Miracle-dz/UniFAR-FTUs huggingface dataset:
Upper bounds on the leakage of private data and an operational approach to Markovianity

(Dated: We quantify the consequences of a private key leakage and private randomness generated during quantum key distribution. We provide simple lower bounds on the one-way distillable key after the leakage has been detected. We also show that the distributed private randomness does not drop by more than twice the number of qubits of the traced-out system. We further focus on irreducible private states, showing that their two-way distillable key is non-lockable. We then strengthen this result by referring to the idea of recovery maps. We further consider the action of special case of side-channels on some of the private states. Finally, we connect the topic of (non)markovian dynamics with that of hacking. In particular, we show that an invertible map is non-CP-divisible if and only if there exists a state whose the key witnessed by a particular privacy witness increases in time. This complements the recent result of J. Ko(cid:32)lody´nski et al. [Phys. Rev. A 101, 020303(R) (2020)] where the log-negativity was connected with the (non)markovianity of the dynamics.
Adaptive Dilated Convolution For Human Pose Estimation

Most existing human pose estimation (HPE) methods exploit multi-scale information by fusing feature maps of four different spatial sizes, \ie $1/4$, $1/8$, $1/16$, and $1/32$ of the input image. There are two drawbacks of this strategy: 1) feature maps of different spatial sizes may be not well aligned spatially, which potentially hurts the accuracy of keypoint location; 2) these scales are fixed and inflexible, which may restrict the generalization ability over various human sizes. Towards these issues, we propose an adaptive dilated convolution (ADC). It can generate and fuse multi-scale features of the same spatial sizes by setting different dilation rates for different channels. More importantly, these dilation rates are generated by a regression module. It enables ADC to adaptively adjust the fused scales and thus ADC may generalize better to various human sizes. ADC can be end-to-end trained and easily plugged into existing methods. Extensive experiments show that ADC can bring consistent improvements to various HPE methods. The source codes will be released for further research.
How to Tell Deep Neural Networks What We Know

We present a short survey of ways in which existing scientiﬁc knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientiﬁc assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may beneﬁt signiﬁcantly from being provided with human-knowledge of the domain encoded in a suf-ﬁciently precise form. This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks. The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed. In each category, we describe techniques that have been shown to yield signiﬁcant changes in network performance.
DENFERT version 2: extension of ab initio structural modelling of hydrated biomolecules to the case of small-angle neutron scattering data

Following the introduction of the program DENFERT [Koutsioubas & Pérez (2013). J. Appl. Cryst. 46, 1884–1888], which takes into account the hydration layer around solvated biological molecules during ab initio restorations of lowresolution molecular envelopes from small-angle X-ray scattering data, the present work introduces the second version of the program, which provides the ability to treat neutron scattering data sets. By considering a fully interconnected and hydrated model during the entire minimization process, it has been possible to simplify the user input and reach more objective shape reconstructions. Additionally, a new method is implemented for the subtraction of the contribution of internal inhomogeneities of biomolecules to the measured scattering. Validation of the overall approach is performed by successfully recovering the shape of various protein molecules from experimental neutron and X-ray solution scattering data.
Least-squares fitting applied to nuclear mass formulas. Solution by the Gauss–Seidel method

In this paper, a numerical method optimizing the coefficients of the semi empirical mass formula or those of similar mass formulas is presented. The optimization is based on the least-squares adjustments method and leads to the resolution of a linear system which is solved by iterations according to the Gauss–Seidel scheme. The steps of the algorithm are given in detail. In practice, the method is very simple to implement and is able to treat large data in a very fast way. In fact, although this method has been illustrated here by specific examples, it can be applied without difficulty to any experimental or statistical data of the same type, i.e. those leading to linear system characterized by symmetric and positive-definite matrices.
Design and synthesis of novel active phosphonate esters and their application in preparation of ceftriaxone

Abstract For a series of active phosphonate esters, the anhydride abbreviated as ANPTA (6a) exhibits the highest reactivity in the preparation of ceftriaxone. The synthesis of ceftriaxone was optimized with the pilot-scale yield reaching 95.7%. The results were explained from the structural viewpoint and supported by analysis of the calculated Mulliken atomic charge distribution.
Low Latency Allcast Over Broadcast Erasure Channels

Consider <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> nodes communicating over an unreliable broadcast channel. Each node has a single packet that needs to be communicated to all other nodes. Time is slotted, and a time slot is long enough for each node to broadcast one packet. Each broadcast reaches a random subset of nodes. The objective is to minimise the time until all nodes have received all packets. We study two schemes, (i) random relaying, and (ii) random linear network coding, and analyse their performance in an asymptotic regime in which <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> tends to infinity. Simulation results for a wide range of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> are also presented.
A Typed Slicing Compilation of the Polymorphic RPC calculus

The polymorphic RPC calculus allows programmers to write succinct multitier programs using polymorphic location constructs. However, until now it lacked an implementation. We develop an experimental programming language based on the polymorphic RPC calculus. We introduce a polymorphic Client-Server (CS) calculus with the client and server parts separated. In contrast to existing untyped CS calculi, our calculus is not only able to resolve polymorphic locations statically, but it is also able to do so dynamically. We design a type-based slicing compilation of the polymorphic RPC calculus into this CS calculus, proving type and semantic correctness. We propose a method to erase types unnecessary for execution but retaining locations at runtime by translating the polymorphic CS calculus into an untyped CS calculus, proving semantic correctness.
Correspondence-Free Point Cloud Registration with SO(3)-Equivariant Implicit Shape Representations

This paper proposes a correspondence-free method for point cloud rotational registration. We learn an embedding for each point cloud in a feature space that preserves the SO(3)-equivariance property, enabled by recent developments in equivariant neural networks. The proposed shape registration method achieves three major advantages through combining equivariant feature learning with implicit shape models. First, the necessity of data association is removed because of the permutation-invariant property in network architectures similar to PointNet. Second, the registration in feature space can be solved in closed-form using Horn's method due to the SO(3)-equivariance property. Third, the registration is robust to noise in the point cloud because of the joint training of registration and implicit shape reconstruction. The experimental results show superior performance compared with existing correspondence-free deep registration methods.
Improving Polyphonic Sound Event Detection on Multichannel Recordings with the Sørensen-Dice Coefficient Loss and Transfer Learning

The S{\o}rensen--Dice Coefficient has recently seen rising popularity as a loss function (also known as Dice loss) due to its robustness in tasks where the number of negative samples significantly exceeds that of positive samples, such as semantic segmentation, natural language processing, and sound event detection. Conventional training of polyphonic sound event detection systems with binary cross-entropy loss often results in suboptimal detection performance as the training is often overwhelmed by updates from negative samples. In this paper, we investigated the effect of the Dice loss, intra- and inter-modal transfer learning, data augmentation, and recording formats, on the performance of polyphonic sound event detection systems with multichannel inputs. Our analysis showed that polyphonic sound event detection systems trained with Dice loss consistently outperformed those trained with cross-entropy loss across different training settings and recording formats in terms of F1 score and error rate. We achieved further performance gains via the use of transfer learning and an appropriate combination of different data augmentation techniques.
Lumen: A machine learning framework to expose influence cues in texts

Phishing and disinformation are popular social engineering attacks with attackers invariably applying influence cues in texts to make them more appealing to users. We introduce Lumen, a learning-based framework that exposes influence cues in text: (i) persuasion, (ii) framing, (iii) emotion, (iv) objectivity/subjectivity, (v) guilt/blame, and (vi) use of emphasis. Lumen was trained with a newly developed dataset of 3K texts comprised of disinformation, phishing, hyperpartisan news, and mainstream news. Evaluation of Lumen in comparison to other learning models showed that Lumen and LSTM presented the best F1-micro score, but Lumen yielded better interpretability. Our results highlight the promise of ML to expose influence cues in text, toward the goal of application in automatic labeling tools to improve the accuracy of human-based detection and reduce the likelihood of users falling for deceptive online content.
Dialogue Object Search

We envision robots that can collaborate and communicate seamlessly with humans. It is necessary for such robots to decide both what to say and how to act, while interacting with humans. To this end, we introduce a new task, dialogue object search: A robot is tasked to search for a target object (e.g. fork) in a human environment (e.g., kitchen), while engaging in a"video call"with a remote human who has additional but inexact knowledge about the target's location. That is, the robot conducts speech-based dialogue with the human, while sharing the image from its mounted camera. This task is challenging at multiple levels, from data collection, algorithm and system development,to evaluation. Despite these challenges, we believe such a task blocks the path towards more intelligent and collaborative robots. In this extended abstract, we motivate and introduce the dialogue object search task and analyze examples collected from a pilot study. We then discuss our next steps and conclude with several challenges on which we hope to receive feedback.
Identification of Tissue Optical Properties During Thermal Laser-Tissue Interactions: Approach and Preliminary Evaluation

In this paper, we propose a computational framework to estimate the physical tissue properties that govern the thermal response of laser-irradiated tissue. We focus in particular on two quantities, the absorption and scattering coefficients, which describe the optical absorption of light in the tissue and whose knowledge is vital to correctly plan medical laser treatments. To perform the estimation, we utilize an implementation of the Ensemble Kalman Filter (EnKF), a type of Bayesian filtering algorithm for data assimilation. Unlike prior approaches, in this work we estimate the tissue optical properties based on the observed surface thermal response to laser irradiation. This method has the potential for straightforward implementation in a clinical setup, as it would only require a simple thermal sensor, e.g., a miniaturized infrared camera. Because the optical properties of tissue can undergo shifts during laser exposure, we employ a variant of EnKF capable of tracking time-varying parameters. Through preliminary evaluation in simulation, we demonstrate the ability of the proposed technique to identify the tissue optical properties and track their dynamic changes during laser exposure, while simultaneously tracking changes in the tissue temperature at locations beneath the surface.
Challenges in cybersecurity: Lessons from biological defense systems

Defending against novel, repeated, or unpredictable attacks, while avoiding attacks on the 'self', are the central problems of both mammalian immune systems and computer systems. Both systems have been studied in great detail, but with little exchange of information across the different disciplines. Here, we present a conceptual framework for structured comparisons across the fields of biological immunity and cybersecurity, by framing the context of defense, considering different (combinations of) defensive strategies, and evaluating defensive performance. Throughout this paper, we pose open questions for further exploration. We hope to spark the interdisciplinary discovery of general principles of optimal defense, which can be understood and applied in biological immunity, cybersecurity, and other defensive realms.
Sturmian subshifts and their C*-algebras

This paper investigates the structure of C∗-algebras built from one-sided Sturmian subshifts. They are parametrised by irrationals in the unit interval and built from a local homeomorphism associated to the subshift. We provide an explicit construction and description of this local homeomorphism. The C∗-algebras are ∗-isomorphic exactly when the systems are conjugate, and they are Morita equivalent exactly when the defining irrationals are equivalent (this happens precisely when the systems are flow equivalent). Using only elementary dynamical tools, we compute the dynamic asymptotic dimension of the (groupoid of the) local homeomorphism to be one, and by a result of Guentner, Willett, and Yu, it follows that the nuclear dimension of the C∗-algebras is one.
Structure Destruction and Content Combination for Face Anti-Spoofing

In pursuit of consolidating the face verification systems, prior face anti-spoofing studies excavate the hidden cues in original images to discriminate real person and diverse attack types with the assistance of auxiliary supervision. However, limited by the following two inherent disturbances in their training process: 1) Complete facial structure in a single image. 2) Implicit subdomains in the whole dataset, these methods are prone to stick on memorization of the entire training dataset and show sensitivity to non-homologous domain distribution. In this paper, we propose Structure Destruction Module and Content Combination Module to address these two limitations separately. The former mechanism destroys images into patches to construct a non-structural input, while the latter mechanism recombines patches from different subdomains or classes into a mixup construct. Based on this splitting-and-splicing operation, Local Relation Modeling Module is further proposed to model the second-order relationship between patches. We evaluate our method on extensive public datasets and promising experimental results to demonstrate the reliability of our method against the state-of-the-art competitors.
Effective dynamics analysis of fMRI data points to reconfiguration of cortical information processing during visual and auditory perception

Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. Nevertheless, the neural mechanisms governing the integration and entrainment of functionally specialized brain areas remain poorly understood. Here, we introduce a theoretical framework that discloses these mechanisms – at the whole brain level – based on fMRI data. Our approach relies on a model of functional connectivity that allows for the estimation of the strengths of (extrinsic) cortico-cortical interactions, as well as the input or baseline activity (intrinsic) to each cortical region. We examine quantitatively the contributions of effective connectivity and intrinsic input to changes in functional integration during movie viewing. Our results show how the interactions between cortical areas are moderately, but significantly, modified to select pathways that integrate sensory influxes from the visual and auditory systems to high-level areas in the frontal and central parts of the brain. Meanwhile, opposing changes in (intrinsic) input and (extrinsic) connectivity preserve a balance of distributed responses. To characterize this balance, we define an ‘effective drive’ that quantifies the propagation of neuronal activity (measured by the amplitude of fMRI fluctuations) between cortical regions. Finally, homotopic regions in opposite hemispheres increased their effective connectivity and drive during movie viewing, whereas they are effectively disconnected when watching a black screen. These findings speak to a dynamic and balanced functional integration that underlies hierarchical processing in the brain.
Communication and Modulation 3.1 Introduction 3.2 Transmission

Surprisingly, the earliest paper in this chapter originates from as late as 1986, testifying that the ”transmission and modulation community” within the Benelux at first did not identify itself with the WIC. Actually, the advent of coded modulation and the interest in modulation issues of people having a background in coding and information theory, led to a growing stream of WIC papers in this field. Also upcoming industrial applications like digital storage and transmission in the eighties (e.g., CD, GSM and DAB), stimulated research and publications within the WIC. The chapter on Communication and Modulation is subdivided into the sections Transmission, Recording and Networking. The papers in each section are rubricated according to their subject. Within each subject the papers are dealt with in chronological order, so that the developments in time of the subjects can be followed (in sofar as results have been published within the WIC). Background information and extensive bibliographies can be found in standard texts like [2, 5, 3, 4].
Competing Pathways for Nucleation of the Double Perovskite Structure in the Epitaxial Synthesis of La2MnNiO6

Double perovskites of the form R2BB′O6 (where R is a rare earth cation and B and B′ are chemically distinct transition metal cations with half-filled and empty eg orbitals, respectively) are of significant interest for their magnetoelectric properties. La2MnNiO6 is particularly attractive because of its large expected ferromagnetic moment per formula unit (5 μB f.u.–1) and its semiconducting character. If the ideal structure nucleates, superexchange coupling can take place via the B—O—B′ bonds that form, and the moment per formula unit can attain its maximum theoretical value. However, we show that even in the case of layer-by-layer deposition via molecular beam epitaxy, the system can follow multiple reaction pathways that lead to deviations from the double perovskite structure. In particular, we observe a spatially extended phase in which B-site cation disorder occurs, resulting in Mn—O—Mn and Ni—O—Ni antiferromagnetic domains, as well as the formation of quasi-epitaxial, antiferromagnetic NiO nanoscale.
Wogonin Alleviates Kidney Tubular Epithelial Injury in Diabetic Nephropathy by Inhibiting PI3K/Akt/NF-κB Signaling Pathways

Introduction Kidney tubular epithelial injury is one of the key factors in the progression of diabetic nephropathy (DN). Wogonin is a kind of flavonoid, which has many pharmacological effects, such as anti-inflammation, anti-oxidation and anti-fibrosis. However, the effect of wogonin in renal tubular epithelial cells during DN is still unknown. Materials and Methods STZ-induced diabetic mice were given doses of wogonin (10, 20, and 40 mg/kg) by intragastric administration for 16 weeks. The metabolic indexes from blood and urine and pathological damage of renal tubules in mice were evaluated. Human tubular epithelial cells (HK-2) were cultured in high glucose (HG) condition containing wogonin (2μM, 4μM, 8μM) for 24 h. Tubular epithelial cell inflammation and autophagic dysfunction both in vivo and in vitro were assessed by Western blot, qRT-PCR, IHC, and IF analyses. Results The treatment of wogonin attenuated urinary albumin and histopathological damage in tubulointerstitium of diabetic mice. We also found that wogonin down-regulated the expression of pro-inflammatory cytokines and autophagic dysfunction in vivo and in vitro. Molecular docking and Cellular Thermal Shift Assay (CETSA) results revealed that mechanistically phosphoinositide 3-kinase (PI3K) was the target of wogonin. We then found that inhibiting PI3K eliminated the protective effect of wogonin. Wogonin regulated autophagy and inflammation via targeting PI3K, the important connection point of PI3K/Akt/NF-κB signaling pathway. Conclusion Our study is the first to demonstrate the novel role of wogonin in mitigating tubulointerstitial fibrosis and renal tubular cell injury via regulating PI3K/Akt/NF-κB signaling pathway-mediated autophagy and inflammation. Wogonin might be a latent remedial drug against tubular epithelial injury in DN by targeting PI3K.
The effects of genetic polymorphisms on benzene‐exposed workers: A systematic review

Benzene is a group I carcinogen, which has been associated with leukemia and myelodysplastic syndrome. Moreover, it has been proposed that polymorphisms in benzene metabolizing genes influence the outcomes of benzene exposure in the human body. This systematic review aims to elucidate the existent relationship between genetic polymorphisms and the risk of developing adverse health effects in benzene‐exposed workers.