Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal index insurance and basis risk decomposition: an application to Kenya

Index insurance is a promising tool to reduce the risk faced by farmers, but high basis risk, which arises from imperfect correlation between the index and individual farm yields, has limited its adoption to date. Basis risk arises from two fundamental sources: the intrinsic heterogeneity within an insurance zone (zonal risk), and the lack of predictive accuracy of the index (design risk). Whereas previous work has focused almost exclusively on design risk, a theoretical and empirical understanding of the role of zonal risk is still lacking. Here we investigate the relative roles of zonal and design risk, using the case of maize yields in Kenya. Our first contribution is to derive a formal decomposition of basis risk, providing a simple upper bound on the insurable basis risk that any index can reach within a given zone. Our second contribution is to provide the first large-scale empirical analysis of the extent of zonal versus design risk. To do so, we use satellite estimates of yields at 10m resolution across Kenya, and investigate the effect of using smaller zones versus using different indices. Our results show a strong local heterogeneity in yields, underscoring the challenge of implementing index insurance in smallholder systems, and the potential benefits of low-cost yield measurement approaches that can enable more local definitions of insurance zones.
Application of Flexible Combine-Clamp in Digital Rapid Production for Aircraft Tube

Aiming at the existing problem of plane tube welding jig manufacturing, many systems developed such as standard components parameterized modeling system, computer intelligent reasoning system, standard components replacement system, accuracy checking system and standard components management system, and also replace the traditional standard samples by three-dimensional model of tube, then realize the digital rapid design and manufacture of combined welding jig.
Investigations of the size distribution and magnetic properties of nanoparticles of Cu2OSeO3

Skyrmions in confined geometries have been a subject of increasing interest due to the different properties that they exhibit compared to their bulk counterparts. In this study, nanoparticles of skyrmion-hosting Cu2OSeO3 have been synthesised using a precipitation method followed by thermal treatment. This enables us to produce nanoparticles whose mean size varies from tens of nanometers to a few micrometers by varying the temperature and duration of the thermal decomposition of the precursor. These sizes span the ∼63 nm diameter of skyrmions in Cu2OSeO3, allowing investigations into how the magnetic state changes when the size of the geometrical confinement is similar to and smaller than the size of an isolated magnetic skyrmion. AC susceptibility measurements performed on nanoparticles with a size distribution from 15 to 250 nm show a change in the magnetic phase diagram compared to bulk Cu2OSeO3.
Tensor network to learn the wavefunction of data

How many different ways are there to handwrite digit 3? To quantify this question imagine extending a dataset of handwritten digits MNIST by sampling additional images until they start repeating. We call the collection of all resulting images of digit 3 the"full set. "To study the properties of the full set we introduce a tensor network architecture which simultaneously accomplishes both classification (discrimination) and sampling tasks. Qualitatively, our trained network represents the indicator function of the full set. It therefore can be used to characterize the data itself. We illustrate that by studying the full sets associated with the digits of MNIST. Using quantum mechanical interpretation of our network we characterize the full set by calculating its entanglement entropy. We also study its geometric properties such as mean Hamming distance, effective dimension, and size. The latter answers the question above -- the total number of black and white threes written MNIST style is $2^{72}$.
Attention-Based Multi-Hypothesis Fusion for Speech Summarization

Speech summarization, which generates a text summary from speech, can be achieved by combining automatic speech recognition (ASR) and text summarization (TS). With this cascade approach, we can exploit state-of-the-art models and large training datasets for both subtasks, i.e., Transformer for ASR and Bidirectional Encoder Representations from Transformers (BERT) for TS. However, ASR errors directly affect the quality of the output summary in the cascade approach. We propose a cascade speech summarization model that is robust to ASR errors and that exploits multiple hypotheses generated by ASR to attenuate the effect of ASR errors on the summary. We investigate several schemes to combine ASR hypotheses. First, we propose using the sum of sub-word embedding vectors weighted by their posterior values provided by an ASR system as an input to a BERT-based TS system. Then, we introduce a more general scheme that uses an attention-based fusion module added to a pre-trained BERT module to align and combine several ASR hypotheses. Finally, we perform speech summarization experiments on the How2 dataset and a newly assembled TED-based dataset that we will release with this paper11https://github.com/nttcslab-sp-admin/TEDSummary. These experiments show that retraining the BERT-based TS system with these schemes can improve summarization performance and that the attention-based fusion module is particularly effective.
Remote Memory-Deduplication Attacks

Memory utilization can be reduced by merging identical memory blocks into copy-on-write mappings. Previous work showed that this so-called memory deduplication can be exploited in local attacks to break ASLR, spy on other programs,and determine the presence of data, i.e., website images. All these attacks exploit memory deduplication across security domains, which in turn was disabled. However, within a security domain or on an isolated system with no untrusted local access, memory deduplication is still not considered a security risk and was recently re-enabled on Windows by default. In this paper, we present the first fully remote memorydeduplication attacks. Unlike previous attacks, our attacks require no local code execution. Consequently, we can disclose memory contents from a remote server merely by sending and timing HTTP/1 and HTTP/2 network requests. We demonstrate our attacks on deduplication both on Windows and Linux and attack widely used server software such as Memcached and InnoDB. Our side channel leaks up to 34.41 B/h over the internet, making it faster than comparable remote memory-disclosure channels. We showcase our remote memory-deduplication attack in three case studies: First, we show that an attacker can disclose the presence of data in memory on a server running Memcached. We show that this information disclosure channel can also be used for fingerprinting and detect the correct libc version over the internet in 166.51 s. Second, in combination with InnoDB, we present an information disclosure attack to leak MariaDB database records. Third, we demonstrate a fully remote KASLR break in less than 4 minutes allowing to derandomize the kernel image of a virtual machine over the Internet, i.e., 14 network hops away. We conclude that memory deduplication must also be considered a security risk if only applied within a single security domain.
Custom Integrated Circuit For Enhanced Real-Time Road Traffic Monitoring

The design and application of a custom IC (integrated circuit), developed to enhance an existing real-time image processing system, is described. The system is used to automatically monitor road traffic at a complex road junction, where data is acquired using a black and white video camera. The new custom IC forms part of a preprocessing circuit to remove binary noise from the image, and is particularly effective when used in conjunction with a technique for background updating [1], also described.
Coarse-to-fine Animal Pose and Shape Estimation

Most existing animal pose and shape estimation approaches reconstruct animal meshes with a parametric SMAL model. This is because the low-dimensional pose and shape parameters of the SMAL model makes it easier for deep networks to learn the high-dimensional animal meshes. However, the SMAL model is learned from scans of toy animals with limited pose and shape variations, and thus may not be able to represent highly varying real animals well. This may result in poor fittings of the estimated meshes to the 2D evidences, e.g. 2D keypoints or silhouettes. To mitigate this problem, we propose a coarse-to-fine approach to reconstruct 3D animal mesh from a single image. The coarse estimation stage first estimates the pose, shape and translation parameters of the SMAL model. The estimated meshes are then used as a starting point by a graph convolutional network (GCN) to predict a per-vertex deformation in the refinement stage. This combination of SMAL-based and vertex-based representations benefits from both parametric and non-parametric representations. We design our mesh refinement GCN (MRGCN) as an encoder-decoder structure with hierarchical feature representations to overcome the limited receptive field of traditional GCNs. Moreover, we observe that the global image feature used by existing animal mesh reconstruction works is unable to capture detailed shape information for mesh refinement. We thus introduce a local feature extractor to retrieve a vertex-level feature and use it together with the global feature as the input of the MRGCN. We test our approach on the StanfordExtra dataset and achieve state-of-the-art results. Furthermore, we test the generalization capacity of our approach on the Animal Pose and BADJA datasets. Our code is available at the project website.
A Survey on Adversarial Attacks for Malware Analysis

Machine learning has witnessed tremendous growth in its adoption and advancement in the last decade. The evolution of machine learning from traditional algorithms to modern deep learning architectures has shaped the way today's technology functions. Its unprecedented ability to discover knowledge/patterns from unstructured data and automate the decision-making process led to its application in wide domains. High flying machine learning arena has been recently pegged back by the introduction of adversarial attacks. Adversaries are able to modify data, maximizing the classification error of the models. The discovery of blind spots in machine learning models has been exploited by adversarial attackers by generating subtle intentional perturbations in test samples. Increasing dependency on data has paved the blueprint for ever-high incentives to camouflage machine learning models. To cope with probable catastrophic consequences in the future, continuous research is required to find vulnerabilities in form of adversarial and design remedies in systems. This survey aims at providing the encyclopedic introduction to adversarial attacks that are carried out against malware detection systems. The paper will introduce various machine learning techniques used to generate adversarial and explain the structure of target files. The survey will also model the threat posed by the adversary and followed by brief descriptions of widely accepted adversarial algorithms. Work will provide a taxonomy of adversarial evasion attacks on the basis of attack domain and adversarial generation techniques. Adversarial evasion attacks carried out against malware detectors will be discussed briefly under each taxonomical headings and compared with concomitant researches. Analyzing the current research challenges in an adversarial generation, the survey will conclude by pinpointing the open future research directions.
Low thermal conductivity and interface thermal conductance in 
SnS2

After the discovery of graphene, there have been tremendous efforts in exploring various layered two-dimensional (2D) materials for their potential applications in electronics, optoelectronics, as well as energy conversion and storage. One of such 2D materials, SnS2, which is earth abundant, low in toxicity, and cost effective, has been reported to show a high on/off current ratio, fast photodetection, and high optical absorption, thus making this material promising for device applications. Further, a few recent theoretical reports predict high electrical conductivity and Seebeck coefficient in its bulk counterparts. However, the thermal properties of SnS2 have not yet been properly explored, which are important to materialize many of its potential applications. Here, we report the thermal properties of SnS2 measured using the optothermal method and supported by density functional theory (DFT) calculations. Our experiments suggest very low in-plane lattice thermal conductivity (κ = 3.20 ± 0.57 W m 1 K) and cross-plane interfacial thermal conductance per unit area (g = 0.53 ± 0.09 MW m K) for monolayer SnS2 supported on a SiO2/Si substrate. The thermal properties show a dependence on the thickness of the SnS2 flake. Based on the findings of our DFT calculations, the very low value of the lattice thermal conductivity can be attributed to low group velocity, a shorter lifetime of the phonons, and strong anharmonicity in the crystal. Materials with low thermal conductivity are important for thermoelectric applications as the thermoelectric power coefficient goes inversely with the thermal conductivity.
Impacts of Jets and winds from primordial black holes

Primordial black holes (PBHs) formed in the early Universe constitute an attractive candidate for dark matter. Within the gaseous environment of the interstellar medium, PBHs with accretion disks naturally launch outflows such as winds and jets. We discuss for the first time how PBHs with significant spin can sustain powerful relativistic jets and generate associated cocoons. Jets and winds can efficiently deposit their kinetic energies and heat the surrounding gas through shocks. Focusing on the Leo T dwarf galaxy, we demonstrate that these effects form novel tests and set new limits on PBHs over a significant ∼10−2M⊙ − 106M⊙ mass range, including the parameter space associated with gravitational wave observations by the LIGO and VIRGO Collaborations. Observing the morphology of emission will allow to distinguish between jet and wind contributions, and hence establishes a new method for identifying spinning PBHs.
Bayesian Optimization for Cascade-Type Multistage Processes

Abstract Complex processes in science and engineering are often formulated as multistage decision-making problems. In this letter, we consider a cascade process, a type of multistage decision-making process. This is a multistage process in which the output of one stage is used as an input for the subsequent stage. When the cost of each stage is expensive, it is difficult to search for the optimal controllable parameters for each stage exhaustively. To address this problem, we formulate the optimization of the cascade process as an extension of the Bayesian optimization framework and propose two types of acquisition functions based on credible intervals and expected improvement. We investigate the theoretical properties of the proposed acquisition functions and demonstrate their effectiveness through numerical experiments. In addition, we consider suspension setting, an extension in which we are allowed to suspend the cascade process at the middle of the multistage decision-making process that often arises in practical problems. We apply the proposed method in a test problem involving a solar cell simulator, the motivation for this study.
Coral: An Approach for Conversational Agents in Mental Health Applications

It may be difficult for some individuals to open up and share their thoughts and feelings in front of a mental health expert. For those who are more at ease with a virtual agent, conversational agents can serve as an intermediate step in the right direction. The conversational agent must therefore be empathetic and able to conduct free-flowing conversations. To this effect, we present an approach for creating a generative empathetic open-domain chatbot that can be used for mental health applications. We leverage large scale pre-training and empathetic conversational data to make the responses more empathetic in nature and a multi-turn dialogue arrangement to maintain context. Our models achieve state-of-the-art results on the Empathetic Dialogues test set.
Phenomenology of one zero texture Yukawa matrix in a flavor symmetric scotogenic model

The scotogenic model is well known for accommodating small neutrino mass and dark matter. Here, we have realized the scotogenic model with the help of discrete flavor symmetry $A_{4}\otimes Z_{4}$. We have obtained three one zero textures of Yukawa coupling matrices from the model and have studied its impact on neutrino phenomenology and related aspects of cosmology. On the basis of $\mu-\tau$ symmetry, we have further discarded two structures of one zero texture Yukawa coupling matrix. We further analyze if the effective mass of active neutrinos obtained by the virtue of the Yukawa coupling matrix is consistent with the KamLAND-Zen limit for 0$\nu\beta\beta$. Also different lepton flavor violating(LFV) processes such as $l_{\alpha}\longrightarrow l_{\beta}\gamma$ and $l_{\alpha}\longrightarrow 3l_{\beta}$ are implemented and their influence on neutrino phenomenology is studied corresponding to the Yukawa coupling matrix. The entire work is carried out considering the dark matter mass($M_{DM}$) in the region $450-750$ GeV. We have also obtained some significant results for baryon asymmetry of the Universe in agreement with the one zero textures of the coupling matrices. Furthermore, interesting results for relic abundance on the basis of distinct mass splittings between the inert scalars.
Trackable species dynamics in reaction network models

In a stochastic reaction network setting we define a subset of species as ’trackable’ if we can consistently follow the fate of its individual molecules. We show that using the classical large volume limit results, we may approximate the dynamics of a single molecule of trackable species in a simple and computationally efficient way. We give examples on how this approach may be used to obtain various characteristics of single-molecule dynamics (for instance, the distribution of the number of infections in a single individual in the course of an epidemic or the activity time of a single enzyme molecule). Moreover, we show how to approximate the overall dynamics of trackable species in the full system with a collection of independent single-molecule trajectories, and give explicit bounds for the approximation error in terms of the reaction rates. This approximation, which is well defined for all times, leads to an efficient and fully parallelizable simulation technique for which we provide some numerical example.
Exploiting Action Impact Regularity and Partially Known Models for Offline Reinforcement Learning

Ofﬂine reinforcement learning—learning a policy from a batch of data—is known to be hard: without making strong assumptions, it is easy to construct counterexamples such that existing algorithms fail. In this work, we instead consider a property of certain real world problems where ofﬂine reinforcement learning should be effective: those where actions only have limited impact for a part of the state. We formalize and introduce this Action Impact Regularity (AIR) property. We further propose an algorithm that assumes and exploits the AIR property, and bound the suboptimality of the output policy when the MDP satisﬁes AIR. Finally, we demonstrate that our algorithm outperforms existing ofﬂine reinforcement learning algorithms across different data collection policies in two simulated environments where the regularity holds.
A Maude Implementation of Rewritable Petri Nets: a Feasible Model for Dynamically Reconfigurable Systems

Petri Nets (PN) are a central, theoretically sound model for concurrent or distributed systems but, at least in their classical definition, not expressive enough to represent dynamic reconfiguration capabilities. On the other side, Rewriting Logic has proved to be a natural semantic framework for several formal models of concurrent/distributed systems. We propose a compact, efficient Maude formalization of dynamically reconfigurable PT nets (with inhibitor arcs), using as a running example the specification of a simple, fault-tolerant manufacturing system. We discuss the advantages of such a combined approach, as well as some concerns that it raises.
Physics-Information-Aided Kriging: Constructing Covariance Functions using Stochastic Simulation Models

In this work, we propose a new Gaussian process regression (GPR) method: physics information aided Kriging (PhIK). In the standard data-driven Kriging, the unknown function of interest is usually treated as a Gaussian process with assumed stationary covariance with hyperparameters estimated from data. In PhIK, we compute the mean and covariance function from realizations of available stochastic models, e.g., from realizations of governing stochastic partial differential equations solutions. Such constructed Gaussian process generally is non-stationary, and does not assume a specific form of the covariance function. Our approach avoids the optimization step in data-driven GPR methods to identify the hyperparameters. More importantly, we prove that the physical constraints in the form of a deterministic linear operator are guaranteed in the resulting prediction. We also provide an error estimate in preserving the physical constraints when errors are included in the stochastic model realizations. To reduce the computational cost of obtaining stochastic model realizations, we propose a multilevel Monte Carlo estimate of the mean and covariance functions. Further, we present an active learning algorithm that guides the selection of additional observation locations. The efficiency and accuracy of PhIK are demonstrated for reconstructing a partially known modified Branin function, studying a three-dimensional heat transfer problem and learning a conservative tracer distribution from sparse concentration measurements.
Benchmarking Machine Learning Techniques for THz Channel Estimation Problems

Terahertz communication is one of the most promising wireless communication technologies for 6G generation and beyond. For THz systems to be practically adopted, channel estimation is one of the key issues. We consider the problem of channel modeling and estimation with deterministic channel propagation and the related physical characteristics of THz bands, and benchmark various machine learning algorithms to estimate THz channel, including neural networks (NN), logistic regression (LR), and projected gradient ascent (PGA). Numerical results show that PGA algorithm yields the most promising performance at SNR=0 dB with NMSE of -12.8 dB.