Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the Expected Influence Capacities of Nodes in Complex Networks under the Susceptible-Infectious-Recovered (SIR) Model

In recent years, epidemic modeling in complex networks has found many applications, including modeling of information or gossip spread in online social networks, modeling of malware spread in communication networks, and the most recent model of the COVID-19 pandemic. If the information disseminated is accurate, for example, maximizing its distribution is desirable, whereas if it is a rumor or a virus, its spread should be minimized. In this context, it is very important to identify super-spreaders that maximize or minimize propagation. Lately, studies for detecting super-spreaders have gained momentum. Most of the studies carried out aim to distinguish the influences of nodes under a specific propagation model (such as SIR) using network centrality measures and subsequently, to rank the nodes accordingly. However, in this study, we developed an algorithm that approximates the expected influence of nodes under the popular SIR model. By considering the behavior of the SIR model and only the shortest paths between nodes, the algorithm ranks the nodes according to this approximated value. Our developed algorithm is named the Expected Value Estimation (EVE). We compared the performance of EVE, using different SIR settings on real datasets, with that of many current well-known centrality measures. The experimental studies demonstrated that the solution quality (ranking capability) of EVE is superior to that of its competitors.
NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation

In this paper, we propose a Chinese multi-turn topic-driven conversation dataset, NaturalConv, which allows the participants to chat anything they want as long as any element from the topic is mentioned and the topic shift is smooth. Our corpus contains 19.9K conversations from six domains, and 400K utterances with an average turn number of 20.1. These conversations contain in-depth discussions on related topics or widely natural transition between multiple topics. We believe either way is normal for human conversation. To facilitate the research on this corpus, we provide results of several benchmark models. Comparative results show that for this dataset, our current models are not able to provide significant improvement by introducing background knowledge/topic. Therefore, the proposed dataset should be a good benchmark for further research to evaluate the validity and naturalness of multi-turn conversation systems. Our dataset is available at https://ai.tencent. com/ailab/nlp/dialogue/#datasets.
An Attention Based Neural Network for Code Switching Detection: English & Roman Urdu

Code-switching is a common phenomenon among people with diverse lingual background and is widely used on the internet for communication purposes. In this paper, we present a Recurrent Neural Network combined with the Attention Model for Language Identification in Code-Switched Data in English and low resource Roman Urdu. The attention model enables the architecture to learn the important features of the languages hence classifying the code switched data. We demonstrated our approach by comparing the results with state of the art models i.e. Hidden Markov Models, Conditional Random Field and Bidirectional LSTM. The models evaluation, using confusion matrix metrics, showed that the attention mechanism provides improved the precision and accuracy as compared to the other models.
Determination of coccidiostat clopidol on an electropolymerized-molecularly imprinted polypyrrole polymer modified screen printed carbon electrode

The clopidol-imprinted polypyrrole films were electrochemically prepared on screen printed carbon electrodes (SPCEs) in aqueous solutions of pyrrole, clopidol and NaClO4 as the supporting electrolyte. The clopidol template molecules were successfully trapped in the polypyrrole (PPy) film where they created artificial recognition sites. After extraction of the template, the PPy film acted as a molecularly imprinted polymer (MIP) for the specific and selective recognition of clopidol. The clopidol-imprinted PPy films were found to selectively detect clopidol. The highest anodic signal of clopidol was obtained in a phosphate buffer solution of pH 9.0. Using differential pulse voltammetry, the calibration curve for clopidol spiked to chicken meat at MIP–SPCE was linear for a concentration range of 0.3–1.8 mg kg−1. The detection limit of clopidol was found as 0.21 mg kg−1 (S/N = 3). The MIP–SPCE showed a stable and reproducible response without any influence of interferents existing in real samples.
Energy-Based Learning for Scene Graph Generation

Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities. Such a formulation, however, ignores the structure in the output space, in an inherently structured prediction problem. In this work, we introduce a novel energy-based learning framework for generating scene graphs. The proposed formulation allows for efficiently incorporating the structure of scene graphs in the output space. This additional constraint in the learning framework acts as an inductive bias and allows models to learn efficiently from a small number of labels. We use the proposed energy-based framework† to train existing stateof-the-art models and obtain a significant performance improvement, of up to 21% and 27%, on the Visual Genome [9] and GQA [5] benchmark datasets, respectively. Furthermore, we showcase the learning efficiency of the proposed framework by demonstrating superior performance in the zero- and few-shot settings where data is scarce.
Marginal deformations from type IIA supergravity

We study marginal deformations of a class of three-dimensional N = 2 SCFTs that admit a holographic dual description in massive type IIA supergravity. We compute the dimension of the conformal manifold in these SCFTs and identify a special submanifold with enhanced flavor symmetry. We show how to apply the TsT transformation of Lunin-Maldacena to construct explicit supersymmetric AdS4 solutions of massive type IIA supergravity with non-trivial internal fluxes that are the holographic dual of these marginal deformations. Finally, we also briefly comment on a class of RG flows between some of these SCFTs that also admit a holographic realization. ∗pieter.bomans@pd.infn. it 1 ar X iv :2 10 3. 02 03 8v 3 [ he pth ] 1 7 M ay 2 02 1
Influence of wetting properties on hydrodynamic boundary conditions at a fluid/solid interface

It is well known that, at a macroscopic level, the boundary condition for a viscous fluid at a solid wall is one of “no-slip’'. The liquid velocity field vanishes at a fixed solid boundary. In this paper, we consider the special case of a liquid that partially wets the solid, i.e., a drop of liquid in equilibrium with its vapor on the solid substrate has a finite contact angle. Using extensive non-equilibrium molecular dynamics (NEMD) simulations, we show that when the contact angle is large enough, the boundary condition can drastically differ (at a microscopic level) from a “no-slip’' condition. Slipping lengths exceeding 30 molecular diameters are obtained for a contact angle of 140°, characteristic of mercury on glass. On the basis of a Kubo expression for δ, we derive an expression for the slipping length in terms of equilibrium quantities of the system. The predicted behaviour is in very good agreement with the numerical results for the slipping length obtained in the NEMD simulations. The existence of large slipping length may have important implications for the transport properties in nanoporous media under such “nonwetting’' conditions.
Probing the Disk–Corona Systems and Broad-line Regions of Changing-look Quasars with X-Ray and Optical Observations

“Changing-look” quasars are a new class of highly variable active galactic nuclei that have changed their spectral type over surprisingly short timescales of just a few years. The origin of this phenomenon is debated, but is likely to reflect some change in the accretion flow. To investigate the disk–corona systems in these objects, we measure optical/UV–X-ray spectral indices ( αOX ) and Eddington ratios ( λEdd ) of 10 previously discovered changing-look quasars at two or more epochs. By comparing these data with simulated results based on the behavior of X-ray binaries, we find possible similarities in spectral indices below the 1% Eddington ratio. We further investigate the Eddington ratios of changing-look quasars before and after their spectral type changes, and find that changing-look quasars cross the 1% Eddington ratio boundary when their broad emission lines disappear/emerge. This is consistent with the disk-wind model as the origin of broad emission lines.
K-FACE: A Large-Scale KIST Face Database in Consideration with Unconstrained Environments

In this paper, we introduce a new large-scale face database from KIST, denoted as K-FACE, and describe a novel capturing device specifically designed to obtain the data. The K-FACE database contains more than 1 million high-quality images of 1,000 subjects selected by considering the ratio of gender and age groups. It includes a variety of attributes, including 27 poses, 35 lighting conditions, three expressions, and occlusions by the combination of five types of accessories. As the K-FACE database is systematically constructed through a hemispherical capturing system with elaborate lighting control and multiple cameras, it is possible to accurately analyze the effects of factors that cause performance degradation, such as poses, lighting changes, and accessories. We consider not only the balance of external environmental factors, such as pose and lighting, but also the balance of personal characteristics such as gender and age group. The gender ratio is the same, while the age groups of subjects are uniformly distributed from the 20s to 50s for both genders. The K-FACE database can be extensively utilized in various vision tasks, such as face recognition, face frontalization, illumination normalization, face age estimation, and three-dimensional face model generation. We expect systematic diversity and uniformity of the K-FACE database to promote these research fields.
On Opportunistic Selection of Common Randomness and LLR generation for Algebraic Group Secret-Key Generation

It is well known that physical-layer key generation methods enable wireless devices to harvest symmetric keys by accessing the randomness offered by the wireless channels. Although two-user key generation is well understood, group secret-key (GSK) generation, wherein more than two nodes in a network generate secret-keys, still poses open problems. Recently, Manish Rao et al., have proposed the Algebraic Symmetrically Quantized GSK (A-SQGSK) protocol for a network of three nodes wherein the nodes share quantized versions of the channel realizations over algebraic rings, and then harvest a GSK. Although A-SQGSK protocol guarantees confidentiality of common randomness to an eavesdropper, we observe that the key-rate of the protocol is poor since only one channel in the network is used to harvest GSK. Identifying this limitation, in this paper, we propose an opportunistic selection method wherein more than one wireless channel is used to harvest GSKs without compromising the confidentiality feature, thereby resulting in remarkable improvements in the key-rate. Furthermore, we also propose a log-likelihood ratio (LLR) generation method for the common randomness observed at various nodes, so that the soft-values are applied to execute LDPC codes based reconciliation to reduce the bit mismatches among the nodes.
Dynamical dark energy models in the light of gravitational-wave transient catalogues

The study of current gravitational waves (GW) catalogues provide an interesting model independent way to understand further the nature of dark energy. In this work, we present an update of the constrains related to dynamical dark energy parametrisations using recent Gravitational-Wave Transient catalogues (GWTC-1 and GWTC-2) along with Type Ia supernova (SNeIa) and Cosmic Chronometers (CC) catalogues. According to our Bayesian results using the full SNeIa+CC+GW database, the ΛCDM model shows a strong preference against two dark energy parameterisation known as Barboza-Alcaniz (BA) and the Low Correlation (LC) models. Also, we obtain a very strong preference against the Chevallier-Polarski-Linder (CPL) model. Furthermore, we generated a mock GW catalogue and estimate that we require approximately 1000 standard sirens to have a constrain of H 0 within 1% relative error, quantity that is out of reach of current standard sirens candidates in GWTC-1 and GWTC-2 catalogues.
S3: Learnable Sparse Signal Superdensity for Guided Depth Estimation

Dense depth estimation plays a key role in multiple applications such as robotics, 3D reconstruction, and augmented reality. While sparse signal, e.g., LiDAR and Radar, has been leveraged as guidance for enhancing dense depth estimation, the improvement is limited due to its low density and imbalanced distribution. To maximize the utility from the sparse source, we propose Sparse Signal Superdensity (S3) technique, which expands the depth value from sparse cues while estimating the confidence of expanded region. The proposed S3 can be applied to various guided depth estimation approaches and trained end-to-end at different stages, including input, cost volume and output. Extensive experiments demonstrate the effectiveness, robustness, and flexibility of the S3 technique on LiDAR and Radar signal.
Why asymmetric molecular coupling to electrodes cannot be at work in real molecular rectifiers

Every one and then one can hear in the molecular electronics community that asymmetric couplings (Γs 6= Γt) of the dominant level (molecular orbital) to electrodes (s and t) having shapes different of each other may be responsible for current rectification observed in experiments. Using a general single level model going beyond the Lorentzian transmission limit, in this work we present a rigorous demonstration that this is not the case. In particular, we deduce an analytical for the bias (V ) driven shift of the level energy δε0(V ) showing that δε0(V )/V scales as Γt/Wt − Γs/Ws, which is merely a tiny quantity because the electrode bandwidths Ws,t are much larger than Γs,t. This result invalidates a previous, never-deduced formula in use in some previous publications that neither could be justified theoretically nor is supported by experiment. To the latter aim, we present new experimental evidence adding to that already inferred in earlier analysis.
A System Level Approach to Robust Model Predictive Control

—Robust tube-based model predictive control (MPC) methods address constraint satisfaction by leveraging an a-priori determined tube controller in the prediction to tighten the constraints. This paper presents a system level tube-MPC (SLTMPC) method derived from the system level parameterization (SLP), which allows optimization over the tube controller online when solving the MPC problem, which can signiﬁcantly reduce conservativeness. We derive the SLTMPC method by establishing an equivalence relation between a class of robust MPC methods and the SLP. Finally, we show that the SLTMPC formulation naturally arises from an extended SLP formulation and show its merits in a numerical example.
Multi-Channel and Multi-Microphone Acoustic Echo Cancellation Using A Deep Learning Based Approach

Building on the deep learning based acoustic echo cancellation (AEC) in the single-loudspeaker (single-channel) and single-microphone setup, this paper investigates multi-channel AEC (MCAEC) and multi-microphone AEC (MMAEC). We train a deep neural network (DNN) to predict the near-end speech from microphone signals with far-end signals used as additional information. We find that the deep learning approach avoids the non-uniqueness problem in traditional MCAEC algorithms. For the AEC setup with multiple microphones, rather than employing AEC for each microphone, a single DNN is trained to achieve echo removal for all microphones. Also, combining deep learning based AEC with deep learning based beamforming further improves the system performance. Experimental results show the effectiveness of both bidirectional long short-term memory (BLSTM) and convolutional recurrent network (CRN) based methods for MCAEC and MMAEC. Furthermore, deep learning based methods are capable of removing echo and noise simultaneously and work well in the presence of nonlinear distortions.
Modeling and control of 5-DoF boom crane

Automation of cranes can have a direct impact on the productivity of construction projects. In this paper, we focus on the control of one of the most used cranes, the boom crane. Tower cranes and overhead cranes have been widely studied in the literature, whereas the control of boom cranes has been investigated only by a few works. Typically, these works make use of simple models making use of a large number of simplifying assumptions (e.g. fixed length cable, assuming certain dynamics are uncoupled, etc.) A first result of this paper is to present a fairly complete nonlinear dynamic model of a boom crane taking into account all coupling dynamics and where the only simplifying assumption is that the cable is considered as rigid. The boom crane involves pitching and rotational movements, which generate complicated centrifugal forces, and consequently, equations of motion highly nonlinear. On the basis of this model, a control law has been developed able to perform position control of the crane while actively damping the oscillations of the load. The effectiveness of the approach has been tested in simulation with realistic physical parameters and tested in the presence of wind disturbances.
Distance to three molecular clouds in the central molecular zone

Context. The determination of absolute and relative distances of molecular clouds along the line-of-sight towards the central molecular zone (CMZ) is crucial for inferring its orbital structure and dynamics and for understanding star formation in the clouds. Aims. Recent work has suggested that the G0.253+0.016 cloud (the Brick) does not belong to the CMZ. This motivated us to crosscheck those results, computing the absolute and relative distances to the Brick as well as to another two molecular clouds (the 50 km s−1 and the 20 km s−1 clouds), and discuss their CMZ membership. Methods. We used the colour magnitude diagrams Ks versus H − Ks to compare stars detected towards the target clouds with stars detected towards three reference regions in the nuclear stellar disc (NSD) and the Galactic bulge. We used red clump (RC) stars to estimate the distance to each region. Results. We found that all the clouds present a double RC feature. Such a double RC has been reported in previous work for the NSD, but not for the bulge adjacent to it. We exclude the possibility that the different RC features are located at significantly different distances. The obtained absolute and relative distances are compatible with the Galactic centre distance (∼8 kpc).
The surface brightness–colour relations based on eclipsing binary stars and calibrated with Gaia EDR3

Aims. The surface brightness–colour relation (SBCR) is a basic tool for establishing precise and accurate distances within the Local Group. Detached eclipsing binary stars with accurately determined radii and trigonometric parallaxes allow calibration of the SBCRs with unprecedented accuracy. Methods. We analysed four nearby eclipsing binary stars containing late F-type main sequence components: AL Ari, AL Dor, FM Leo, and BN Scl. We determined very precise spectroscopic orbits and combined them with high-precision ground- and space-based photometry. We derived the astrophysical parameters of their components with mean errors of 0.1% for mass and 0.4% for radius. We combined those four systems with another 24 nearby eclipsing binaries with accurately known radii from the literature for which Gaia EDR3 parallaxes are available in order to derive the SBCRs. Results. The resulting SBCRs cover stellar spectral types from B9 V to G7 V. For calibrations, we used Johnson optical B and V, Gaia GBP and G, and 2MASS JHK bands. The most precise relations are calibrated using the infrared K band and allow angular diameters of A-, F-, and G-type dwarf and subgiant stars to be predicted with a precision of 1%.
Continuous Speech Separation with Ad Hoc Microphone Arrays

Speech separation has been shown effective for multi-talker speech recognition. Under the ad hoc microphone array setup where the array consists of spatially distributed asynchronous microphones, additional challenges must be overcome as the geometry and number of microphones are unknown beforehand. Prior studies show, with a spatial-temporal-interleaving structure, neural networks can efficiently utilize the multi-channel signals of the ad hoc array. In this paper, we further extend this approach to continuous speech separation. Several techniques are introduced to enable speech separation for real continuous recordings. First, we apply a transformer-based network for spatio-temporal modeling of the ad hoc array signals. In addition, two methods are proposed to mitigate a speech duplication problem during single talker segments, which seems more severe in the ad hoc array scenarios. One method is device distortion simulation for reducing the acoustic mismatch between simulated training data and real recordings. The other is speaker counting to detect the single speaker segments and merge the output signal channels. Experimental results for AdHoc-LibiCSS, a new dataset consisting of continuous recordings of concatenated LibriSpeech utterances obtained by multiple different devices, show the proposed separation method can significantly improve the ASR accuracy for overlapped speech with little performance degradation for single talker segments.
Dimensional reduction in complex living systems: Where, why, and how

The unprecedented prowess of measurement techniques provides a detailed, multi‐scale look into the depths of living systems. Understanding these avalanches of high‐dimensional data—by distilling underlying principles and mechanisms—necessitates dimensional reduction. We propose that living systems achieve exquisite dimensional reduction, originating from their capacity to learn, through evolution and phenotypic plasticity, the relevant aspects of a non‐random, smooth physical reality. We explain how geometric insights by mathematicians allow one to identify these genuine hallmarks of life and distinguish them from universal properties of generic data sets. We illustrate these principles in a concrete example of protein evolution, suggesting a simple general recipe that can be applied to understand other biological systems.
The exact value of Hausdorff dimension of escaping sets of class B meromorphic functions

We consider the subclass of class B consisting of meromorphic functions f : C → (cid:2) C for which inﬁnity is not an asymptotic value and whose all poles have orders uniformly bounded from above. This class was introduced in Bergweiler and Kotus (Trans Am Math Soc 364(10):5369–5394, 2012) and the Hausdorﬀ dimension HD( I ( f )) of the set I ( f ) of all points escaping to inﬁnity under forward iteration of f was estimated therein. In this paper we provide a closed formula for the exact value of HD( I ( f )) identifying it with the critical exponent of the natural series introduced in Bergweiler and Kotus (2012). This exponent is very easy to calculate for many concrete functions. In particular, we construct a function from this class which is of inﬁnite order and for which HD( I ( f )) = 0.