Miracle-dz/UniFAR-FTUs huggingface dataset:
Orchid bees (Hymenoptera: Apidae) in the coastal forests of southern Brazil: diversity, efficiency of sampling methods and comparison with other Atlantic forest surveys

Os levantamentos da fauna de abelhas euglossineas realizados ate o momento ao longo da floresta Atlântica sao restritos a poucas regioes e nao permitem um entendimento mais aprofundado dos padroes latitudinais de distribuicao e diversidade dessas abelhas. Por este motivo, duas areas de floresta Atlântica do sul do Brasil, na planicie costeira de Sao Paulo (Sete Barras, Faz. Morro do Capim: SP3) e do Parana (Antonina, Reserva Natural do Rio Cachoeira: PR3), tiveram sua fauna de euglossineos amostrada. Em PR3, foi avaliada tambem a eficiencia de dois metodos alternativos de coleta, fazendo-se a comparacao entre armadilhas plasticas, iscadas com fragrâncias, e coleta direta com rede entomologica em iscas odoriferas. A diversidade e abundância das abelhas foram muito baixas: apenas 39 machos de oito especies foram coletados em SP3 (Euglossa iopoecila, Euglossa roderici, Eulaema nigrita, Euglossa annectans, Eulaema cingulata, Euglossa pleosticta, Euglossa viridis e Exaerete smaragdina) e 254 machos de seis especies em PR3 (Euglossa iopoecila, Euglossa annectans, Euglossa stellfeldi, Euglossa roderici, Euglossa pleosticta e Eulaema nigrita). A comparacao entre os metodos de amostragem mostrou que a coleta direta (seis especies; 221 especimes) foi mais eficiente do que o uso de armadilhas (tres especies; 33 especimes). A analise de correspondencia (DCA) mostrou que os dois levantamentos apresentados aqui se posicionaram relativamente proximos entre si, porem afastados dos outros locais comparados, nao se agrupando com o levantamento mais meridional nos dominios da floresta Atlântica do Rio Grande do Sul, nem com aqueles conduzidos em areas de terras baixas mais ao norte nesse bioma.
Uses of Sub-sample Estimates to Reduce Errors in Stochastic Optimization Models

Optimization software enables the solution of problems with millions of variables and associated parameters. These parameters are, however, often uncertain and represented with an analytical description of the parameter's distribution or with some form of sample. With large numbers of such parameters, optimization of the resulting model is often driven by mis-specifications or extreme sample characteristics, resulting in solutions that are far from a true optimum. This paper describes how asymptotic convergence results may not be useful in large-scale problems and how the optimization of problems based on sub-sample estimates may achieve improved results over models using full-sample solution estimates. A motivating example and numerical results from a portfolio optimization problem demonstrate the potential improvement. A theoretical analysis also provides insight into the structure of problems where sub-sample optimization may be most beneficial.
X-Ray Spectral Variations of Circinus X-1 Observed with NICER throughout an Entire Orbital Cycle

Circinus X-1 (Cir X-1) is a neutron star binary with an elliptical orbit of 16.6 days. The source is unique for its extreme youth, providing a key to understanding early binary evolution. However, its X-ray variability is too complex to reach a clear interpretation. We conducted the first high-cadence (every 4 hr, on average) observations covering one entire orbit using the NICER X-ray telescope. The X-ray flux behavior can be divided into stable, dip, and flaring phases. The X-ray spectra in all phases can be described by a common model consisting of a partially covered disk blackbody emission and the line features from a highly ionized photoionized plasma. The spectral change over the orbit is attributable to rapid changes of the partial covering medium in the line of sight and gradual changes of the disk blackbody emission. Emission lines of H- and He-like Mg, Si, S, and Fe are detected, most prominently in the dip phase. The Fe emission lines change to absorption in the course of the transition from the dip phase to the flaring phase. The estimated ionization degree indicates no significant changes, suggesting that the photoionized plasma is stable over the orbit. We propose a simple model in which the disk blackbody emission is partially blocked by a local medium in the line of sight that has spatial structures depending on the azimuth of the accretion disk. Emission lines upon the continuum emission are from the photoionized plasma located outside of the blocking material.
Denoising Task Routing for Diffusion Models

Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL). Despite the inherent connection between diffusion models and MTL, there remains an unexplored area in designing neural architectures that explicitly incorporate MTL into the framework of diffusion models. In this paper, we present Denoising Task Routing (DTR), a simple add-on strategy for existing diffusion model architectures to establish distinct information pathways for individual tasks within a single architecture by selectively activating subsets of channels in the model. What makes DTR particularly compelling is its seamless integration of prior knowledge of denoising tasks into the framework: (1) Task Affinity: DTR activates similar channels for tasks at adjacent timesteps and shifts activated channels as sliding windows through timesteps, capitalizing on the inherent strong affinity between tasks at adjacent timesteps. (2) Task Weights: During the early stages (higher timesteps) of the denoising process, DTR assigns a greater number of task-specific channels, leveraging the insight that diffusion models prioritize reconstructing global structure and perceptually rich contents in earlier stages, and focus on simple noise removal in later stages. Our experiments reveal that DTR not only consistently boosts diffusion models' performance across different evaluation protocols without adding extra parameters but also accelerates training convergence. Finally, we show the complementarity between our architectural approach and existing MTL optimization techniques, providing a more complete view of MTL in the context of diffusion training. Significantly, by leveraging this complementarity, we attain matched performance of DiT-XL using the smaller DiT-L with a reduction in training iterations from 7M to 2M.
Metric properties of boundary maps, Hilbert entropy and non-differentiability

We interpret the Hilbert entropy of a convex projective structure on a closed higher-genus surface as the Hausdorff dimension of the non-differentiability points of the limit set in the full flag space $\mathcal F(\mathbb R^3)$. Generalizations for regularity properties of boundary maps between locally conformal representations are also discussed. An ingredient for the proofs is the concept of hyperplane conicality that we introduce for a $\theta$-Anosov representation into a reductive real-algebraic Lie group $G$. In contrast with directional conicality, hyperplane-conical points always have full mass for the corresponding Patterson-Sullivan measure.
On Stable and Unstable Rossby Waves in Non-Zonal Oceanic Shear Flow

Abstract We derive linear equations for the study of baroclinic instability of a non-zonal oceanic shear flow whose direction is allowed to change with depth. These equations can be used to study unstable disturbances as well as stable Rossby waves in such a flow. We find an approximate analytic solution of the equations by using a two-level model. According to this solution a non-zonal shear flow is always unstable, and the length scales of the unstable disturbances are larger than the internal Rossby radius of deformation (corresponding wavelengths are larger than 2π times the internal Rossby radius of deformation). In all our examples of non-zonal shear flow, and strongly unstable zonal shear flow, the wavelength of the fastest growing disturbance is ∼10 times the internal Rossby radius of deformation. Only for weak westward unstable zonal shear flow do we find the fastest growing unstable disturbance to have a wavelength close to 2π times the internal Rossby radius of deformation. We also compute nume.
A Cacophony of Echoes from Daily Monitoring of the Crab Pulsar at Jodrell Bank

Using archival data from the 42-foot telescope at the Jodrell Bank Observatory, we produce daily stacks of aligned giant pulses for the Crab pulsar, to study changes to the daily profiles between 2012 April and 2016 December. From these, we identify echoes, where intervening material away from the line of sight causes pulsed emission to be redirected toward the observer, with delay corresponding to the increased distance of travel, resulting in additional profile components. These observations show that such echoes may be far more common than implied by the previous rate of detections. All the observed echoes are consistent with approaching zero delay at their closest approach to the normal giant pulse emission. This indicates that the structures responsible for producing these events must be highly anisotropic, with typical lengths greater than ∼4 au, typical widths on the sky of ∼0.1 au, and typical depths of ∼5 au, given the previously observed electron densities of the nebular filaments, on the order of 1000 cm−3. This suggests that these inhomogeneities are likely to be offshoot substructure from the larger nebular filaments of the Crab nebula.
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL

Dyna-style model-based reinforcement learning contains two phases: model rollouts to generate sample for policy learning and real environment exploration using current policy for dynamics model learning. However, due to the complex real-world environment, it is inevitable to learn an imperfect dynamics model with model prediction error, which can further mislead policy learning and result in sub-optimal solutions. In this paper, we propose $\texttt{COPlanner}$, a planning-driven framework for model-based methods to address the inaccurately learned dynamics model problem with conservative model rollouts and optimistic environment exploration. $\texttt{COPlanner}$ leverages an uncertainty-aware policy-guided model predictive control (UP-MPC) component to plan for multi-step uncertainty estimation. This estimated uncertainty then serves as a penalty during model rollouts and as a bonus during real environment exploration respectively, to choose actions. Consequently, $\texttt{COPlanner}$ can avoid model uncertain regions through conservative model rollouts, thereby alleviating the influence of model error. Simultaneously, it explores high-reward model uncertain regions to reduce model error actively through optimistic real environment exploration. $\texttt{COPlanner}$ is a plug-and-play framework that can be applied to any dyna-style model-based methods. Experimental results on a series of proprioceptive and visual continuous control tasks demonstrate that both sample efficiency and asymptotic performance of strong model-based methods are significantly improved combined with $\texttt{COPlanner}$.
Changes in Accommodative Function of Young Adults in their Twenties following Smartphone Use

목적: 본 연구에서는스마트폰을 이용한 근거리 작업 시 조절 기능의 변화가 유도되는가를 알아보고자 하였다. 방법: 20대의 총 63명 (남성 26명, 여성 37명)을 대상으로 조절 기능과 교정 전 등가구면도수를 측정하여 대조군 값으로 하였다. 실내조명 아래에서 연구대상자들에게 30분 동안 책을 읽게 한 후 30분간의 휴식 시간을 가지게 하고 순차적으로 동일한 시간 동안 스마트폰으로 영화를 보게 하거나 작업순서에 따른 오차를 배제하기 위하여 반대의 순서로 영화보기와 책읽기를 하게 하였다. 연구대상자들의 조절기능은 1) 최대조절력, 2) 조절 용이성, 3) 양성 및 음성상대조절, 4) 조절 래그 및 교정하지 않은 등가구면도수를 각 작업 후에 다시 검사하고 대조군 값과 비교하였다. 결과: 단안 최대조절력은 독서 후와 비교하여 스마트폰 시청 후 유의하게 감소하였다. 단안 및 양안 조절용이성은 독서나 스마트폰 사용 후 모두 감소하는 경향을 보였으나 통계적으로 유의한 차이는 아니었으며, 양성상대조절값은 독서나 스마트폰 사용 후 모두 유의한 변화가 없었다. 스마트폰 사용 후 음성상대조절 값은 독서 후와는 달리 거의 변화가 없었다. 스마트폰 사용 후 조절래그 값은 독서 후의 경우보다 유의하게 높았다. 결론: 30분 동안의 스마트폰을 사용한 근거리 작업은 일부 조절기능의 변화를 유발하였으며, 그 변화는 동일한 작업 환경에서의 독서 시 보다 유의하게 컸다. 【Purpose: The present study was conducted to investigate whether near work with smartphone could induce the change of accommodative function. Methods: Total 63 subjects(26 male and 37 female) in their 20s were firstly examined their visual functions related to accommodation and uncorrected spherical equivalent power as the control. After that, the subjects were asked to read a book for 30 min and sequentially watch a movie on smartphone for the same time after 30 min-break under the indoor light or as it was in the reverse order to avoid time-ordered effect. Their accommodative functions, 1) accommodative amplitude, 2) accommodative facility, 3) relative positive/negative accommodation, and 4) accommodative lag and their uncorrected spherical equivalent power were examined again following each task and compared with the control values. Results: The monocular accommodative amplitude was significantly decreased after smartphone watching compared with it after reading. The monocular and binocular accommodative facilities were tended to reduce after smartphone watching and book reading but were not significantly different. Neither significant change in positive relative accommodation was determined after smartphone watching nor book reading. Negative relative accommodation after smartphone watching was almost unchanged unlike reading a book. The accommodative lag after smartphone work was significantly higher than after book reading. Conclusions: The near work with a smartphone for 30 min induced the change of some accommodative functions, which was significantly greater than when reading under the same working environment. 】
Investment prioritization based on broadscale spatial budgeting to meet downstream targets for suspended sediment loads

On the basis of a spatially distributed sediment budget across a large basin, costs of achieving certain sediment reduction targets in rivers were estimated. A range of investment prioritization scenarios were tested to identify the most cost‐effective strategy to control suspended sediment loads. The scenarios were based on successively introducing more information from the sediment budget. The relationship between spatial heterogeneity of contributing sediment sources on cost effectiveness of prioritization was investigated. Cost effectiveness was shown to increase with sequential introduction of sediment budget terms. The solution which most decreased cost was achieved by including spatial information linking sediment sources to the downstream target location. This solution produced cost curves similar to those derived using a genetic algorithm formulation. Appropriate investment prioritization can offer large cost savings because the magnitude of the costs can vary by several times depending on what type of erosion source or sediment delivery mechanism is targeted. Target settings which only consider the erosion source rates can potentially result in spending more money than random management intervention for achieving downstream targets. Coherent spatial patterns of contributing sediment emerge from the budget model and its many inputs. The heterogeneity in these patterns can be summarized in a succinct form. This summary was shown to be consistent with the cost difference between local and regional prioritization for three of four test catchments. To explain the effect for the fourth catchment, the detail of the individual sediment sources needed to be taken into account.
Lateral slab deformation and the origin of the western Mediterranean arcs

The western Mediterranean subduction zone (WMSZ) extends from the northern Apennine to southern Spain and turns around forming the narrow and tight Calabrian and Gibraltar Arcs. The evolution of the WMSZ is characterized by a first phase of orogenic wedging followed, from 30 Ma on, by trench retreat and back‐arc extension. Combining new and previous geological data, new tomographic images of the western Mediterranean mantle, and plate kinematics, we describe the evolution of the WMSZ during the last 35 Myr. Our reconstruction shows that the two arcs form by fragmentation of the 1500 km long WMSZ in small, narrow slabs. Once formed, these two narrow slabs retreat outward, producing back‐arc extension and large scale rotation of the flanks, shaping the arcs. The Gibraltar Arc first formed during the middle Miocene, while the Calabrian Arc formed later, during the late Miocene‐Pliocene. Despite the different paleogeographic settings, the mechanism of rupture and backward migration of the narrow slabs presents similarities on both sides of the western Mediterranean, suggesting that the slab deformation is also driven by lateral mantle flow that is particularly efficient in a restricted (upper mantle) style of mantle convection.
Infra-slow Fluctuation Training in Clinical Practice: A Technical History

Infra-slow Fluctuation (ISF) electroencephalogram (EEG) biofeedback is a recent development in neurofeedback training. This form of training is focused on the lowest energy the brain produces (< 0.1 Hz). The intervention is performed with a Direct Current (DC) coupled neurofeedback amplifier. It is distinct from Slow Cortical Potential (SCP) training and Infra-Low Frequency (ILF) training. It shares a similar optimization process with ILF that focuses on emergent state shifts within sessions. These state shifts require frequency adjustments that optimize client response to the training in real time. Due to the technical difficulties inherent in recording these frequencies, EEG investigators largely neglected this low energy until recently. As DC amplifiers improved, the slow frequencies became a signal of increasing interest to researchers. Research has demonstrated an important role for the infra-slow oscillations in clinical work. Positive clinical case outcomes suggest that a larger controlled study is warranted. The technical, clinical, and equipment requirements of the intervention make this form of neurofeedback unique in the pantheon of EEG biofeedback interventions.
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks

The most recent large language models(LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide range of NLP tasks with little or no adaptation. How effective are such models in the financial domain? Understanding this basic question would have a significant impact on many downstream financial analytical tasks. In this paper, we conduct an empirical study and provide experimental evidences of their performance on a wide variety of financial text analytical problems, using eight benchmark datasets from five categories of tasks. We report both the strengths and limitations of the current models by comparing them to the state-of-the-art fine-tuned approaches and the recently released domain-specific pretrained models. We hope our study can help understand the capability of the existing models in the financial domain and facilitate further improvements.
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages

Plasticity, the ability of a neural network to evolve with new data, is crucial for high-performance and sample-efficient visual reinforcement learning (VRL). Although methods like resetting and regularization can potentially mitigate plasticity loss, the influences of various components within the VRL framework on the agent's plasticity are still poorly understood. In this work, we conduct a systematic empirical exploration focusing on three primary underexplored facets and derive the following insightful conclusions: (1) data augmentation is essential in maintaining plasticity; (2) the critic's plasticity loss serves as the principal bottleneck impeding efficient training; and (3) without timely intervention to recover critic's plasticity in the early stages, its loss becomes catastrophic. These insights suggest a novel strategy to address the high replay ratio (RR) dilemma, where exacerbated plasticity loss hinders the potential improvements of sample efficiency brought by increased reuse frequency. Rather than setting a static RR for the entire training process, we propose Adaptive RR, which dynamically adjusts the RR based on the critic's plasticity level. Extensive evaluations indicate that Adaptive RR not only avoids catastrophic plasticity loss in the early stages but also benefits from more frequent reuse in later phases, resulting in superior sample efficiency.
Imitation Learning from Purified Demonstration

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, expert demonstrations are often imperfect, leading to challenges in effectively applying imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential perturbations in imperfect demonstrations and subsequently conduct imitation learning from purified demonstrations. Motivated by the success of diffusion models, we introduce a two-step purification via the diffusion process. In the first step, we apply a forward diffusion process to effectively smooth out the potential perturbations in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal expert demonstrations from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that total variance distance between the purified and optimal demonstration distributions can be upper-bounded. The evaluation results on MuJoCo demonstrate the effectiveness of our method from different aspects.
Negotiating Knowledge Production: The Everyday Inclusions, Exclusions, and Contradictions of Participatory GIS Research*

Abstract Although participatory approaches to geographic information system (GIS) use have significantly altered the technological and social practices of GIS-based research and decision making, they have received relatively little attention within discussions of participatory research. This paper examines how participation and representation are negotiated in participatory GIS research through everyday practices of knowledge production. Inclusion and exclusion in the production of knowledge in participatory GIS are mediated within several intersecting arenas of research practice simultaneously, often with contradictory implications, priorities, and outputs.
Self-supervised Object-Centric Learning for Videos

Unsupervised multi-object segmentation has shown impressive results on images by utilizing powerful semantics learned from self-supervised pretraining. An additional modality such as depth or motion is often used to facilitate the segmentation in video sequences. However, the performance improvements observed in synthetic sequences, which rely on the robustness of an additional cue, do not translate to more challenging real-world scenarios. In this paper, we propose the first fully unsupervised method for segmenting multiple objects in real-world sequences. Our object-centric learning framework spatially binds objects to slots on each frame and then relates these slots across frames. From these temporally-aware slots, the training objective is to reconstruct the middle frame in a high-level semantic feature space. We propose a masking strategy by dropping a significant portion of tokens in the feature space for efficiency and regularization. Additionally, we address over-clustering by merging slots based on similarity. Our method can successfully segment multiple instances of complex and high-variety classes in YouTube videos.