Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Analysis of Signal-Dependent Noise: Application in Blind Localization of Image Splicing Forgery

Visual noise is often regarded as a disturbance in image quality, whereas it can also provide a crucial clue for image-based forensic tasks. Conventionally, noise is assumed to comprise an additive Gaussian model to be estimated and then used to reveal anomalies. However, for real sensor noise, it should be modeled as signal-dependent noise (SDN). In this work, we apply SDN to splicing forgery localization tasks. Through statistical analysis of the SDN model, we assume that noise can be modeled as a Gaussian approximation for a certain brightness and propose a likelihood model for a noise level function. By building a maximum a posterior Markov random field (MAP-MRF) framework, we exploit the likelihood of noise to reveal the alien region of spliced objects, with a probability combination refinement strategy. To ensure a completely blind detection, an iterative alternating method is adopted to estimate the MRF parameters. Experimental results demonstrate that our method is effective and provides a comparative localization performance.
Bayes-Adaptive Deep Model-Based Policy Optimisation

We introduce a Bayesian (deep) model-based reinforcement learning method (RoMBRL) that can capture model uncertainty to achieve sample-efficient policy optimisation. We propose to formulate the model-based policy optimisation problem as a Bayes-adaptive Markov decision process (BAMDP). RoMBRL maintains model uncertainty via belief distributions through a deep Bayesian neural network whose samples are generated via stochastic gradient Hamiltonian Monte Carlo. Uncertainty is propagated through simulations controlled by sampled models and history-based policies. As beliefs are encoded in visited histories, we propose a history-based policy network that can be end-to-end trained to generalise across history space and will be trained using recurrent Trust-Region Policy Optimisation. We show that RoMBRL outperforms existing approaches on many challenging control benchmark tasks in terms of sample complexity and task performance. The source code of this paper is also publicly available on this https URL.
A Cross-lingual Natural Language Processing Framework for Infodemic Management

The COVID-19 pandemic has put immense pressure on health systems which are further strained due to the misinformation surrounding it. Under such a situation, providing the right information at the right time is crucial. There is a growing demand for the management of information spread using Artificial Intelligence. Hence, we have exploited the potential of Natural Language Processing for identifying relevant information that needs to be disseminated amongst the masses. In this work, we present a novel Cross-lingual Natural Language Processing framework to provide relevant information by matching daily news with trusted guidelines from the World Health Organization. The proposed pipeline deploys various techniques of NLP such as summarizers, word embeddings, and similarity metrics to provide users with news articles along with a corresponding healthcare guideline. A total of 36 models were evaluated and a combination of LexRank based summarizer on Word2Vec embedding with Word Mover distance metric outperformed all other models. This novel open-source approach can be used as a template for proactive dissemination of relevant healthcare information in the midst of misinformation spread associated with epidemics.
Ammonium and nitrate uptake by Eucalyptus nitens

Low nitrogen availability commonly limits the growth of Eucalyptus miens (Deane and Maiden) Maiden plantations in south-eastern Australia. However, until this study was undertaken little was known of the nitrogen uptake and assimilation processes of temperate eucalypt species like E. nitens. In this study ammonium and nitrate uptake by young roots of solution cultured E. nitens were characterised under a variety of conditions using two different techniques: net uptake on a macro scale measured as depletion from solution, and net fluxes on a micro scale estimated from measurements of concentration gradients near root surfaces using microelectrodes. Datasets taken from the literature were used to both validate the use of the depletion method for nitrogen, and to find the most appropriate method to use for the estimation of kinetic parameters from depletion datasets. Ammonium and nitrate depletion from solution was characterised with respect to nitrogen source, pH, temperature, and N status. Ammonium uptake rates were consistently higher than nitrate uptake rates in all experiments. Uptake rates for both ammonium and nitrate were higher at pH 4 than at pH 6, and they were reduced to a similar extent with a decrease in temperature from 20°C to 10°C (Q10 values of 1.3 to 1.9). For ammonium uptake, there was evidence for rapid adaptation of uptake processes (within 24 hrs) to changing temperature. Nitrogen status, which was correlated with relative growth rate (RGR), had unclear effects on uptake characteristics and highlighted deficiencies of measuring uptake by long term (up to 10 hours) nutrient depletion. Ion selective electrodes were used in the MIFE (microelectrode ion flux estimation) technique to simultaneously measure ammonium, nitrate, and proton fluxes within the unstirred layer surrounding roots of E. nitens in solution culture. Measurements were within the region 20 to 50 mm from the tip of primary roots approximately 80 mm long. Within this region fluxes of ammonium, nitrate, and protons varied little, spatially or temporally. Under these conditions there was a consistent flux stoichiometry for ammonium: nitrate: and protons of 3.1: 1: -6.0. When ammonium and nitrate concentrations were both set at 100 µM there was no inhibition of nitrate uptake by ammonium, but at concentrations of ammonium and nitrate in the range 200-1000 µM there was an apparent suppression of nitrate uptake. There was evidence for two mechanisms of ammonium uptake. The mechanism at low concentrations (<200 µM) showed saturation kinetics with a K m of 15 µM whereas at high concentrations (200-1000µM) an approximately linear increase in uptake occurred. Excision of the shoot brought about a rapid and dramatic reduction in fluxes of ammonium, nitrate, and protons. The apparent preference shown here for ammonium over nitrate could be indicative of an adaptation by E. nitens to grow in cold, acidic, forest soils where ammonium is more readily available than nitrate. These results suggest that nitrogen uptake rates of E. nitens will be maximised if nitrogen is supplied predominantly in the ammonium form.
Introduction to virtual Morse-Bott theory on analytic spaces, moduli spaces of SO(3) monopoles, and applications to four-manifolds.

We introduce an approach to Morse-Bott theory, called virtual Morse-Bott theory, for Hamiltonian functions of circle actions on closed, real analytic, almost Hermitian spaces. In the case of Hamiltonian functions of circle actions on closed, smooth, almost Kaehler (symplectic) manifolds, virtual Morse-Bott theory coincides with classical Morse-Bott theory due to Bott (1954) and Frankel (1959). Using Embedded Resolution of Singularities, we prove that positivity of virtual Morse-Bott indices implies downward gradient flow in the top stratum of smooth points in the analytic space. When applied to moduli spaces of Higgs pairs on a smooth Hermitian vector bundle over a Riemann surface of genus two or more, virtual Morse-Bott theory allows one to relax assumptions due to Hitchin (1987) that ensure that those moduli spaces are smooth, for example, that the degree and rank of the vector bundle are coprime and hence that the defining Higgs equations for Higgs pairs are unobstructed in the sense of Kuranishi (1965) and that gauge transformations act freely. Real analytic spaces are Whitney-stratified and virtual Morse-Bott theory provides new techniques complementing the stratified Morse theory methods of Goresky and MacPherson (1988). In this article, we apply our method to the moduli space of SO(3) monopoles over a complex, Kaehler surface, we use the Atiyah-Singer Index Theorem to compute virtual Morse-Bott indices of all critical strata (Seiberg-Witten moduli subspaces), and we prove that these indices are positive in a setting with implications for the geography of closed, smooth four-manifolds of Seiberg-Witten simple type.
A Survey and Analysis of TLS Interception Mechanisms and Motivations: Exploring how end-to-end TLS is made “end-to-me” for web traffic

TLS is an end-to-end protocol designed to provide confidentiality and integrity guarantees that improve end-user security and privacy. While TLS helps defend against pervasive surveillance of intercepted unencrypted traffic, it also hinders several common beneficial operations typically performed by middleboxes on the network traffic. Consequently, various methods have been proposed that “bypass” the confidentiality goals of TLS by playing with keys and certificates essentially in a man-in-the-middle solution, as well as new proposals that extend the protocol to accommodate third parties, delegation schemes to trusted middleboxes, and fine-grained control and verification mechanisms. We first review the use cases expecting plain HTTP traffic and discuss the extent to which TLS hinders these operations. We retain 19 scenarios where access to unencrypted traffic is still relevant and evaluate the incentives of the stakeholders involved. Second, we survey 30 schemes by which TLS no longer delivers end-to-end security and by which the notion of an “end” changes, including caching middleboxes such as Content Delivery Networks. Finally, we compare each scheme based on deployability and security characteristics and evaluate their compatibility with the stakeholders’ incentives. Our analysis leads to a number of key findings, observations, and research questions that we believe will be of interest to practitioners, policy makers, and researchers.
COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We introduce an agent-based compartmental simulator we call COVI-AgentSim, integrating detailed consideration of virology, disease progression, social contact networks, and mobility patterns, based on parameters derived from empirical research. We verify by comparing to real data that COVI-AgentSim is able to reproduce realistic COVID-19 spread dynamics, and perform a sensitivity analysis to verify that the relative performance of contact tracing methods are consistent across a range of settings. We use COVI-AgentSim to perform cost-benefit analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features. We find all DCT methods consistently reduce the spread of the disease, and that the advantage of FCT over BCT is maintained over a wide range of adoption rates. Feature-based methods of contact tracing avert more disability-adjusted life years (DALYs) per socioeconomic cost (measured by productive hours lost). Our results suggest any DCT method can help save lives, support re-opening of economies, and prevent second-wave outbreaks, and that FCT methods are a promising direction for enriching BCT using self-reported symptoms, yielding earlier warning signals and a significantly reduced spread of the virus per socioeconomic cost.
Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach

Nowadays, there exist powerful language models such as OpenAI's GPT-2 that can generate readable text and can be fine-tuned to generate text for a specific domain. Considering GPT-2, it cannot directly generate synthetic news with respect to a given topic and the output of the language model cannot be explicitly controlled. In this paper, we study the novel problem of topic-preserving synthetic news generation. We propose a novel deep reinforcement learning-based method to control the output of GPT-2 with respect to a given news topic. When generating text using GPT-2, by default, the most probable word is selected from the vocabulary. Instead of selecting the best word each time from GPT-2's output, an RL agent tries to select words that optimize the matching of a given topic. In addition, using a fake news detector as an adversary, we investigate generating realistic news using our proposed method. In this paper, we consider realistic news as news that cannot be easily detected by a fake news classifier. Experimental results demonstrate the effectiveness of the proposed framework on generating topic-preserving news content than state-of-the-art baselines.
PATHoBot: A Robot for Glasshouse Crop Phenotyping and Intervention

We present PATHoBot an autonomous crop surveying and intervention robot for glasshouse environments. The aim of this platform is to autonomously gather high quality data and also estimate key phenotypic parameters. To achieve this we retro-fit an off-the-shelf pipe-rail trolley with an array of multi-modal cameras, navigation sensors and a robotic arm for close surveying tasks and intervention. In this paper we describe PATHoBot design choices made to ensure proper operation in a commercial glasshouse environment. As a surveying platform we collect a number of datasets which include both sweet pepper and tomatoes. We show how PATHoBot enables novel surveillance approaches by first improving our previous work on fruit counting by incorporating wheel odometry and depth information. We find that by introducing re-projection and depth information we are able to achieve an absolute improvement of 20 points over the baseline technique in an "in the wild" situation. Finally, we present a 3D mapping case study, further showcasing PATHoBot’s crop surveying capabilities.
Hessian of Hausdorff dimension on purely imaginary directions

Bridgeman–Taylor (Math. Ann. 341 (2008), 927–943) and McMullen (Invent. Math. 173 (2008), 365–425) showed that the Weil–Petersson metric on Teichmüller space can be realized by looking at the infinitesimal change of the Hausdorff dimension of certain quasi‐Fuchsian deformations. In this article, we give a similar geometric interpretation of the spectral gap pressure metric introduced by Bridgeman–Canary–Labourie–Sambarino (Geom. Dedicata 192 (2018), 57–86) on the Hitchin component for PSLd(R)${{\mathsf {PSL}}}_d(\mathbb {R})$ . More generally, we investigate the Hessian of the Hausdorff dimension as a function on the space of (1,1,2)‐hyperconvex representations, a class introduced in (J. reine angew. Math. 774 (2021), 1–51) which includes small complex deformations of Hitchin representations and of Θ$\Theta$ ‐positive representations. As another application, we prove that the Hessian of the Hausdorff dimension of the limit set at the inclusion Γ→PO(n,1)→PU(n,1)${{\Gamma }}\rightarrow {{\mathsf {PO}}}(n,1)\rightarrow {{\mathsf {PU}}}(n,1)$ is positive definite when Γ${{\Gamma }}$ is co‐compact in PO(n,1)${{\mathsf {PO}}}(n,1)$ (unless n=2$n=2$ and the deformation is tangent to X(Γ,PO(2,1))$\mathfrak {X}({{\Gamma }}, {{\mathsf {PO}}}(2,1))$ ).
Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations

Effective communication is an important skill for enabling information exchange and cooperation in multi-agent settings. Indeed, emergent communication is now a vibrant field of research, with common settings involving discrete cheap-talk channels. One limitation of this setting is that it does not allow for the emergent protocols to generalize beyond the training partners. Furthermore, so far emergent communication has primarily focused on the use of symbolic channels. In this work, we extend this line of work to a new modality, by studying agents that learn to communicate via actuating their joints in a 3D environment. We show that under realistic assumptions, a non-uniform distribution of intents and a common-knowledge energy cost, these agents can find protocols that generalize to novel partners. We also explore and analyze specific difficulties associated with finding these solutions in practice. Finally, we propose and evaluate initial training improvements to address these challenges, involving both specific training curricula and providing the latent feature that can be coordinated on during training.
Semantic Labeling Using a Deep Contextualized Language Model

Generating schema labels automatically for column values of data tables has many data science applications such as schema matching, and data discovery and linking. For example, automatically extracted tables with missing headers can be filled by the predicted schema labels which significantly minimizes human effort. Furthermore, the predicted labels can reduce the impact of inconsistent names across multiple data tables. Understanding the connection between column values and contextual information is an important yet neglected aspect as previously proposed methods treat each column independently. In this paper, we propose a context-aware semantic labeling method using both the column values and context. Our new method is based on a new setting for semantic labeling, where we sequentially predict labels for an input table with missing headers. We incorporate both the values and context of each data column using the pre-trained contextualized language model, BERT, that has achieved significant improvements in multiple natural language processing tasks. To our knowledge, we are the first to successfully apply BERT to solve the semantic labeling task. We evaluate our approach using two real-world datasets from different domains, and we demonstrate substantial improvements in terms of evaluation metrics over state-of-the-art feature-based methods.
TILT: Achieving Higher Fidelity on a Trapped-Ion Linear-Tape Quantum Computing Architecture

Trapped-ion qubits are a leading technology for practical quantum computing. In this work, we present an architectural analysis of a linear-tape architecture for trapped ions. In order to realize our study, we develop and evaluate mapping and scheduling algorithms for this architecture. In particular, we introduce TILT, a linear “Turing-machinelike” architecture with a multilaser control “head,” where a linear chain of ions moves back and forth under the laser head. We find that TILT can substantially reduce communication as compared with comparable-sized Quantum Charge Coupled Device (QCCD) architectures. We also develop two important scheduling heuristics for TILT. The first heuristic reduces the number of swap operations by matching data traveling in opposite directions into an “opposing swap. ”, and also avoids the maximum swap distance across the width of the head, as maximum swap distances make scheduling multiple swaps in one head position difficult. The second heuristic minimizes ion chain motion by scheduling the tape to the position with the maximal executable operations for every movement. We provide application performance results from our simulation, which suggest that TILT can outperform QCCD in a range of NISQ applications in terms of success rate (up to 4.35x and 1.95x on average). We also discuss using TILT as a building block to extend existing scalable trapped-ion quantum computing proposals.
Cluster-based network modeling -- automated robust modeling of complex dynamical systems

We propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science and engineering. Data-driven dynamic modeling often assumes a low-dimensional subspace or manifold for the state. We liberate ourselves from this assumption by proposing cluster-based network modeling (CNM) bridging machine learning, network science, and statistical physics. CNM only assumes smoothness of the dynamics in the state space, robustly describes short- and long-term behavior and is fully automatable as it does not rely on application-specific knowledge. CNM is demonstrated for the Lorenz attractor, ECG heartbeat signals, Kolmogorov flow, and a high-dimensional actuated turbulent boundary layer. Even the notoriously difficult modeling benchmark of rare events in the Kolmogorov flow is solved. This automatable universal data-driven representation of complex nonlinear dynamics complements and expands network connectivity science and promises new fast-track avenues to understand, estimate, predict and control complex systems in all scientific fields.
An Efficient QAM Detection via Nonlinear Post-distortion for SC-FDE Transmission under PA Impairments with Memory

In this paper, we propose a novel receiver structure for single carrier transmission with frequency domain equalization (FDE) that is exposed to power amplifier (PA) nonlinearities. A two stage approach is adopted, in which linear communication channel is equalized at first stage and it is followed by a post-distortion unit where nonlinear distortion is reduced. In literature, there are some techniques that are proposed for the memoryless compensation of nonlinear distortion together with FDE. However, in this study, we show that even if a memoryless nonlinearity is utilized, received signal is impaired by nonlinear inter-symbol interference (ISI). Therefore, we propose a class of post-distortion techniques, which use neighbouring received symbols to supress the nonlinear interference. Two different, post-distortion techniques, namely Gaussian process regression (GPR) and neural network (NN) based post-distorters, are considered. In addition, a receiver structure, combining outputs of fractional delayed bank of FDE's, is proposed to overcome performance degradation problem of FDE for highly frequency selective channels under nonlinear distortion. Performance of the proposed techniques are compared with that of the state-of-the-art techniques in terms of bit error rate (BER) and achievable information rate (AIR) metrics via simulations. Simulation results demonstrate that GPR and NN based post-distortion methods outperform state-of-the-art techniques. Lastly, it is observed that proposed receiver architecture performs very close to linear system with the help of proposed fractionally delayed FDE bank.
Endoscopic ultrasound-guided radiofrequency ablation for hepatocellular carcinoma in cirrhosis: a case report test for efficacy and future perspectives

Abstract Background The percutaneous approach allows for effective and safe treatment of liver lesions. But in case of subcapsular or left segments location, this approach seems to be less effective or unsafe. Endoscopic ultrasound-guided radiofrequency ablation (EUS-RFA) is a new technique used to treat pancreatic and neuroendocrine tumors in patients unfit for surgery. Methods Hereby, we describe the case of a 70-year-old patient with cirrhosis with a large subcapsular hepatocellular carcinoma (HCC) in II-III-IVb segments, in which surgery or percutaneous therapies were not feasible, treated with EUS-RFA. The HCC was treated using an EUS-RFA (EUSRA) system, which consists of a 19G water-cooled monopolar RFA needle and a dedicated generator system. Results After a multidisciplinary discussion, the lesion was ablated in two different sessions, which resulted in destruction of about 70 % of neoplastic tissue. A second step surgery was required but initially refused by the patient. Conclusions EUS-RFA could be an effective way to treat left hepatic lesions not manageable with conventional percutaneous methods. This case report does not highlight concerns about safety of this approach and this observation needs to be validated in a larger cohort of patients with cirrhosis.