Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of temperature on development and survival of burnt pine longhorn Arhopalus ferus (Mulsant) (Coleoptera: Cerambycidae) eggs

ABSTRACT The burnt pine longhorn beetle, Arhopalus ferus (Mulsant) (Cerambycidae: Coleoptera), is an introduced species in New Zealand that can occur as a hitch-hiker in association with export logs and sawn timber. Its life cycle is completed in decaying pine. We determined the effect of 14 constant temperatures from 3 to 41 °C on the development and survival of burnt pine longhorn eggs. The lower temperature limit for development was estimated to be 10.4 °C, 6.3 °C and 7.6 °C by a linear model and two non-linear models, respectively. The latter two estimates may be more accurate than the former as the development curve deviates from linearity at low temperatures. The optimum temperature for development, when development rate is at a maximum, was estimated by the non-linear models as 33.8 °C and 34.7 °C. Eggs hatched within the range 10–38 °C with duration ranging from a maximum of 92 days at 10 °C to a minimum of 4.3 days at 35 °C. Survival exceeded 90% in the range 13–32 °C and declined to zero at 7 °C and 41 °C.
End-to-end Music-mixed Speech Recognition

Automatic speech recognition (ASR) in multimedia content is one of the promising applications, but speech data in this kind of content are frequently mixed with background music, which is harmful for the performance of ASR. In this study, we propose a method for improving ASR with background music based on time-domain source separation. We utilize Conv-TasNet as a separation network, which has achieved state-of-the-art performance for multi-speaker source separation, to extract the speech signal from a speech-music mixture in the waveform domain. We also propose joint fine-tuning of a pre-trained Conv-TasNet front-end with an attention-based ASR back-end using both separation and ASR objectives. We evaluated our method through ASR experiments using speech data mixed with background music from a wide variety of Japanese animations. We show that time-domain speech-music separation drastically improves ASR performance of the back-end model trained with mixture data, and the joint optimization yielded a further significant WER reduction. The time-domain separation method outperformed a frequency-domain separation method, which reuses the phase information of the input mixture signal, both in simple cascading and joint training settings. We also demonstrate that our method works robustly for music interference from classical, jazz and popular genres.
Swirling Instability of the Microtubule Cytoskeleton

In the cellular phenomena of cytoplasmic streaming, molecular motors carrying cargo along a network of microtubules entrain the surrounding fluid. The piconewton forces produced by individual motors are sufficient to deform long microtubules, as are the collective fluid flows generated by many moving motors. Studies of streaming during oocyte development in the fruit fly D. melanogaster have shown a transition from a spatially-disordered cytoskeleton, supporting flows with only short-ranged correlations, to an ordered state with a cell-spanning vortical flow. To test the hypothesis that this transition is driven by fluid-structure interactions we study a discrete-filament model and a coarse-grained continuum theory for motors moving on a deformable cytoskeleton, both of which are shown to exhibit a swirling instability to spontaneous large-scale rotational motion, as observed.
Generalization of the Circular Dichroism from Metallic Arrays That Support Bloch-Like Surface Plasmon Polaritons

The broken mirror symmetry in subwavelength photonic systems has manifested many interesting chiroptical effects such as optical rotation and circular dichroism. When such systems are placed periodically in a lattice form, in addition to intrinsic chirality, extrinsic chirality also takes part, and the overall effect depends not only on the basis and lattice but also the excitation configuration. Here, we study planar chiral nanohole arrays in square lattice that support Bloch-like surface plasmon polaritons (SPPs) and clarify how the system geometry and the excitation contribute to circular dichroism. By using temporal coupled mode theory (CMT), the dissymmetry factor and the scattering matrix of the arrays are analytically formulated. Remarkably, we find the dissymmetry factor depends only on the coupling polarization angle and the in-coupling phase difference between the p- and s-polarizations. Besides, the upper limit of the dissymmetry factor at +/-2 can be reached simply by orienting the lattice of the arrays for properly exciting the Bloch-like SPPs and at the same time making the basis mimic two orthogonal and relatively displaced dipoles, demonstrating the interplay between extrinsic and intrinsic chirality. The models have been verified by numerical simulations and experiments, yielding the dissymmetry factors to be 1.82 and 1.55, respectively, from the proposed dual slot system.
CO observations towards H i-rich Ultradiffuse Galaxies

We present CO observations towards a sample of six H i-rich Ultradiffuse galaxies (UDGs) as well as one UDG (VLSB-A) in the Virgo Cluster with the Institut de RadioAstronomie Millimétrique (IRAM) 30-m telescope. CO J = 1–0 is marginally detected at 4σ level in AGC 122966, as the first detection of CO emission in UDGs. We estimate upper limits of molecular mass in other galaxies from the non-detection of CO lines. These upper limits and the marginal CO detection in AGC 122966 indicate low mass ratios between molecular and atomic gas masses. With the star formation efficiency derived from the molecular gas, we suggest that the inefficiency of star formation in such H i-rich UDGs is likely caused by the low efficiency in converting molecules from atomic gas, instead of low efficiency in forming stars from molecular gas.
Approximate Message Passing algorithms for rotationally invariant matrices

Approximate Message Passing (AMP) algorithms have seen widespread use across a variety of applications. However, the precise forms for their Onsager corrections and state evolutions depend on properties of the underlying random matrix ensemble, limiting the extent to which AMP algorithms derived for white noise may be applicable to data matrices that arise in practice. In this work, we study more general AMP algorithms for random matrices $W$ that satisfy orthogonal rotational invariance in law, where $W$ may have a spectral distribution that is different from the semicircle and Marcenko-Pastur laws characteristic of white noise. The Onsager corrections and state evolutions in these algorithms are defined by the free cumulants or rectangular free cumulants of the spectral distribution of $W$. Their forms were derived previously by Opper, Cakmak, and Winther using non-rigorous dynamic functional theory techniques, and we provide rigorous proofs. Our motivating application is a Bayes-AMP algorithm for Principal Components Analysis, when there is prior structure for the principal components (PCs) and possibly non-white noise. For sufficiently large signal strengths and any non-Gaussian prior distributions for the PCs, we show that this algorithm provably achieves higher estimation accuracy than the sample PCs.
Scaled minimax optimality in high-dimensional linear regression: A non-convex algorithmic regularization approach.

The question of fast convergence in the classical problem of high dimensional linear regression has been extensively studied. Arguably, one of the fastest procedures in practice is Iterative Hard Thresholding (IHT). Still, IHT relies strongly on the knowledge of the true sparsity parameter $s$. In this paper, we present a novel fast procedure for estimation in the high dimensional linear regression. Taking advantage of the interplay between estimation, support recovery and optimization we achieve both optimal statistical accuracy and fast convergence. The main advantage of our procedure is that it is fully adaptive, making it more practical than state of the art IHT methods. Our procedure achieves optimal statistical accuracy faster than, for instance, classical algorithms for the Lasso. Moreover, we establish sharp optimal results for both estimation and support recovery. As a consequence, we present a new iterative hard thresholding algorithm for high dimensional linear regression that is scaled minimax optimal (achieves the estimation error of the oracle that knows the sparsity pattern if possible), fast and adaptive.
A Degree Sequence Strengthening of the Vertex Degree Threshold for a Perfect Matching in 3-Uniform Hypergraphs

The study of asymptotic minimum degree thresholds that force matchings and tilings in hypergraphs is a lively area of research in combinatorics. A key breakthrough in this area was a result of H\`{a}n, Person and Schacht who proved that the asymptotic minimum vertex degree threshold for a perfect matching in an $n$-vertex $3$-graph is $\left(\frac{5}{9}+o(1)\right)\binom{n}{2}$. In this paper we improve on this result, giving a family of degree sequence results, all of which imply the result of H\`{a}n, Person and Schacht, and additionally allow one third of the vertices to have degree $\frac{1}{9}\binom{n}{2}$ below this threshold. Furthermore, we show that this result is, in some sense, tight.
Inner Eye Canthus Localization for Human Body Temperature Screening

In this paper, we propose an automatic approach for localizing the inner eye canthus in thermal face images. We first coarsely detect 5 facial keypoints corresponding to the center of the eyes, the nosetip and the ears. Then we compute a sparse 2D-3D points correspondence using a 3D Morphable Face Model (3DMM). This correspondence is used to project the entire 3D face onto the image, and subsequently locate the inner eye canthus. Detecting this location allows to obtain the most precise body temperature measurement for a person using a thermal camera. We evaluated the approach on a thermal face dataset provided with manually annotated landmarks. However, such manual annotations are normally conceived to identify facial parts such as eyes, nose and mouth, and are not specifically tailored for localizing the eye canthus region. As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images. Then, by manually selecting a small region corresponding to the eye canthus, we enrich the dataset with additional annotations. By using the manual landmarks, we ensure the correctness of the 3DMM projection, which can be used as ground-truth for future evaluations. Moreover, we supply the dataset with the 3D head poses and per-point visibility masks for detecting self-occlusions. The data is publicly available at https://www.micc. unifi.it/resources/datasets/thermal-face/.
Moderately supervised learning: definition and framework

Supervised learning (SL) has achieved remarkable success in numerous artificial intelligence applications. In the current literature, by referring to the properties of the ground-truth labels prepared for a training data set, SL is roughly categorized as fully supervised learning (FSL) and weakly supervised learning (WSL). However, solutions for various FSL tasks have shown that the given ground-truth labels are not always learnable, and the target transformation from the given ground-truth labels to learnable targets can significantly affect the performance of the final FSL solutions. Without considering the properties of the target transformation from the given ground-truth labels to learnable targets, the roughness of the FSL category conceals some details that can be critical to building the optimal solutions for some specific FSL tasks. Thus, it is desirable to reveal these details. This article attempts to achieve this goal by expanding the categorization of FSL and investigating the subtype that plays the central role in FSL. Taking into consideration the properties of the target transformation from the given ground-truth labels to learnable targets, we first categorize FSL into three narrower subtypes. Then, we focus on the subtype moderately supervised learning (MSL). MSL concerns the situation where the given ground-truth labels are ideal, but due to the simplicity in annotation of the given ground-truth labels, careful designs are required to transform the given ground-truth labels into learnable targets. From the perspectives of definition and framework, we comprehensively illustrate MSL to reveal what details are concealed by the roughness of the FSL category. Finally, discussions on the revealed details suggest that MSL should be given more attention.
Multi-wavelength photometry during the 2018 superoutburst of the WZ Sge-type dwarf nova EG Cancri

We report on the multi-wavelength photometry of the 2018 superoutburst in EG Cnc. We have detected stage A superhumps and long-lasting late-stage superhumps via the optical photometry and have constrained the binary mass ratio and its possible range. The median value of the mass ratio is 0.048 and the upper limit is 0.057, which still implies that EG Cnc is one of the possible candidates for period bouncers. This object also showed multiple rebrightenings in this superoutburst which are the same as those in its previous superoutburst in 1996–1997, despite the difference in the main superoutburst. This would represent that the rebrightening type is inherent to each object and is independent of the initial disk mass at the beginning of superoutbursts. We also found that B − I and J − Ks colors were unusually red just before the rebrightening phase and became bluer during the quiescence between rebrightenings, which would mean that the low-temperature mass reservoir at the outermost disk accreted with time after the main superoutburst. Also, the ultraviolet flux was sensitive to rebrightenings as well as the optical flux, and the U − B color became redder during the rebrightening phase, which would indicate that the inner disk became cooler when this object repeated rebrightenings. Our results thus basically support the idea that the cool mass reservoir in the outermost disk is responsible for rebrightenings.
On the SAV‐DG method for a class of fourth order gradient flows

For a class of fourth order gradient flow problems, integration of the scalar auxiliary variable (SAV) time discretization with the penalty‐free discontinuous Galerkin (DG) spatial discretization leads to SAV‐DG schemes. These schemes are linear and shown unconditionally energy stable. However, the reduced linear systems are rather expensive to solve due to the dense coefficient matrices. In this paper, we provide a procedure to pre‐evaluate the auxiliary variable in the piecewise polynomial space. As a result the computational complexity of ON2$$ O\left({\mathcal{N}}^2\right) $$ reduces to O(N)$$ O\left(\mathcal{N}\right) $$ when exploiting the conjugate gradient (CG) solver. This hybrid SAV‐DG method is more efficient and able to deliver satisfactory results of high accuracy. This was also compared with solving the full augmented system of the SAV‐DG schemes.
Network satisfaction for symmetric relation algebras with a flexible atom

Robin Hirsch posed in 1996 the Really Big Complexity Problem: classify the computational complexity of the network satisfaction problem for all finite relation algebras A. We provide a complete classification for the case that A is symmetric and has a flexible atom; the problem is in this case NP-complete or in P. If a finite integral relation algebra has a flexible atom, then it has a normal representation B. We can then study the computational complexity of the network satisfaction problem of A using the universal-algebraic approach, via an analysis of the polymorphisms of B. We also use a Ramsey-type result of Nešetřil and Rödl and a complexity dichotomy result of Bulatov for conservative finite-domain constraint satisfaction problems.
A Spatial analysis of disposable income in Ireland: A GWR approach

This paper examines the spatial distribution of income in Ireland. Median gross household disposable income data from the CSO, available at the Electoral Division (ED) level, is used to explore the spatial variability in income. Geary's C highlights the spatial dependence of income, highlighting that the distribution of income is not random across space and is influenced by location. Given the presence of spatial autocorrelation, utilising a global OLS regression will lead to biased results. Geographically Weighted Regression (GWR) is used to examine the spatial heterogeneity of income and the impact of local demographic drivers on income. GWR results show the demographic drivers have varying levels of influence on income across locations. Lone parent has a stronger negative impact in the Cork commuter belt than it does in the Dublin commuter belt. The relationship between household income and the demographic context of the area is a complicated one. This paper attempts to examine these relationships acknowledging the impact of space.
Deep Inverse Reinforcement Learning for Structural Evolution of Small Molecules

The size and quality of chemical libraries to the drug discovery pipeline are crucial for developing new drugs or repurposing existing drugs. Existing techniques such as combinatorial organic synthesis and high-throughput screening usually make the process extraordinarily tough and complicated since the search space of synthetically feasible drugs is exorbitantly huge. While reinforcement learning has been mostly exploited in the literature for generating novel compounds, the requirement of designing a reward function that succinctly represents the learning objective could prove daunting in certain complex domains. Generative adversarial network-based methods also mostly discard the discriminator after training and could be hard to train. In this study, we propose a framework for training a compound generator and learn a transferable reward function based on the entropy maximization inverse reinforcement learning (IRL) paradigm. We show from our experiments that the IRL route offers a rational alternative for generating chemical compounds in domains where reward function engineering may be less appealing or impossible while data exhibiting the desired objective is readily available.
Automatic Speech Summarisation: A Scoping Review

Speech summarisation techniques take human speech as input and then output an abridged version as text or speech. Speech summarisation has applications in many domains from information technology to health care, for example improving speech archives or reducing clinical documentation burden. This scoping review maps the speech summarisation literature, with no restrictions on time frame, language summarised, research method, or paper type. We reviewed a total of 110 papers out of a set of 153 found through a literature search and extracted speech features used, methods, scope, and training corpora. Most studies employ one of four speech summarisation architectures: (1) Sentence extraction and compaction; (2) Feature extraction and classification or rank-based sentence selection; (3) Sentence compression and compression summarisation; and (4) Language modelling. We also discuss the strengths and weaknesses of these different methods and speech features. Overall, supervised methods (e.g. Hidden Markov support vector machines, Ranking support vector machines, Conditional random fields) performed better than unsupervised methods. As supervised methods require manually annotated training data which can be costly, there was more interest in unsupervised methods. Recent research into unsupervised methods focusses on extending language modelling, for example by combining Uni-gram modelling with deep neural networks. Protocol registration: The protocol for this scoping review is registered at this https URL.
Robustness Hidden in Plain Sight: Can Analog Computing Defend Against Adversarial Attacks?

The ever-increasing computational demand of Deep Learning has propelled research in special-purpose inference accelerators based on emerging non-volatile memory (NVM) technologies. Such NVM crossbars promise fast and energy-efficient in-situ matrix vector multiplications (MVM) thus alleviating the long-standing von Neuman bottleneck in today's digital hardware. However the analog nature of computing in these NVM crossbars introduces approximations in the MVM operations. In this paper, we study the impact of these non-idealities on the performance of DNNs under adversarial attacks. The non-ideal behavior interferes with the computation of the exact gradient of the model, which is required for adversarial image generation. In a non-adaptive attack, where the attacker is unaware of the analog hardware, we show that analog computing offers a varying degree of intrinsic robustness, with a peak adversarial accuracy improvement of 35.34%, 22.69%, and 31.70% for white box PGD ($\epsilon$=1/255, iter=30) for CIFAR-10, CIFAR-100, and ImageNet(top-5) respectively. We also demonstrate "hardware-in-loop" adaptive attacks that circumvent this robustness by utilizing the knowledge of the NVM model. To the best of our knowledge, this is the first work that explores the non-idealities of analog computing for adversarial robustness at the time of submission to NeurIPS 2020.
Braking indices of young radio pulsars: theoretical perspective

Recently, Parthsarathy et al. analysed long-term timing observations of 85 young radio pulsars. They found that 11 objects have braking indices ranging $\sim 10-100$, far from the classical value $n=3$. They also noted a mild correlation between measured value of $n$ and characteristic age of a radio pulsar. In this article we systematically analyse possible physical origin of large braking indices. We find that a small fraction of these measurements could be caused by gravitational acceleration from an unseen ultra-wide companion of a pulsar or by precession. Remaining braking indices cannot be explained neither by pulsar obliquity angle evolution, nor by complex high-order multipole structure of the poloidal magnetic field. The most plausible explanation is a decay of the poloidal dipole magnetic field which operates on a time scale $\sim 10^4-10^5$ years in some young objects, but has significantly longer time scale in other radio pulsars. This decay can explain both amplitude of measured $n$ and some correlation between $n$ and characteristic age. The decay can be caused by either enhanced crystal impurities in the crust of some isolated radio pulsars, or more likely, by enhanced resistivity related to electron scattering off phonons due to slow cooling of low-mass neutron stars. If this effect is indeed the main cause of the rapid magnetic field decay manifesting as large braking indices, we predict that pulsars with large braking indices are hotter in comparison to those with $n\approx 3$.