Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the antigenicity of the lyssavirus glycoprotein

Rabies, the archetypal lyssavirus, is one of the most feared viruses known to man and globally, is the cause of more than 55,000 deaths per year. Alongside rabies virus, numerous related lyssaviruses exist that are also capable of causing fatal disease, clinically indistinguishable from rabies. Whilst the human burden of these non-rabies lyssaviruses remains unknown, fatalities have been reported. The lyssavirus glycoprotein is the sole target for virus neutralising antibodies and several amino acid epitopes have been linked to virus neutralisation. Lyssaviruses are genetically and antigenically categorised into phylogroups that indicate the level of protection afforded by current vaccines. It is generally accepted that an antibody response to the currently available rabies vaccines affords protection against all viruses that are categorised into phylogroup I. However, this antibody response does not protect against lyssavirus species within phylogroups II and III. Indeed, experimental data has shown that the antibody repertoire induced by rabies virus vaccines is unable to neutralise viruses in these phylogroups. In this study we have generated lentivirus pseudotypes representing all currently defined lyssaviruses as well as including chimeric lyssavirus glycoproteins that have had their antigenic sites swapped between phylogroup I and II viruses. Using the wildtype lyssavirus pseudotypes we have confirmed a strong level of intra-phylogroup neutralisation in addition to very limited inter-phylogroup neutralisation. The antigenic site swap constructs have shown an alteration in both glycoprotein functionality alongside altered neutralisation profiles using a variety of vaccine induced and divergent lyssavirus specific sera. Promising cross protective candidates have been cloned into a vaccine strain full length backbone and reverse genetics has enabled rescue of these viruses. The growth kinetics of recombinant viruses have been studied in vitro and an investigation into the pathogenicity and degree of vaccine induced protection against these viruses in vivo has been conducted.
Combinatorial Geometry of Threshold-Linear Networks

The architecture of a neural network constrains the potential dynamics that can emerge. Some architectures may only allow for a single dynamic regime, while others display a great deal of flexibility with qualitatively different dynamics that can be reached by modulating connection strengths. In this work, we develop novel mathematical techniques to study the dynamic constraints imposed by different network architectures in the context of competitive threshold-linear networks (TLNs). Any given TLN is naturally characterized by a hyperplane arrangement in $\mathbb{R}^n$, and the combinatorial properties of this arrangement determine the pattern of fixed points of the dynamics. This observation enables us to recast the question of network flexibility in the language of oriented matroids, allowing us to employ tools and results from this theory in order to characterize the different dynamic regimes a given architecture can support. In particular, fixed points of a TLN correspond to cocircuits of an associated oriented matroid; and mutations of the matroid correspond to bifurcations in the collection of fixed points. As an application, we provide a complete characterization of all possible sets of fixed points that can arise in networks through size $n=3$, together with descriptions of how to modulate synaptic strengths of the network in order to access the different dynamic regimes. These results provide a framework for studying the possible computational roles of various motifs observed in real neural networks.
The pursuit of beauty: Converting image labels to meaningful vectors

A challenge of the computer vision community is to understand the semantics of an image, in order to allow image reconstruction based on existing high-level features or to better analyze (semi-)labelled datasets. Towards addressing this challenge, this paper introduces a method, called Occlusion-based Latent Representations (OLR), for converting image labels to meaningful representations that capture a significant amount of data semantics. Besides being informational rich, these representations compose a disentangled low-dimensional latent space where each image label is encoded into a separate vector. We evaluate the quality of these representations in a series of experiments whose results suggest that the proposed model can capture data concepts and discover data interrelations.
Galactic extinction laws – II. Hidden in plain sight, a new interstellar absorption band at 7700 Å broader than any known DIB

We have detected a broad interstellar absorption band centred close to 7700 A and with a FWHM of 176.6$\pm$3.9 A. This is the first such absorption band detected in the optical range and is significantly wider than the numerous diffuse interstellar bands (DIBs). It remained undiscovered until now because it is partially hidden behind the A telluric band produced by O$_2$. The band was discovered using STIS@HST spectra and later detected in a large sample of stars of diverse type (OB stars, BA supergiants, red giants) using further STIS and ground-based spectroscopy. The EW of the band is measured and compared with our extinction and K I $\lambda\lambda$7667.021,7701.093 measurements for the same sample. The carrier is ubiquitous in the diffuse and translucent Galactic ISM but is depleted in the environment around OB stars. In particular, it appears to be absent or nearly so in sightlines rich in molecular carbon. This behaviour is similar to that of the $\sigma$-type DIBs, which originate in the low/intermediate-density UV-exposed ISM but are depleted in the high-density UV-shielded molecular clouds. We also present an update on our previous work on the relationship between $E(4405-5495)$ and $R_{5495}$ and incorporate our results into a general model of the ISM.
Asteroseismology of two Kepler detached eclipsing binaries

The present work contains light curve, spectroscopic, and asteroseismic analyses for KIC 04851217 and KIC 10686876. These systems are detached eclipsing binaries hosting a pulsating component of δ Scuti type and have been observed with the unprecedented accuracy of the Kepler space telescope. Using ground-based spectroscopic observations, the spectral types of the primary components of the systems were estimated as A6V and A5V for KIC 04851217 and KIC 10686876, respectively, with an uncertainty of one subclass. The present spectral classification, together with literature radial velocity curves, were used to model the light curves of the systems and, therefore, to calculate the absolute parameters of their components with a higher degree of certainty. The photometric data were analysed using standard eclipsing binary modeling techniques, while their residuals were further analysed using Fourier transformation techniques to extract the pulsation frequencies of their host δ Scuti stars. The oscillation modes of the independent frequencies were identified using theoretical models of δ Scuti stars. The distances of the systems were calculated using the relation between the luminosity and the pulsation period for δ Scuti stars. Here, the physical and the oscillation properties of the pulsating components of these systems are discussed and compared with others of the same type. Moreover, using all the currently known cases of δ Scuti stars in detached binaries, updated correlations between orbital and dominant pulsation periods and between log g and pulsation periods are derived. It can concluded that the proximity of the companion plays significant role in the evolution of the pulsational frequencies.
Accurate Mapping and Planning for Autonomous Racing

This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student Germany (FSG) 2019 driverless competition, where it won 1st place overall. The presented solution combines early fusion of camera and LiDAR data, a layered mapping approach, and a planning approach that uses Bayesian filtering to achieve high-speed driving on unknown race tracks while creating accurate maps. We benchmark the method against our team’s previous solution, which won FSG 2018, and show improved accuracy when driving at the same speeds. Furthermore, the new pipeline makes it possible to reliably raise the maximum driving speed in unknown environments from 3 m/s to 12 m/s while still mapping with an acceptable RMSE of 0.29 m.
Pharmacokinetic analysis for the differentiation of pituitary microadenoma subtypes through dynamic contrast-enhanced magnetic resonance imaging

The value of pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in distinguishing pituitary microadenoma subtypes was investigated in the present study. Pathology and follow-up outcomes were applied as the gold standard for differentiating between 76 patients with pituitary microadenomas (38 prolactin-producing tumors, 17 adrenocorticotropic hormone adenomas and 21 growth hormone-producing tumors) and 20 patients with normal pituitary glands. DCE-MRI was conducted to obtain the following quantitative permeability parameters: Volume transfer constant (Ktrans), rate constant (Kep) and extracellular extravascular volume fraction (Ve). Among the 76 cases included, 61 were visually diagnosed using conventional MRI. The Ktrans, Kep and Ve of the microadenoma cases were 0.472±0.292/min, 0.765±0.359/min and 0.792±0.345, respectively. The Ktrans, Kep and Ve of the normal control group were 0.902±0.238/min, 1.208±0.599/min and 0.928±0.378, respectively. The Ktrans and Kep of patients with microadenomas were significantly lower compared with those of the normal controls (P<0.05). However, the Ve of the two groups did not significantly differ. Subtype differentiation analysis revealed that patients with growth hormone-producing tumors exhibited the highest Ktrans value (P<0.05). Kep significantly differed between growth hormone-producing tumors and the other two subtypes (P<0.05), but did not significantly differ among three subtypes. Receiver-operator characteristic analysis indicated that the area under the curve values of Ktrans and Kep were 0.884 and 0.728, respectively. Sensitivity and specificity were 95.0 and 82.6%, respectively, when Ktrans was set to 0.614/min as the cut-off value, and when the Kep cut-off value was set to 0.985/min, sensitivity and specificity were 60.0 and 81.3%, respectively. In conclusion, Ktrans and Kep derived from DCE-MRI could be applied to detect and identify microadenoma subtypes. Ktrans better reflects the blood perfusion alterations exhibited by patients with different microadenoma subtypes.
Singer Identification Using Convolutional Acoustic Motif Embeddings

Flamenco singing is characterized by pitch instability, micro-tonal ornamentations, large vibrato ranges, and a high degree of melodic variability. These musical features make the automatic identification of flamenco singers a difficult computational task. In this article we present an end-to-end pipeline for flamenco singer identification based on acoustic motif embeddings. In the approach taken, the fundamental frequency obtained directly from the raw audio signal is approximated. This approximation reduces the high variability of the audio signal and allows for small melodic patterns to be discovered using a sequential pattern mining technique, thus creating a dictionary of motifs. Several acoustic features are then used to extract fixed length embeddings of variable length motifs by using convolutional architectures. We test the quality of the embeddings in a flamenco singer identification task, comparing our approach with previous deep learning architectures, and study the effect of motivic patterns and acoustic features in the identification task. Results indicate that motivic patterns play a crucial role in identifying flamenco singers by minimizing the size of the signal to be learned, discarding information that is not relevant in the identification task. The deep learning architecture presented outperforms denser models used in large-scale audio classification problems.
POTUS: Predictive Online Tuple Scheduling for Data Stream Processing Systems

Most online service providers deploy their own data stream processing systems in the cloud to conduct large-scale and real-time data analytics. However, such systems, e.g., Apache Heron, often adopt naive scheduling schemes to distribute data streams (in the units of tuples) among processing instances, which may result in workload imbalance and system disruption. Hence, there still exists a mismatch between the temporal variations of data streams and such inflexible scheduling scheme designs. Besides, the fundamental limits of benefits of predictive scheduling to data stream processing systems remain unexplored. In this article, we focus on the problem of tuple scheduling with predictive service in Apache Heron. With a careful choice in the granularity of system modeling and decision making, we formulate the problem as a stochastic network optimization problem and propose POTUS, an online predictive scheduling scheme that aims to minimize the response time of data stream processing by steering data streams in a distributed fashion. Theoretical analysis and simulation results show that POTUS achieves an ultra-low response time with a stability guarantee. Moreover, POTUS only requires mild-value of future information to effectively reduce the response time, even with mis-prediction.
Learning to Drive Small Scale Cars from Scratch

We consider the problem of learning to drive low-cost small scale cars using reinforcement learning. It is challenging to handle the long-tailed distributions of events in the real-world with handcrafted logical rules and reinforcement learning could be a potentially more scalable solution to deal with them. We adopt an existing platform called Donkey car for low-cost repeatable and reproducible research in autonomous driving. We consider the task of learning to drive around a track, given only monocular image observations from an on-board camera. We demonstrate that the soft actor-critic algorithm combined with state representation learning using a variational autoencoder can learn to drive around randomly generated tracks on the Donkey car simulator and a real-world track using the Donkey car platform. Our agent can learn from scratch using sparse and noisy rewards within just 10 minutes of driving experience.
Improving accuracy in the Leray model for incompressible nonisothermal flows via adaptive deconvolution‐based nonlinear filtering

This paper considers a Leray regularization model of incompressible, nonisothermal fluid flows which uses nonlinear filtering based on indicator functions and introduces an efficient numerical method for solving it. The proposed method uses a multistep, second‐order temporal discretization with a finite element (FE) spatial discretization in such a way that the resulting algorithm is linear at each time level and decouples the evolution equations from the velocity filter step. Since the indicator function chosen in this model is mathematically based on approximation theory, the proposed numerical algorithm can be analyzed robustly, i.e., the stability and convergence of the method is provable. A series of numerical tests are carried out to verify the theoretical convergence rates and to compare the algorithm with direct numerical simulation and the usual Leray‐α model of the flow problem.
Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy

We study the global convergence and global optimality of actor-critic, one of the most popular families of reinforcement learning algorithms. While most existing works on actor-critic employ bi-level or two-timescale updates, we focus on the more practical single-timescale setting, where the actor and critic are updated simultaneously. Specifically, in each iteration, the critic update is obtained by applying the Bellman evaluation operator only once while the actor is updated in the policy gradient direction computed using the critic. Moreover, we consider two function approximation settings where both the actor and critic are represented by linear or deep neural networks. For both cases, we prove that the actor sequence converges to a globally optimal policy at a sublinear $O(K^{-1/2})$ rate, where $K$ is the number of iterations. To the best of our knowledge, we establish the rate of convergence and global optimality of single-timescale actor-critic with linear function approximation for the first time. Moreover, under the broader scope of policy optimization with nonlinear function approximation, we prove that actor-critic with deep neural network finds the globally optimal policy at a sublinear rate for the first time.
Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification

In this paper, we propose a domain adaptation framework to address the device mismatch issue in acoustic scene classification leveraging upon neural label embedding (NLE) and relational teacher student learning (RTSL). Taking into account the structural relationships between acoustic scene classes, our proposed framework captures such relationships which are intrinsically device-independent. In the training stage, transferable knowledge is condensed in NLE from the source domain. Next in the adaptation stage, a novel RTSL strategy is adopted to learn adapted target models without using paired source-target data often required in conventional teacher student learning. The proposed framework is evaluated on the DCASE 2018 Task1b data set. Experimental results based on AlexNet-L deep classification models confirm the effectiveness of our proposed approach for mismatch situations. NLE-alone adaptation compares favourably with the conventional device adaptation and teacher student based adaptation techniques. NLE with RTSL further improves the classification accuracy.
Structure and Automatic Segmentation of Dhrupad Vocal Bandish Audio

A Dhrupad vocal concert comprises a composition section that is interspersed with improvised episodes of increased rhythmic activity involving the interaction between the vocals and the percussion. Tracking the changing rhythmic density, in relation to the underlying metric tempo of the piece, thus facilitates the detection and labeling of the improvised sections in the concert structure. This work concerns the automatic detection of the musically relevant rhythmic densities as they change in time across the bandish (composition) performance. An annotated dataset of Dhrupad bandish concert sections is presented. We investigate a CNN-based system, trained to detect local tempo relationships, and follow it with temporal smoothing. We also employ audio source separation as a pre-processing step to the detection of the individual surface densities of the vocals and the percussion. This helps us obtain the complete musical description of the concert sections in terms of capturing the changing rhythmic interaction of the two performers.
GmFace: A Mathematical Model for Face Image Representation Using Multi-Gaussian

Establishing mathematical models is a ubiquitous and effective method to understand the objective world. Due to complex physiological structures and dynamic behaviors, mathematical representation of the human face is an especially challenging task. A mathematical model for face image representation called GmFace is proposed in the form of a multi-Gaussian function in this paper. The model utilizes the advantages of two-dimensional Gaussian function which provides a symmetric bell surface with a shape that can be controlled by parameters. The GmNet is then designed using Gaussian functions as neurons, with parameters that correspond to each of the parameters of GmFace in order to transform the problem of GmFace parameter solving into a network optimization problem of GmNet. The face modeling process can be described by the following steps: (1) GmNet initialization; (2) feeding GmNet with face image(s); (3) training GmNet until convergence; (4) drawing out the parameters of GmNet (as the same as GmFace); (5) recording the face model GmFace. Furthermore, using GmFace, several face image transformation operations can be realized mathematically through simple parameter computation.
TA-Dash: An Interactive Dashboard for Spatial-Temporal Traffic Analytics

In recent years, a large number of research efforts aimed at the development of machine learning models to predict complex spatial-temporal mobility patterns and their impact on road traffic and infrastructure. However, the utility of these models is often diminished due to the lack of accessible user interfaces to view and analyse prediction results. In this paper, we present the Traffic Analytics Dashboard (TA-Dash), an interactive dashboard that enables the visualisation of complex spatial-temporal urban traffic patterns. We demonstrate the utility of TA-Dash at the example of two recently proposed spatial-temporal models for urban traffic and urban road infrastructure analysis. In particular, the use cases include the analysis, prediction and visualisation of the impact of planned special events on urban road traffic as well as the analysis and visualisation of structural dependencies within urban road networks. The lightweight TA-Dash dashboard aims to address non-expert users involved in urban traffic management and mobility service planning. The TA-Dash builds on a flexible layer-based architecture that is easily adaptable to the visualisation of new models.
Active Object Search

In this work, we investigate an Active Object Search (AOS) task that is not explicitly addressed in the literature. It aims to actively perform as few action steps as possible to search and locate the target object in a 3D indoor scene. Different from classic object detection that passively receives visual information, this task encourages an intelligent agent to perform active search via reasonable action planning; thus it can better recall the target objects, especially for the challenging situations that the target is far from the agent, blocked by an obstacle and out of view. To handle this AOS task, we formulate a reinforcement learning framework that consists of a 3D object detector, a state controller and a cross-modal action planner to work cooperatively to find out the target object with minimal action steps. During training, we design a novel cost-sensitive active search reward that penalizes inaccurate object search and redundant action steps. To evaluate this novel task, we construct an Active Object Search (AOS) benchmark that contains 5,845 samples from 30 diverse indoor scenes. We conduct extensive qualitative and quantitative evaluations on this benchmark to demonstrate the effectiveness of the proposed approach and analyze the key factors that contribute more to address this task.