is significant and the rate of cyber risk events is increasing. Existing cyber risk insurance products as well as academic studies have been focusing on classifying cyber risk events and developing models of these events, but little attention has been paid to proposing insurance risk transfer strategies that incentivize mitigation of cyber loss through adjusting the premium of the risk transfer product. To address this important gap, we develop a Bonus-Malus model for cyber risk insurance. Specifically, we propose a mathematical model of cyber risk insurance and cybersecurity provisioning supported with an efficient numerical algorithm based on dynamic programming. Through a numerical experiment, we demonstrate how a properly designed cyber risk insurance contract with a Bonus-Malus system can resolve the issue of moral hazard and benefit the insurer.
Some properties of K-frame in n-Hilbert space

The notion of a K-frame in n-Hilbert space is presented and some of their characterizations are given. We verify that sum of two K-frames is also a K-frame in n-Hilbert space. Also, the concept of tight K-frame in n-Hilbert space is described and some properties of its are going to be established.
Toward Safe and Efficient Human–Robot Interaction via Behavior-Driven Danger Signaling

This article introduces the notion of danger awareness in the context of human–robot interaction, which decodes whether a human is aware of the existence of the robot, and illuminates whether the human is willing to engage in ensuring safety. This article also quantifies the notion as a single binary variable, called danger awareness coefficient, and provides a game-theoretic interpretation for that. Employing an online Bayesian learning method to update the robot’s belief about the human’s danger awareness by observing their actions, it is shown how the robot can build a predictive human model to anticipate the human’s future actions. To enrich robot’s observations, and thus to improve safety and efficiency of the robot, the robot is equipped with a danger signaling system to generate awareness in the human. Finally, a planning scheme is proposed to provide an efficient and probabilistically safe plan for the robot. The effectiveness of the proposed scheme is demonstrated through simulation studies on an interaction between a self-driven car and a pedestrian.
Locally Free Weight Sharing for Network Width Search

Searching for network width is an effective way to slim deep neural networks with hardware budgets. With this aim, a one-shot supernet is usually leveraged as a performance evaluator to rank the performance \wrt~different width. Nevertheless, current methods mainly follow a manually fixed weight sharing pattern, which is limited to distinguish the performance gap of different width. In this paper, to better evaluate each width, we propose a locally free weight sharing strategy (CafeNet) accordingly. In CafeNet, weights are more freely shared, and each width is jointly indicated by its base channels and free channels, where free channels are supposed to loCAte FrEely in a local zone to better represent each width. Besides, we propose to further reduce the search space by leveraging our introduced FLOPs-sensitive bins. As a result, our CafeNet can be trained stochastically and get optimized within a min-min strategy. Extensive experiments on ImageNet, CIFAR-10, CelebA and MS COCO dataset have verified our superiority comparing to other state-of-the-art baselines. For example, our method can further boost the benchmark NAS network EfficientNet-B0 by 0.41\% via searching its width more delicately.
It’s Cloud’s Illusions I Recall: Mixing Drives the Acceleration of Clouds from Ram Pressure Stripped Galaxies

Ram pressure stripping can remove gas from satellite galaxies in clusters via a direct interaction between the intracluster medium (ICM) and the interstellar medium. This interaction is generally thought of as a contact force per area; however, we point out that these gases must interact in a hydrodynamic fashion and argue that this will lead to the mixing of the galactic gas with the ICM wind. We develop an analytic framework for how mixing is related to the acceleration of stripped gas from a satellite galaxy. We then test this model using three “wind-tunnel” simulations of Milky Way–like galaxies interacting with a moving ICM and find excellent agreement with predictions using the analytic framework. Focusing on the dense clumps in the stripped tails, we find that they are nearly uniformly mixed with the ICM, indicating that all gas in the tail mixes with the surroundings and dense clumps are not separate entities to be modeled differently than diffuse gas. We find that while mixing drives the acceleration of stripped gas, the density and velocity of the surrounding wind will determine whether the mixing results in the heating of stripped gas into the ICM or the cooling of the ICM into dense clouds.
NOELLE Offers Empowering LLVM Extensions

Modern and emerging architectures demand increasingly complex compiler analyses and transformations. As the emphasis on compiler infrastructure moves beyond support for peephole optimizations and the extraction of instruction-level parallelism, compilers should support custom tools designed to meet these demands with higher-level analysis-powered abstractions and functionalities of wider program scope. This paper introduces NOELLE, a robust open-source domain-independent compilation layer built upon LLVM providing this support. NOELLE extends abstractions and functionalities provided by LLVM enabling advanced, program-wide code analyses and transformations. This paper shows the power of NOELLE by presenting a diverse set of 11 custom tools built upon it.
Sparse Antenna Array Design for MIMO Radar Using Softmax Selection

MIMO transmit arrays allow for flexible design of the transmit beampattern. However, the large number of elements required to achieve certain performance using uniform linear arrays (ULA) maybe be too costly. This motivated the need for thinned arrays by appropriately selecting a small number of elements so that the full array beampattern is preserved. In this paper, we propose Learn-to-Select (L2S), a novel machine learning model for selecting antennas from a dense ULA employing a combination of multiple Softmax layers constrained by an orthogonalization criterion. The proposed approach can be efficiently scaled for larger problems as it avoids the combinatorial explosion of the selection problem. It also offers a flexible array design framework as the selection problem can be easily formulated for any metric.
Memory-Associated Differential Learning

Conventional Supervised Learning approaches focus on the mapping from input features to output labels. After training, the learnt models alone are adapted onto testing features to predict testing labels in isolation, with training data wasted and their associations ignored. To take full advantage of the vast number of training data and their associations, we propose a novel learning paradigm called Memory-Associated Differential (MAD) Learning. We first introduce an additional component called Memory to memorize all the training data. Then we learn the differences of labels as well as the associations of features in the combination of a differential equation and some sampling methods. Finally, in the evaluating phase, we predict unknown labels by inferencing from the memorized facts plus the learnt differences and associations in a geometrically meaningful manner. We gently build this theory in unary situations and apply it on Image Recognition, then extend it into Link Prediction as a binary situation, in which our method outperforms strong state-of-the-art baselines on ogbl-ddi dataset.
Massive Self-Assembly in Grid Environments

Self-assembly plays an essential role in many natural processes, involving the formation and evolution of living or non-living structures, and shows potential applications in many emerging domains. In existing research and practice, there still lacks an ideal self-assembly mechanism that manifests efficiency, scalability, and stability at the same time. Inspired by phototaxis observed in nature, we propose a computational approach for massive self-assembly of connected shapes in grid environments. The key component of this approach is an artificial light field superimposed on a grid environment, which is determined by the positions of all agents and at the same time drives all agents to change their positions, forming a dynamic mutual feedback process. This work advances the understanding and potential applications of self-assembly.
Proximal Decoding for LDPC-coded Massive MIMO Channels

We propose a novel optimization-based decoding algorithm for LDPC-coded massive MIMO channels. The proposed decoding algorithm is based on a proximal gradient method for solving an approximate maximum a posteriori (MAP) decoding problem. The key idea is the use of a code-constraint polynomial penalizing a vector far from a codeword as a regularizer in the approximate MAP objective function. The code proximal operator is naturally derived from code-constraint polynomials. The proposed algorithm, called proximal decoding, can be described by a simple recursion consisting of the gradient descent step for a negative log-likelihood function and the code proximal operation. Several numerical experiments show that the proposed algorithm outperforms known massive MIMO detection algorithms, such as an MMSE detector with belief propagation decoding.
Knowledge Graph Generation and Entity Prediction for Contextual Malware Threat Intelligence

Large amounts of threat intelligence information about malware attacks are available in disparate, typically unstructured, formats. Knowledge graphs can capture this information and its context using RDF triples represented by entities and relations. Sparse or inaccurate threat information, however, leads to challenges such as incomplete or erroneous triples. Named entity recognition (NER) and relation extraction (RE) models used to populate the knowledge graph cannot fully guarantee accurate information retrieval, further exacerbating this problem. This paper proposes an end-to-end approach to generate a Malware Knowledge Graph called MalKG, the ﬁrst open-source automated knowledge graph for malware threat intelligence. MalKG dataset called MT40K 1 , contains approximately 40,000 triples generated from 27,354 unique entities and 34 relations. We demonstrate the application of MalKG in predicting missing malware threat intelligence information in the knowledge graph. For ground truth, we manually curate a knowledge graph called MT3K, with 3,027 triples generated from 5,741 unique entities and 22 relations. For entity prediction via a state-of-the-art entity prediction model (TuckER), our approach achieves 80.4 for the hits@10 metric (predicts the top 10 options for missing entities in the knowledge graph), and 0.75 for the MRR (mean reciprocal rank). We also propose a framework to automate the extraction of thousands of entities and relations into RDF triples, both manually and automatically, at the sentence level from 1,100 malware threat intelligence reports and from the common vulnerabilities and exposures (CVE) database.
Confirming the Calibration of ALMA Using Planck Observations

We test the accuracy of ALMA flux density calibration by comparing ALMA flux density measurements of extragalactic sources to measurements made by the Planck mission; Planck is absolutely calibrated to subpercent precision using the dipole signal induced by the satellite’s orbit around the solar system barycenter. Planck observations ended before ALMA began systematic observations, however, and many of the sources are variable, so we employ measurements by the Atacama Cosmology Telescope (ACT) to bridge the two epochs. We compare ACT observations at 93 and ∼145 GHz to Planck measurements at 100 and 143 GHz and to ALMA measurements made at 91.5 and 103.5 GHz in Band 3. For both comparisons, flux density measurements were corrected to account for the small differences in frequency using the best available spectral index for each source. We find the ALMA flux density scale (based on observations of Uranus) is consistent with Planck. All methods used to make the comparison are consistent with ALMA flux densities in Band 3 averaging 0.99 times those measured by Planck. One specific test gives ALMA/Planck = 0.996 ± 0.024. We also test the absolute calibration of both ACT at 93 and ∼145 GHz and the South Pole Telescope (SPT) at 97.43, 152.9, and 215.8 GHz, again with reference to Planck measurements at 100, 143, and 217 GHz, as well as the internal consistency of measurements of compact sources made by all three instruments.
Pretreatment Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio as Prognostic Factors and Reference Markers of Treatment Options for Locally Advanced Squamous Cell Carcinoma Located in the Middle and Upper Esophagus

Background Various inflammatory biomarkers, such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), have been well authenticated to predict clinical outcomes in numerous types of cancer. The optimal treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC) located in the middle or upper region is still inconclusive. The aim of the study was to examine pretreatment NLR and PLR to select from radical surgery or definitive chemoradiotherapy (dCRT) for these patients. The linkage between pretreatment NLR/PLR and prognosis was also analyzed. Methods NLR and PLR were calculated in 113 locally advanced ESCC located in the middle or upper esophagus of patients who underwent radical surgery or dCRT between January 2014 and December 2019. A receiver operating characteristic curve was plotted to select the best cut-off value of NLR and PLR for predicting survival. A survival curve was plotted using the Kaplan–Meier method. Univariate and multivariate Cox regression analyses were applied to assess predictors for survival. Results NLR and PLR were associated with the extent of lymph node metastasis (NLR: P = 0.045; PLR: P = 0.002). Additionally, high PLR and recurrence with distant organ metastasis were closely related (P = 0.014), and NLR was related to the tumor stage (P = 0.043). The results of the multivariate analysis revealed that NLR (>2.07) and PLR (>183.06) were independently associated with poor prognosis. It is noteworthy that surgery was associated with a superior OS compared with dCRT in the low NLR population (P = 0.045). Conclusion Low pretreatment NLR patients are fit to undergo radical surgery with a substantial therapeutic benefit. Pretreatment NLR and PLR are independent predictors for patients with locally advanced ESCC located in the middle and upper esophagus who underwent radical surgery or dCRT.
An incomplete form of anti-ganglioside antibody-positive Miller Fisher syndrome after an Epstein-Barr virus infection

Abstract Rationale: The Miller Fisher syndrome (MFS) is an acute polyradiculoneuritis regarded as an uncommon clinical variant of the Guillain-Barre syndrome (GBS). It is characterized by the clinical triad of ophthalmoplegia, ataxia, and areflexia. The diagnosis of MFS is based on clinical presentation, presence of albuminocytologic dissociation in the cerebrospinal fluid (CSF), and normal brain imaging results. The presence of anti-ganglioside antibodies (GQlb) in the serum is helpful for the diagnosis. A history of upper respiratory tract infection or diarrhea 3 days to 6 weeks before the onset of MFS is common. However, there are some patients with atypical manifestations who are difficult to diagnose. Here, we present an incomplete form of MFS where antibodies against GQ1b were detected in the serum following an Epstein Barr virus (EBV) infection. Patient concerns: A 77-year-old Chinese woman was admitted to the hospital with acute diplopia and right blepharoptosis. She had a history of mild upper respiratory tract infection 2 weeks ago. In 1 week, the symptoms rapidly progressed into bilateral ophthalmoplegia and hyporeflexia of the limbs without ataxia. CSF analysis on the third day after onset was normal, without albuminocytologic dissociation. EBV immunoglobulin G (IgG) antibodies were detected in the CSF. GQ1b and GD1b IgG antibodies were positive in the serum and negative in the CSF. No responsible lesion was found on brain imaging examination. Diagnoses: In accordance with the progressive bilateral ophthalmoplegia and hyporeflexia, the history of upper respiratory tract infection, the detection of EBV and GQ1b antibodies, and the negative brain imaging examination, the diagnosis of MFS was confirmed. Interventions: The patient was administered intravenous immunoglobulin for 5 days. Outcomes: She had a favorable outcome after treatment. At the 6-week follow-up, bilateral ocular movement limitation and tendon reflexes had recovered. Lessons: The diagnosis of MFS can be challenging, especially when encountered with incomplete symptoms and normal CSF results. Attention should be paid to the presence of anti-GQ1b IgG antibodies when the clinical manifestations are incomplete. Furthermore, EBV primary infection could be associated with MFS and considered a potential causative agent.
Hot Flashes and Cardiovascular Disease Risk Indices Among Women With HIV

Abstract Women with HIV (WWH) transitioning through menopause have heightened cardiovascular disease (CVD) risk. In the general population, hot flash burden relates to CVD risk indices. We found higher hot flash burden among women with vs without HIV. Further, among WWH, hot flash burden related to select CVD risk indices. ClinicalTrials.gov Registration NCT02874703.