Miracle-dz/UniFAR-FTUs huggingface dataset:
Humor in library instruction: a narrative review with implications for the health sciences

Objective The review sought to gain a better understanding of humor’s use and impact as a teaching and learning strategy in academic library and health sciences instruction and to determine if the most common techniques across both disciplines can be adapted to increase engagement in medical libraries’ information literacy efforts. Methods This narrative review involved retrieving citations from several subject databases, including Library, Information Science & Technology Abstracts; Information Science & Technology Abstracts; Library & Information Science Source; PubMed; and CINAHL. The author limited her review to those publications that explicitly addressed the use of humor in relation to some form of academic library or health sciences instruction. Studies examining use of humor in patient education were excluded. Results Scholars and practitioners have consistently written about humor as an instructional strategy from the 1980s onward, in both the library literature and health sciences literature. These authors have focused on instructors’ attitudes, benefits to students, anecdotes, and best practices summaries. Overall, both librarians and health sciences educators have a positive opinion of humor, and many instructors make use of it in their classrooms, though caution and careful planning is advised. Conclusions Commonalities between the library and information science literature and health sciences literature provide a cohesive set of best practices and strategies for successfully incorporating comedy into library instruction sessions. Health sciences librarians can adapt several of the most commonly used types of instructional humor (e.g., silly examples, cartoons, storytelling, etc.) to their own contexts with minimal risk.
Deep Joint Source-Channel Coding for Wireless Image Transmission

We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions by two convolutional neural networks (CNNs), which are trained jointly, and can be considered as an autoencoder with a non-trainable layer in the middle that represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms digital transmission concatenating JPEG or JPEG2000 compression with a capacity achieving channel code at low signal-to-noise ratio (SNR) and channel bandwidth values in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the “cliff effect,” and it provides a graceful performance degradation as the channel SNR varies with respect to the SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC learns noise resilient coded representations and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values.
Accurate Retinal Vessel Segmentation via Octave Convolution Neural Network

Retinal vessel segmentation is a crucial step in diagnosing and screening various diseases, including diabetes, ophthalmologic diseases, and cardiovascular diseases. In this paper, we propose an effective and efficient method for vessel segmentation in color fundus images using encoder-decoder based octave convolution network. Compared with other convolution networks utilizing vanilla convolution for feature extraction, the proposed method adopts octave convolution for learning multiple-spatial-frequency features, thus can better capture retinal vasculatures with varying sizes and shapes. It is demonstrated that the feature maps of low-frequency kernels respond mainly to the major vascular tree, whereas the high-frequency feature maps can better capture the fine details of thin vessels. To provide the network the capability of learning how to decode multifrequency features, we extend octave convolution and propose a new operation named octave transposed convolution. A novel architecture of convolutional neural network is proposed based on the encoder-decoder architecture of UNet, which can generate high resolution vessel segmentation in one single forward feeding. The proposed method is evaluated on four publicly available datasets, including DRIVE, STARE, CHASE_DB1, and HRF. Extensive experimental results demonstrate that the proposed approach achieves better or comparable performance to the state-of-the-art methods with fast processing speed.
Genus-zero and genus-one string amplitudes and special multiple zeta values

In this paper we show that in perturbative string theory the genus-one contribution to formal 2-point amplitudes can be related to the genus-zero contribution to 4-point amplitudes. This is achieved by studying special linear combinations of multiple zeta values that appear as coefficients of the amplitudes. We also exploit our results to relate closed strings to open strings at genus one using Brown's single-valued projection, proving a conjecture of Broedel, Schlotterer and the second author.
Hierarchical domain model explains multifractal scaling of chromosome contact maps

Modern experimental techniques such as Hi-C make it possible to measure the probability that different chromosomal regions are close in space. Usually, these measurements are characterized by the scaling of the contact probability as a function of the genomic distance between regions. In this work, we introduce a multifractal analysis of chromosomal contact maps. Our analysis shows that Hi-C maps display a non-trivial multifractal spectrum. We introduce a simple analytical model that describes the structure of chromosomes as a hierarchical set of domains nested in each other and we solve it exactly. The predicted multifractal spectrum is characterized by a phase transition between two phases with different fractal dimension, in excellent agreement with experimental data. These results support the view that there is no privileged level in the hierarchy of conformational domains. Within the same model, the scaling exponent of the contact probability can also be calculated. We argue that such procedure leads to a much more precise estimate of the contact probability exponent than previous approaches. By applying this method to experimental data, we demonstrate that it can capture subtle conformational differences among chromosomes that are robust in different realizations of the same experiment.
Search for gravitational waves from Scorpius X-1 in the second Advanced LIGO observing run with an improved hidden Markov model

We present results from a semicoherent search for continuous gravitational waves from the low-mass X-ray binary Scorpius X-1, using a hidden Markov model (HMM) to track spin wandering. This search improves on previous HMM-based searches of LIGO data by using an improved frequency domain matched filter, the $\mathcal{J}$-statistic, and by analysing data from Advanced LIGO's second observing run. In the frequency range searched, from $60$ to $650\,\mathrm{Hz}$, we find no evidence of gravitational radiation. At $194.6\,\mathrm{Hz}$, the most sensitive search frequency, we report an upper limit on gravitational wave strain (at 95\% confidence) of $h_0^{95\%} = 3.47 \times 10^{-25}$ when marginalising over source inclination angle. This is the most sensitive search for Scorpius X-1, to date, that is specifically designed to be robust in the presence of spin wandering.
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

Self-supervision provides effective representations for downstream tasks without requiring labels. However, existing approaches lag behind fully supervised training and are often not thought beneficial beyond obviating the need for annotations. We find that self-supervision can benefit robustness in a variety of ways, including robustness to adversarial examples, label corruption, and common input corruptions. Additionally, self-supervision greatly benefits out-of-distribution detection on difficult, near-distribution outliers, so much so that it exceeds the performance of fully supervised methods. These results demonstrate the promise of self-supervision for improving robustness and uncertainty estimation and establish these tasks as new axes of evaluation for future self-supervised learning research.
The Warm Gaseous Disk and the Anisotropic Circumgalactic Medium of the Milky Way

Warm ( ) gas is an important gaseous component in the galaxy baryonic cycle, which is important for understanding both gas accretion and galactic feedback processes. We built a two-dimensional disk–circumgalactic medium (CGM) model to study the warm gas distribution of the Milky Way (MW) using the absorption-line surveys of Si iv and O vi. In this model, the disk component of both ions has the same density profile ( ) with a scale height of z0 = 2.6 ± 0.4 kpc and a scale length of r0 = 6.1 ± 1.2 kpc. For this disk component, we calculate the warm gas mass of . The similar disk density profiles and total masses of the Si iv- and O vi-bearing gas set constraints on the ionization mechanisms. We suggest that the warm gas disk might be dominated by the Galactic fountain mechanism, which ejects and recycles gas to set both the scale height and the scale length of the warm gas disk. The CGM component in our model has a dependence on Galactic latitude, with a higher column density along the direction perpendicular to the Galactic plane (b = 90°) than along the radial direction (b = 0°). The column density difference between these two directions is 0.82 ± 0.32 dex at 6. 3σ for both ions. This difference may be due to either the enrichment of Galactic feedback to the entire CGM or an additional interaction layer between the warm gas disk and the CGM; existing data cannot distinguish between these two scenarios. If this higher column density at b = 90° is for the entire CGM, the total warm CGM mass is within the MW virial radius of 250 kpc.
Search for spectral features in extragalactic background light with gamma-ray telescopes

Context. Cumulative optical and infrared emission from galaxies accumulated over cosmological time scales, the extragalactic background light (EBL), could be probed by complementary techniques of direct observations and source counting in the visible and infrared as well as via its imprint on the signal of distant active galactic nuclei in γ-rays. Aims. We compare the visible and infrared measurements with the γ-ray constraints and study if the discrepancies of the measurements with different methods could be due to the presence of features in the EBL spectrum that are localised in the micron wavelength range. Methods. We combined data on time-averaged spectra of selected blazars that were obtained by Fermi and ground-based gamma-ray telescopes. We also modelled the effect of absorption on EBL while allowing for the existence of a previously unaccounted spectral feature. Results. We show that a previously reported “excess” in EBL flux in the ∼1 micron wavelength range is consistent with γ-ray measurements, that is, if the excess has the form of a narrow feature of the width δλ < λ and an overall flux of up to 15 nW m−2 sr−1 above the “minimal” EBL, which is estimated from the visible and infrared source counts. Such ’bump-like’ spectral features could originate, for example, from decaying dark-matter particles, or either axions or peculiar astrophysical processes in the course of star-formation history. We discuss the possibilities for the search of spectral features in the EBL with the Cherenkov Telescope Array.
Size of data in implicit function problems and singular perturbations for nonlinear Schrödinger systems

We investigate a general question about the size and regularity of the data and the solutions in implicit function problems with loss of regularity. First, we give a heuristic explanation of the fact that the optimal data size found by Ekeland and Sere with their recent non-quadratic version of the Nash-Moser theorem can also be recovered, for a large class of nonlinear problems, with quadratic schemes. Then we prove that this heuristic observation applies to the singular perturbation Cauchy problem for the nonlinear Schrodinger system studied by Metivier, Rauch, Texier, Zumbrun, Ekeland, Sere. Using a "free flow component" decomposition and applying an abstract Nash-Moser-Hormander theorem, we improve the existing results regarding both the size of the data and the regularity of the solutions.
Harnessing Fluctuations in Thermodynamic Computing via Time-Reversal Symmetries

Author(s): Wimsatt, Gregory; Saira, Olli-Pentti; Boyd, Alexander B; Matheny, Matthew H; Han, Siyuan; Roukes, Michael L; Crutchfield, James P | Abstract: We experimentally demonstrate that highly structured distributions of work emerge during even the simple task of erasing a single bit. These are signatures of a refined suite of time-reversal symmetries in distinct functional classes of microscopic trajectories. As a consequence, we introduce a broad family of conditional fluctuation theorems that the component work distributions must satisfy. Since they identify entropy production, the component work distributions encode both the frequency of various mechanisms of success and failure during computing, as well giving improved estimates of the total irreversibly-dissipated heat. This new diagnostic tool provides strong evidence that thermodynamic computing at the nanoscale can be constructively harnessed. We experimentally verify this functional decomposition and the new class of fluctuation theorems by measuring transitions between flux states in a superconducting circuit.
Green’s Function Estimates for Time-Fractional Evolution Equations

We look at estimates for the Green’s function of time-fractional evolution equations of the form D 0 + * ν u = L u , where D 0 + * ν is a Caputo-type time-fractional derivative, depending on a Lévy kernel ν with variable coefficients, which is comparable to y - 1 - β for β ∈ ( 0 , 1 ) , and L is an operator acting on the spatial variable. First, we obtain global two-sided estimates for the Green’s function of D 0 β u = L u in the case that L is a second order elliptic operator in divergence form. Secondly, we obtain global upper bounds for the Green’s function of D 0 β u = Ψ ( - i ∇ ) u where Ψ is a pseudo-differential operator with constant coefficients that is homogeneous of order α . Thirdly, we obtain local two-sided estimates for the Green’s function of D 0 β u = L u where L is a more general non-degenerate second order elliptic operator. Finally we look at the case of stable-like operator, extending the second result from a constant coefficient to variable coefficients. In each case, we also estimate the spatial derivatives of the Green’s functions. To obtain these bounds we use a particular form of the Mittag-Leffler functions, which allow us to use directly known estimates for the Green’s functions associated with L and Ψ , as well as estimates for stable densities. These estimates then allow us to estimate the solutions to a wide class of problems of the form D 0 ( ν , t ) u = L u , where D ( ν , t ) is a Caputo-type operator with variable coefficients.
PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors

We present PUFFINN, a parameterless LSH-based index for solving the $k$-nearest neighbor problem with probabilistic guarantees. By parameterless we mean that the user is only required to specify the amount of memory the index is supposed to use and the result quality that should be achieved. The index combines several heuristic ideas known in the literature. By small adaptions to the query algorithm, we make heuristics rigorous. We perform experiments on real-world and synthetic inputs to evaluate implementation choices and show that the implementation satisfies the quality guarantees while being competitive with other state-of-the-art approaches to nearest neighbor search. We describe a novel synthetic data set that is difficult to solve for almost all existing nearest neighbor search approaches, and for which PUFFINN significantly outperform previous methods.
Certifiable Robustness and Robust Training for Graph Convolutional Networks

Recent works show that Graph Neural Networks (GNNs) are highly non-robust with respect to adversarial attacks on both the graph structure and the node attributes, making their outcomes unreliable. We propose the first method for certifiable (non-)robustness of graph convolutional networks with respect to perturbations of the node attributes. We consider the case of binary node attributes (e.g. bag-of-words) and perturbations that are L_0-bounded. If a node has been certified with our method, it is guaranteed to be robust under any possible perturbation given the attack model. Likewise, we can certify non-robustness. Finally, we propose a robust semi-supervised training procedure that treats the labeled and unlabeled nodes jointly. As shown in our experimental evaluation, our method significantly improves the robustness of the GNN with only minimal effect on the predictive accuracy.
The origin of spin in binary black holes

Context. After years of scientific progress, the origin of stellar binary black holes is still a great mystery. Several formation channels for merging black holes have been proposed in the literature. As more merger detections are expected with future gravitational-wave observations, population synthesis studies can help to distinguish between them. Aims. We study the formation of coalescing binary black holes via the evolution of isolated field binaries that go through the common envelope phase in order to obtain the combined distributions of observables such as black-hole spins, masses and cosmological redshifts of mergers. Methods. To achieve this aim, we used a hybrid technique that combines the parametric binary population synthesis code COMPAS with detailed binary evolution simulations performed with the MESA code. We then convolved our binary evolution calculations with the redshift- and metallicity-dependent star-formation rate and the selection effects of gravitational-wave detectors to obtain predictions of observable properties. Results. By assuming efficient angular momentum transport, we are able to present a model that is capable of simultaneously predicting the following three main gravitational-wave observables: the effective inspiral spin parameter χeff, the chirp mass Mchirp and the cosmological redshift of merger zmerger. We find an excellent agreement between our model and the ten events from the first two advanced detector observing runs. We make predictions for the third observing run O3 and for Advanced LIGO design sensitivity. We expect approximately 80% of events with χeff < 0.1, while the remaining 20% of events with χeff ≥ 0.1 are split into ∼10% with Mchirp < 15 M⊙ and ∼10% with Mchirp ≥ 15 M⊙. Moreover, we find that Mchirp and χeff distributions are very weakly dependent on the detector sensitivity. Conclusions. The favorable comparison of the existing LIGO/Virgo observations with our model predictions gives support to the idea that the majority, if not all of the observed mergers, originate from the evolution of isolated binaries. The first-born black hole has negligible spin because it lost its envelope after it expanded to become a giant star, while the spin of the second-born black hole is determined by the tidal spin up of its naked helium star progenitor by the first-born black hole companion after the binary finished the common-envelope phase.
A Bayesian phylogenetic hidden Markov model for B cell receptor sequence analysis

The human body generates a diverse set of high affinity antibodies, the soluble form of B cell receptors (BCRs), that bind to and neutralize invading pathogens. The natural development of BCRs must be understood in order to design vaccines for highly mutable pathogens such as influenza and HIV. BCR diversity is induced by naturally occurring combinatorial “V(D)J” rearrangement, mutation, and selection processes. Most current methods for BCR sequence analysis focus on separately modeling the above processes. Statistical phylogenetic methods are often used to model the mutational dynamics of BCR sequence data, but these techniques do not consider all the complexities associated with B cell diversification such as the V(D)J rearrangement process. In particular, standard phylogenetic approaches assume the DNA bases of the progenitor (or “naive”) sequence arise independently and according to the same distribution, ignoring the complexities of V(D)J rearrangement. In this paper, we introduce a novel approach to Bayesian phylogenetic inference for BCR sequences that is based on a phylogenetic hidden Markov model (phylo-HMM). This technique not only integrates a naive rearrangement model with a phylogenetic model for BCR sequence evolution but also naturally accounts for uncertainty in all unobserved variables, including the phylogenetic tree, via posterior distribution sampling.
An Image Source Method Framework for Arbitrary Reflecting Boundaries.

We propose a theoretical framework for the image source method that generalizes to arbitrary reflecting boundaries, e.g. boundaries that are curved or even with certain openings. Furthermore, it can seamlessly incorporate boundary absorption, source directivity, and nonspecular reflections. This framework is based on the notion of reflection paths that allows the introduction of the concepts of validity and visibility of virtual sources. These definitions facilitate the determination, for a given source and receiver location, of the distribution of virtual sources that explain the boundary effects of a wide range of reflecting surfaces. The structure of the set of virtual sources is then more general than just punctual virtual sources. Due to this more diverse configuration of image sources, we represent the room impulse response as an integral involving the temporal excitation signal against a measure determined by the source and receiver locations, and the original boundary. The latter smoothly enables, in an analytically tractable manner, the incorporation of more general boundary shapes as well as directivity of sources and boundary absorption while, at the same time, maintaining the conceptual benefits of the image source method.
New pointwise convolution in Deep Neural Networks through Extremely Fast and Non Parametric Transforms

Some conventional transforms such as Discrete Walsh-Hadamard Transform (DWHT) and Discrete Cosine Transform (DCT) have been widely used as feature extractors in image processing but rarely applied in neural networks. However, we found that these conventional transforms have the ability to capture the cross-channel correlations without any learnable parameters in DNNs. This paper firstly proposes to apply conventional transforms to pointwise convolution, showing that such transforms significantly reduce the computational complexity of neural networks without accuracy performance degradation. Especially for DWHT, it requires no floating point multiplications but only additions and subtractions, which can considerably reduce computation overheads. In addition, its fast algorithm further reduces complexity of floating point addition from $\mathcal{O}(n^2)$ to $\mathcal{O}(n\log n)$. These nice properties construct extremely efficient networks in the number parameters and operations, enjoying accuracy gain. Our proposed DWHT-based model gained 1.49\% accuracy increase with 79.1\% reduced parameters and 48.4\% reduced FLOPs compared with its baseline model (MoblieNet-V1) on the CIFAR 100 dataset.