Miracle-dz/UniFAR-FTUs huggingface dataset:
Cppless: Productive and Performant Serverless Programming in C++

The rise of serverless introduced a new class of scalable, elastic and highly available parallel workers in the cloud. Many systems and applications benefit from offloading computations and parallel tasks to dynamically allocated resources. However, the developers of C++ applications found it difficult to integrate functions due to complex deployment, lack of compatibility between client and cloud environments, and loosely typed input and output data. To enable single-source and efficient serverless acceleration in C++, we introduce Cppless, an end-to-end framework for implementing serverless functions which handles the creation, deployment, and invocation of functions. Cppless is built on top of LLVM and requires only two compiler extensions to automatically extract C++ function objects and deploy them to the cloud. We demonstrate that offloading parallel computations from a C++ application to serverless workers can provide up to 30x speedup, requiring only minor code modifications and costing less than one cent per computation.
Optoacoustic entanglement in a continuous Brillouin-active solid state system

Entanglement in hybrid quantum systems comprised of fundamentally different degrees of freedom, such as light and mechanics is of interest for a wide range of applications in quantum technologies. Here, we propose to engineer bipartite entanglement between traveling acoustic phonons in a Brillouin active solid state system and the accompanying light wave. The effect is achieved by applying optical pump pulses to state-of-the-art waveguides, exciting a Brillouin Stokes process. This pulsed approach, in a system operating in a regime orthogonal to standard optomechanical setups, allows for the generation of entangled photon-phonon pairs, resilient to thermal fluctuations. We propose an experimental platform where readout of the optoacoustics entanglement is done by the simultaneous detection of Stokes and Anti-Stokes photons in a two-pump configuration. The proposed mechanism presents an important feature in that it does not require initial preparation of the quantum ground state of the phonon mode.
Deterministic Simple $(1+\varepsilon)\Delta$-Edge-Coloring in Near-Linear Time

We study the edge-coloring problem in simple $n$-vertex $m$-edge graphs with maximum degree $\Delta$. This is one of the most classical and fundamental graph-algorithmic problems. Vizing's celebrated theorem provides $(\Delta+1)$-edge-coloring in $O(m\cdot n)$ deterministic time. This running time was improved to $O\left(m\cdot\min\left\{\Delta\cdot\log n, \sqrt{n}\right\}\right)$. It is also well-known that $3\left\lceil\frac{\Delta}{2}\right\rceil$-edge-coloring can be computed in $O(m\cdot\log\Delta)$ time deterministically. Duan et al. devised a randomized $(1+\varepsilon)\Delta$-edge-coloring algorithm with running time $O\left(m\cdot\frac{\log^6 n}{\varepsilon^2}\right)$. It was however open if there exists a deterministic near-linear time algorithm for this basic problem. We devise a simple deterministic $(1+\varepsilon)\Delta$-edge-coloring algorithm with running time $O\left(m\cdot\frac{\log n}{\varepsilon}\right)$. We also devise a randomized $(1+\varepsilon)\Delta$-edge-coloring algorithm with running time $O(m\cdot(\varepsilon^{-18}+\log(\varepsilon\cdot\Delta)))$. For $\varepsilon\geq\frac{1}{\log^{1/18}\Delta}$, this running time is $O(m\cdot\log\Delta)$.
From Market Saturation to Social Reinforcement: Understanding the Impact of Non-Linearity in Information Diffusion Models

Diffusion of information in networks is at the core of many problems in AI. Common examples include the spread of ideas and rumors as well as marketing campaigns. Typically, information diffuses at a non-linear rate, for example, if markets become saturated or if users of social networks reinforce each other's opinions. Despite these characteristics, this area has seen little research, compared to the vast amount of results for linear models, which exhibit less complex dynamics. Especially, when considering the possibility of re-infection, no fully rigorous guarantees exist so far. We address this shortcoming by studying a very general non-linear diffusion model that captures saturation as well as reinforcement. More precisely, we consider a variant of the SIS model in which vertices get infected at a rate that scales polynomially in the number of their infected neighbors, weighted by an infection coefficient $\lambda$. We give the first fully rigorous results for thresholds of $\lambda$ at which the expected survival time becomes super-polynomial. For cliques we show that when the infection rate scales sub-linearly, the threshold only shifts by a poly-logarithmic factor, compared to the standard SIS model. In contrast, super-linear scaling changes the process considerably and shifts the threshold by a polynomial term. For stars, sub-linear and super-linear scaling behave similar and both shift the threshold by a polynomial factor. Our bounds are almost tight, as they are only apart by at most a poly-logarithmic factor from the lower thresholds, at which the expected survival time is logarithmic.
Null Space Properties of Neural Networks with Applications to Image Steganography

This paper explores the null space properties of neural networks. We extend the null space definition from linear to nonlinear maps and discuss the presence of a null space in neural networks. The null space of a given neural network can tell us the part of the input data that makes no contribution to the final prediction so that we can use it to trick the neural network. This reveals an inherent weakness in neural networks that can be exploited. One application described here leads to a method of image steganography. Through experiments on image datasets such as MNIST, we show that we can use null space components to force the neural network to choose a selected hidden image class, even though the overall image can be made to look like a completely different image. We conclude by showing comparisons between what a human viewer would see, and the part of the image that the neural network is actually using to make predictions and, hence, show that what the neural network ``sees'' is completely different than what we would expect.
Sublinear expanders and their applications

In this survey we aim to give a comprehensive overview of results using sublinear expanders. The term sublinear expanders refers to a variety of definitions of expanders, which typically are defined to be graphs $G$ such that every not-too-small and not-too-large set of vertices $U$ has neighbourhood of size at least $\alpha |U|$, where $\alpha$ is a function of $n$ and $|U|$. This is in contrast with linear expanders, where $\alpha$ is typically a constant. :We will briefly describe proof ideas of some of the results mentioned here, as well as related open problems.
Separating a particle's mass from its momentum

The Quantum Cheshire Cat experiment showed that when weak measurements are performed on pre- and post-selected system, the counterintuitive result has been obtained that a neutron is measured to be in one place without its spin, and its spin is measured to be in another place without the neutron. A generalization of this effect is presented with a massive particle whose mass is measured to be in one place with no momentum, while the momentum is measured to be in another place without the mass. The new result applies to any massive particle, independent of its spin or charge. A gedanken experiment which illustrates this effect is presented using a nested pair of Mach-Zehnder interferometers, but with some of the mirrors and beam splitters moving relative to the laboratory frame. The analysis of this experiment using the counterparticle ontology of Aharonov et al. is also given.
The Lorenz system as a gradient-like system

We formulate, for continuous-time dynamical systems, a sufficient condition to be a gradient-like system, i.e. that all bounded trajectories approach stationary points and therefore that periodic orbits, chaotic attractors, etc. do not exist. This condition is based upon the existence of an auxiliary function defined over the state space of the system, in a way analogous to a Lyapunov function for the stability of an equilibrium. For polynomial systems, Lyapunov functions can be found computationally by using sum-of-squares optimisation. We demonstrate this method by finding such an auxiliary function for the Lorenz system. We are able to show that the system is gradient-like for $0\leq\rho\leq12$ when $\sigma=10$ and $\beta=8/3$, significantly extending previous results. The results are rigorously validated by a novel procedure: First, an approximate numerical solution is found using finite-precision floating-point sum-of-squares optimisation. We then prove that there exists an exact solution close to this using interval arithmetic.
Diffusive coupling facilitates and impedes noise-induced escape in interacting bistable elements

Diverse complex systems often undergo sudden changes in their states, such as epileptic seizures, climate changes, and social uprisings. Such behavior has been modeled by noise-induced escape of bistable elements, which is the escape from an attracting state driven by a fluctuation in the system's state. We consider a system of interacting bistable elements and investigate the effect of diffusive coupling among elements on the process of noise-induced escape. We focus on the influence of the coupling strength over the escape time, which is the time it takes for noise-induced escape to occur. We performed numerical simulations and observed that weak coupling reduced the mean escape time, whereas strong coupling impeded escape. We argue that, although diffusive coupling both facilitates and impedes escape, the facilitating effect is dominant when coupling is weak. For weak coupling cases, we develop an approximate theory that can predict the mean and variance of escape times. In contrast, strong coupling reduces the effective noise intensity to impede escape. Our results suggest that diffusive coupling among multistable elements contributes to regulating the rate of transitions among attracting states.
Low-loss, compact, fibre-integrated cell for quantum memories

We present a low-loss, compact, hollow core optical fibre (HCF) cell integrated with single mode fibre (SMF). The cell is designed to be filled with atomic vapour and used as a component in photonic quantum technologies, with applications in quantum memory and optical switching. We achieve a total insertion loss of 0.6(2) dB at 780 nm wavelength via graded index fibre to ensure efficient mode matching coupled with anti-reflection coatings to minimise loss at the SMF-HCF interfaces. We also present numerical modelling of these interfaces, which can be undertaken efficiently without the need for finite element simulation. We encapsulate the HCF core by coupling to the SMF inside a support capillary, enhancing durability and facilitating seamless integration into existing fibre platforms.
PoseScript: Linking 3D Human Poses and Natural Language

Natural language plays a critical role in many computer vision applications, such as image captioning, visual question answering, and cross-modal retrieval, to provide fine-grained semantic information. Unfortunately, while human pose is key to human understanding, current 3D human pose datasets lack detailed language descriptions. To address this issue, we have introduced the PoseScript dataset. This dataset pairs more than six thousand 3D human poses from AMASS with rich human-annotated descriptions of the body parts and their spatial relationships. Additionally, to increase the size of the dataset to a scale that is compatible with data-hungry learning algorithms, we have proposed an elaborate captioning process that generates automatic synthetic descriptions in natural language from given 3D keypoints. This process extracts low-level pose information, known as"posecodes", using a set of simple but generic rules on the 3D keypoints. These posecodes are then combined into higher level textual descriptions using syntactic rules. With automatic annotations, the amount of available data significantly scales up (100k), making it possible to effectively pretrain deep models for finetuning on human captions. To showcase the potential of annotated poses, we present three multi-modal learning tasks that utilize the PoseScript dataset. Firstly, we develop a pipeline that maps 3D poses and textual descriptions into a joint embedding space, allowing for cross-modal retrieval of relevant poses from large-scale datasets. Secondly, we establish a baseline for a text-conditioned model generating 3D poses. Thirdly, we present a learned process for generating pose descriptions. These applications demonstrate the versatility and usefulness of annotated poses in various tasks and pave the way for future research in the field.
Characterizing Exoplanets for Habitability

A habitable exoplanet is a world that can maintain stable liquid water on its surface. Techniques and approaches to characterizing such worlds are essential, as performing a census of Earth-like planets that may or may not have life will inform our understanding of how frequently life originates and is sustained on worlds other than our own. Observational techniques like high contrast imaging and transit spectroscopy can reveal key indicators of habitability for exoplanets. Both polarization measurements and specular reflectance from oceans (also known as"glint") can provide direct evidence for surface liquid water, while constraining surface pressure and temperature (from moderate resolution spectra) can indicate liquid water stability. Observations of variability (that indicates weather) from, as well as mapping of, exoplanets can provide indirect evidence of habitability, and measurements of water vapor or cloud profiles that indicate condensation near a surface could also provide evidence for habitability. Approaches to making the types of measurements that indicate habitability are diverse, and have different considerations for the required wavelength range, spectral resolution, maximum noise levels, stellar host temperature, and observing geometry.
BSAlign: a library for nucleotide sequence alignment

Increasing the accuracy of the nucleotide sequence alignment is an essential issue in genomics research. Although classic dynamic-programming algorithms (e.g., Smith-Waterman and Needleman–Wunsch) guarantee to produce the optimal result, their time complexity hinders the application of large-scale sequence alignment. Many optimization efforts that aim to accelerate the alignment process generally come from three perspectives: re-designing data structures (e.g., diagonal or striped Single Instruction Multiple Data (SIMD) implementations), increasing the number of parallelisms in SIMD operations (e.g., difference recurrence relation), or reducing searching space (e.g., banded dynamic programming). However, no methods combine all these three aspects to build an ultra-fast algorithm. We have developed a Banded Striped Aligner(library) named BSAlign that delivers accurate alignment results at an ultra-fast speed by knitting a series of novel methods together to take advantage of all of the aforementioned three perspectives with highlights such as active F-loop in striped vectorization and striped move in banded dynamic programming. We applied our new acceleration design on both regular and edit-distance pairwise alignment. BSAlign achieved 2-fold speed-up than other SIMD based implementations for regular pairwise alignment, and 1.5 to 4-fold speedup in edit distance based implementations for long reads. BSAlign is implemented in C programing language and is available at https://github.com/ruanjue/bsalign.
Bridging Viruses and Prokaryotic Host through Miniature Inverted-repeat Transposable Elements (MITEs)

Transposable elements (TEs) have a pivotal role in the evolution of genomes across all life domains. “Miniature Inverted-repeat Transposable-Elements” (MITEs) are non-autonomous TEs mainly located in intergenic regions, relying on external transposases for mobilization. The boundaries of MITEs’ mobilome were explored across nearly 1700 prokaryotic genera, 183232 genomes, revealing a widespread distribution. MITEs were identified in 56.5% of genomes, totaling over 1.4 million cMITEs (cellular). Cluster analysis revealed that a significant 97.4% of cMITEs were conserved within genera boundaries, with up to 23% being species-specific. Subsequently, this genus-specificity was evaluated as a tool to link microbial host to their viruses. A total of 51655 cMITEs had counterparts in viral sequences, termed vMITE (viral), resulting in the identification of 2798 viral sequences with vMITEs. Among these, 1501 sequences were positively assigned to a previously known host (41.8% were isolated virus, and 12.3% were assigned through CRISPR data), while 379 new host-virus associations were predicted. Deeper analysis in Neisseria and Bacteroidetes groups allowed the association of 242 and 530 new additional viral sequences, respectively. Given the abundance of non-culturable virus sequences accumulated in databases lacking affiliations with their microbial targets, MITEs are proposed as a novel approach to establishing valid virus-host relationships. GRAPHICAL ABSTRACT
EvoAug-TF: Extending evolution-inspired data augmentations for genomic deep learning to TensorFlow

Deep neural networks (DNNs) have been widely applied to predict the molecular functions of regulatory regions in the non-coding genome. DNNs are data hungry and thus require many training examples to fit data well. However, functional genomics experiments typically generate limited amounts of data, constrained by the activity levels of the molecular function under study inside the cell. Recently, EvoAug was introduced to train a genomic DNN with evolution-inspired augmentations. EvoAug-trained DNNs have demonstrated improved generalization and interpretability with attribution analysis. However, EvoAug only supports PyTorch-based models, which limits its applications to a broad class of genomic DNNs based in TensorFlow. Here, we extend EvoAug’s functionality to TensorFlow in a new package we call EvoAug-TF. Through a systematic benchmark, we find that EvoAug-TF yields comparable performance with the original EvoAug package. Availability EvoAug-TF is freely available for users and is distributed under an open-source MIT license. Researchers can access the open-source code on GitHub (https://github.com/p-koo/evoaug-tf). The pre-compiled package is provided via PyPI (https://pypi.org/project/evoaug-tf) with in-depth documentation on ReadTheDocs (https://evoaug-tf.readthedocs.io). The scripts for reproducing the results are available at (https://github.com/p-koo/evoaug-tf_analysis).
Early evolution of the ecdysozoan body plan

Extant ecdysozoans (moulting animals) are represented by a great variety of vermiform or articulated organisms. However, controversies remain about the nature of their ancestral body plan although the vermiform hypothesis seems to prevail. We describe here Beretella spinosa gen et sp. nov. a tiny ecdysozoan from the early Cambrian, Yanjiahe Formation, South China, with an unusual sack-like appearance, single opening, and spiny ornament. Beretella has no equivalent among animals, except Saccorhytus from the basal Cambrian. Phylogenetic analyses resolve both forms as a sister group (Saccorhytida) to all known Ecdysozoa, thus suggesting that ancestral ecdysozoans may have been non-vermiform animals. Saccorhytids are likely to represent an early dead-end off-shot along the stem-line Ecdysozoa that possibly evolved through anatomical simplification (e.g. lack of anus). Although extinct during the Cambrian, this animal lineage provides precious insight into the early evolution of Ecdysozoa and the nature (possibly non-vermiform) of the earliest representatives of the group.
Evanescent acoustic-gravity modes in the isothermal atmosphere: systematization and applications to the Earth and solar atmospheres

Abstract. The objects of research in this work are evanescent wave modes in a gravitationally stratified atmosphere and their associated pseudo-modes. Whereas the former, according to the dispersion relation, rapidly decrease with distance from a certain surface, the latter, having the same dispersion law, differ from the first by the form of polarization and the nature of decrease from the surface. Within a linear hydrodynamic model, the propagation features of evanescent wave modes in an isothermal atmosphere are studied. Research is carried out for different assumptions about the properties of the disturbances. In this way, a new wave mode – anelastic evanescent wave mode – was discovered that satisfies the dispersion relation ω2=kxgγ-1. Also, the possibility of the existence of a pseudo-mode related to it is indicated. The case of two isothermal media differing in temperature at the interface is studied in detail. It is shown that a non-divergent pseudo-mode with a horizontal scale kx∼1/2H1 can be realized on the interface with dispersion ω2=kxg. Dispersion relation ω2=kxgγ-1 at the interface of two media is satisfied by the wave mode, which has different types of amplitude versus height dependencies at different horizontal scales kx. The applicability of the obtained results to clarify the properties of the f-mode observed on the Sun is analyzed.
Planner: Cost-eﬀicient Execution Plans Placement for Uniform Stream Analytics on Edge and Cloud

—Stream processing applications handle unbounded and continuous ﬂows of data items which are generated from multiple geographically distributed sources. Two approaches are commonly used for processing: Cloud-based analytics and Edge analytics. The ﬁrst one routes the whole data set to the Cloud, incurring signiﬁcant costs and late results from the high latency networks that are traversed. The latter can give timely results but forces users to manually deﬁne which part of the computation should be executed on Edge and to interconnect it with the remaining part executed in the Cloud, leading to sub-optimal placements. In this paper, we introduce Planner, a middleware for uniform and transparent stream processing across Edge and Cloud. Planner automatically selects which parts of the execution graph will be executed at the Edge in order to minimize the network cost. Real-world micro-benchmarks show that Planner reduces the network usage by 40% and the makespan (end-to-end processing time) by 15% compared to state-of-the-art.
Purpose, place, and people: How the pandemic helped foster open and inclusive course design

In the spring of 2020, many institutions of higher education rapidly adopted new models of course delivery to support the ongoing need for instructional flexibility in response to the SARS-CoV-2 pandemic. This article discusses how the transition to a flexible instructional model at Furman University created space for faculty to consider the value of intentional learning environment design to meet students where they are both literally and figuratively. Reporting on a course planning framework focused on the alignment of learning outcomes, learning space, and considerations of student engagement and accessibility, this article reviews data from a faculty survey on pedagogical adaptations to highlight how the pandemic provided an opportunity to expand notions of educational architecture in course design efforts by reimagining how, where, and through which mediums teaching and learning occur. The results presented underscore the need for sustained attention to open educational architecture as a part of course and curriculum development beyond the period of pandemic teaching and reveal important considerations for educational developers working to support inclusive pedagogy in the 21st century.