Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph-Based Global Robot Localization Informing Situational Graphs with Architectural Graphs

In this paper, we propose a solution for legged robot localization using architectural plans. Our specific contributions towards this goal are several. Firstly, we develop a method for converting the plan of a building into what we denote as an architectural graph (A-Graph). When the robot starts moving in an environment, we assume it has no knowledge about it, and it estimates an online situational graph representation (S-Graph) of its surroundings. We develop a novel graph-to-graph matching method, in order to relate the S-Graph estimated online from the robot sensors and the A-Graph extracted from the building plans. Note the challenge in this, as the S-Graph may show a partial view of the full A-Graph, their nodes are heterogeneous and their reference frames are different. After the matching, both graphs are aligned and merged, resulting in what we denote as an informed Situational Graph (is-Graph), with which we achieve global robot localization and exploitation of prior knowledge from the building plans. Our experiments show that our pipeline shows a higher robustness and a significantly lower pose error than several LiDAR localization baselines. Paper Video: https://youtu.be/3Pv7y8aOsUY
On the maximum number of connected induced subgraphs of a graph

We characterise the structure of those graphs of a given order which maximise the number of connected induced subgraphs for seven different graph classes, each with other prescribed parameters like minimum degree, independence number, vertex cover number, vertex connectivity, edge connectivity, chromatic number, number of bridges, thereby contributing to filling a gap in the literature.
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins

Coarse-graining (CG) accelerates molecular simulations of protein dynamics by simulating sets of atoms as singular beads. Backmapping is the opposite operation of bringing lost atomistic details back from the CG representation. While machine learning (ML) has produced accurate and efficient CG simulations of proteins, fast and reliable backmapping remains a challenge. Rule-based methods produce poor all-atom geometries, needing computationally costly refinement through additional simulations. Recently proposed ML approaches outperform traditional baselines but are not transferable between proteins and sometimes generate unphysical atom placements with steric clashes and implausible torsion angles. This work addresses both issues to build a fast, transferable, and reliable generative backmapping tool for CG protein representations. We achieve generalization and reliability through a combined set of innovations: representation based on internal coordinates; an equivariant encoder/prior; a custom loss function that helps ensure local structure, global structure, and physical constraints; and expert curation of high-quality out-of-equilibrium protein data for training. Our results pave the way for out-of-the-box backmapping of coarse-grained simulations for arbitrary proteins.
Automatic Increase Market Systems (AIMS): Towards a deterministic theory for cryptocurrencies

The popularity of cryptocurrencies has grown significantly in recent years, and they have become an important asset for internet trading. One of the main drawbacks of cryptocurrencies is the high volatility and fluctuation in value. The value of cryptocurrencies can change rapidly and dramatically, making them a risky investment. Cryptocurrencies are largely unregulated, which can exacerbate their volatility. The high volatility of cryptocurrencies has also led to a speculative bubble, with many investors buying and selling cryptocurrencies based on short-term price fluctuations rather than their underlying values. Therefore, how to reduce the fluctuation risk introduced by exchanges, transform uncertain prices to deterministic value, and promote the benefits of decentralized finance are critical for the future development of cryptos and Web 3.0. To address the issues, this paper proposes a novel theory as Automatic Increase Market Systems (AIMS) for cryptos, which could potentially be designed to automatically adjust the value of a cryptocurrency helping to stabilize the price and increase its value over time in a deterministic manner. We build a crypto, WISH (https://wishbank.wtf), based on AIMS in order to demonstrate how the automatic increase market system would work in practice, and how it would influence the supply of the cryptocurrency in response to market demand and finally make itself to be a stable medium of exchange, ensuring that the AIMS is fair and transparent.
Automated Machine Learning for Deep Learning based Malware Detection

Deep learning (DL) has proven to be effective in detecting sophisticated malware that is constantly evolving. Even though deep learning has alleviated the feature engineering problem, finding the most optimal DL model, in terms of neural architecture search (NAS) and the model's optimal set of hyper-parameters, remains a challenge that requires domain expertise. In addition, many of the proposed state-of-the-art models are very complex and may not be the best fit for different datasets. A promising approach, known as Automated Machine Learning (AutoML), can reduce the domain expertise required to implement a custom DL model. AutoML reduces the amount of human trial-and-error involved in designing DL models, and in more recent implementations can find new model architectures with relatively low computational overhead. This work provides a comprehensive analysis and insights on using AutoML for static and online malware detection. For static, our analysis is performed on two widely used malware datasets: SOREL-20M to demonstrate efficacy on large datasets; and EMBER-2018, a smaller dataset specifically curated to hinder the performance of machine learning models. In addition, we show the effects of tuning the NAS process parameters on finding a more optimal malware detection model on these static analysis datasets. Further, we also demonstrate that AutoML is performant in online malware detection scenarios using Convolutional Neural Networks (CNNs) for cloud IaaS. We compare an AutoML technique to six existing state-of-the-art CNNs using a newly generated online malware dataset with and without other applications running in the background during malware execution.In general, our experimental results show that the performance of AutoML based static and online malware detection models are on par or even better than state-of-the-art models or hand-designed models presented in literature.
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations

Speech restoration (SR) is a task of converting degraded speech signals into high-quality ones. In this study, we propose a robust SR model called Miipher, and apply Miipher to a new SR application: increasing the amount of high-quality training data for speech generation by converting speech samples collected from the Web to studio-quality. To make our SR model robust against various degradation, we use (i) a speech representation extracted from w2v-BERT for the input feature, and (ii) a text representation extracted from transcripts via PnG-BERT as a linguistic conditioning feature. Experiments show that Miipher (i) is robust against various audio degradation and (ii) enable us to train a high-quality text-to-speech (TTS) model from restored speech samples collected from the Web. Audio samples are available at our demo page: google.github. io/df-conformer/miipher/.
Unproportional mosaicing

Data shift is a gap between data distribution used for training and data distribution encountered in the real-world. Data augmentations help narrow the gap by generating new data samples, increasing data variability, and data space coverage. We present a new data augmentation: Unproportional mosaicing (Unprop). Our augmentation randomly splits an image into various-sized blocks and swaps its content (pixels) while maintaining block sizes. Our method achieves a lower error rate when combined with other state-of-the-art augmentations.
Decoding and Visualising Intended Emotion in an Expressive Piano Performance

Expert musicians can mould a musical piece to convey specific emotions that they intend to communicate. In this paper, we place a mid-level features based music emotion model in this performer-to-listener communication scenario, and demonstrate via a small visualisation music emotion decoding in real time. We also extend the existing set of mid-level features using analogues of perceptual speed and perceived dynamics.
MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices

High-quality 3D ground-truth shapes are critical for 3D object reconstruction evaluation. However, it is difficult to create a replica of an object in reality, and even 3D reconstructions generated by 3D scanners have artefacts that cause biases in evaluation. To address this issue, we introduce a novel multi-view RGBD dataset captured using a mobile device, which includes highly precise 3D ground-truth annotations for 153 object models featuring a diverse set of 3D structures. We obtain precise 3D ground-truth shape without relying on high-end 3D scanners by utilising LEGO models with known geometry as the 3D structures for image capture. The distinct data modality offered by high-resolution RGB images and low-resolution depth maps captured on a mobile device, when combined with precise 3D geometry annotations, presents a unique opportunity for future research on high-fidelity 3D reconstruction. Furthermore, we evaluate a range of 3D reconstruction algorithms on the proposed dataset.
Comparing metrics for mixed quantum states: Sjöqvist and Bures

It is known that there are infinitely many distinguishability metrics for mixed quantum states. This freedom, in turn, leads to metric-dependent interpretations of physically meaningful geometric quantities such as complexity and volume of quantum states. In this paper, we first present an explicit and unabridged mathematical discussion on the relation between the Sjoqvist metric and the Bures metric for arbitrary nondegenerate mixed quantum states, using the notion of decompositions of density operators by means of ensembles of pure quantum states. Then, to enhance our comprehension of the difference between these two metrics from a physics standpoint, we compare the formal expressions of these two metrics for arbitrary thermal quantum states specifying quantum systems in equilibrium with a reservoir at non-zero temperature. For illustrative purposes, we show the difference between these two metrics in the case of a simple physical system characterized by a spin-qubit in an arbitrarily oriented uniform and stationary external magnetic field in thermal equilibrium with a finite-temperature bath. Finally, we compare the Bures and Sjoqvist metrics in terms of their monotonicity property.
Distribution Decomposition and Sum-Capacity Results of Two-User Optical Intensity Multiple Access Channels

This paper investigates the sum-capacity of two-user optical intensity multiple access channels with per-user peak- or/and average-intensity constraints. By leveraging tools from the decomposition of certain distributions, we derive several lower bounds on the sum-capacity. In the high signal-to-noise ratio (SNR) regime, some bounds asymptotically match or approach the sum-capacity, thus closing or reducing the existing gaps to the high-SNR asymptotic sum-capacity. At moderate SNR, some bounds are also fairly close to the sum-capacity.
How video calls affect mimicry and trust during interactions

Many social species, humans included, mimic emotional expressions, with important consequences for social bonding. Although humans increasingly interact via video calls, little is known about the effect of these online interactions on the mimicry of scratching and yawning, and their linkage with trust. The current study investigated whether mimicry and trust are affected by these new communication media. Using participant-confederate dyads (n = 27), we tested the mimicry of four behaviours across three different conditions: watching a pre-recorded video, online video call, and face-to-face. We measured mimicry of target behaviours frequently observed in emotional situations, yawn and scratch and control behaviours, lip-bite and face-touch. In addition, trust in the confederate was assessed via a trust game. Our study revealed that (i) mimicry and trust did not differ between face-to-face and video calls, but were significantly lower in the pre-recorded condition; and (ii) target behaviours were significantly more mimicked than the control behaviours. This negative relationship can possibly be explained by the negative connotation usually associated with the behaviours included in this study. Overall, this study showed that video calls might provide enough interaction cues for mimicry to occur in our student population and during interactions between strangers. This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’.
Minimum description length modelling of musical structure

This article presents a method of inductive inference whose aim is to build formal quantitative models of musical structure. The models are constructed by statistical extraction of significant patterns from a musical corpus. The minimum description length (MDL) principle is used to select the best model from among the members of a non-parametric model family characterized by an unbounded parameter set. The chosen model achieves optimal compromise between goodness-of-fit and model complexity, thereby avoiding the over-fitting normally associated with such a family of models. The MDL method is illustrated through its application to the Hidden Markov Model (HMM) framework. We derive an original mathematical expression for the MDL complexity of HMMs that employ a finite alphabet of symbols; these models are particularly suited to the symbolic modelling of musical structure. As an illustration, we use the proposed HMM complexity expression to construct a model for a common melodic formula in Greek church chant. Such formulas are characterized by text–tune association that is governed by complex rules. We show that MDL-guided model construction gradually ‘learns’ important aspects of the melodic formula's structure, and that the MDL principle terminates the process when nothing significant is left to learn. We outline how the musical applications of MDL can be extended beyond the HMM framework, and how they address general methodological concerns in empirical music research.
A Multi-Source Information Dissemination Model Based on Edge Evolution Game

The influence of user behavioral diversity and multi-source information on dissemination behavior has been neglected in the current research in social networks. Therefore, the multi-source information dissemination in social networks from the micro level is analyzed, and a multi-source information dissemination model based on edge evolutionary game (MSID-EEG) is proposed in this paper. Firstly, the evolution of user strategies and relationships in the network is simulated by edge evolutionary games, and user types are separated from behavioral intentions to diversify user behaviors. Secondly, the information dissemination process among users is analyzed based on the competitive and cooperative relationships of multi-source information, and the relationships between information are quantified by introducing influence coefficients and combining information incomes. Finally, the dissemination process of competitive and cooperative information is experimentally simulated, and the influence of the influence coefficient on the dissemination process of competitive and cooperative information is analyzed.
Lexicon-matched Word Injection for Chinese NER

Recently, Chinese named entity recognition has attracted a lot of attention. Most of the work utilizes words matching with lexicon which integrates potential word information with lattice structure or graph structure. Although existing approaches have been proved to be effective for exploiting abundant word boundary information, it is difficult to model global semantic interactions due to the inherent one-way sequential nature of the DAG structure. Meanwhile, more interfering lexicon words have been introduced leading to word boundary conflicts. To address the above issues, this paper proposes a knowledge fusion method based on lexicon matching word injection, which captures sentence context features through a pre-trained learning model and then injects lexicon knowledge into each character. With the power of Transformer and well-designed encoding, it becomes easier to obtain accurate word information using the character encoding vector of transformer encoder model. Besides, the self-attention module integrating characters with different words is fully leveraged to improve recognition accuracy. Experiments on three Chinese public datasets show that the proposed method outperformed other lexicon-based methods in performance and efficiency.
Predicting the Stock Market Volatility Based on Deep Learning and Boosting Tree Methods

Accurate forecasting of volatility is critical for options trading, as option prices are directly related to the volatility of the underlying product. However, industry-leading pricing algorithms never stop evolving. In order to create better trading channels and prices for options, cash stocks, bonds and foreign exchange on numerous exchanges around the world, we need to take volatility forecasting models to the next level. For that purpose, based on the data provided by Optiver, we convert the fine-grained data in the stock trading panel into the feature data. Then we use the K-Means algorithm to cluster the features, and introduce the KNN algorithm to calculate the features at similar times. After the feature engineering is completed, we build a combined machine learning method comprising of LightGBM and neural networks, where RMSPE is the loss function and evaluation metric. Our integrated model reached 0.2176 on the test set and 0.2193 on the forecast dataset for the next three months, which can get a silver medal prize in the Optiver Realized Volatility Prediction Kaggle competition.
The Secondary Flow of Newtonian Fluids in Cone‐and‐Plate Viscometers

Secondary flow in cone‐and‐plate viscometers is studied by numerical integration of the equations of motion for steady incompressible flow of Newtonian fluids. Solutions over wide ranges of the two principal parameters, Reynolds number and gap angle, yield detailed information on the flow fields and elements of the rate of deformation tensor. Secondary flows are shown to cause large deviations in certain elements of the rate of deformation at Reynolds numbers more than an order of magnitude lower than those at which the torque is appreciably changed. Comparisons are given with prior analytical and experimental work.
Effective albumin – A novel paradigm in the management of decompensated liver cirrhosis

Synthesis Human albumin, a 66.5-kDa globular protein synthesized by hepatocytes, is the most abundant serum protein whose concentration ranges from 3.5 to 5 g/dL. Under normal conditions, albumin production is stable (10–15 g daily), but can increase up to 3–4 fold. Hormonal factors, such as insulin, cortisol, and growth hormone, stimulate, while mediators of inflammation, such as proinflammatory cytokines, inhibit albumin synthesis. [1–3]
Fault detection for a class of network‐based nonlinear systems with communication constraints and random packet dropouts

This paper is dealt with the fault detection (FD) problem for a class of network‐based nonlinear systems with communication constraints and random packet dropouts. The plant is described by a Takagi–Sugeno fuzzy time‐delay model, it has multiple sensors and only one of them is actually communicated with the FD filter at each transmission instant, and the packet dropouts occur randomly. The goal is to design a FD filter such that, for all unknown inputs, control inputs, time delays and incomplete data conditions, the estimation error between the residual and ‘fault’ (or, more generally, the weighted fault) is minimized. By casting the addressed FD problem into an auxiliary H∞ filtering problem of a stochastic switched fuzzy time‐delay system, a sufficient condition for the existence of the desired FD filter is established in terms of linear matrix inequalities. A numerical example is provided to illustrate the effectiveness and applicability of the proposed technique. Copyright © 2011 John Wiley & Sons, Ltd.
The relationship analysis between variable selection and basis pursuit

In signal processing, great interest has been widely focused on the sparsest represent. Variable selection is a principle for decomposing a signal into "optimal" superposition bases, where optimal means having small value under some criterion among all such decomposition. Basis pursuit is a principle for decomposing a signal into "optimal" superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients instead of l0 among all such decomposition. In this paper we present a relation between the variable selection and basis pursuit. After the most widely used Cp criterion is further discussed, variable selection is extended to the overcomplete dictionaries case. Based on the equivalence conditions of l1 norm and l0 in signal decomposing, the relationship between variable selection method and the basis pursuit is discussed. Finally, the example of spectrum estimation is given to demonstrate the equivalence of these two methods.
First-principles study on the stability and mechanical properties of TiC x N 1 (cid:2) x

First-principles evolutionary simulations are used to predict the stable compounds of TiC x N 1 (cid:2) x . Our structural searching results reveal that TiC x N 1 (cid:2) x system has a wide compositional range and several TiC x N 1 (cid:2) x structures with new stoichiometries are found. All the predicted TiC x N 1 (cid:2) x compounds are dynamically stable at ambient pressure. The elastic properties, hardness and fracture toughness of these TiC x N 1 (cid:2) x compounds have been systematically investigated. New ternary compound Ti 5 C 3 N 2 is predicted to be the hardest and has a Vickers hardness of 29 GPa and a high fracture toughness (3.38 MPa m 1 = 2 ). The high hardness for Ti 5 C 3 N 2 originates from the strong interactions between Ti-3 d and C-2 p electrons according to the electronic structure analysis. The relationship between elastic properties and valence electron concentration (VEC) is further discussed. The results show that the hardness and shear moduli of TiC x N 1 (cid:2) x compounds simultaneously reach their maximum when the VEC is around 8.4
Effects of the central armchair nanotube size on the conductance of carbon nanotube quantum dots

Recently, one of the most significant topics in electronic devices is miniaturization. It has been a growing interest in some mesoscopic systems such as quantum dots. The size of these quantum dots approaches to the atomic scale, which contributes to interesting new behaviors. Understanding their properties is an important problem in the fields of nano electronics. Here we study the transport properties of the single wall carbon nanotubes quantum dots. Considering Carbon nanotube (2n,0)/1(n,n)/m(2n,0) quantum dot, we have investigated the effects of the central cell size on the conductance of the system. By increasing the length of armchair carbon nanotube in metalmetal- metal quantum dot m(12,0)/1(6,6)/m(12,0) , we have observed reduction in the conductance. In semiconductor- metal- semiconductor quantum dots (8,0)/1(4,4)/m(8,0), increasing the length of armchair part causes the scattering rate raising. For more than special length, due to the destructive and constructive interference of the wave functions, the conductance gap oscillates near the Fermi energy. Therefore, by controlling on cell size characteristics, it is possible to manipulate some efficient devices in nano-electronics.