Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpret Neural Networks by Identifying Critical Data Routing Paths

Interpretability of a deep neural network aims to explain the rationale behind its decisions and enable the users to understand the intelligent agents, which has become an important issue due to its importance in practical applications. To address this issue, we develop a Distillation Guided Routing method, which is a flexible framework to interpret a deep neural network by identifying critical data routing paths and analyzing the functional processing behavior of the corresponding layers. Specifically, we propose to discover the critical nodes on the data routing paths during network inferring prediction for individual input samples by learning associated control gates for each layer's output channel. The routing paths can, therefore, be represented based on the responses of concatenated control gates from all the layers, which reflect the network's semantic selectivity regarding to the input patterns and more detailed functional process across different layer levels. Based on the discoveries, we propose an adversarial sample detection algorithm by learning a classifier to discriminate whether the critical data routing paths are from real or adversarial samples. Experiments demonstrate that our algorithm can effectively achieve high defense rate with minor training overhead.
Computer-aided Diagnosis via Hierarchical Density Based Clustering

When applying non-supervised clustering, the concepts discovered by the clustering algorithm hardly match business concepts. Hierarchical clustering then proves to be a useful tool to exhibit sets of clusters according to a hierarchy. Data can be analyzed in layers and the user has a full spectrum of clusterings to which he can give meaning. This paper presents a new hierarchical density-based algorithm that advantageously works from compacted data. The algorithm is applied to the monitoring of a process benchmark, illustrating its value in identifying different types of situations , from normal to highly critical.
A Fast Resection-Intersection Method for the Known Rotation Problem

The known rotation problem refers to a special case of structure-from-motion where the absolute orientations of the cameras are known. When formulated as a minimax ($$) problem on reprojection errors, the problem is an instance of pseudo-convex programming. Though theoretically tractable, solving the known rotation problem on large-scale data (1,000's of views, 10,000's scene points) using existing methods can be very time-consuming. In this paper, we devise a fast algorithm for the known rotation problem. Our approach alternates between pose estimation and triangulation (i.e., resection-intersection) to break the problem into multiple simpler instances of pseudo-convex programming. The key to the vastly superior performance of our method lies in using a novel minimum enclosing ball (MEB) technique for the calculation of updating steps, which obviates the need for convex optimisation routines and greatly reduces memory footprint. We demonstrate the practicality of our method on large-scale problem instances which easily overwhelm current state-of-the-art algorithms.
Outbursts of the intermediate-mass black hole HLX-1: a wind-instability scenario

We model the intermediate-mass black hole HLX-1, using the Hubble Space Telescope, XMM-Newton and Swift. We quantify the relative contributions of a bluer component, function of X-ray irradiation, and a redder component, constant and likely coming from an old stellar population. We estimate a black hole mass of about (2^{+2}_{-1}) x 10^4 M_{sun}, a spin parameter a/M ~ 0.9 for moderately face-on view, and a peak outburst luminosity of about 0.3 times the Eddington luminosity. We discuss the discrepancy between the characteristic sizes inferred from the short X-ray timescale (R ~ a few 10^{11} cm) and from the optical emitter (R sqrt[cos theta] ~ 2.2 x 10^{13} cm). One possibility is that the optical emitter is a circumbinary disk; however, we disfavour this scenario because it would require a very small donor star. A more plausible scenario is that the disk is large but only the inner annuli are involved in the X-ray outburst. We propose that the recurrent outbursts are caused by an accretion-rate oscillation driven by wind instability in the inner disk. We argue that the system has a long-term-average accretion rate of a few percent Eddington, just below the upper limit of the low/hard state; a wind-driven oscillation can trigger transitions to the high/soft state, with a recurrence period of ~1 year (much longer than the binary period, which we estimate as ~10 days). The oscillation that dominated the system in the last decade is now damped such that the accretion rate no longer reaches the level required to trigger a transition. Finally, we highlight similarities between disk winds in HLX-1 and in the Galactic black hole V404 Cyg.
Using ERA-40 products to verify and improve the Met Office climate model

Accurate measurements of the Earth’s climate system and its variability on a variety of time and space-scales are vital in verifying and improving the quality of climate model simulations. However such comparisons are impeded by incomplete observational coverage. Reanalyses address this problem by using the limited array of observations to continually force a model via an assimilation system, thereby providing information as to the true nature of the atmosphere on a grid suitable for consistent comparison with climate and numerical prediction models. The degree to which observational information is lost or spurious climate signals created in the assimilation process is not fully understood and therefore requires careful checking of reanalysis products. Thus, with the aim of utilising the ECMWF 40-year reanalysis data (ERA-40) in conjunction with observations to verify the Met Office climate model, the present study seeks to:
Temporal Hallucinating for Action Recognition with Few Still Images

Action recognition in still images has been recently promoted by deep learning. However, the success of these deep models heavily depends on huge amount of training images for various action categories, which may not be available in practice. Alternatively, humans can classify new action categories after seeing few images, since we may not only compare appearance similarities between images on hand, but also attempt to recall importance motion cues from relevant action videos in our memory. To mimic this capacity, we propose a novel Hybrid Video Memory (HVM) machine, which can hallucinate temporal features of still images from video memory, in order to boost action recognition with few still images. First, we design a temporal memory module consisting of temporal hallucinating and predicting. Temporal hallucinating can generate temporal features of still images in an unsupervised manner. Hence, it can be flexibly used in realistic scenarios, where image and video categories may not be consistent. Temporal predicting can effectively infer action categories for query image, by integrating temporal features of training images and videos within a domain-adaptation manner. Second, we design a spatial memory module for spatial predicting. As spatial and temporal features are complementary to represent different actions, we apply spatial-temporal prediction fusion to further boost performance. Finally, we design a video selection module to select strongly-relevant videos as memory. In this case, we can balance the number of images and videos to reduce prediction bias as well as preserve computation efficiency. To show the effectiveness, we conduct extensive experiments on three challenging data sets, where our HVM outperforms a number of recent approaches by temporal hallucinating from video memory.
Supplementary Material for Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

The detail of experiment on toy dataset is shown in this section. When generating target samples, we set the rotation angle 30 in experiments of our main paper. We used Adam with learning rate 2.0×10−4 to optimizer the model. The batch size was set to 200. For a feature generator, we used 3-layered fully-connected networks with 15 neurons in hidden layer, in which ReLU is used as the activation function. For classifiers, we used three-layed fully-connected networks with 15 neurons in hidden layer and 2 neurons in output layer. The decision boundary shown in the main paper is obtained when we rotate the source samples 30 degrees to generate target samples. We set n to 3 in this experiment.
Building a Virtual Enterprise Network Environment for APT Experimentation

Advanced Persistent Threat (APT) represents a class of the most sophisticated, targeted, stealthy, and potentially devastating cyber-attacks. Detecting these threats has proven to be very difficult due to the potency of these attacks and the general lack of defensive capabilities by the victims. Going forward, this asymmetric situation is expected to get worse as the attacking tools can be more readily acquired, modified, and enhanced to not only proliferate more broadly but also become more lethal. To reverse this trend, cyber defenders will need not only better tactics, techniques and procedures, but also a workforce better trained in preventing, defending against, detecting, responding to, and mitigating the effects of APTs. Given the nature of the APTs, training cyber defenders will not be an easy task. An effective learning environment must be provided for the learners to gain hands-on knowledge, skills, and experience. This paper presents the design and implementation of our first-iteration virtual lab environment for learning and experimenting with those APTs that are commonly used to attack enterprise networks. This virtual environment is designed to operate on the XenServer virtualized infrastructure, and composed of a number of building blocks (e.g., router/firewall, Microsoft Domain Controller, web and email servers, Kali Linux), as well as components for the Gh0st malware which is chosen as a case study of the APTs as it has been widely employed worldwide to take full control remotely of vulnerable Windows platforms. Keywords—Advanced persistent threat; cyber kill chain; Gh0st; malware; XenServer
Interpenetrating networks of carboxymethyl tamarind gum and chitosan for sustained delivery of aceclofenac

The aim of present investigation was to characterize carboxymethyl tamarind gum (CMTG) based interpenetrating networks (IPNs) of aceclofenac for site specific sustained delivery. The drug loaded IPNs were prepared by using chitosan and CMTG as polymers and gluteraldehyde as crosslinking agent. The IPNs were characterized by Attenuated total reflectance- Fourier transform infrared (ATR-FTIR) spectroscopy, thermal analysis, X-ray powder diffraction and solid state 13C-nuclear magnetic resonance spectroscopy. The prepared IPNs were evaluated for the drug entrapment efficiency and equilibrium swelling. The drug release from IPNs was studied in 0.1NHCl for 2h followed by phosphate buffer pH 6.8 for further 10h and compared with commercial tablet. The results of ATR-FTIR and thermal analysis for blank IPNs indicated intercalation of polymeric chains of crosslinked CMTG and chitosan. The results of solid state characterization revealed that the aceclofenac is compatible with IPNs. Entrapment efficiency of IPNs was found to be increased with increase in crosslinker concentration as well as amount of CMTG. The equilibrium swelling study indicated pH dependent swelling of IPNs. The drug release by IPNs showed sustained release of aceclofenac upto 12h while commercial formulation showed fast release within 8h. From the results, it can be concluded that the IPNs of CMTG and chitosan has potential in development of site specific sustained drug delivery.
Supplementary Materials : Generative Image Inpainting with Contextual Attention

CelebA-HQ [? ] We show results from our full model trained on CelebA-HQ dataset in Figure 1. Note that the original image resolution of CelebA-HQ dataset is 1024 × 1024. We resize image to 256 × 256 for both training and evaluation. CelebA [? ] We show more results from our full model trained on CelebA dataset in Figure 2. Note that the original image resolution of CelebA dataset is 218× 178. We resize image to 315×256 and do a random crop of size 256×256 to make face landmarks roughly unaligned for both training and evaluation. ImageNet [? ] We show more results from our full model trained on ImageNet dataset in Figure 3. DTD textures [? ] We show more results from our full model trained on DTD dataset in Figure 4.
Metabolite Production and Antibacterial Activities of Callus Cultures from Rosa damascena Mill. Petals

Rosa damascena Mill. is a very fragrant member of Rosaceae family, exhibiting strong antioxidant and antimicrobial activity. In this study, in vitro cell cultures from Rosa damascena Mill. flower petals were established and the effects of precursor L-phenylalanine, the elicitor methyl jasmonate and light/dark treatments on the accumulation of characteristic rose volatile compounds, tocopherols and β-carotene in addition to the antibacterial activity of petal callus extracts were investigated with Solid-Phase Microextraction Combined with Gas Chromatography–Mass Spectrometry (GC–MS). The amount of β-pinene increased by 236 times when 500 μM Phe was added to the culture medium or under light treatment. Strong antibacterial activity from petal callus extracts, up to 2.6- fold compared to amikacin as well as original petal tissues, was obtained under dark treatment for P. aeruginosa ATCC 27853 and S. aureus ATCC 25923. These results revealed that petal callus extracts are sources of natural antioxidants and antibacterial compounds.
2 FINITE ELEMENT ANALYSIS 2 . 1 Large Scale Triaxial Test Confi guration

Large and frequent cyclic train loading from heavy haul and passenger trains often leads to progressive track deterioration. The excessive deformation and degradation of ballast and unacceptable differential settlement of track and/or pumping of underlying soft subgrade soils necessitate frequent and costly track maintenance. A proper understanding of load transfer mechanisms and subsequent deformations in track layers is the key element for safe and economical track design and optimum maintenance procedures. Many simplifi ed analytical and empirical design methods have been used to estimate the settlement and stress-transfer between the track layers. However, these design methods are based on the linear elastic approach, and often only give crude estimates. Given the complexities of the behaviour of the composite track system consisting of rail, sleeper, ballast, sub-ballast and subgrade subject to repeated traffi c loads in a real track environment, the current track design techniques are overly simplifi ed. The track design should also account for the deterioration of ballast due to breakage and subsequent implications on the track deformations. Considering this, an elasto-plastic constitutive model of a composite multi-layer track system is proposed. Constitutive models and material parameters adopted in this numerical model are discussed. A hardening soil model with a non-associative fl ow rule is introduced to accurately simulate the strain-hardening behaviour of ballast. The breakage of ballast observed in large scale triaxial tests is also simulated based on this model. In conjunction, numerical simulations are also performed using a two-dimensional plane-strain fi nite element analysis (PLAXIS) capturing the effects of ballast breakage and track confi ning pressure. The paper also demonstrates the advantages of the proposed elasto-plastic fi nite element simulations when compared to conventional analytical methods used by practitioners that are primarily based on a linear elastic approach.
Radioactive permeability studies of doxycycline hyclate from microemulsion and solution

Doxycycline Hyclate (DOX) is an antibacterial drug which is member of the second tetracycline group and the absorption of DOX is reduced about 20% in the presence of skimmed milk. Therefore new drug delivery system can be advisable for DOX to reduce the food and drug interaction and improve the bioavailability. The aim of the present study is to prepare a microemulsion system of DOX which could result in reducing in food interaction and an improvement of oral bioavailability by increasing the drug's permeability. For this purposes, DOX was radiolabeled with 99m Tc. Radiochemical purity was determined with radioactive thin layer chromatography (RTLC) studies. Permeability of DOX from 99m Tc-DOX solution ( 99m Tc-DOX-S) and 99m Tc-DOX loaded microemulsion ( 99m Tc-DOX-M) was investigated with in vitro cell culture studies by using human colonic adenocarcinoma cell line (Caco-2). The radioactivity of 99m Tc-DOX-M for the apical to basolateral direction (P app (A→B)) and basolateral to apical direction (P app (B→A)) were found higher than 99m Tc-DOX-S. Based on the in vitro cell culture studies, this dosage form is a promising formulation as an alternative for oral drug delivery of DOX.
Application source code Annotated executable Memory usage statistics Program input Program execution Compile with site annotations MemBrain runtime Annotated executable Architectural profiling Site →

Computer systems with multiple tiers of memory devices with different latency, bandwidth, and capacity characteristics are quickly becoming mainstream. Due to cost and physical limitations, device tiers that enable better performance typically include less capacity. Such heterogeneous memory systems require alternative data management strategies to utilize the capacity-constrained resources efficiently. However, current techniques are often limited because they rely on inflexible hardware caching or manual modifications to source code. This paper introduces MemBrain, a new memory management framework that automates the production and use of data-tiering guidance for applications on hybrid memory systems. MemBrain employs program profiling and source code analysis to enable transparent and efficient data placement across different types of memory. It automatically clusters data with similar expected usage patterns into page-aligned regions of virtual addresses (arenas), and uses offline profile feedback to direct low-level tier assignments for each region. We evaluate MemBrain on an Intel Knights Landing server machine with an upper tier of limited capacity (but higher bandwidth) MCDRAM and a lower tier of conventional DDR4 using a selection of high-bandwidth benchmarks from SPEC CPU 2017 as well as two proxy apps (Lulesh and AMG), and one full scale scientific application (QMCPACK). Our evaluation shows that MemBrain can achieve significant performance and efficiency improvements compared to current guided and unguided management strategies.
Alternating-Stereo VINS: Observability Analysis and Performance Evaluation

One approach to improve the accuracy and robustness of vision-aided inertial navigation systems (VINS) that employ low-cost inertial sensors, is to obtain scale information from stereoscopic vision. Processing images from two cameras, however, is computationally expensive and increases latency. To address this limitation, in this work, a novel two-camera alternating-stereo VINS is presented. Specifically, the proposed system triggers the left-right cameras in an alternating fashion, estimates the poses corresponding to the left camera only, and introduces a linear interpolation model for processing the alternating right camera measurements. Although not a regular stereo system, the alternating visual observations when employing the proposed interpolation scheme, still provide scale information, as shown by analyzing the observability properties of the vision-only corresponding system. Finally, the performance gain, of the proposed algorithm over its monocular and stereo counterparts is assessed using various datasets.
Translative Sets and Functions and their Applications to Risk Measure Theory and Nonlinear Separation

Recently defined concepts such as nonlinear separation functionals due to Tammer and Weidner, coherent risk measures due to Delbaen et al., topical functions due to Gunawardena and Keane as well as isotonic Banach functionals coincide and can be traced back to books by Day from 1957 and Krasnosel’skij’s from 1964. This paper is to get out the common background of these concepts and to give an extension to set–valued functions. Translativity with respect to one or a finite collection of elements turns out to be the key property of sets and functions in this note.
Data Mining Models Comparison for Diabetes Prediction

From the past few years, data mining got a lot of attention for extracting information from large datasets to find patterns and to establish relationships to solve problems. Well known data mining algorithms include classification, association, Naive Bayes, clustering and decision tree. In medical science field, these algorithms help to predict a disease at early stage for future diagnosis. Diabetes mellitus is the most growing disease that needs to be predicted at its early stage as it is lifelong disease and there is no cure for it. This research is intended to provide comparison for different data mining algorithms on PID dataset for early prediction of diabetes.
Entropy Contribution to the Line Tension: Insights from Polymer Physics, Water String Theory, and the Three-Phase Tension

The notion of three-phase (line) tension remains one of the most disputable notions in surface science. A very broad range of its values has been reported. Experts even do not agree on the sign of line tension. The polymer-chain-like model of three-phase (triple) line enables rough estimation of entropic input into the value of line tension, estimated as Γen≅kBTdm≅10−11N, where dm is the diameter of the liquid molecule. The introduction of the polymer-chain-like model of the triple line is justified by the “water string” model of the liquid state, predicting strong orientation effects for liquid molecules located near hydrophobic moieties. The estimated value of the entropic input into the line tension is close to experimental findings, reported by various groups, and seems to be relevant for the understanding of elastic properties of biological membranes.
Robust Hough Transform Based 3D Reconstruction from Circular Light Fields

Light-field imaging is based on images taken on a regular grid. Thus, high-quality 3D reconstructions are obtainable by analyzing orientations in epipolar plane images (EPIs). Unfortunately, such data only allows to evaluate one side of the object. Moreover, a constant intensity along each orientation is mandatory for most of the approaches. This paper presents a novel method which allows to reconstruct depth information from data acquired with a circular camera motion, termed circular light fields. With this approach it is possible to determine the full 360Â° view of target objects. Additionally, circular light fields allow retrieving depth from datasets acquired with telecentric lenses, which is not possible with linear light fields. The proposed method finds trajectories of 3D points in the EPIs by means of a modified Hough transform. For this purpose, binary EPI-edge images are used, which not only allow to obtain reliable depth information, but also overcome the limitation of constant intensity along trajectories. Experimental results on synthetic and real datasets demonstrate the quality of the proposed algorithm.