Miracle-dz/UniFAR-FTUs huggingface dataset:
Serostatus testing and dengue vaccine cost–benefit thresholds

The World Health Organisation currently recommends pre-screening for past infection prior to administration of the only licensed dengue vaccine, CYD-TDV. Using a bounding analysis, we show that despite additional testing costs, this approach can improve the economic viability of CYD-TDV: effective testing reduces unnecessary vaccination costs while increasing the health benefit for vaccine recipients. When testing is cheap enough, those trends outweigh additional screening costs and make test-then-vaccinate strategies net-beneficial in many settings. We derived these results using a general approach for determining price thresholds for testing and vaccination, as well as indicating optimal start and end ages of routine test-then-vaccinate programs. This approach only requires age-specific seroprevalence and a cost estimate for second infections. We demonstrate this approach across settings commonly used to evaluate CYD-TDV economics, and highlight implications of our simple model for more detailed studies. We found trends showing test-then-vaccinate strategies are generally more beneficial starting at younger ages, and that in some settings multiple years of testing can be more beneficial than only testing once, despite increased investment in testing.
Activity, movement, home range and habitat use of an adult gray wolf in a Mediterranean landscape of northern Greece

Abstract We present the results of a study on the activity patterns of a wolf in northwestern Greece (2011–2012). The home range of the wolf was 460.5 km2, while the core area was 88.9 km2 and included mainly areas with tree cover and agricultural areas. On three occasions the wolf unsuccessfully tried to cross a highway, highlighting the necessity to identify habitat corridors and effectively mitigate the effects of transportation infrastructure on the species in Greece. Based on these results we propose the increase of research and management actions for the effective protection of wolves in the country.
Non-Rigid 3D Shape Retrieval Based on Multi-view Metric Learning

This study presents a novel multi-view metric learning algorithm, which aims to improve 3D non-rigid shape retrieval. With the development of non-rigid 3D shape analysis, there exist many shape descriptors. The intrinsic descriptors can be explored to construct various intrinsic representations for non-rigid 3D shape retrieval task. The different intrinsic representations (features) focus on different geometric properties to describe the same 3D shape, which makes the representations are related. Therefore, it is possible and necessary to learn multiple metrics for different representations jointly. We propose an effective multi-view metric learning algorithm by extending the Marginal Fisher Analysis (MFA) into the multi-view domain, and exploring Hilbert-Schmidt Independence Criteria (HSCI) as a diversity term to jointly learning the new metrics. The different classes can be separated by MFA in our method. Meanwhile, HSCI is exploited to make the multiple representations to be consensus. The learned metrics can reduce the redundancy between the multiple representations, and improve the accuracy of the retrieval results. Experiments are performed on SHREC'10 benchmarks, and the results show that the proposed method outperforms the state-of-the-art non-rigid 3D shape retrieval methods.
The Radio-to-X-Ray Spectrum of GRB 970508 on 1997 May 21.0 UT

We have reconstructed the spectrum of the afterglow of GRB 970508 on 1997 May 21.0 UT (12.1 days after the gamma-ray burst) on the basis of observations spanning the X-ray-to-radio range. The low-frequency power-law index of the spectrum, α=0.44 ± 0.07 (Fν ∝ να), is in agreement with the expected value α=1/3 for optically thin synchrotron radiation. The 1.4 GHz emission is self-absorbed. We infer constraints on the break frequencies νc and νm on 1997 May 21.0 UT from a spectral transition from Fν ~ν−0.6 to Fν ~ν−1.1 in the optical passband around 1.4 days. A model of an adiabatically expanding blast wave emitting synchrotron radiation, in which a significant fraction of the electrons cool rapidly, provides a successful and consistent description of the afterglow observations over nine decades in frequency, ranging in time from trigger until several months later.
DC SQUID as a sensitive detector of dark matter.

The gauge-gravity duality has been applied to examine the properties of holographic superconducting quantum device (SQUID), composed of two S-N-S Josephson junctions, influenced by dark matter sector modeled by the auxiliary $U(1)$-gauge field coupled to the ordinary Maxwell one. The dark matter sector is known to affect the properties of superconductor and is expected to enter the current-phase relation. While the single junctions between two superconductors turn out to be very weakly sensitive to the presence of dark matter sector, the results obtained for the SQUID are encouraging. We find that the analyzed properties of the device may be used in the future experiments aimed at the detection of the dark sector of our Universe.
Impact of new ICRU 90 key data on stopping-power ratios and beam quality correction factors for carbon ion beams

The recent update of key dosimetric data by the International Commission on Radiation Units and Measurements (ICRU) makes several changes to the computation of beam quality correction factors kQ with regard to, for example, the mean excitation energies, I, which enter the stopping power computation for water and air, the computation procedure itself, the average energy expended in the production of an ion pair in air, W/e, as well as chamber-specific factors for cobalt-60. With the new recommendations an accurate assessment of the water-to-air stopping-power ratio, , in reference conditions is necessary to update the dosimetry protocols for carbon ion beams. The ICRU 90 key data were considered for computation of for carbon ion beams using Monte Carlo transport simulations for a number of reference conditions, namely monoenergetic carbon ion beams with a range in water from 3 to 30 cm and spread-out Bragg peaks (SOBPs) of different widths and depths in water. New recommendations for are presented, namely 1.1247 for the reference condition of depth 1 g cm−2 for monoenergetic carbon ion beams and 1.1273 at the center of physically optimized SOBPs. The recommendation of a constant value (1.126) represents the stopping-power ratio within a 0.3% variation of for all reference conditions considered. The impact of these new values and the updated key data on kQ for carbon ion beams was evaluated in a second step. Changes and the difference from experimental data were found to be non-significant, but larger discrepancies to measurements were observed for plane-parallel ionization chambers. The combined uncertainty for kQ in carbon ion beams decreased to 2.4%. In future, it could be further lowered by using chamber-specific Monte Carlo transport simulations, for which the implementation of ICRU 90 key data as done in this study is a prerequisite.
On the interplay between multiscaling and stock dependence

We find a nonlinear dependence between an indicator of the degree of multiscaling of log-price time series of a stock and the average correlation of the stock with respect to the other stocks traded in the same market. This result is a robust stylized fact holding for different financial markets. We investigate this result conditional on the stocks' capitalization and on the kurtosis of stocks' log-returns in order to search for possible confounding effects. We show that a linear dependence with the logarithm of the capitalization and the logarithm of kurtosis does not explain the observed stylized fact, which we interpret as being originated from a deeper relationship.
Mean field limit for Coulomb-type flows

We establish the mean-field convergence for systems of points evolving along the gradient flow of their interaction energy when the interaction is the Coulomb potential or a super-coulombic Riesz potential, for the first time in arbitrary dimension. The proof is based on a modulated energy method using a Coulomb or Riesz distance, assumes that the solutions of the limiting equation are regular enough and exploits a weak-strong stability property for them. The method can handle the addition of a regular interaction kernel, and applies also to conservative and mixed flows. In the appendix, it is also adapted to prove the mean-field convergence of the solutions to Newton's law with Coulomb or Riesz interaction in the monokinetic case to solutions of an Euler-Poisson type system.
Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues

The recent surge of text-based online counseling applications enables us to collect and analyze interactions between counselors and clients. A dataset of those interactions can be used to learn to automatically classify the client utterances into categories that help counselors in diagnosing client status and predicting counseling outcome. With proper anonymization, we collect counselor-client dialogues, define meaningful categories of client utterances with professional counselors, and develop a novel neural network model for classifying the client utterances. The central idea of our model, ConvMFiT, is a pre-trained conversation model which consists of a general language model built from an out-of-domain corpus and two role-specific language models built from unlabeled in-domain dialogues. The classification result shows that ConvMFiT outperforms state-of-the-art comparison models. Further, the attention weights in the learned model confirm that the model finds expected linguistic patterns for each category.
Topics of Concern: Identifying User Issues in Reviews of IoT Apps and Devices

Internet of Things (IoT) systems are bundles of networked sensors and actuators that are deployed in an environment and act upon the sensory data that they receive. These systems, especially consumer electronics, have two main cooperating components: a device and a mobile app. The unique combination of hardware and software in IoT systems presents challenges that are lesser known to mainstream software developers. They might require innovative solutions to support the development and integration of such systems. In this paper, we analyze more than 90,000 reviews of ten IoT devices and their corresponding apps and extract the issues that users encountered while using these systems. Our results indicate that issues with connectivity, timing, and updates are particularly prevalent in the reviews. Our results call for a new software-hardware development framework to assist the development of reliable IoT systems.
Rational design, preparation and characterization of recombinant Ag85B variants and their glycoconjugates with T-cell antigenic activity against Mycobacterium tuberculosis

Tuberculosis is the deadliest infectious disease in the world. The variable efficacy of the current treatments highlights the need for more effective agents against this disease. In the past few years, we focused on the investigation of antigenic glycoconjugates starting from recombinant Ag85B (rAg85B), a potent protein antigen from Mycobacterium tuberculosis. In this paper, structural modifications were rationally designed in order to obtain a rAg85B variant protein able to maintain its immunogenicity after glycosylation. Lysine residues involved in the main T-epitope sequences (namely, K30 and K282) have been substituted with arginine to prevent their glycosylation by a lysine-specific reactive linker. The effectiveness of the mutation strategy and the detailed structure of resulting neo-glycoconjugates have been studied by intact mass spectrometry, followed by peptide and glycopeptide mapping. The effect of K30R and K282R mutations on the T-cell activity of rAg85B has also been investigated with a preliminary immunological evaluation performed by enzyme-linked immunospotting on the different variant proteins and their glycosylation products. After glycosylation, the two variant proteins with an arginine in position 30 completely retain the original T-cell activity, thus representing adequate antigenic carriers for the development of efficient glycoconjugate vaccines against tuberculosis.
Experimental data-based reduced-order model for analysis and prediction of flame transition in gas turbine combustors

In lean premixed combustors, flame stabilisation is an important operational concern that can affect efficiency, robustness and pollutant formation. The focus of this paper is on flame lift-off and re-attachment to the nozzle of a swirl combustor. Using time-resolved experimental measurements, a data-driven approach known as cluster-based reduced-order modelling (CROM) is employed to (1) isolate key flow patterns and their sequence during the flame transitions, and (2) formulate a forecasting model to predict the flame instability. The flow patterns isolated by the CROM methodology confirm some of the experimental conclusions about the flame transition mechanism. In particular, CROM highlights the key role of the precessing vortex core (PVC) in the flame detachment process in an unsupervised manner. For the attachment process, strong flow recirculation far from the nozzle appears to drive the flame upstream, thus initiating re-attachment. Different data-types (velocity field, OH concentration) were processed by the modelling tool, and the predictive capabilities of these different models are also compared. It was found that the swirling velocity possesses the best predictive properties, which gives a supplemental argument for the role of the PVC in causing the flame transition. The model is tested against unseen data and successfully predicts the probability of flame transition (both detachment and attachment) when trained with swirling velocity with minimal user input. The model trained with OH-PLIF data was only successful at predicting the flame attachment, which implies that different physical mechanisms are present for different types of flame transition. Overall, these aspects show the great potential of data-driven methods, particularly probabilistic forecasting techniques, in analysing and predicting large-scale features in complex turbulent combustion problems.
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs

To make machines better understand sentiments, research needs to move from polarity identification to understanding the reasons that underlie the expression of sentiment. Categorizing the goals or needs of humans is one way to explain the expression of sentiment in text. Humans are good at understanding situations described in natural language and can easily connect them to the character’s psychological needs using commonsense knowledge. We present a novel method to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. We efficiently integrate the acquired knowledge paths in a neural model that interfaces context representations with knowledge using a gated attention mechanism. We assess the model’s performance on a recently published dataset for categorizing human needs. Selectively integrating knowledge paths boosts performance and establishes a new state-of-the-art. Our model offers interpretability through the learned attention map over commonsense knowledge paths. Human evaluation highlights the relevance of the encoded knowledge.
Damped and Divergence Exact Solutions for the Duffing Equation Using Leaf Functions and Hyperbolic Leaf Functions

According to the wave power rule, the second derivative of a function with respect to the variable t is equal to negative n times the function raised to the power of 2n-1. Solving the ordinary differential equations numerically results in waves appearing in the figures. The ordinary differential equation is very simple; however, waves, including the regular amplitude and period, are drawn in the figure. In this study, the function for obtaining the wave is called the leaf function. Based on the leaf function, the exact solutions for the undamped and unforced Duffing equations are presented. In the ordinary differential equation, in the positive region of the variable, the second derivative becomes negative. Therefore, in the case that the curves vary with the time under the condition x(t)>0, the gradient constantly decreases as time increases. That is, the tangential vector on the curve of the graph changes from the upper right direction to the lower right direction as time increases. On the other hand, in the negative region of the variable, the second derivative becomes positive. The gradient constantly increases as time decreases. That is, the tangent vector on the curve changes from the lower right direction to the upper right direction as time increases. Since the behavior occurring in the positive region of the variable and the behavior occurring in the negative region of the variable alternately occur in regular intervals, waves appear by these interactions. In this paper, I present seven types of damped and divergence exact solutions by combining trigonometric functions, hyperbolic functions, hyperbolic leaf functions, leaf functions, and exponential functions. In each type, I show the derivation method and numerical examples, as well as describe the features of the waveform.
Learn2MAC: Online Learning Multiple Access for URLLC Applications

This paper addresses a fundamental limitation of previous random access protocols, their lack of latency performance guarantees. We consider K IoT transmitters competing for uplink resources and we design a fully distributed protocol for deciding how they access the medium. Specifically, each transmitter restricts decisions to a locally-generated dictionary of transmission patterns. At the beginning of a frame, pattern i is chosen with probability pI, and an online exponentiated gradient algorithm is used to adjust this probability distribution. The performance of the proposed scheme is showcased in simulations, where it is compared with a basline random access protocol. Simulation results show that (a) the proposed scheme achieves good latent throughput performance and low energy consumption, while (b) it outperforms by a big margin random transmissions.
Layered Image Compression Using Scalable Auto-Encoder

This paper presents a novel convolutional neural network (CNN) based image compression framework via scalable auto-encoder (SAE). Specifically, our SAE based deep image codec consists of hierarchical coding layers, each of which is an end-to-end optimized auto-encoder. The coarse image content and texture are encoded through the first (base) layer while the consecutive (enhance) layers iteratively code the pixel-level reconstruction errors between the original and former reconstructed images. The proposed SAE structure alleviates the need to train multiple models for different bit-rate points by recently proposed auto-encoder based codecs. The SAE layers can be combined to realize multiple rate points, or to produce a scalable stream. The proposed method has similar rate-distortion performance in the low-to-medium rate range as the state-of-the-art CNN based image codec (which uses different optimized networks to realize different bit rates) over a standard public image dataset. Furthermore, the proposed codec generates better perceptual quality in this bit rate range.
Time dispersion in quantum mechanics

In quantum mechanics the time dimension is treated as a parameter, while the three space dimensions are treated as observables. This assumption is both untested and inconsistent with relativity. From dimensional analysis, we expect quantum effects along the time axis to be of order an attosecond. Such effects are not ruled out by current experiments. But they are large enough to be detected with current technology, if sufficiently specific predictions can be made. To supply such we use path integrals. The only change required is to generalize the usual three dimensional paths to four. We predict a large variety of testable effects. The principal effects are additional dispersion in time and full equivalence of the time/energy uncertainty principle to the space/momentum one. Additional effects include interference, diffraction, and entanglement in time. The usual ultraviolet divergences do not appear: they are suppressed by a combination of dispersion in time and entanglement in time. The approach here has no free parameters; it is therefore falsifiable. As it treats time and space with complete symmetry and does not suffer from the ultraviolet divergences, it may provide a useful starting point for attacks on quantum gravity.
PyramidBox++: High Performance Detector for Finding Tiny Face

With the rapid development of deep convolutional neural network, face detection has made great progress in recent years. WIDER FACE dataset, as a main benchmark, contributes greatly to this area. A large amount of methods have been put forward where PyramidBox designs an effective data augmentation strategy (Data-anchor-sampling) and context-based module for face detector. In this report, we improve each part to further boost the performance, including Balanced-data-anchor-sampling, Dual-PyramidAnchors and Dense Context Module. Specifically, Balanced-data-anchor-sampling obtains more uniform sampling of faces with different sizes. Dual-PyramidAnchors facilitate feature learning by introducing progressive anchor loss. Dense Context Module with dense connection not only enlarges receptive filed, but also passes information efficiently. Integrating these techniques, PyramidBox++ is constructed and achieves state-of-the-art performance in hard set.
Learning Affective Correspondence between Music and Image

We introduce the problem of learning affective correspondence between audio (music) and visual data (images). For this task, a music clip and an image are considered similar (having true correspondence) if they have similar emotion content. In order to estimate this crossmodal, emotion-centric similarity, we propose a deep neural network architecture that learns to project the data from the two modalities to a common representation space, and performs a binary classification task of predicting the affective correspondence (true or false). To facilitate the current study, we construct a large scale database containing more than 3, 500 music clips and 85, 000 images with three emotion classes (positive, neutral, negative). The proposed approach achieves 61.67% accuracy for the affective correspondence prediction task on this database, outperforming two relevant and competitive baselines. We also demonstrate that our network learns modality-specific representations of emotion (without explicitly being trained with emotion labels), which are useful for emotion recognition in individual modalities.