Miracle-dz/UniFAR-FTUs huggingface dataset:
Early-onset scoliosis

Purpose of review The aim of this review is to provide the reader with the most updated available information so that it can be helpful in the approach of patients with early-onset scoliosis (EOS). Recent findings While confirming the efficacy and safety of classic techniques for the treatment of EOS such as traditional growing rods or Mehta casting, recent research suggests that there is room for improvement with less invasive techniques. Summary The most important goal when treating patients with EOS should be to promote rib cage expansion and lung development. Different techniques have been described and may be used depending on the specific patient's characteristics.
Nonlinear Quantum Photonics with a Tin-Vacancy Center Coupled to a One-Dimensional Diamond Waveguide

Color-centers integrated with nanophotonic devices have emerged as a compelling platform for quantum science and technology. Here we integrate tin-vacancy centers in a diamond waveguide and investigate the interaction with light at the single-photon level. We observe single-emitter induced extinction of the transmitted light up to 25% and measure the nonlinear effect on the photon statistics. Furthermore, we demonstrate fully tunable interference between the reflected single-photon field and laser light back-scattered at the fiber end and show the corresponding controlled change between bunched and anti-bunched photon statistics in the reflected field.
On factorization hierarchy of equations for banana Feynman amplitudes

We present a review of the relations between various equations for maximal cut banana Feynman diagrams, i.e. amplitudes with propagators substituted with $\delta$-functions. We consider both equal and generic masses. There are three types of equation to consider: those in coordinate space, their Fourier transform and Picard-Fuchs equations originating from the parametric representation. First, we review the properties of the corresponding differential operators themselves, mainly their factorization properties at the equal mass locus and their form at special values of the dimension. Then we study the relation between the Fourier transform of the coordinate space equations and the Picard-Fuchs equations and show that they are related by factorization as well. The equations in question are the counterparts of the Virasoro constraints in the much-better studied theory of eigenvalue matrix models and are the first step towards building a full-fledged theory of Feynman integrals, which will reveal their hidden integrable structure.
Solution discovery via reconfiguration for problems in P

In the recently introduced framework of solution discovery via reconfiguration [Fellows et al., ECAI 2023], we are given an initial configuration of $k$ tokens on a graph and the question is whether we can transform this configuration into a feasible solution (for some problem) via a bounded number $b$ of small modification steps. In this work, we study solution discovery variants of polynomial-time solvable problems, namely Spanning Tree Discovery, Shortest Path Discovery, Matching Discovery, and Vertex/Edge Cut Discovery in the unrestricted token addition/removal model, the token jumping model, and the token sliding model. In the unrestricted token addition/removal model, we show that all four discovery variants remain in P. For the toking jumping model we also prove containment in P, except for Vertex/Edge Cut Discovery, for which we prove NP-completeness. Finally, in the token sliding model, almost all considered problems become NP-complete, the exception being Spanning Tree Discovery, which remains polynomial-time solvable. We then study the parameterized complexity of the NP-complete problems and provide a full classification of tractability with respect to the parameters solution size (number of tokens) $k$ and transformation budget (number of steps) $b$. Along the way, we observe strong connections between the solution discovery variants of our base problems and their (weighted) rainbow variants as well as their red-blue variants with cardinality constraints.
Systematic word meta-sense extension

The meaning of polysemous words often varies in a highly productive yet predictable way. Generalizing the regularity between conventional senses to derive novel word meaning is crucial for automated processing of non-literal language uses such as figurative expressions. We introduce a novel task called systematic word meta-sense extension (SWORME) to test and improve language models' ability to extend word meaning to denote new semantic domains (also called meta-senses) that bear regular semantic relations with existing senses. We found that language models prefer incremental lexical semantic change toward conceptually similar meta-senses such as logical metonymy, and are much worse at predicting highly non-literal meaning extensions such as metaphors. We propose a novel analogy-based method of word meaning extension, and show that it effectively improves language model systematicity in making both gradual and radical types of meta-sense extension. We further demonstrate that learning systematic meta-sense extensions benefits language models on multiple benchmarks of figurative language understanding.
Synchronous Measurement of Land Surface Processes in Typical Vegetation Landscape Zones in the Hei River Basin

On the review of some international research projects related to land surface processes and on the context that sustainable development of environment\|resource\|economy system are restricted to scarce water resource in arid area in northwest China, the researches presented in this paper are aimed to understand the processes related to water and energy cycle and ecological process at different vegetation landscape zones of inland river basins. Understanding of the land surface hydrological processes of energy and water exchange can help us accomplish efficient water resource management, such as water resource allotment, desert vegetation protection, and find the equivalent value which different ecosystem can supply to us at different scenes in inland river basin. Therefore synchronous measurement and experiment on land surface processes, energy and water fluxes in the soil vegetation atmosphere system in typical vegetation landscape zones are expected to find a key for these problems. In the present study, three synchronous measurement fields were set up in upper\|stream mountain vegetation zone, oasis\|desert transitional zone between desert and oasis, the natural oasis zone at the downstream riparian tamarisk forest of the Hei River Basin in the arid area of northwest China. The three fields of measurement and experiment represent typical vegetation landscape zones in a inland river basin. Three environment measurement systems of ENVIS, which can measure standard meteorological factors and other ecological factors were installed in the three typical vegetation landscape zones in Hei river basin. The comparative analysis is carried out on the measurements of SVAT system in three vegetation landscape zones, and theories and approaches to parameterize the water and energy fluxes in SVAT system at the three measurement locations are discussed. The preliminary results obtained indicate that the forest\|grassland\|shrubbery zone in mountain vegetation landscape zones is a cold and wet surface, and the oasis\|desert transitional zone in middle river is a relative wet but dry land surface, while the riparian tamarisk forests and their adjacent places in the downstream area are drier and hotter land surface. In the aspect of carbon cycle and sequestration, the forest\|grassland\|shrubbery is a sink for carbon sequestration according to preliminary research, and there have distinct discriminations between the middle stream and the downstream area.
The Journey to Serverless Migration: An Empirical Analysis of Intentions, Strategies, and Challenges

Serverless is an emerging cloud computing paradigm that facilitates developers to focus solely on the application logic rather than provisioning and managing the underlying infrastructure. The inherent characteristics such as scalability, flexibility, and cost efficiency of serverless computing, attracted many companies to migrate their legacy applications toward this paradigm. However, the stateless nature of serverless requires careful migration planning, consideration of its subsequent implications, and potential challenges. To this end, this study investigates the intentions, strategies, and technical and organizational challenges while migrating to a serverless architecture. We investigated the migration processes of 11 systems across diverse domains by conducting 15 in-depth interviews with professionals from 11 organizations. we also presented a detailed discussion of each migration case. Our findings reveal that large enterprises primarily migrate to enhance scalability and operational efficiency, while smaller organizations intend to reduce the cost. Furthermore, organizations use a domain-driven design approach to identify the use case and gradually migrate to serverless using a strangler pattern. However, migration encounters technical challenges i.e., testing event-driven architecture, integrating with the legacy system, lack of standardization, and organizational challenges i.e., mindset change and hiring skilled serverless developers as a prominent. The findings of this study provide a comprehensive understanding that can guide future implementations and advancements in the context of serverless migration.
Magnetic, GPR and geoelectrical measurements for studying the archaeological site of ‘Masseria Nigro’ (Viggiano, southern Italy)

The results of a geophysical survey carried out at the archaeological site of Masseria Nigro, located on a fluvial terrace on the north-eastern flank of the Agri valley (Viggiano, Basilicata Region), are presented and discussed. The site includes an important ancient structure, developed in successive phases between the 4th and 3rd centuries BC and destroyed after the Roman conquest. Magnetic mapping, GPR profiling and electrical resistivity tomography were applied in the course of geophysical exploration in the archaeological area. The application of these methods revealed information about buried ancient walls, helping to define the boundaries of the whole building. During the first phase of the excavation, archaeologists targeted only the western part of the ancient structure (about 1000 m 2 ), but the geophysical survey performed in a second phase was able to cover the entire ancient structure (1400 m 2 ), enabling the archaeologists to define the extent of the buried structures. Our findings confirmed the hypothesis of the larger extent of the archaeological area in the eastern part and revealed the complete plan of the ancient building.
On the pointwise existence of Cauchy $\rm{P.V.}$ integrals

The Riemann-Hilbert (RH) approach, whose origins can be traced back to Riemann's PhD thesis, is well known to be far-reaching. It provides a general framework for expressing solutions of integrable problems such as ODEs or PDEs. Its generalization concerning monodromy groups of Fuchsian systems is one of Hilbert's 23 problems. In this paper we extend a basic result in scalar RH theory, the Sokhotski-Plemelj formula. Classically, this formula is derived under assumptions of H\"older continuity, although it also holds true under weaker, Dini, continuity conditions. Dini continuity is still too restrictive. We prove the Sokhotski-Plemelj formula under weaker assumptions, namely continuity at a point and an $L^1$ condition. Furthermore, we provide sufficient conditions for the existence of the Cauchy $\rm{P.V. }$ integral which are in a precise sense also necessary: weakening them runs into obstructions of a mathematical foundations nature.
Learning to Fly in Seconds

Learning-based methods, particularly Reinforcement Learning (RL), hold great promise for streamlining deployment, enhancing performance, and achieving generalization in the control of autonomous multirotor aerial vehicles. Deep RL has been able to control complex systems with impressive fidelity and agility in simulation but the simulation-to-reality transfer often brings a hard-to-bridge reality gap. Moreover, RL is commonly plagued by prohibitively long training times. In this work, we propose a novel asymmetric actor-critic-based architecture coupled with a highly reliable RL-based training paradigm for end-to-end quadrotor control. We show how curriculum learning and a highly optimized simulator enhance sample complexity and lead to fast training times. To precisely discuss the challenges related to low-level/end-to-end multirotor control, we also introduce a taxonomy that classifies the existing levels of control abstractions as well as non-linearities and domain parameters. Our framework enables Simulation-to-Reality (Sim2Real) transfer for direct RPM control after only 18 seconds of training on a consumer-grade laptop as well as its deployment on microcontrollers to control a multirotor under real-time guarantees. Finally, our solution exhibits competitive performance in trajectory tracking, as demonstrated through various experimental comparisons with existing state-of-the-art control solutions using a real Crazyflie nano quadrotor. We open source the code including a very fast multirotor dynamics simulator that can simulate about 5 months of flight per second on a laptop GPU. The fast training times and deployment to a cheap, off-the-shelf quadrotor lower the barriers to entry and help democratize the research and development of these systems.
Guided Flows for Generative Modeling and Decision Making

Classifier-free guidance is a key component for enhancing the performance of conditional generative models across diverse tasks. While it has previously demonstrated remarkable improvements for the sample quality, it has only been exclusively employed for diffusion models. In this paper, we integrate classifier-free guidance into Flow Matching (FM) models, an alternative simulation-free approach that trains Continuous Normalizing Flows (CNFs) based on regressing vector fields. We explore the usage of \emph{Guided Flows} for a variety of downstream applications. We show that Guided Flows significantly improves the sample quality in conditional image generation and zero-shot text-to-speech synthesis, boasting state-of-the-art performance. Notably, we are the first to apply flow models for plan generation in the offline reinforcement learning setting, showcasing a 10x speedup in computation compared to diffusion models while maintaining comparable performance.
Applications of Spiking Neural Networks in Visual Place Recognition

In robotics, Spiking Neural Networks (SNNs) are increasingly recognized for their largely-unrealized potential energy efficiency and low latency particularly when implemented on neuromorphic hardware. Our paper highlights three advancements for SNNs in Visual Place Recognition (VPR). First, we propose Modular SNNs, where each SNN represents a set of non-overlapping geographically distinct places, enabling scalable networks for large environments. Secondly, we present Ensembles of Modular SNNs, where multiple networks represent the same place, significantly enhancing accuracy compared to single-network models. Our SNNs are compact and small, comprising only 1500 neurons and 474k synapses, which makes them ideally suited for ensembling due to this small size. Lastly, we investigate the role of sequence matching in SNN-based VPR, a technique where consecutive images are used to refine place recognition. We analyze the responsiveness of SNNs to ensembling and sequence matching compared to other VPR techniques. Our contributions highlight the viability of SNNs for VPR, offering scalable and robust solutions, paving the way for their application in various energy-sensitive robotic tasks.
Controlled expansion for transport in a class of non-Fermi liquids

Non-Fermi liquids arise when strong interactions destroy stable fermionic quasiparticles. The simplest models featuring this phenomenon involve a Fermi surface coupled to fluctuating gapless bosonic order parameter fields, broadly referred to as"Hertz-Millis"models. We revisit a controlled approach to Hertz-Millis models that combines an expansion in the inverse number ($N$) of fermion species with an expansion in the deviation of the boson dynamical critical exponent $z$ from 2. The structure of the expansion is found to be qualitatively different in the quantum critical regime $\Omega \ll q$ and in the transport regime $\Omega \gg q$. In particular, correlation functions in the transport regime involve infinitely many diagrams at each order in perturbation theory. We provide an explicit and tractable recipe to classify and resum these diagrams. For the simplest Hertz-Millis models, we show that this recipe is consistent with non-perturbative anomaly arguments and correctly captures the fixed point optical conductivity as well as leading corrections from irrelevant operators. We comment on potential applications of this expansion to transport in more complicated Hertz-Millis models as well as certain beyond-Landau metallic quantum critical points.
Spectral condition for the existence of a chorded cycle

A chord of a cycle $C$ is an edge joining two non-consecutive vertices of $C$. A cycle $C$ in a graph $G$ is chorded if the vertex set of $C$ induces at least one chord. In this paper, we prove that if $G$ is a graph with order $n\geq 6$ and $\rho(G)\geq \rho(K_{2,n-2})$, then $G$ contains a chorded cycle unless $G\cong K_{2,n-2}$. This gives one answer to a question posed by Gould [Results and problems on chorded cycles: A survey, Graphs Combin. 38 (2022) 189].
SpecHD: Hyperdimensional Computing Framework for FPGA-based Mass Spectrometry Clustering

Mass spectrometry-based proteomics is a key enabler for personalized healthcare, providing a deep dive into the complex protein compositions of biological systems. This technology has vast applications in biotechnology and biomedicine but faces significant computational bottlenecks. Current methodologies often require multiple hours or even days to process extensive datasets, particularly in the domain of spectral clustering. To tackle these inefficiencies, we introduce SpecHD, a hyperdimensional computing (HDC) framework supplemented by an FPGA-accelerated architecture with integrated near-storage preprocessing. Utilizing streamlined binary operations in an HDC environment, SpecHD capitalizes on the low-latency and parallel capabilities of FPGAs. This approach markedly improves clustering speed and efficiency, serving as a catalyst for real-time, high-throughput data analysis in future healthcare applications. Our evaluations demonstrate that SpecHD not only maintains but often surpasses existing clustering quality metrics while drastically cutting computational time. Specifically, it can cluster a large-scale human proteome dataset-comprising 25 million MS/MS spectra and 131 GB of MS data-in just 5 minutes. With energy efficiency exceeding 31x and a speedup factor that spans a range of 6x to 54x over existing state of-the-art solutions, SpecHD emerges as a promising solution for the rapid analysis of mass spectrometry data with great implications for personalized healthcare.
Unsupervised Multimodal Surface Registration with Geometric Deep Learning

This paper introduces GeoMorph, a novel geometric deep-learning framework designed for image registration of cortical surfaces. The registration process consists of two main steps. First, independent feature extraction is performed on each input surface using graph convolutions, generating low-dimensional feature representations that capture important cortical surface characteristics. Subsequently, features are registered in a deep-discrete manner to optimize the overlap of common structures across surfaces by learning displacements of a set of control points. To ensure smooth and biologically plausible deformations, we implement regularization through a deep conditional random field implemented with a recurrent neural network. Experimental results demonstrate that GeoMorph surpasses existing deep-learning methods by achieving improved alignment with smoother deformations. Furthermore, GeoMorph exhibits competitive performance compared to classical frameworks. Such versatility and robustness suggest strong potential for various neuroscience applications.
Spectroscopy of a single-carrier bilayer graphene quantum dot from time-resolved charge detection

We measured the spectrum of a single-carrier bilayer graphene quantum dot as a function of both parallel and perpendicular magnetic fields, using a time-resolved charge detection technique that gives access to individual tunnel events. Thanks to our unprecedented energy resolution of 4$\mu~$eV, we could distinguish all four levels of the dot's first orbital, in particular in the range of magnetic fields where the first and second excited states cross ($B_\perp\lesssim 100~$mT). We thereby experimentally establish, the hitherto extrapolated, single-charge carrier spectrum picture and provide a new upper bound for the inter-valley mixing, equal to our energy resolution.
Extensibility governs the flow-induced alignment of polymers and rod-like colloids

Polymers and rod-like colloids (PaRC) adopt a favorable orientation under sufficiently strong flows. However, how the flow kinematics affect the alignment of such nanostructures according to their extensibility remains unclear. By analysing the shear- and extension-induced alignment of chemically and structurally different PaRC, we show that extensibility is a key determinant of the structural response to the imposed kinematics. We propose a unified description of the effectiveness of extensional flow, compared to shearing flow, at aligning PaRC of different extensibility.
Medical Image Retrieval Using Pretrained Embeddings

A wide range of imaging techniques and data formats available for medical images make accurate retrieval from image databases challenging. Efficient retrieval systems are crucial in advancing medical research, enabling large-scale studies and innovative diagnostic tools. Thus, addressing the challenges of medical image retrieval is essential for the continued enhancement of healthcare and research. In this study, we evaluated the feasibility of employing four state-of-the-art pretrained models for medical image retrieval at modality, body region, and organ levels and compared the results of two similarity indexing approaches. Since the employed networks take 2D images, we analyzed the impacts of weighting and sampling strategies to incorporate 3D information during retrieval of 3D volumes. We showed that medical image retrieval is feasible using pretrained networks without any additional training or fine-tuning steps. Using pretrained embeddings, we achieved a recall of 1 for various tasks at modality, body region, and organ level.