Miracle-dz/UniFAR-FTUs huggingface dataset:
Manifestly Causal Loop-Tree Duality

Loop-Tree Duality (LTD) is a framework in which the energy components of all loop momenta of a Feynman integral are integrated out using residue theorem, resulting in a sum over tree-like structures. Originally, the LTD expression exhibits cancellations of non-causal thresholds between summands, also known as dual cancellations. As a result, the expression exhibits numerical instabilities in the vicinity of non-causal thresholds and for large loop momenta. In this work we derive a novel, generically applicable, Manifestly Causal LTD (cLTD) representation whose only thresholds are causal thresholds, i.e. it manifestly realizes dual cancellations. Consequently, this result also serves as a general proof for dual cancellations. We show that LTD, cLTD, and the expression stemming from Time Ordered Perturbation Theory (TOPT) are locally equivalent. TOPT and cLTD both feature only causal threshold singularities, however LTD features better scaling with the number of propagators. On top of the new theoretical perspectives offered by our representation, it has the useful property that the ultraviolet (UV) behaviour of the original 4D integrand is maintained for every summand. We show that the resulting LTD integrand expression is completely stable in the UV region which is key for practical applications of LTD to the computation of amplitudes and cross sections. We present explicit examples of the LTD expression for a variety of up to four-loop integrals and show that its increased computational complexity can be efficiently mitigated by optimising its numerical implementation. Finally, we provide computer code that automatically generates the LTD expression for an arbitrary topology.
Multi-Central Differential Privacy

Differential privacy is typically studied in the central model where a trusted "aggregator" holds the sensitive data of all the individuals and is responsible for protecting their privacy. A popular alternative is the local model in which the aggregator is untrusted and instead each individual is responsible for their own privacy. The decentralized privacy guarantee of the local model comes at a high price in statistical utility or computational complexity. Thus intermediate models such as the shuffled model and pan privacy have been studied in an attempt to attain the best of both worlds. In this note, we propose an intermediate trust model for differential privacy, which we call the multi-central model. Here there are multiple aggregators and we only assume that they do not collude nefariously. This model relaxes the trust requirements of the central model while avoiding the price of the local model. We motivate this model and provide some simple and efficient algorithms for it. We argue that this model is a promising direction for further research.
Volatility Forecasting with 1-dimensional CNNs via transfer learning

Volatility is a natural risk measure in finance as it quantifies the variation of stock prices. A frequently considered problem in mathematical finance is to forecast different estimates of volatility. What makes it promising to use deep learning methods for the prediction of volatility is the fact, that stock price returns satisfy some common properties, referred to as `stylized facts'. Also, the amount of data used can be high, favoring the application of neural networks. We used 10 years of daily prices for hundreds of frequently traded stocks, and compared different CNN architectures: some networks use only the considered stock, but we tried out a construction which, for training, uses much more series, but not the considered stocks. Essentially, this is an application of transfer learning, and its performance turns out to be much better in terms of prediction error. We also compare our dilated causal CNNs to the classical ARIMA method using an automatic model selection procedure.
Development of data-driven spd tight-binding models of Fe—parameterisation based on QSGW and DFT calculations including information about higher-order elastic constants

Quantum-mechanical (QM) simulations, thanks to their predictive power, can provide significant insights into the nature and dynamics of defects such as vacancies, dislocations and grain boundaries. These considerations are essential in the context of the development of reliable, inexpensive and environmentally friendly alloys. However, despite significant progress in computer performance, QM simulations of defects are still extremely time-consuming with ab-initio/non-parametric methods. The two-centre Slater–Koster (SK) tight-binding (TB) models can achieve significant computational efficiency and provide an interpretable picture of the electronic structure. In some cases, this makes TB a compelling alternative to models based on abstraction of the electronic structure, such as the embedded atom model. The biggest challenge in the implementation of the SK method is the estimation of the optimal and transferable parameters that are used to construct the Hamiltonian matrix. In this paper, we will present results of the development of a data-driven framework, following the classical approach of adjusting parameters in order to recreate properties that can be measured or estimated using ab-initio or non-parametric methods. Distinct features include incorporation of data from QSGW (quasi-particle self-consistent GW approximation) calculations, as well as consideration of higher-order elastic constants. Furthermore, we provide a description of the optimisation procedure, omitted in many publications, including the design stage. We also apply modern optimisation techniques that allow us to minimise constraints on the parameter space. In summary, this paper introduces some methodological improvements to the semi-empirical approach while addressing associated challenges and advantages.
CasGCN: Predicting future cascade growth based on information diffusion graph

Sudden bursts of information cascades can lead to unexpected consequences such as extreme opinions, changes in fashion trends, and uncontrollable spread of rumors. It has become an important problem on how to effectively predict a cascade' size in the future, especially for large-scale cascades on social media platforms such as Twitter and Weibo. However, existing methods are insufficient in dealing with this challenging prediction problem. Conventional methods heavily rely on either hand crafted features or unrealistic assumptions. End-to-end deep learning models, such as recurrent neural networks, are not suitable to work with graphical inputs directly and cannot handle structural information that is embedded in the cascade graphs. In this paper, we propose a novel deep learning architecture for cascade growth prediction, called CasGCN, which employs the graph convolutional network to extract structural features from a graphical input, followed by the application of the attention mechanism on both the extracted features and the temporal information before conducting cascade size prediction. We conduct experiments on two real-world cascade growth prediction scenarios (i.e., retweet popularity on Sina Weibo and academic paper citations on DBLP), with the experimental results showing that CasGCN enjoys a superior performance over several baseline methods, particularly when the cascades are of large scale.
Practical Cross-modal Manifold Alignment for Grounded Language

We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items. Our approach learns these embeddings by sampling triples of anchor, positive, and negative data points from RGB-depth images and their natural language descriptions. We show that our approach can benefit from, but does not require, post-processing steps such as Procrustes analysis, in contrast to some of our baselines which require it for reasonable performance. We demonstrate the effectiveness of our approach on two datasets commonly used to develop robotic-based grounded language learning systems, where our approach outperforms four baselines, including a state-of-the-art approach, across five evaluation metrics.
HECTOR-V: A Heterogeneous CPU Architecture for a Secure RISC-V Execution Environment

To ensure secure and trustworthy execution of applications in potentially insecure environments, vendors frequently embed trusted execution environments (TEE) into their systems. Applications executed in this safe, isolated space are protected from adversaries, including a malicious operating system. TEEs are usually build by integrating protection mechanisms directly into the processor or by using dedicated external secure elements. However, both of these approaches only cover a narrow threat model resulting in limited security guarantees. Enclaves nested into the application processor typically provide weak isolation between the secure and non-secure domain, especially when considering side-channel attacks. Although external secure elements do provide strong isolation, the slow communication interface to the application processor is exposed to adversaries and restricts the use cases. Independently of the used approach, TEEs often lack the possibility to establish secure communication to peripherals, and most operating systems executed inside TEEs do not provide state-of-the-art defense strategies, making them vulnerable to various attacks. We argue that TEEs, such as Intel SGX or ARM TrustZone, implemented on the main application processor, are insecure, especially when considering side-channel attacks. In this paper, we demonstrate how a heterogeneous multicore architecture can be utilized to realize a secure TEE design. We directly embed a secure processor into our HECTOR-V architecture to provide strong isolation between the secure and non-secure domain. The tight coupling of the TEE and the application processor enables HECTOR-V to provide mechanisms for establishing secure communication channels between different devices. We further introduce RISC-V Secure Co-Processor (RVSCP), a security-hardened processor tailored for TEEs. To secure applications executed inside the TEE, RVSCP provides hardware enforced control-flow integrity and rigorously restricts I/O accesses to certain execution states. RVSCP reduces the trusted computing base to a minimum by providing operating system services directly in hardware.
Performance Analysis of Fingerprint Denoising Using Stationary Wavelet Transform

—Finger print is the finest and cheapest recognition system because of its easy extraction of unique features like bifurcation and termination. But the quality of fingerprint data are easily degraded by dryness of skin, wet, wound and other types of noises. Hence, denoising of fingerprint image is vital step for automatic fingerprint recognition system. In the proposed paper the removal of noise from fingerprint images by using stationary wavelet transform and adaptive thresholding method is analysed. The proposed algorithm is developed using MATLAB (R2010b) and tested in the fingerprint images collected from FVC2004 database and R303A optical scanner. The performance of the method is analysed by calculating the quality metrics like Peak Signal to Noise Ratio, Universal Quality Index , Structure Similarity and Multi-Scale Structure Similarity (MS-SSIM). The quality of fingerprint image after noise removal using proposed analysis confirms the suggested method is better than the conventional techniques.
Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error rate (BER). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols. To this end, we develop a DTL detection framework which consists of offline learning, transfer learning, and online detection. Specifically, a DTL-based likelihood ratio test (DTL-LRT) is derived based on the minimum error probability (MEP) criterion. As a realization of the developed framework, we then apply convolutional neural networks (CNN) to intelligently explore the features of the sample covariance matrix, which facilitates the design of a CNN-based algorithm for tag signal detection. Exploiting the powerful capability of CNN in extracting features of data in the matrix formation, the proposed method is able to further improve the system performance. In addition, an asymptotic explicit expression is also derived to characterize the properties of the proposed CNN-based method when the number of samples is sufficiently large. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.
Combinatorial Ricci flow on cusped 3-manifolds

Combinatorial Ricci flow on a cusped $3$-manifold is an analogue of Chow-Luo's combinatorial Ricci flow on surfaces and Luo's combinatorial Ricci flow on compact $3$-manifolds with boundary for finding complete hyperbolic metrics on cusped $3$-manifolds. Dual to Casson and Rivin's program of maximizing the volume of angle structures, combinatorial Ricci flow finds the complete hyperbolic metric on a cusped $3$-manifold by minimizing the co-volume of decorated hyperbolic polyhedral metrics. The combinatorial Ricci flow may develop singularities. We overcome this difficulty by extending the flow through the potential singularities using Luo-Yang's extension. It is shown that the existence of a complete hyperbolic metric on a cusped $3$-manifold is equivalent to the convergence of the extended combinatorial Ricci flow, which gives a new characterization of existence of a complete hyperbolic metric on a cusped $3$-manifold dual to Casson and Rivin's program. The extended combinatorial Ricci flow also provides an effective algorithm for finding complete hyperbolic metrics on cusped $3$-manifolds.
SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context

We present SONYC-UST-V2, a dataset for urban sound tagging with spatiotemporal information. This dataset is aimed for the development and evaluation of machine listening systems for real-world urban noise monitoring. While datasets of urban recordings are available, this dataset provides the opportunity to investigate how spatiotemporal metadata can aid in the prediction of urban sound tags. SONYC-UST-V2 consists of 18510 audio recordings from the "Sounds of New York City" (SONYC) acoustic sensor network, including the timestamp of audio acquisition and location of the sensor. The dataset contains annotations by volunteers from the Zooniverse citizen science platform, as well as a two-stage verification with our team. In this article, we describe our data collection procedure and propose evaluation metrics for multilabel classification of urban sound tags. We report the results of a simple baseline model that exploits spatiotemporal information.
Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems using Feature Importance Fusion

When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output is produced. Although feature importance calculation approaches assist in interpretation, there is a lack of consensus regarding how features’ importance is quantified, which makes the explanations offered for the outcomes mostly unreliable. A possible solution to address the lack of agreement is to combine the results from multiple feature importance quantifiers to reduce the variance in estimates and to improve the quality of explanations. Our hypothesis is that this leads to more robust and trustworthy explanations of the contribution of each feature to machine learning predictions. To test this hypothesis, we propose an extensible model-agnostic framework divided in four main parts: (i) traditional data pre-processing and preparation for predictive machine learning models, (ii) predictive machine learning, (iii) feature importance quantification, and (iv) feature importance decision fusion using an ensemble strategy. Our approach is tested on synthetic data, where the ground truth is known. We compare different fusion approaches and their results for both training and test sets. We also investigate how different characteristics within the datasets affect the quality of the feature importance ensembles studied. The results show that, overall, our feature importance ensemble framework produces 15% less feature importance errors compared with existing methods. Additionally, the results reveal that different levels of noise in the datasets do not affect the feature importance ensembles’ ability to accurately quantify feature importance, whereas the feature importance quantification error increases with the number of features and number of orthogonal informative features. We also discuss the implications of our findings on the quality of explanations provided to safety-critical systems.
Super Star Clusters in the Central Starburst of NGC 4945

The nearby (3.8 Mpc) galaxy NGC 4945 hosts a nuclear starburst and Seyfert type 2 active galactic nucleus (AGN). We use the Atacama Large Millimeter/submillimeter Array (ALMA) to image the 93 GHz (3.2 mm) free–free continuum and hydrogen recombination line emission (H40α and H42α) at 2.2 pc (0.″12) resolution. Our observations reveal 27 bright, compact sources with FWHM sizes of 1.4–4.0 pc, which we identify as candidate super star clusters. Recombination line emission, tracing the ionizing photon rate of the candidate clusters, is detected in 15 sources, six of which have a significant synchrotron component to the 93 GHz continuum. Adopting an age of ∼5 Myr, the stellar masses implied by the ionizing photon luminosities are (M⋆/M⊙) ≈ 4.7–6.1. We fit a slope to the cluster mass distribution and find β = −1.8 ± 0.4. The gas masses associated with these clusters, derived from the dust continuum at 350 GHz, are typically an order of magnitude lower than the stellar mass. These candidate clusters appear to have already converted a large fraction of their dense natal material into stars and, given their small freefall times of ∼0.05 Myr, are surviving an early volatile phase. We identify a pointlike source in 93 GHz continuum emission that is presumed to be the AGN. We do not detect recombination line emission from the AGN and place an upper limit on the ionizing photons that leak into the starburst region of Q0 < 1052 s−1.
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification

Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic. However, due to complex feature behaviors and expensive annotations of US images, it is difficult to apply Artificial Intelligence (AI) assisting approaches for the lung's multi-symptom (multi-label) classification. To overcome these difficulties, we propose a novel semi-supervised Two-Stream Active Learning (TSAL) method to model complicated features and reduce labeling costs in an iterative manner. The core component of TSAL is the multi-label learning mechanism, in which label correlation information is used to design a multi-label margin (MLM) strategy and a confidence validation for automatically selecting informative samples and confident labels. In this framework, a multi-symptom multi-label (MSML) classification network is proposed to learn discriminative features of lung symptoms, and a human-machine interaction (HMI) is exploited to confirm the final annotations that are used to fine-tune MSML. Moreover, a novel lung US dataset named COVID19-LUSMS is built, currently containing 71 clinical patients with 6,836 images sampled from 678 videos. Experimental evaluations show that TSAL can achieve superior performance to the baseline and the state-of-the-art using only 20% data. Qualitatively, visualization of the attention map confirms a good consistency between the model prediction and the clinical knowledge.
A new heuristic algorithm for fast k-segmentation

The k-segmentation of a time-indexed data stream is used to partition it into k piecewise-linear segments, so that each linear segment has a meaningful interpretation. Such segmentation may be used to summarize large datasets with small sub-samples, to identify anomalies within segments, detect change points between segments, and to select critical subsets for training machine learning models. Exact and approximate segmentation methods for k-segmentation exist in the literature. Each of these algorithms provides a different trade-off between computational complexity and accuracy. A novel heuristic algorithm is proposed in this paper which provides accuracies competitive with exact methods at a fraction of the computational expense.The new algorithm is inspired by Lloyd’s algorithm for K-Means and Lloyd-Max algorithm for scalar quantization, and is called the LM algorithm for convenience. This algorithm iteratively minimizes a cost function from any given initialisation of partitions; the commonly used L2 cost is chosen in this paper. The greedy nature of cost minimization makes the algorithm sensitive to the choice of initialisation. However, the same sensitivity allows convergence to different local optima when starting with different initialisations. Three variants of the proposed algorithm are tested over a large number of synthetic datasets, one being a standalone LM implementation, and two others that combine with existing algorithms. One of the latter two — LM-enhanced-Bottom-Up segmentation — is found to have the best accuracy and the lowest computational complexity among all algorithms. This variant of LM can provide k-segmentations over data sets with up to a million high-dimensional data points within several seconds.
E7 oncoprotein from human papillomavirus 16 alters claudins expression and the sealing of epithelial tight junctions

Tight junctions (TJs) are cell-cell adhesion structures frequently altered by oncogenic transformation. In the present study the role of human papillomavirus (HPV) 16 E7 oncoprotein on the sealing of TJs was investigated and also the expression level of claudins in mouse cervix and in epithelial Madin-Darby Canine Kidney (MDCK) cells. It was found that there was reduced expression of claudins -1 and -10 in the cervix of 7-month-old transgenic K14E7 mice treated with 17β-estradiol (E2), with invasive cancer. In addition, there was also a transient increase in claudin-1 expression in the cervix of 2-month-old K14E7 mice, and claudin-10 accumulated at the border of cells in the upper layer of the cervix in FvB mice treated with E2, and in K14E7 mice treated with or without E2. These changes were accompanied by an augmented paracellular permeability of the cervix in 2- and 7-monthold FvB mice treated with E2, which became more pronounced in K14E7 mice treated with or without E2. In MDCK cells the stable expression of E7 increased the space between adjacent cells and altered the architecture of the monolayers, induced the development of an acute peak of transepithelial electrical resistance accompanied by a reduced expression of claudins -1, -2 and -10, and an increase in claudin-4. Moreover, E7 enhances the ability of MDCK cells to migrate through a 3D matrix and induces cell stiffening and stress fiber formation. These observations revealed that cell transformation induced by HPV16 E7 oncoprotein was accompanied by changes in the pattern of expression of claudins and the degree of sealing of epithelial TJs.