Miracle-dz/UniFAR-FTUs huggingface dataset:
RECORDING FORAGING SEABIRDS AT SEA STANDARDISED RECORDING AND CODING OF FORAGING BEHAVIOUR AND MULTI-SPECIES FORAGING ASSOCIATIONS

Camphuysen, C.J. & Garthe, S. 2004. Recording foraging seabirds at sea. Stadardised recording and coding of foraging behaviour and multi-species foraging assocations. Atlantic Seabirds 6(1): 1-32. The European Seabirds at Sea (ESAS) database was established in the early 1980s using a common format. It contains the results of ship-based and aerial seabird surveys in the Northwest European waters, collected using standard methods. The emphasis has always been on mapping distribution patterns and variations in relative abundance, from which seabird abundance estimates for certain sea areas could be made. The data have been used first to evaluate different sea areas in terms of their vulnerability for surface pollutants. Later studies put more emphasis on ecological aspects underlying seabird distribution and from this work a growing need for more adequate, but still standardised, coding of behaviour types emerged. In this manual, a coding system is introduced that allows specific coding of associations of birds and marine mammals with certain surface phenomena (including land), coding of multi-species feeding associations (feeding flocks) and coding of a variety of behaviour types, with emphasis on feeding behaviour and foraging interactions. In this coding method, the original aim and style of data collecting of seabirds at sea remains intact, and new data are therefore directly comparable with historical material. The coding is thought to be of interest for ESAS participants as well as other groups studying the behaviour of seabirds at sea.
An Efficient Framework for Global Non-Convex Polynomial Optimization with Nonlinear Polynomial Constraints

We present an efficient framework for solving constrained global non-convex polynomial optimization problems. We prove the existence of an equivalent nonlinear reformulation of such problems that possesses essentially no spurious local minima. We show through numerical experiments that polynomial scaling in dimension and degree is achievable for computing the optimal value and location of previously intractable global constrained polynomial optimization problems in high dimension.
Applications of a new coefficient of similarity to pollution surveys

are many measures of similarity between samples. Their successful use in pollution surveys rests with the as sumption that (a) local ecosystems re ceiving the same pollutant support biologi cal communities similar to one another, and ( b ) the analytical techniques available are capable of truthfully and accurately re flecting the extent to which these com munities are similar or dissimilar. Much has been published 1? 2 to support the general veracity of the first assumption; that is, if other things were equal, polluted communities would have different species occurrences and abundances than nonpol luted communities. There is an equal mass of evidence in the form of the multi tude of techniques that have been tried, especially community structure and sim ilarity indexes, that implies indirectly that the second assumption is not necessarily true. Such a profusion of measures attests to the general dissatisfaction with the exist ing indexes and the complexity of the problem. Before the authors' solution is offered, it would be appropriate and help ful to examine the shortcomings and mis uses associated with the common indexes currently being used in pollution studies.
Photon-resolved Floquet theory in open quantum systems

Photon-resolved Floquet theory keeps track of the photon exchange of a quantum system with a coherent driving field. It thus complements the standard full-counting statistics that counts the number of photons exchanged with incoherent photon modes giving rise to dissipation. In this paper, we introduce a unifying framework describing both situations. We develop methods suitable for an analytical evaluation of low-order cumulants of photonic probability distributions. Within this framework we analyze the two-mode Jaynes-Cummings model to demonstrate that the Photon-resolved Floquet theory and the standard full-counting statistics make consistent statistical predictions. Interestingly, we find that the photon-flux fluctuations diverge for vanishing dissipation, which can be related to an entanglement effect between the driven matter system and the driving field. To substantiate our results, we use our framework to describe efficient photon up-conversion in an ac-driven lambda system, that is characterized by a high signal-to-noise ratio. As the framework is non-perturbative and predicts fluctuations, it paves the way towards non-perturbative spectroscopy, which will assist to improve metrological methods.
Does Difficulty even Matter? Investigating Difficulty Adjustment and Practice Behavior in an Open-Ended Learning Task

Difficulty adjustment in practice exercises has been shown to be beneficial for learning. However, previous research has mostly investigated close-ended tasks, which do not offer the students multiple ways to reach a valid solution. Contrary to this, in order to learn in an open-ended learning task, students need to effectively explore the solution space as there are multiple ways to reach a solution. For this reason, the effects of difficulty adjustment could be different for open-ended tasks. To investigate this, as our first contribution, we compare different methods of difficulty adjustment in a user study conducted with 86 participants. Furthermore, as the practice behavior of the students is expected to influence how well the students learn, we additionally look at their practice behavior as a post-hoc analysis. Therefore, as a second contribution, we identify different types of practice behavior and how they link to students' learning outcomes and subjective evaluation measures as well as explore the influence the difficulty adjustment methods have on the practice behaviors. Our results suggest the usefulness of taking into account the practice behavior in addition to only using the practice performance to inform adaptive intervention and difficulty adjustment methods.
FiloBass: A Dataset and Corpus Based Study of Jazz Basslines

We present FiloBass: a novel corpus of music scores and annotations which focuses on the important but often overlooked role of the double bass in jazz accompaniment. Inspired by recent work that sheds light on the role of the soloist, we offer a collection of 48 manually verified transcriptions of professional jazz bassists, comprising over 50,000 note events, which are based on the backing tracks used in the FiloSax dataset. For each recording we provide audio stems, scores, performance-aligned MIDI and associated metadata for beats, downbeats, chord symbols and markers for musical form. We then use FiloBass to enrich our understanding of jazz bass lines, by conducting a corpus-based musical analysis with a contrastive study of existing instructional methods. Together with the original FiloSax dataset, our work represents a significant step toward a fully annotated performance dataset for a jazz quartet setting. By illuminating the critical role of the bass in jazz, this work contributes to a more nuanced and comprehensive understanding of the genre.
DRNet: A Decision-Making Method for Autonomous Lane Changingwith Deep Reinforcement Learning

Machine learning techniques have outperformed numerous rule-based methods for decision-making in autonomous vehicles. Despite recent efforts, lane changing remains a major challenge, due to the complex driving scenarios and changeable social behaviors of surrounding vehicles. To help improve the state of the art, we propose to leveraging the emerging \underline{D}eep \underline{R}einforcement learning (DRL) approach for la\underline{NE} changing at the \underline{T}actical level. To this end, we present"DRNet", a novel and highly efficient DRL-based framework that enables a DRL agent to learn to drive by executing reasonable lane changing on simulated highways with an arbitrary number of lanes, and considering driving style of surrounding vehicles to make better decisions. Furthermore, to achieve a safe policy for decision-making, DRNet incorporates ideas from safety verification, the most important component of autonomous driving, to ensure that only safe actions are chosen at any time. The setting of our state representation and reward function enables the trained agent to take appropriate actions in a real-world-like simulator. Our DRL agent has the ability to learn the desired task without causing collisions and outperforms DDQN and other baseline models.
Cross-modal Consistency Learning with Fine-grained Fusion Network for Multimodal Fake News Detection

Previous studies on multimodal fake news detection have observed the mismatch between text and images in the fake news and attempted to explore the consistency of multimodal news based on global features of different modalities. However, they fail to investigate this relationship between fine-grained fragments in multimodal content. To gain public trust, fake news often includes relevant parts in the text and the image, making such multimodal content appear consistent. Using global features may suppress potential inconsistencies in irrelevant parts. Therefore, in this paper, we propose a novel Consistency-learning Fine-grained Fusion Network (CFFN) that separately explores the consistency and inconsistency from high-relevant and low-relevant word-region pairs. Specifically, for a multimodal post, we divide word-region pairs into high-relevant and low-relevant parts based on their relevance scores. For the high-relevant part, we follow the cross-modal attention mechanism to explore the consistency. For low-relevant part, we calculate inconsistency scores to capture inconsistent points. Finally, a selection module is used to choose the primary clue (consistency or inconsistency) for identifying the credibility of multimodal news. Extensive experiments on two public datasets demonstrate that our CFFN substantially outperforms all the baselines. Our code can be found at: https://github.com/uestc-lj/CFFN/.
Alloying Effects on the Microstructure and Properties of Laser Additively Manufactured Tungsten Materials

A large body of literature within the additive manufacturing (AM) community has focused on successfully creating stable tungsten (W) microstructures due to significant interest in its application for extreme environments. However, solidification cracking and additional embrittling features at grain boundaries have resulted in poorly performing microstructures, stymying the application of AM as a manufacturing technique for W. Several alloying strategies, such as ceramic particles and ductile elements, have emerged with the promise to eliminate solidification cracking while simultaneously enhancing stability against recrystallization. In this work, we provide new insights regarding the defects and microstructural features that result from the introduction of ZrC for grain refinement and NiFe as a ductile reinforcement phase - in addition to the resulting thermophysical and mechanical properties. ZrC is shown to promote microstructural stability with increased hardness due to the formation of ZrO2 dispersoids. Conversely, NiFe forms into micron-scale FCC phase regions within a BCC W matrix, producing enhanced toughness relative to pure AM W. A combination of these effects is realized in the WNiFe+ZrC system and demonstrates that complex chemical environments coupled with the tuning of AM microstructures provides an effective pathway for enabling laser AM W materials with enhanced stability and performance.
PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion

Recent evaluations of Large Language Models (LLMs) have centered around testing their zero-shot/few-shot capabilities for basic natural language tasks and their ability to translate instructions into tool APIs. However, the evaluation of LLMs utilizing complex tools to finish multi-turn, multi-modal instructions in a complex multi-modal environment has not been investigated. To address this gap, we introduce the PowerPoint Task Completion (PPTC) benchmark to assess LLMs' ability to create and edit PPT files based on user instructions. It contains 279 multi-turn sessions covering diverse topics and hundreds of instructions involving multi-modal operations. We also propose the PPTX-Match Evaluation System that evaluates if LLMs finish the instruction based on the prediction file rather than the label API sequence, thus it supports various LLM-generated API sequences. We measure 3 closed LLMs and 6 open-source LLMs. The results show that GPT-4 outperforms other LLMs with 75.1\% accuracy in single-turn dialogue testing but faces challenges in completing entire sessions, achieving just 6\% session accuracy. We find three main error causes in our benchmark: error accumulation in the multi-turn session, long PPT template processing, and multi-modality perception. These pose great challenges for future LLM and agent systems. We release the data, code, and evaluation system of PPTC at \url{https://github.com/gydpku/PPTC}.
Ramsey properties of randomly perturbed hypergraphs

Let $\tilde K_t$ denote the $3$-uniform linear clique of order $t$. Given an even integer $t \geq 4$, let $M$ denote the asymmetric maximal density of $\tilde K_t$ and $\tilde K_{t/2}$. We prove that there exists a constant $C>0$ such that, if $(H_n)_{n \in \mathbb{N}}$ is any sequence of dense $3$-uniform hypergraphs and $p := p(n) \geq Cn^{-1/M}$, then $$ \lim_{n \to \infty} \Pr[H_n \cup R_n \to (\tilde K_t)_2] =1 $$ holds, whenever $R_n \sim \mathbb{H}(n,p)$, where the latter denotes the binomial $3$-uniform random hypergraph distribution. We conjecture that this result uncovers the threshold for the property in question for $t \geq 6$. The key tools of our proof are a new variant of the Strong Hypergraph Regularity Lemma along with a new Tuple Lemma accompanying it. Our variant incorporates both the strong and the weak hypergraph regularity lemmas into a single lemma; allowing for graph-like applications of the strong lemma to be carried out in $3$-uniform hypergraphs.
Dynamic Redundancy-aware Blockchain-based Partial Computation Offloading for the Metaverse in In-network Computing

The computing in the network (COIN) paradigm has emerged as a potential solution for computation-intensive applications like the metaverse by utilizing unused network resources. The blockchain (BC) guarantees task-offloading privacy, but cost reduction, queueing delays, and redundancy elimination remain open problems. This paper presents a redundancy-aware BC-based approach for the metaverse's partial computation offloading (PCO). Specifically, we formulate a joint BC redundancy factor (BRF) and PCO problem to minimize computation costs, maximize incentives, and meet delay and BC offloading constraints. We proved this problem is NP-hard and transformed it into two subproblems based on their temporal correlation: real-time PCO and Markov decision process-based BRF. We formulated the PCO problem as a multiuser game, proposed a decentralized algorithm for Nash equilibrium under any BC redundancy state, and designed a double deep Q-network-based algorithm for the optimal BRF policy. The BRF strategy is updated periodically based on user computation demand and network status to assist the PCO algorithm. The experimental results suggest that the proposed approach outperforms existing schemes, resulting in a remarkable 47% reduction in cost overhead, delivering approximately 64% higher rewards, and achieving convergence in just a few training episodes.
NON-METRIC CAMERA CALIBRATION AND DOCUMENTATION OF HISTORICAL BUILDINGS

The market of digital amateur cameras is growing rapidly. It turned out, that these cameras could change the field of close range photogrammetry almost revolutionary: Inconvenient in handling and expensive metric cameras could be replaced by “off-the-shelf” cameras if these had the same stability qualities and high enough resolution. Especially the documentation of (historical) buildings would profit from that development, if it was proved that this kind of cameras were good enough for photogrammetric purposes. The proposed paper describes the investigation on the digital non-metric “off-the-shelf” camera Olympus C-5050 Zoom. After a short
Reconstruction of long‐term aerosol optical depth series with sunshine duration records

We report the suitability of sunshine duration (SD) records as a proxy for the reconstruction of atmospheric aerosol content, for which little information exists, especially prior to the 1980s. Specifically, we have treated cloudless summer days in 16 stations throughout Spain. For almost all sites we find statistically significant relationships between aerosol optical depth (AOD) and daily SD. The correlation coefficient presents a mean value of −0.72, and slope values of the linear regressions are within the range [−0.11, −0.36]. The relationships are used to generate AOD series back to the 1960s (to the 1920s for Madrid). These reconstructed series show an increase in AOD from the mid‐1960s to the 1980s, followed by a decrease until the present, in agreement with changes in anthropogenic aerosol emissions and with opposite trends of solar irradiance. The method can be used to reconstruct AOD from the late nineteenth century at many stations worldwide.
An Unsupervised Approach to Anonymous Crowd Monitoring

With over 4.2 million CCTV cameras in the UK alone [2], it would be useful haveto an automated system to monitor over wide, open spaces. In such areas, we canobserve emergent behaviour in crowd movements, often changing dynamicall over timeas crowds form and disperse. Trained operators can often notice trouble the moment,if not before, it happens. Unfortunately, the high number of cameras watching over thepublic has generated a feeling of unease within the populus as people feel increasinglythat their privacy is being invaded.We propose a system that, using an ofﬂine, unsupervised lear ning process, willanonymously detect patterns of motion within a scene and describe it as usual or un-usual. The system is trained on footage of the scene, recorded using a single camera.Flow is detected using the KLT tracker [3] to accumulate, at chosen granularity, ‘track-lets’ [1] of elemental motion.These tracks are quantised and their distribution in spatial and temporal windowsaround each pixel clustered to generate acceptable patterns. In scenes with changingbehaviour, there may be several candidates at each postion. During testing, similarlygenerated patterns are tested for plausibility by proximity to acceptable clusters.Early results show promise and may be tuned via various parameters of the system.
Relating sex differences in cortical and hippocampal microstructure to sex hormones

Sex hormone receptors are expressed widely in both neurons and glial cells, which allows them to interact with the brain’s major cell groups via several molecular mechanisms. These mechanisms lead to sex differences in brain structure as well as hormone-induced plasticity in the female brain across the menstrual cycle. Adding to the literature on volumetric changes in cortical structure, here we set out to investigate sex differences in the microstructure of the human cortex in relation to sex hormones. We assessed regional variation in cortical microstructure as a function of sex, hormonal status and sex hormone receptor gene expression distribution based on quantitative intracortical profiling in vivo using the magnetic resonance imaging based T1w/T2w ratio in 992 healthy females and males of the Human Connectome Project young adult sample. We demonstrate that microstructure in isocortex and hippocampus differs regionally between males and females, that this effect varies with hormone levels of females and that implicated brain regions overlap with estrogen receptor and sex steroid synthesis gene expression. Lastly, we show that sex and sex hormone related brain structure variations are most pronounced in areas of low laminar cortical complexity (agranular cortex), which are also predicted to be most plastic based on their cytoarchitectural properties. Together, our data thus are suggestive of sex differences in cortical and hippocampal microstructure, as well as the modulatory function of sex hormones on these measures. Albeit correlative, this study underscores the importance of incorporating sex hormone variables into the investigation of brain structure and plasticity.
Detection, characterization, and phylogenetic analysis of a near-whole genome sequence of a novel astrovirus in an endemic Malagasy fruit bat, Rousettus madagascariensis

Bats (order: Chiroptera) are known to host a diverse range of viruses, some of which present a public health risk. Thorough viral surveillance is therefore essential to predict and potentially mitigate zoonotic spillover. Astroviruses (family: Astroviridae) are an understudied group of viruses with a growing amount of indirect evidence for zoonotic transfer. Astroviruses have been detected in bats with significant prevalence and diversity, suggesting that bats may act as important astrovirus hosts. Most astrovirus surveillance in wild bat hosts has, to date, been restricted to single-gene PCR detection and concomitant Sanger sequencing; additionally, many bat species and many geographic regions have not yet been surveyed for astroviruses at all. Here, we use metagenomic Next Generation Sequencing (mNGS) to detect astroviruses in three species of Madagascar fruit bats, Eidolon dupreanum, Pteropus rufus, and Rousettus madagascariensis. We detect numerous partial sequences from all three species and one near-full length astrovirus sequence from Rousettus madagascariensis, which we use to characterize the evolutionary history of astroviruses both within bats and the broader mammalian clade, Mamastrovirus. Taken together, applications of mNGS implicate bats as important astrovirus hosts and demonstrate novel patterns of bat astrovirus evolutionary history, particularly in the Southwest Indian Ocean region.