Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Label Feature Selection Using Adaptive and Transformed Relevance

Multi-label learning has emerged as a crucial paradigm in data analysis, addressing scenarios where instances are associated with multiple class labels simultaneously. With the growing prevalence of multi-label data across diverse applications, such as text and image classification, the significance of multi-label feature selection has become increasingly evident. This paper presents a novel information-theoretical filter-based multi-label feature selection, called ATR, with a new heuristic function. Incorporating a combinations of algorithm adaptation and problem transformation approaches, ATR ranks features considering individual labels as well as abstract label space discriminative powers. Our experimental studies encompass twelve benchmarks spanning various domains, demonstrating the superiority of our approach over ten state-of-the-art information-theoretical filter-based multi-label feature selection methods across six evaluation metrics. Furthermore, our experiments affirm the scalability of ATR for benchmarks characterized by extensive feature and label spaces. The codes are available at https://github.com/Sadegh28/ATR
Absence of nematic instability in the kagome metal CsV$_3$Sb$_5$

Ever since the discovery of the charge density wave (CDW) transition in the kagome metal CsV$_3$Sb$_5$, the nature of its symmetry breaking is under intense debate. While evidence suggests that the rotational symmetry is already broken at the CDW transition temperature ($T_{\rm CDW}$), an additional electronic nematic instability well below $T_{\rm CDW}$ was reported based on the diverging elastoresistivity coefficient in the anisotropic channel ($m_{E_{2g}}$). Verifying the existence of a nematic transition below $T_{\rm CDW}$ is not only critical for establishing the correct description of the CDW order parameter, but also important for understanding the low-temperature superconductivity. Here, we report elastoresistivity measurements of CsV$_3$Sb$_5$ using three different techniques probing both isotropic and anisotropic symmetry channels. Contrary to previous reports, we find the anisotropic elastoresistivity coefficient $m_{E_{2g}}$ is temperature-independent except for a step jump at $T_{\rm CDW}$. The absence of nematic fluctuations is further substantiated by measurements of the elastocaloric effect, which show no enhancement associated with nematic susceptibility. On the other hand, the symmetric elastoresistivity coefficient $m_{A_{1g}}$ increases below $T_{\rm CDW}$, reaching a peak value of 90 at $T^* = 20$ K. Our results strongly indicate that the phase transition at $T^*$ is not nematic in nature and the previously reported diverging elastoresistivity is due to the contamination from the $A_{1g}$ channel.
Soil Nutrients and Other Major Properties in Grassland Fertilized with Nitrogen and Phosphorus

Soil Sci. Soc. Am. J. doi:10.2136/sssaj2012.0178 Received 5 June 2012. *Corresponding author (Noura.Ziadi@agr.gc.ca). © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Soil Nutrients and Other Major Properties in Grassland Fertilized with Nitrogen and Phosphorus Nutrient Management & Soil & Plant Analysis
Smooth Exact Gradient Descent Learning in Spiking Neural Networks

Artificial neural networks are highly successfully trained with backpropagation. For spiking neural networks, however, a similar gradient descent scheme seems prohibitive due to the sudden, disruptive (dis-)appearance of spikes. Here, we demonstrate exact gradient descent learning based on spiking dynamics that change only continuously. These are generated by neuron models whose spikes vanish and appear at the end of a trial, where they do not influence other neurons anymore. This also enables gradient-based spike addition and removal. We apply our learning scheme to induce and continuously move spikes to desired times, in single neurons and recurrent networks. Further, it achieves competitive performance in a benchmark task using deep, initially silent networks. Our results show how non-disruptive learning is possible despite discrete spikes.
SOFIA FEEDBACK Survey: The Pillars of Creation in [C ii] and Molecular Lines

We investigate the physical structure and conditions of photodissociation regions (PDRs) and molecular gas within the Pillars of Creation in the Eagle Nebula using SOFIA FEEDBACK observations of the [C ii] 158 μm line. These observations are velocity resolved to 0.5 km s−1 and are analyzed alongside a collection of complimentary data with similar spatial and spectral resolution: the [O i] 63 μm line, also observed with SOFIA, and rotational lines of CO, HCN, HCO+, CS, and N2H+. Using the superb spectral resolution of SOFIA, APEX, CARMA, and BIMA, we reveal the relationships between the warm PDR and cool molecular gas layers in context of the Pillars’ kinematic structure. We assemble a geometric picture of the Pillars and their surroundings informed by illumination patterns and kinematic relationships and derive physical conditions in the PDRs associated with the Pillars. We estimate an average molecular gas density nH2∼1.3×105 cm−3 and an average atomic gas density n H ∼ 1.8 × 104 cm−3 and infer that the ionized, atomic, and molecular phases are in pressure equilibrium if the atomic gas is magnetically supported. We find pillar masses of 103, 78, 103, and 18 M ⊙ for P1a, P1b, P2, and P3, respectively, and evaporation times of ∼1–2 Myr. The dense clumps at the tops of the pillars are currently supported by the magnetic field. Our analysis suggests that ambipolar diffusion is rapid and these clumps are likely to collapse within their photoevaporation timescales.
MEMPSEP II. -- Forecasting the Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach

Solar Energetic Particles (SEPs) form a critical component of Space Weather. The complex, intertwined dynamics of SEP sources, acceleration, and transport make their forecasting very challenging. Yet, information about SEP arrival and their properties (e.g., peak flux) is crucial for space exploration on many fronts. We have recently introduced a novel probabilistic ensemble model called the Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). Its primary aim is to forecast the occurrence and physical properties of SEPs. The occurrence forecasting, thoroughly discussed in a preceding paper (Chatterjee et al., 2023), is complemented by the work presented here, which focuses on forecasting the physical properties of SEPs. The MEMPSEP model relies on an ensemble of Convolutional Neural Networks, which leverage a multi-variate dataset comprising full-disc magnetogram sequences and numerous derived and in-situ data from various sources. Skill scores demonstrate that MEMPSEP exhibits improved predictions on SEP properties for the test set data with SEP occurrence probability above 50%, compared to those with a probability below 50%. Results present a promising approach to address the challenging task of forecasting SEP physical properties, thus improving our forecasting capabilities and advancing our understanding of the dominant parameters and processes that govern SEP production.
Leveraging Datapath Propagation in IC3 for Hardware Model Checking

IC3 is a famous bit-level framework for safety verification. By incorporating datapath abstraction, a notable enhancement in the efficiency of hardware verification can be achieved. However, datapath abstraction entails a coarse level of abstraction where all datapath operations are approximated as uninterpreted functions. This level of abstraction, albeit useful, can lead to an increased computational burden during the verification process as it necessitates extensive exploration of redundant abstract state space. In this paper, we introduce a novel approach called datapath propagation. Our method involves leveraging concrete constant values to iteratively compute the outcomes of relevant datapath operations and their associated uninterpreted functions. Meanwhile, we generate potentially useful datapath propagation lemmas in abstract state space and tighten the datapath abstraction. With this technique, the abstract state space can be reduced, and the verification efficiency is significantly improved. We implemented the proposed approach and conducted extensive experiments. The results show promising improvements of our approach compared to the state-of-the-art verifiers.
Spatial wavefront shaping with a multipolar-resonant metasurface for structured illumination microscopy

Structured illumination microscopy (SIM) achieves superresolution in fluorescence imaging through patterned illumination and computational image reconstruction, yet current methods require bulky, costly modulation optics and high-precision optical alignment. This work demonstrates how nano-optical metasurfaces, rationally designed to tailor the optical wavefront at sub-wavelength dimensions, hold great potential as ultrathin, single-surface, all-optical wavefront modulators for SIM. We computationally demonstrate this principle with a multipolar-resonant metasurface composed of silicon nanostructures which generate versatile optical wavefronts in the far field upon variation of the polarization or angle of incident light. Algorithmic optimization is performed to identify the seven most suitable illumination patterns for SIM generated by the metasurface based on three key criteria. We find that multipolar-resonant metasurface SIM (mrm-SIM) achieves resolution comparable to conventional methods by applying the seven optimal metasurface-generated wavefronts to simulated fluorescent objects and reconstructing the objects using proximal gradient descent. The work presented here paves the way for a metasurface-enabled experimental simplification of structured illumination microscopy.
Flow Cytometric Analysis of Coastal Lagoon Bacterioplankton and Picophytoplankton: Fixation and Storage Effects

Abstract The effect of different fixation and storage protocols on the flow cytometric (FCM) simultaneous analysis of bacterioplankton and phytoplankton in coastal seawater samples (Mediterranean coastal lagoons) was investigated. FCM measurements (cell number, fluorescence and scatter characteristics) were obtained through DAPI staining. Three fixatives [glutaraldehyde (GA), formaldehyde (FA) and paraformaldehyde (PFA)] and two storage (3 months duration) methods (5 °C and −196 °C) were tested. Two dominant populations were detected in studied samples: bacteria and eukaryotic picophytoplankton. Adding fixatives (2% final concentration) appears necessary to obtain FCM exhaustive counts of all the bacteria and phytoplanktonic cells. This was related to the permeation effect of fixatives which allowed a better DAPI staining of the cells. Maximum fluorescence, i.e. optimal staining of the cells was obtained with FA or PFA, and significant lower fluorescences with GA. Fixed samples stored at 5 °C induced rapid cell loss. Only storage in liquid nitrogen of samples fixed with FA or PFA, allows mid-term (≥4 months) preservation of bacteria or picophytoplankton cell numbers, and limited evolution of DAPI-induced fluorescence and scatter characteristics.
PTPerf: On the Performance Evaluation of Tor Pluggable Transports

Tor, one of the most popular censorship circumvention systems, faces regular blocking attempts by censors. Thus, to facilitate access, it relies on "pluggable transports" (PTs) that disguise Tor's traffic and make it hard for the adversary to block Tor. However, these are not yet well studied and compared for the performance they provide to the users. Thus, we conduct a first comparative performance evaluation of a total of 12 PTs-the ones currently supported by the Tor project and those that can be integrated in the future. Our results reveal multiple facets of the PT ecosystem. (1) PTs' download time significantly varies even under similar network conditions. (2) All PTs are not equally reliable. Thus, clients who regularly suffer censorship may falsely believe that such PTs are blocked. (3) PT performance depends on the underlying communication primitive. (4) PTs performance significantly depends on the website access method (browser or command-line). Surprisingly, for some PTs, website access time was even less than vanilla Tor. Based on our findings from more than 1.25M measurements, we provide recommendations about selecting PTs and believe that our study can facilitate access for users who face censorship.
Language-EXtended Indoor SLAM (LEXIS): A Versatile System for Real-time Visual Scene Understanding

Versatile and adaptive semantic understanding would enable autonomous systems to comprehend and interact with their surroundings. Existing fixed-class models limit the adaptability of indoor mobile and assistive autonomous systems. In this work, we introduce LEXIS, a real-time indoor Simultaneous Localization and Mapping (SLAM) system that harnesses the open-vocabulary nature of Large Language Models (LLMs) to create a unified approach to scene understanding and place recognition. The approach first builds a topological SLAM graph of the environment (using visual-inertial odometry) and embeds Contrastive Language-Image Pretraining (CLIP) features in the graph nodes. We use this representation for flexible room classification and segmentation, serving as a basis for room-centric place recognition. This allows loop closure searches to be directed towards semantically relevant places. Our proposed system is evaluated using both public, simulated data and real-world data, covering office and home environments. It successfully categorizes rooms with varying layouts and dimensions and outperforms the state-of-the-art (SOTA). For place recognition and trajectory estimation tasks we achieve equivalent performance to the SOTA, all also utilizing the same pre-trained model. Lastly, we demonstrate the system's potential for planning.
On subsurface stormflow: Predictions with simple kinematic theory for saturated and unsaturated flows

This paper provides analytical solutions for some simple cases of subsurface stormflow. The solutions are based on kinematic approximations in both unsaturated and saturated zones. Solutions for rising, falling, and partial equilibrium hydrographs are given. The analytical model has been applied to data collected on a hillslope of the East Twin Brook catchment, Mendip, United Kingdom. Measured and predicted hillslope hydrographs were in good agreement.
Air-sea heat exchange along the northern sea surface temperature front in the eastern tropical Pacific

Abstract The atmospheric response to the oceanic forcing in the eastern Pacific along the northern equatorial sea surface temperature (SST) front is investigated in terms of sensible and latent heat flux during the 6-month period from 28 July 1999 to 27 January 2000 and the 7-month period from 28 June 2000 to 27 January 2001. Of particular interest is the atmospheric boundary layer (ABL) response to oceanic tropical instability waves (TIWs) that distort the SST front during May–January in normal years. In previous studies, time series of surface heat fluxes clearly show the influence of TIWs, but the relationship to spatial patterns of SST and wind speed has been inferred only from sparse in situ data. In this study, satellite observations are used as a basis for compositing in situ data from moorings to compensate for the lack of a spatially dense mooring array. The variability in the position of the SST front caused by westward-propagating TIWs enables fixed mooring locations to measure the ABL response.
Characterization and drug release behavior of highly responsive chip-like electrically modulated reduced graphene oxide–poly(vinyl alcohol) membranes

Hybrid hydrogel membranes composed of reduced graphene oxide (rGO) nanosheets and a poly (vinyl alcohol) (PVA) matrix were investigated as an electrically responsive drug release system. The rGO nanosheets in the matrix act as a physical barrier to inhibit the release of the model anesthetic drug, lidocaine hydrochloride, but the highly responsive release can be enhanced by the presence of rGO when exposed to an electrical stimulus. More interestingly, the on-demand drug release profiles of the assembled rGO–PVA hydrogel into a chip-like device can be highly controlled by an external electrical field. Release profiles ranging from a slow-elution pattern to a rapid release under electrical field treatment can be achieved by manipulating the rGO–PVA composition. Moreover, under cyclic exposure to an electrical stimulus, a highly controllable and repeatedly pulsatile release with desirable precision is obtained from the rGO–PVA hydrogel, implying that the hydrogel exhibits excellent anti-fatigue properties. By combining with the enhanced structural integrity and biocompatibility, the electrically responsive rGO–PVA hydrogel has demonstrated potential biological applications in drug delivery systems. In addition, the easy loading of hydrophilic drugs into the rGO–PVA hydrogel opens up a promising future in biological applications, such as transdermal therapy and wound healing.
Use of the Underwater Video Profiler for the Study of Aggregate Dynamics in the North Mediterranean

Abstract The Underwater Video Profiler is a vertically deployed survey system designed for the quantification of particles >280 μm and of large zooplankton in the 0–1000 m water column. Light reflected by undisturbed target objects forms a dark-field image, which is recorded at 25 Hz frequency. The recorded images are automatically digitized and analysed. The results are expressed as abundance or size distributions and they can be converted to volume or mass units. The system can be configured as a multi-instrument array and can simultaneously acquire biological and physical data. From May 1994 to April 1995 a monthly survey was performed across a frontal structure associated to the North Ligurian geostrophic current. In winter and spring 1995 the front, as well as the offshore dispersion limit of particles, were located near to the coast. In contrast, during autumn 1994, its position was open sea and the terrestrial matter was dispersed far from the coast. The continuous presence of intermediate nepheloid layers along the continental slope indicates that different processes may supply and transport the particulate matter to deeper layers, from where it can be diffused into the basin.
Sampling Ground Water for Organic Contaminants

The proper collection of ground-water samples for analysis of organic compounds differs substantially from routine inorganic constituents because the former are easily contaminated and generally appear in the parts per billion range. Much of the sampling equipment that has been developed at the Robert S. Kerr Environmental Research Laboratory during the past few years is constructed from Teflon or glass. While the former is expensive and the latter is fragile, neither contaminate or modify a water sample as does rubber, metal and most plastics. Designs are provided for a grab sampler, a continuous sampler consisting of adsorbent columns, a protective housing for a sampling system, a continuous discharge/high lift glass pump, and a system for obtaining highly volatile organic compounds from the unsaturated zone.
Irrigation Scheduling Impact Assessment MODel (ISIAMOD): a Decision Tool for Irrigation Scheduling

This paper presents a process-based simulation known as Irrigation Scheduling Impact Assessment MODel (ISIAMOD). It was developed to simulate crop growth&yield, soil water balance and water management response indices to define the impact of irrigation scheduling decisions. ISIAMOD was calibrated and validated using data from field experiments on the irrigated maize crop conducted in an irrigation scheme located in south western Tanzania. The model adequately simulates crop biomass yield, grain yield, seasonal evapotranspiration and average soil moisture content in the crop effective rooting depth. Some unique features of this model make it a major improvement over the existing crop-soil simulation models.
Learning to Assist Different Wearers in Multitasks: Efficient and Individualized Human-In-the-Loop Adaption Framework for Exoskeleton Robots

One of the typical purposes of using lower-limb exoskeleton robots is to provide assistance to the wearer by supporting their weight and augmenting their physical capabilities according to a given task and human motion intentions. The generalizability of robots across different wearers in multiple tasks is important to ensure that the robot can provide correct and effective assistance in actual implementation. However, most lower-limb exoskeleton robots exhibit only limited generalizability. Therefore, this paper proposes a human-in-the-loop learning and adaptation framework for exoskeleton robots to improve their performance in various tasks and for different wearers. To suit different wearers, an individualized walking trajectory is generated online using dynamic movement primitives and Bayes optimization. To accommodate various tasks, a task translator is constructed using a neural network to generalize a trajectory to more complex scenarios. These generalization techniques are integrated into a unified variable impedance model, which regulates the exoskeleton to provide assistance while ensuring safety. In addition, an anomaly detection network is developed to quantitatively evaluate the wearer's comfort, which is considered in the trajectory learning procedure and contributes to the relaxation of conflicts in impedance control. The proposed framework is easy to implement, because it requires proprioceptive sensors only to perform and deploy data-efficient learning schemes. This makes the exoskeleton practical for deployment in complex scenarios, accommodating different walking patterns, habits, tasks, and conflicts. Experiments and comparative studies on a lower-limb exoskeleton robot are performed to demonstrate the effectiveness of the proposed framework.
Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfaces

Large language models (LLMs) exhibit dynamic capabilities and appear to comprehend complex and ambiguous natural language prompts. However, calibrating LLM interactions is challenging for interface designers and end-users alike. A central issue is our limited grasp of how human cognitive processes begin with a goal and form intentions for executing actions, a blindspot even in established interaction models such as Norman’s gulfs of execution and evaluation. To address this gap, we theorize how end-users ‘envision’ translating their goals into clear intentions and craft prompts to obtain the desired LLM response. We define a process of Envisioning by highlighting three misalignments: (1) knowing whether LLMs can accomplish the task, (2) how to instruct the LLM to do the task, and (3) how to evaluate the success of the LLM’s output in meeting the goal. Finally, we make recommendations to narrow the envisioning gulf in human-LLM interactions