Miracle-dz/UniFAR-FTUs huggingface dataset:
Sub-clusters of Normal Data for Anomaly Detection

Anomaly detection in data analysis is an interesting but still challenging research topic in real world applications. As the complexity of data dimension increases, it requires to understand the semantic contexts in its description for effective anomaly characterization. However, existing anomaly detection methods show limited performances with high dimensional data such as ImageNet. Existing studies have evaluated their performance on low dimensional, clean and well separated data set such as MNIST and CIFAR-10. In this paper, we study anomaly detection with high dimensional and complex normal data. Our observation is that, in general, anomaly data is defined by semantically explainable features which are able to be used in defining semantic sub-clusters of normal data as well. We hypothesize that if there exists reasonably good feature space semantically separating sub-clusters of given normal data, unseen anomaly also can be well distinguished in the space from the normal data. We propose to perform semantic clustering on given normal data and train a classifier to learn the discriminative feature space where anomaly detection is finally performed. Based on our careful and extensive experimental evaluations with MNIST, CIFAR-10, and ImageNet with various combinations of normal and anomaly data, we show that our anomaly detection scheme outperforms state of the art methods especially with high dimensional real world images.
Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

Simple Summary Determination of human papillomavirus (HPV) status for oropharyngeal cancer patients depicts a essential diagnostic factor and is important for treatment decisions. Current histological methods are invasive, time consuming and costly. We tested the ability of deep learning models for HPV status testing based on routinely acquired diagnostic CT images. A network trained for sports video clip classification was modified and then fine tuned for HPV status prediction. In this way, very basic information about image structures is induced into the model before training is started, while still allowing for exploitation of full 3D information in the CT images. Usage of this approach helps the network to cope with a small number of training examples and makes it more robust. For comparison, two other models were trained, one not relying on a pre-training task and another one pre-trained on 2D Data. The pre-trained video model preformed best. Abstract Infection with the human papillomavirus (HPV) has been identified as a major risk factor for oropharyngeal cancer (OPC). HPV-related OPCs have been shown to be more radiosensitive and to have a reduced risk for cancer related death. Hence, the histological determination of HPV status of cancer patients depicts an essential diagnostic factor. We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets, we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine-tuned, such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases, with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet, the video pre-trained model performed best. Deep learning models are capable of CT image-based HPV status determination. Video based pre-training has the ability to improve training for 3D medical data, but further studies are needed for verification.
Optimal diameter computation within bounded clique-width graphs

Coudert et al. (SODA'18) proved that under the Strong Exponential-Time Hypothesis, for any $\epsilon >0$, there is no ${\cal O}(2^{o(k)}n^{2-\epsilon})$-time algorithm for computing the diameter within the $n$-vertex cubic graphs of clique-width at most $k$. We present an algorithm which given an $n$-vertex $m$-edge graph $G$ and a $k$-expression, computes all the eccentricities in ${\cal O}(2^{{\cal O}(k)}(n+m)^{1+o(1)})$ time, thus matching their conditional lower bound. It can be modified in order to compute the Wiener index and the median set of $G$ within the same amount of time. On our way, we get a distance-labeling scheme for $n$-vertex $m$-edge graphs of clique-width at most $k$, using ${\cal O}(k\log^2{n})$ bits per vertex and constructible in ${\cal O}(k(n+m)\log{n})$ time from a given $k$-expression. Doing so, we match the label size obtained by Courcelle and Vanicat (DAM 2016), while we considerably improve the dependency on $k$ in their scheme. As a corollary, we get an ${\cal O}(kn^2\log{n})$-time algorithm for computing All-Pairs Shortest-Paths on $n$-vertex graphs of clique-width at most $k$. This partially answers an open question of Kratsch and Nelles (STACS'20).
Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence

Solving Rank Constrained Least Squares via Recursive Importance Sketching In statistics and machine learning, we sometimes run into the rank-constrained least squares problems, for which we need to find the best low-rank fit between sets of data, such as trying to figure out what factors are affecting the data, filling in missing information, or finding connections between different sets of data. This paper introduces a new method for solving this problem called the recursive importance sketching algorithm (RISRO), in which the central idea is to break the problem down into smaller, easier parts using a unique technique called “recursive importance sketching. ” This new method is not only easy to use, but it is also very efficient and gives accurate results. We prove that RISRO converges in a local quadratic-linear and quadratic rate under some mild conditions. Simulation studies also demonstrate the superior performance of RISRO.
Explaining the Adapative Generalisation Gap

We conjecture that the reason for the diﬀer-ence in generalisation between adaptive and non adaptive gradient methods stems from the failure of adaptive methods to account for the greater levels of noise associated with ﬂatter directions in their estimates of local curvature. This conjecture—motivated by results in random matrix theory [1, 2]—has implications for optimisation in both simple convex settings and deep neural networks. We demonstrate that typical schedules used for adaptive meth-ods (with low numerical stability or damping constants) serve to bias relative movement towards ﬂat directions relative to sharp directions, eﬀectively amplifying the noise-to-signal ratio and harming generalisation. We show that the numerical stability/damping constant used in these methods can be decomposed into a learning rate reduction and linear shrinkage of the estimated curvature matrix. We then demonstrate signiﬁcant generalisation improvements by increasing the shrinkage coeﬃcient, closing the generalisation gap entirely in our neural network experiments. Finally, we show that other popular modiﬁcations to adaptive methods, such as decoupled weight decay [3] and partial adaptivity [4] can be shown to calibrate parameter updates to make better use of sharper, more reliable directions.
Automatically Repairing Programs Using Both Tests and Bug Reports

The success of automated program repair (APR) depends significantly on its ability to localize the defects it is repairing. For fault localization (FL), APR tools typically use either spectrum-based (SBFL) techniques that use test executions or information-retrieval-based (IRFL) techniques that use bug reports. These two approaches often complement each other, patching different defects. No existing repair tool uses both SBFL and IRFL. We develop RAFL (Rank-Aggregation-Based Fault Localization), a novel FL approach that combines multiple FL techniques. We also develop Blues, a new IRFL technique that uses bug reports, and an unsupervised approach to localize defects. On a dataset of 818 real-world defects, SBIR (combined SBFL and Blues) consistently localizes more bugs and ranks buggy statements higher than the two underlying techniques. For example, SBIR correctly identifies a buggy statement as the most suspicious for 18.1% of the defects, while SBFL does so for 10.9% and Blues for 3.1%. We extend SimFix, a state-of-the-art APR tool, to use SBIR, SBFL, and Blues. SimFix using SBIR patches 112 out of the 818 defects; 110 when using SBFL, and 55 when using Blues. The 112 patched defects include 55 defects patched exclusively using SBFL, 7 patched exclusively using IRFL, 47 patched using both SBFL and IRFL and 3 new defects. SimFix using Blues significantly outperforms iFixR, the state-of-the-art IRFL-based APR tool. Overall, SimFix using our FL techniques patches ten defects no prior tools could patch. By evaluating on a benchmark of 818 defects, 442 previously unused in APR evaluations, we find that prior evaluations on the overused Defects4J benchmark have led to overly generous findings. Our paper is the first to (1) use combined FL for APR, (2) apply a more rigorous methodology for measuring patch correctness, and (3) evaluate on the new, substantially larger version of Defects4J.
Properties of loss cone stars in a cosmological galaxy merger remnant

Aims. We investigate the orbital and phase space properties of loss cone stars that interact strongly with a hard, high-redshift binary supermassive black hole (SMBH) system formed in a cosmological scenario. Methods. We use a novel hybrid integration approach that combines the direct N-body code φ-GRAPE with ETICS, a collisionless code that employs the self-consistent field method for force calculation. The hybrid approach shows considerable speed-up over direct summation for particle numbers > 106, while retaining accuracy of direct N-body for a subset of particles. During the SMBH binary evolution we monitor individual stellar interactions with the binary in order to identify stars that noticeably contribute to the SMBH binary hardening. Results. We successfully identify and analyze in detail the properties of stars that extract energy from the binary. We find that the summed energy changes seen in these stars match very well with the overall binary energy change, demonstrating that stellar interactions are the primary drivers of SMBH binary hardening in triaxial, gas-poor systems. We find that 76% of these stars originate from centrophilic orbits, only possible in a triaxial system. As a result, even the slight triaxiality of our system results in efficient refilling of the loss cone, avoiding the final parsec problem. We distinguish three different populations of interactions based on their apocenter. We find a clear prevalence of interactions co-rotating with the binary. Nevertheless, retrograde interactions are the most energetic, contributing only slightly less than the prograde population to the overall energy exchange. The most energetic interactions are also likely to result in a change of sign in the angular momentum of the star. We estimate the merger timescale of the binary to be ≈20 Myr, a value larger by a factor of two than the timescale reported in a previous study.
A High-Realism and Cost-Effective Training Simulator for Extracorporeal Membrane Oxygenation

Medical simulators, employed in extracorporeal membrane oxygenation (ECMO), are burdened with costly equipment and low-fidelity methodologies. This dichotomy necessitated a new approach that eliminates high-costs and integrates with the critical care environment. This is especially applicable after the Coronavirus pandemic, where resources and supplies are evermore scarce. After examining the state-of-the-art and establishing a close collaboration with Hamad Medical Corporation (HMC), the main healthcare provider in Qatar, several criteria were identified to advance the cutting-edge. In this article, a high-realism, cost-effective ECMO simulator is presented. It runs on a novel blood simulation technology along with simulation modules. An instructor tablet application enables instructors to orchestrate the training experience wirelessly with real-time performance. It also includes a novel scenario designer for implementing consistent simulation curricula. A product-level simulator with high-fidelity casings is in the final integration phases. Current results include developing and testing the simulated blood circuit, simulation modules for hemorrhaging, line chattering, air bubbles noise, and a replicated console along with an integrated communications system. Nineteen specialists rated the fidelity of the system as highly realistic during a questionnaire-based study. It is expected to run a second study to evaluate the educational efficacy of the simulator as a first-of-its-kind in the region.
Multi-SQL: An extensible multi-model data query language

Big data management aims to establish data hubs that support data in multiple models and types in an all-around way. Thus, the multi-model database system is a promising architecture for building such a multi-model data store. For an integrated data hub, a unified and flexible query language is incredibly necessary. In this paper, an extensible and practical query language--Multi-SQL is proposed to realize the unified management of multi-model data considering the co-processing of multi-model data. To the best of our knowledge, Multi-SQL is the first query language based on various data models. We take four popular data models, i.e., RELATION, GRAPH, KV, DOCUMENT, as the foundation of Multi-SQL design. Multi-SQL can also be expanded to suit more complicated scenarios as it is flexible to add other data models. Moreover, we provide a formal semantic definition of the core features of Multi-SQL and propose an effective algorithm based on equivalence class rule (ECT) to process multi-query. Compared with some existing popular databases' query languages, experimental results demonstrate that Multi-SQL is remarkable in the case of complex multi-queries.
Dynamic Valuation of Battery Lifetime in Electricity Markets

Our world is now booming with new and used batteries. By responding to electricity price signals to charge or discharge, these batteries can aid grid operations while profiting their owners, but careful planning must be made to maximize their lifetime utilization following unique degradation characteristics. This paper presents a dynamic valuation framework that optimally determines the value of batteries participating in electricity markets. A comprehensive case study using historical price data from the New York Independent System Operator shows that a battery's value is highly sensitive to both the external market environment as well as its internal state of health. The results conclude second-life batteries are superior choices for conducting electricity price arbitrage compared to new batteries, and as batteries become cheaper, their manufacturing cost will no longer determine how batteries should be operated in the grid.
Political Partisanship and Anti-Science Attitudes in Online Discussions about Covid-19

BACKGROUND The novel coronavirus pandemic continues to ravage communities across the United States. Opinion surveys identified the importance of political ideology in shaping perceptions of the pandemic and compliance with preventive measures. OBJECTIVE The aim of this study was to measure political partisanship and antiscience attitudes in the discussions about the pandemic on social media, as well as their geographic and temporal distributions. METHODS We analyzed a large set of tweets from Twitter related to the pandemic, collected between January and May 2020, and developed methods to classify the ideological alignment of users along the moderacy (hardline vs moderate), political (liberal vs conservative), and science (antiscience vs proscience) dimensions. RESULTS We found a significant correlation in polarized views along the science and political dimensions. Moreover, politically moderate users were more aligned with proscience views, while hardline users were more aligned with antiscience views. Contrary to expectations, we did not find that polarization grew over time; instead, we saw increasing activity by moderate proscience users. We also show that antiscience conservatives in the United States tended to tweet from the southern and northwestern states, while antiscience moderates tended to tweet from the western states. The proportion of antiscience conservatives was found to correlate with COVID-19 cases. CONCLUSIONS Our findings shed light on the multidimensional nature of polarization and the feasibility of tracking polarized opinions about the pandemic across time and space through social media data.
Examining the Feasibility of Off-the-Shelf Algorithms for Masking Directly Identifiable Information in Social Media Data

The identification and removal/replacement of protected information from social media data is an understudied problem, despite being desirable from an ethical and legal perspective. This paper identifies types of potentially directly identifiable information (inspired by protected health information in clinical texts) contained in tweets that may be readily removed using off-the-shelf algorithms, introduces an English dataset of tweets annotated for identifiable information, and compiles these off-the-shelf algorithms into a tool (Nightjar) to evaluate the feasibility of using Nightjar to remove directly identifiable information from the tweets. Nightjar as well as the annotated data can be retrieved from this https URL.
Optimal Accuracy-Time Trade-off for Deep Learning Services in Edge Computing Systems

With the increasing demand for computationally intensive services like deep learning tasks, emerging distributed computing platforms such as edge computing (EC) systems are becoming more popular. Edge computing systems have shown promising results in terms of latency reduction compared to the traditional cloud systems. However, their limited processing capacity imposes a trade-off between the potential latency reduction and the achieved accuracy in computationally-intensive services such as deep learning-based services. In this paper, we focus on finding the optimal accuracy-time trade-off for running deep learning services in a three-tier EC platform where several deep learning models with different accuracy levels are available. Specifically, we cast the problem as an Integer Linear Program, where optimal task scheduling decisions are made to maximize overall user satisfaction in terms of accuracy-time trade-off. We prove that our problem is NP-hard and then provide a polynomial constant-time greedy algorithm, called GUS, that is shown to attain near-optimal results. Finally, upon vetting our algorithmic solution through numerical experiments and comparison with a set of heuristics, we deploy it on a testbed implemented to measure for real-world results. The results of both numerical analysis and real-world implementation show that GUS can outperform the baseline heuristics in terms of the average percentage of satisfied users by a factor of at least 50%.
Optimizing Graph Processing and Preprocessing with Hardware Assisted Propagation Blocking

Extensive prior research has focused on alleviating the characteristic poor cache locality of graph analytics workloads. However, graph pre-processing tasks remain relatively unexplored. In many important scenarios, graph pre-processing tasks can be as expensive as the downstream graph analytics kernel. We observe that Propagation Blocking (PB), a software optimization designed for SpMV kernels, generalizes to many graph analytics kernels as well as common pre-processing tasks. In this work, we identify the lingering inefficiencies of a PB execution on conventional multicores and propose architecture support to eliminate PB's bottlenecks, further improving the performance gains from PB. Our proposed architecture -- COBRA -- optimizes the PB execution of both graph processing and pre-processing alike to provide end-to-end speedups of up to 4.6x (3.5x on average).
Caught in the Web I: environmental effect on halo concentrations, shape and spin

Using a set of high-resolution simulations we study the statistical correlation of dark matter halo properties with the large-scale environment. We consider halo populations split into four Cosmic Web (CW) elements: voids, walls, filaments, and nodes. For the first time we present a study of CW effects for halos covering six decades in mass: $10^{8}-10^{14}{h^{-1}{\rm M}_{\odot}}$. We find that the fraction of halos living in various web components is a strong function of mass, with the majority of $M>10^{12}{h^{-1}{\rm M}_{\odot}}$ halos living in filaments and nodes. Low mass halos are more equitably distributed in filaments, walls, and voids. For halo density profiles and formation times we find a universal mass threshold of $M_{th}\sim6\times10^{10}{h^{-1}{\rm M}_{\odot}}$ below which these properties vary with environment. Here, filament halos have the steepest concentration-mass relation, walls are close to the overall mean, and void halos have the flattest relation. This amounts to $c_{200}$ for filament and void halos that are respectively $14\%$ higher and $7\%$ lower than the mean at $M=2\times10^8{h^{-1}{\rm M}_{\odot}}$, with low-mass node halos are most likely splashed-back. We find double power-law fits that very well describe $c(M)$ for the four environments in the whole probed mass range. A complementary picture is found for the average formation times, with the mass-formation time relations following trends shown for the concentrations: the nodes halos being the oldest and void halo the youngest. The CW environmental effect is much weaker when studying the halo spin and shapes. The trends with halo mass is reversed: the small halos with $M<10^{10}{h^{-1}{\rm M}_{\odot}}$ seem to be unaffected by the CW environment. Some weak trends are visible for more massive void and walls halos, which, on average, are characterized by lower spin and higher triaxiality parameters.
GSSMD: A new standardized effect size measure to improve robustness and interpretability in biological applications

In many biological applications, the primary objective of study is to quantity the magnitude of treatment effect between two groups. Cohens’d or strictly standardized mean difference (SSMD) can be used to measure effect size however, it is sensitive to violation of assumption of normality. Here, we propose an alternative metric of standardized effect size measure to improve robustness and interpretability, based on the overlap between two sample distributions. The proposed method is a non-parametric generalized variant of SSMD (Strictly Standardized Mean Difference). We characterized proposed measure in various simulation settings to illustrate its behavior. We also investigated finite sample properties on the estimation of effect size and draw some guidelines. As a case study, we applied our measure for hit selection problem in an RNAi experiment and showed superiority of proposed method.
The Unruh Effect in Slow Motion

We show under what conditions an accelerated detector (e.g., an atom/ion/molecule) thermalizes while interacting with the vacuum state of a quantum field in a setup where the detector’s acceleration alternates sign across multiple optical cavities. We show (non-perturbatively) in what regimes the probe `forgets’ that it is traversing cavities and thermalizes to a temperature proportional to its acceleration, the same as it would in free space. Then we analyze in detail how this thermalization relates to the renowned Unruh effect. Finally, we use these results to propose an experimental testbed for the direct detection of the Unruh effect at relatively low probe speeds and accelerations, potentially orders of magnitude below previous proposals.