Miracle-dz/UniFAR-FTUs huggingface dataset:
Dodging DeepFake Detection via Implicit Spatial-Domain Notch Filtering

The current high-fidelity generation and high-precision detection of DeepFake images are at an arms race. We believe that producing DeepFakes that are highly realistic and 'detection evasive' can serve the ultimate goal of improving future generation DeepFake detection capabilities. In this paper, we propose a simple yet powerful pipeline to reduce the artifact patterns of fake images without hurting image quality by performing implicit spatial-domain notch filtering. We first demonstrate that frequency-domain notch filtering, although famously shown to be effective in removing periodic noise in the spatial domain, is infeasible for our task at hand due to the manual designs required for the notch filters. We, therefore, resort to a learning-based approach to reproduce the notch filtering effects, but solely in the spatial domain. We adopt a combination of adding overwhelming spatial noise for breaking the periodic noise pattern and deep image filtering to reconstruct the noise-free fake images, and we name our method DeepNotch. Deep image filtering provides a specialized filter for each pixel in the noisy image, producing filtered images with high fidelity compared to their DeepFake counterparts. Moreover, we also use the semantic information of the image to generate an adversarial guidance map to add noise intelligently. Our large-scale evaluation on 3 representative state-of-the-art DeepFake detection methods (tested on 16 types of DeepFakes) has demonstrated that our technique significantly reduces the accuracy of these 3 fake image detection methods, 36.79% on average and up to 97.02% in the best case.
On Strong f-Electron Localization Effect in a Topological Kondo Insulator

We study a strong f-electron localization effect on the surface state of a generic topological Kondo insulator (TKI) system by performing a mean-field theoretic (MFT) calculation within the framework of the periodic Anderson model (PAM) using the slave boson technique. The surface metallicity, together with bulk insulation, requires this type of localization. A key distinction between surface states in a conventional insulator and a topological insulator is that, along a course joining two time-reversal invariant momenta (TRIM) in the same BZ, there will be an intersection of these surface states, an even/odd number of times, with the Fermi energy inside the spectral gap. For an even (odd) number of surface state crossings, the surface states are topologically trivial (non-trivial). The symmetry consideration and the pictorial representation of the surface band structure obtained here show an odd number of crossings, leading to the conclusion that, at least within the PAM framework, the generic system is a strong topological insulator.
Road-model-based and graph-structure-based hierarchical path-planning approach for autonomous vehicles

This paper presents a path-planning strategy for autonomous vehicles which aims to provide safe and feasible manoeuvres in various driving environments. Our strategy uses a hierarchical architecture which consists of three components: a behaviour planner, a map and path selector and a local-path planner. The behaviour planner performs the rule-based decision process which determines the overall vehicle manoeuvres. The map and path selector preprocesses perception data and chooses a local-path-planning algorithm using the results of the behaviour planner. From this selection, the local-path planner generates a driveable and collision-free path. For reliable path generation under various driving conditions, the proposed local path planner employs two algorithms: a road-model-based path planning algorithm and a graph-structure-based path-planning algorithm. The former is used for structured road driving, such as lane keeping or changing, and the latter is used for unstructured road driving. The proposed hierarchical path-planning algorithm was implemented in the autonomous vehicle called A1, which was applied with an in-vehicle-network-based distributed system architecture. A1 won the 2012 Autonomous Vehicle Competition organized by the Hyundai Motor Group in Korea.
Space by slowground : A critical look on our pace in work life

The increase in the pace of life is becoming very tangible yet it is not visible to us in a qualitative way. Technological advancements offer us a life without friction where we can go full speed, but with this change we lose our windows for reflection and slowing down. Our pace is mostly determined by people or machines around us and we rarely have a chance to listen to our own rhythm, users and designers alike.With this project, the sustainability of the human being was questioned, particularly in the work life, where our pace is the highest. An exploratory and partially critical approach was engaged in the process and different methodologies have been explored. The result, a system of objects, aim to visualize one’s pace while criticizing the frictionless environments we create as designers.
Machine learning assisted Bayesian model comparison: learnt harmonic mean estimator

We resurrect the infamous harmonic mean estimator for computing the marginal likelihood (Bayesian evidence) and solve its problematic large variance. The marginal likelihood is a key component of Bayesian model selection to evaluate model posterior probabilities; however, its computation is challenging. The original harmonic mean estimator, first proposed by Newton and Raftery in 1994, involves computing the harmonic mean of the likelihood given samples from the posterior. It was immediately realised that the original estimator can fail catastrophically since its variance can become very large (possibly not finite). A number of variants of the harmonic mean estimator have been proposed to address this issue although none have proven fully satisfactory. We present the \emph{learnt harmonic mean estimator}, a variant of the original estimator that solves its large variance problem. This is achieved by interpreting the harmonic mean estimator as importance sampling and introducing a new target distribution. The new target distribution is learned to approximate the optimal but inaccessible target, while minimising the variance of the resulting estimator. Since the estimator requires samples of the posterior only, it is agnostic to the sampling strategy used. We validate the estimator on a variety of numerical experiments, including a number of pathological examples where the original harmonic mean estimator fails catastrophically. We also consider a cosmological application, where our approach leads to $\sim$ 3 to 6 times more samples than current state-of-the-art techniques in 1/3 of the time. In all cases our learnt harmonic mean estimator is shown to be highly accurate. The estimator is computationally scalable and can be applied to problems of dimension $O(10^3)$ and beyond. Code implementing the learnt harmonic mean estimator is made publicly available
Look at here : Utilizing supervision to attend subtle key regions

Despite the success of deep learning in computer vision, algorithms to recognize subtle and small objects (or regions) is still challenging. For example, recognizing a baseball or a frisbee on a ground scene or a bone fracture in an X-ray image can easily result in overfitting, unless a huge amount of training data is available. To mitigate this problem, we need a way to force a model should identify subtle regions in limited training data. In this paper, we propose a simple but efficient supervised augmentation method called Cut\&Remain. It achieved better performance on various medical image domain (internally sourced- and public dataset) and a natural image domain (MS-COCO$_s$) than other supervised augmentation and the explicit guidance methods. In addition, using the class activation map, we identified that the Cut\&Remain methods drive a model to focus on relevant subtle and small regions efficiently. We also show that the performance monotonically increased along the Cut\&Remain ratio, indicating that a model can be improved even though only limited amount of Cut\&Remain is applied for, so that it allows low supervising (annotation) cost for improvement.
Calculation of impulse current distributions and magnetic fields in lightning protection structures-a computer program and its laboratory validation

A circuit model and an ad hoc computer program were set up to evaluate electromagnetic interference in the vicinity of protective structures struck by lightning. This program permits the evaluation of the impulsive magnetic fields by calculating the impulse current distribution in different parts of such structures. It also allows the evaluation of the electromagnetic interference induced on susceptible victim circuits to which electric equipment may be connected. To validate the approach, a series of impulse tests was performed in the laboratory on conductive structures designed to highlight the features of the program and to make evident the dependence of the impulse current distributions on some particular phenomena that need simplified modeling, such as the effect of induced or return currents through the ground. >
Elliptic complexes on G2 and Spin(7)−manifolds

There are three types of generalized de Rham complexes arising from representations of holonomy group on a smooth Riemannian manifold, two of which are dual to each other. We prove that a complex is elliptic if and only if its generator satisfies an algebraic condition. As applications, we give all elliptic complexes on a Riemannian manifold with exceptional holonomy.
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation

Automatic brain tumor segmentation from Magnetic Resonance Imaging (MRI) data plays an important role in assessing tumor response to therapy and personalized treatment stratification. Manual segmentation is tedious and subjective. Deep-learning based algorithms for brain tumor segmentation have the potential to provide objective and fast tumor segmentation. However, the training of such algorithms requires large datasets which are not always available. Data augmentation techniques may reduce the need for large datasets. However current approaches are mostly parametric and may result in suboptimal performance. We introduce two non-parametric methods of data augmentation for brain tumor segmentation: the mixed structure regularization (MSR) and shuffle pixels noise (SPN). We evaluated the added value of the MSR and SPN augmentation on the brain tumor segmentation (BraTS) 2018 challenge dataset with the encoder-decoder nnU-Net architecture as the segmentation algorithm. Both MSR ans SPN improve the nnU-Net segmentation accuracy compared to parametric Gaussian noise augmentation. (Mean dice score increased from 80% to 82% and p-values=0.0022, 0.0028 when comparing MSR to non parametric augmentation for the tumor core and whole tumor experiments respectively. The proposed MSR and SPN augmentations has the potential to improve neural-networks performance in other tasks as well.
Ab-initio investigation and thermodynamic assessment of the FeTi hydrogenation

FeTi-based hydrides have recently re-attracted attention as stationary hydrogen storage materials due to favorable reversibility, good sorption kinetics and relatively low costs compared to alternative intermetallic hydrides. Employing the OpenCalphad software, the thermodynamics of the (FeTi)1−xHx (0 ≤ x ≤ 1) system were assessed within the Calphad (CALculation of PHAse Diagrams) framework as a key basis for modeling hydrogenation of FeTi-based alloys. Thermodynamic data were acquired from our experimental pressurecomposition-isotherm (PCI) curves, as well as first-principles calculations utilizing density functional theory (DFT). The thermodynamic phase models were carefully selected based on critical analysis of literature information and ab-initio investigations. Key thermodynamic properties such as dissociation pressure, formation enthalpies and phase diagrams were calculated in good agreement to our performed experiments and literature-reported data. This work provides an initial perspective, which can be extended to account for higher-order thermodynamic assessments and subsequently enables the design of novel FeTi-based hydrides. In addition, the assessed thermodynamic data can serve as key inputs for kinetic models and hydride microstructure simulations.
High-dimensional spatial teleportation enabled by nonlinear optics

Bereneice Sephton, Adam Vallés, 2, 3, ∗ Isaac Nape, Mitchell A. Cox, Fabian Steinlechner, 6 Thomas Konrad, 8 Juan P. Torres, 9 Filippus S. Roux, and Andrew Forbes † School of Physics, University of the Witwatersrand, Private Bag 3, Wits 2050, South Africa Molecular Chirality Research Center, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan ICFO Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona) 08860, Spain School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa Fraunhofer Institute for Applied Optics and Precision Engineering, Albert-Einstein-Str. 7, 07745 Jena, Germany Friedrich Schiller University Jena, Abbe Center of Photonics, Albert-Einstein-Str. 6, 07745 Jena, Germany School of Chemistry and Physics, University of KwaZulu-Natal, Durban, South Africa National Institute of Theoretical and Computational Sciences (NITheCS), KwaZulu-Natal, South Africa Department of Signal Theory and Communications, Universitat Politecnica de Catalunya, Campus Nord D3, 08034 Barcelona, Spain National Metrology Institute of South Africa, Meiring Naudé Road, Brummeria, Pretoria 0040, South Africa
Inside Out Visual Place Recognition

Visual Place Recognition (VPR) is generally concerned with localizing outdoor images. However, localizing indoor scenes that contain part of an outdoor scene can be of large value for a wide range of applications. In this paper, we introduce Inside Out Visual Place Recognition (IOVPR), a task aiming to localize images based on outdoor scenes visible through windows. For this task we present the new large-scale dataset Amsterdam-XXXL, with images taken in Amsterdam, that consists of 6.4 million panoramic street-view images and 1000 user-generated indoor queries. Additionally, we introduce a new training protocol Inside Out Data Augmentation to adapt Visual Place Recognition methods for localizing indoor images, demonstrating the potential of Inside Out Visual Place Recognition. We empirically show the benefits of our proposed data augmentation scheme on a smaller scale, whilst demonstrating the difficulty of this large-scale dataset for existing methods. With this new task we aim to encourage development of methods for IOVPR. The dataset and code are available for research purposes at https://github.com/saibr/IOVPR
An Augmented Likelihood Approach for the Discrete Proportional Hazards Model Using Auxiliary and Validated Outcome Data -- with Application to the HCHS/SOL Study

In large epidemiologic studies, it is typical for an inexpensive, non-invasive procedure to be used to record disease status during regular follow-up visits, with less frequent assessment by a gold standard test. Inexpensive outcome measures like self-reported disease status are practical to obtain, but can be error-prone. Association analysis reliant on error-prone outcomes may lead to biased results; however, restricting analyses to only data from the less frequently observed error-free outcome could be inefficient. We have developed an augmented likelihood that incorporates data from both error-prone outcomes and a gold standard assessment. We conduct a numerical study to show how we can improve statistical efficiency by using the proposed method over standard approaches for interval-censored survival data that do not leverage auxiliary data. We extend this method for the complex survey design setting so that it can be applied in our motivating data example. Our method is applied to data from the Hispanic Community Health Study/Study of Latinos to assess the association between energy and protein intake and the risk of incident diabetes. In our application, we demonstrate how our method can be used in combination with regression calibration to additionally address the covariate measurement error in self-reported diet.
A Learned SVD approach for Inverse Problem Regularization in Diffuse Optical Tomography

Diﬀuse Optical Tomography (DOT) is an emerging technology in medical imaging which employs near– infra–red light to estimate the distribution of optical coeﬃcients in biological tissues for diagnostic purposes. The DOT approach involves the solution of a severely ill–posed inverse problem, for which regularization techniques are mandatory in order to achieve reasonable results. Traditionally, regularization techniques put a variance prior on the desired solution/gradient via regularization parameters, whose choice requires a ﬁne tuning. In this work we explore deep learning techniques in a fully data–driven approach, able of reconstructing the generating signal (absorption coeﬃcient) in an automated way. We base our approach on the so-called learned Singular Value Decomposition, which has been proposed for general inverse problems, and we tailor it to the DOT application. We test our approach on a 2D synthetic dataset, with increasing levels of noise on the measure.
Atomic scale strain engineering of layered sheets on the surfaces of two-dimensional materials

Atomic modulations of two-dimensional materials using scanning tunneling microscope (STM) tip-induced forces modifies their mechanical and electrical properties. In situ topographic and spectroscopic probing through electrical tunneling has been used for straining sheets of graphite, monolayer graphene and NbSe2. The findings also resolve a thirty-five-year-old controversy involving numerous proposed models to explain the source of anomalously high measured atomic amplitudes (of up to 24 Å, expected 0.2Å) from atomic corrugation on graphite surfaces. Our findings attributes the anomaly to surface elastic deformation characteristics of ‘soft’ 2D monatomic sheets of graphene and graphite in contrast to NbSe2 which is associated with their local bonding configurations. The tip-induced deformations are shown to induce controlled strain on the material surface atomically and it offers a new way for strain engineering. Topographic deformation of formed graphitic Moiré patterns reveals the inter-layer van der Waals (vdW) strength varying across its domains. In-situ tunneling spectroscopy associated with straining of the Moiré domains reveals electronic flat band formation controllably thereby creating a platform for many-body correlations. The paper cautions anomalous observations when probing 2D materials at small gap distances with their strain induced effects and provides guidelines to exploit or avoid this effect.
Self-supervised Pretraining with Classification Labels for Temporal Activity Detection

Temporal Activity Detection aims to predict activity classes per frame, in contrast to video-level predictions as done in Activity Classiﬁcation (i.e., Activity Recognition). Due to the expensive frame-level annotations required for detection, the scale of detection datasets is limited. Thus, commonly, previous work on temporal activity detection resorts to ﬁne-tuning a classiﬁcation model pretrained on large-scale classiﬁcation datasets (e.g., Kinetics-400). However, such pretrained models are not ideal for downstream detection performance due to the disparity between the pre-training and the downstream ﬁne-tuning tasks. This work proposes a novel self-supervised pretraining method for detection leveraging classiﬁcation labels to mitigate such disparity by introducing frame-level pseudo labels, multi-action frames, and action segments. We show that the models pre-trained with the proposed self-supervised detection task out-perform prior work on multiple challenging activity detection benchmarks, including Charades and MultiTHUMOS. Our extensive ablations further provide insights on when and how to use the proposed models for activity detection. Code and models will be released online.
Mentoring in the Writing Café: Identity, Belonging and Ownership

The historical legacy of the coffeehouse, the predecessor to the cafe, is rooted in a culture of community, collaboration and social activity. In January 2014, the Learning Development team at Plymouth University opened the Writing Cafe; a developmental, creative space that offers the potential to reframe their work and provides the opportunity to work in partnership with students. Along with serving great coffee and locally sourced food, conversations and activities in the cafe provide a space for students and staff to meet and discuss writing practices and gain support from the Learning Development team and trained student Writing Mentors. This paper reports on a small-scale action research project undertaken to investigate to what extent the Writing Mentors benefitted from being partners in this project; as co-designers, advisors, writing mentors, baristas and more.
Lensless multicore-fiber microendoscope for real-time tailored light field generation with phase encoder neural network (CoreNet)

JIAWEI SUN,1,2,6,† JIACHEN WU,1,3,7,† NEKTARIOS KOUKOURAKIS, 1,2 ROBERT KUSCHMIERZ, 1,2 LIANGCAI CAO3 AND JÜRGEN CZARSKE, 1,2,4,5,8 1Laboratory of Measurement and Sensor System Technique, TU Dresden, Helmholtzstrasse 18, 01069 Dresden, Germany 2Competence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, Dresden, Germany 3State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, China 4Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany 5Institute of Applied Physics, TU Dresden, Dresden, Germany 6jiawei.sun@tu-dresden.de 7wjc18@mails.tsinghua. edu.cn 8juergen.czarske@tu-dresden.de † These authors contributed equally to this work
Unscented Kalman Filter for Long-Distance Vessel Tracking in Geodetic Coordinates

This paper describes a novel tracking filter, designed primarily for use in collision avoidance systems on autonomous surface vehicles (ASVs). The proposed methodology leverages real-time kinematic information broadcast via the Automatic Information System (AIS) messaging protocol, in order to estimate the position, speed, and heading of nearby cooperative targets. The state of each target is recursively estimated in geodetic coordinates using an unscented Kalman filter (UKF) with kinematic equations derived from the spherical law of cosines. This improves upon previous approaches, many of which employ the extended Kalman filter (EKF), and thus require the specification of a local planar coordinate frame, in order to describe the state kinematics in an easily differentiable form. The proposed geodetic UKF obviates the need for this local plane. This feature is particularly advantageous for long-range ASVs, which must otherwise periodically redefine a new local plane to curtail linearization error. In real-world operations, this recurring redefinition can introduce error and complicate mission planning. It is shown through both simulation and field testing that the proposed geodetic UKF performs as well as, or better than, the traditional plane-Cartesian EKF, both in terms of estimation error and stability.