Miracle-dz/UniFAR-FTUs huggingface dataset:
The Comparative Morphology of the Pistils of the Ranunculaceæ, Alismaceæ, and Rosaceæ

THERE are two types of pistil present in this family. In the first there is but a single ovule in each pistil; in a specialized form of this type there are, besides the one principal ovule, two to six rudimentary ovules subsequently developed, but never reaching maturity. The second type consists of those pistils which have two to many ovules, all or nearly all reaching maturity. Pistils of the first type are found in the Ranunculec, Anemoncx, and Clematidex, and of the second type in the Helleborex and Paeoniee. The species studied to determine the mode of development of the pistils were the following: Raniuiculus abortivus L. , R. erernogenes Greene, R. delp/ibllfolius Torr. , R. ovalis Raf. , R. glaberrimlnus? iHook. , and Myosurus inimyinyus L. , all representing the strictly uniovulate type; and Anemone Caroliniana Walt. , A. cylillcdrica Gray, A. C(anadlensis L., P11/satilla A1irsutissimIa (Ptursh) Britt. , and Cleniatis ligzslicifolia Nutt. , representing the uniovulate type where rudimentary ovules are developed. For the study
A verifiably secure and proportional committee election rule

The concept of proportional representation in approval-based committee elections has appeared in the social choice literature for over a century and is typically understood as avoiding the underrepresentation of minorities. However, we argue that the security of some distributed systems critically depends on the opposite goal of preventing the overrepresentation of any minority, a goal not previously formalized that leads us to an optimization objective known as maximin support. After providing a thorough analysis of the computational complexity of this objective, we propose a new efficient election rule that simultaneously achieves a) a constant-factor approximation guarantee for it, and b) the property of proportional justified representation (PJR) - one of the strongest forms of proportional representation. However, the most striking feature of the new rule is that one can verify in linear time that the winning committee satisfies the two aforementioned guarantees, even when the algorithm is executed by an untrusted party who only communicates the output. As a result, the rule can be adapted into a verifiable computing scheme. Moreover, its verification procedure easily admits parallel processing for further efficiency. Our work is motivated by an application on blockchain networks that implement Nominated Proof-of-Stake, where the community elects a committee of validators to participate in the consensus protocol, and where preventing overrepresentation protects the network against attacks by an adversarial minority. Our election rule enables a validator selection protocol with formal guarantees on security and proportionality, and its adaptation as a verifiable computing scheme with a parallelized verification proves to be key for its successful implementation given the computationally limited nature of the blockchain architecture.
Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning in 6G Networks

Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth generation (6G) networks, which implies the intelligence over the whole network from the core to the edge including end devices. Nevertheless, fulfilling such vision, particularly the intelligence at the edge, is extremely challenging, due to the limited resources of edge devices as well as the ubiquitous coverage envisioned by 6G. To empower the edge intelligence, in this article, we propose a novel framework called AGIFL (Air-Ground Integrated Federated Learning), which organically integrates air-ground integrated networks and federated learning (FL). In the AGIFL, leveraging the flexible on-demand 3D deployment of aerial nodes such as unmanned aerial vehicles (UAVs), all the nodes can collaboratively train an effective learning model by FL. We also conduct a case study to evaluate the effect of two different deployment schemes of the UAV over the learning and network performance. Last but not the least, we highlight several technical challenges and future research directions in the AGIFL.
Exploratory Cumulative Risk Assessment (CRA) Approaches Using Secondary Data

ABSTRACT Cumulative risk assessments (CRAs) include the examination of risks posed by multiple stressors and include population-specific vulnerabilities and susceptibilities. In this case study, we assess potential hearing impairment hazard due to joint exposure from noise and volatile organic compounds (VOCs) in order to examine the strengths and limitations of using secondary data on exposure and health effects for a CRA. Block group-level noise categories were estimated using modeled street-level data. A quantile regression model of sociodemographic and personal predictors from the 1999–2000 U.S. National Health and Nutrition Examination Survey VOC dataset was used along with block group-level sociodemographic and personal variables to estimate VOC exposures. Hazard indices (HIs) for potential hearing impairment due to joint noise and VOC exposures were calculated. County-averaged HIs for hearing impairment ranged from 0.8 (10th total VOCs percentile and 45–60 dB) to 1.7 (90th total VOCs percentile and 71–75 dB). Limitations of the exposure and health effects data included issues combining heterogeneous data and a lack of established threshold levels for combined low-level exposures; yet, this case study illustrates that screening-level CRAs, including non-chemical stressors, can be accomplished with publicly available data and existing methods.
Consequences of varying regional heterogeneity in source-sink metacommunities

Mouquet, N. , Miller, T. E. , Daufresne, T. and Kneitel, J. M. 2006. Consequencesof varying regional heterogeneity in source sink metacommunities. Oikos 113:481 488.Although the influence of dispersal on coexistence mechanisms in metacommunitieshas received great emphasis, few studies have addressed how such influence is affectedvarying regional heterogeneity. We present a mechanistic model of resourcecompetition in a metacommunity based on classical models of plant competition forlimiting resources. We defined regional heterogeneity as the differences in resourcesupply rates (or resource availabilities) across local communities. As suggested byprevious work, the highest diversify occurred at intermediate levels of dispersal amonglocal communities. However our model shows how the effects of dispersal depend onthe amount of heterogeneity among local communities and vice versa. Both regionaland local species richness were the highest when heterogeneity was intermediate. Wesuggest that empirical studies that found no evidence for source sink or mass effects atthe community level may have examined communities with limited ranges of dispersaland regional heterogeneity. This model of species coexistence contributes to a broaderunderstanding of patterns in real communities.N. Mouquet, UMR 5554, ISEM, Univ. Montpellier II, Place Euge`ne Bataillon, CC 065,FR-34095 Montpellier Cedex 5, France (nmouquet@univ-montp2.fr). T. E. Miller,Dept of Biological Science, Florida State Univ., Tallahassee, FL 32306-1100, USA. T.Daufresne, Dept of Ecology and Evolutionary Biology, Princeton Univ., Princeton, NJ08544, USA. Present address: INRA Centre d’Etude de la Faune Sauvage, Chemin deBorde 16, Rouge-Auzeville, FR-31326 Castanet-Tolosan, France. J. M. Kneitel,Washington Univ., Campus Box 1137, St. Louis, MO 63130. Present address: Dept ofBiological Sciences, California State Univ., 6000 J Street, Sacramento, CA 95819-6077,USA.
Reproducing fractional monomials: Weakening of the Strang-Fix conditions

A method to reproduce causal and symmetric monomials of fractional degree by integer shifts of the corresponding fractional B-splines, introduced by M. Unser and Th. Blue, is presented. Thus the traditional relation between the degree of reproduced monomials and the order of approximation holds. Bivariate, obtained by tensor product, fractional B-splines are introduced; and reproducing of bivariate causal and symmetric monomials is shown. Demonstration that the method is based on a weakening of the Strang-Fix conditions is presented.
HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images

Training end-to-end networks for classifying gigapixel size histopathological images is computationally intractable. Most approaches are patch-based and first learn local representations (patch-wise) before combining these local representations to produce image-level decisions. However, dividing large tissue structures into patches limits the context available to these networks, which may reduce their ability to learn representations from clinically relevant structures. In this paper, we introduce a novel attention-based network, the Holistic ATtention Network (HATNet) to classify breast biopsy images. We streamline the histopathological image classification pipeline and show how to learn representations from gigapixel size images end-to-end. HATNet extends the bag-of-words approach and uses self-attention to encode global information, allowing it to learn representations from clinically relevant tissue structures without any explicit supervision. It outperforms the previous best network Y-Net, which uses supervision in the form of tissue-level segmentation masks, by 8%. Importantly, our analysis reveals that HATNet learns representations from clinically relevant structures, and it matches the classification accuracy of human pathologists for this challenging test set. Our source code is available at \url{this https URL}
Decomposed Generation Networks with Structure Prediction for Recipe Generation from Food Images

Recipe generation from food images and ingredients is a challenging task, which requires the interpretation of the information from another modality. Different from the image captioning task, where the captions usually have one sentence, cooking instructions contain multiple sentences and have obvious structures. To help the model capture the recipe structure and avoid missing some cooking details, we propose a novel framework: Decomposed Generation Networks (DGN) with structure prediction, to get more structured and complete recipe generation outputs. To be specific, we split each cooking instruction into several phases, and assign different sub-generators to each phase. Our approach includes two novel ideas: (i) learning the recipe structures with the global structure prediction component and (ii) producing recipe phases in the sub-generator output component based on the predicted structure. Extensive experiments on the challenging large-scale Recipe1M dataset validate the effectiveness of our proposed model DGN, which improves the performance over the state-of-the-art results.
Origins of eukaryotic excitability

All living cells interact dynamically with a constantly changing world. Eukaryotes, in particular, evolved radically new ways to sense and react to their environment. These advances enabled new and more complex forms of cellular behaviour in eukaryotes, including directional movement, active feeding, mating, and responses to predation. But what are the key events and innovations during eukaryogenesis that made all of this possible? Here we describe the ancestral repertoire of eukaryotic excitability and discuss five major cellular innovations that enabled its evolutionary origin. The innovations include a vastly expanded repertoire of ion channels, the emergence of cilia and pseudopodia, endomembranes as intracellular capacitors, a flexible plasma membrane and the relocation of chemiosmotic ATP synthesis to mitochondria, which liberated the plasma membrane for more complex electrical signalling involved in sensing and reacting. We conjecture that together with an increase in cell size, these new forms of excitability greatly amplified the degrees of freedom associated with cellular responses, allowing eukaryotes to vastly outperform prokaryotes in terms of both speed and accuracy. This comprehensive new perspective on the evolution of excitability enriches our view of eukaryogenesis and emphasizes behaviour and sensing as major contributors to the success of eukaryotes. This article is part of the theme issue ‘Basal cognition: conceptual tools and the view from the single cell’.
WGANVO: Monocular Visual Odometry based on Generative Adversarial Networks

In this work we present WGANVO, a Deep Learning based monocular Visual Odometry method. In particular, a neural network is trained to regress a pose estimate from an image pair. The training is performed using a semi-supervised approach. Unlike geometry based monocular methods, the proposed method can recover the absolute scale of the scene without neither prior knowledge nor extra information. The evaluation of the system is carried out on the well-known KITTI dataset where it is shown to work in real time and the accuracy obtained is encouraging to continue the development of Deep Learning based methods.
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs

Meta-planning, or learning to guide planning from experience, is a promising approach to improving the computational cost of planning. A general meta-planning strategy is to learn to impose constraints on the states considered and actions taken by the agent. We observe that (1) imposing a constraint can induce context-specific independences that render some aspects of the domain irrelevant, and (2) an agent can take advantage of this fact by imposing constraints on its own behavior. These observations lead us to propose the context-specific abstract Markov decision process (CAMP), an abstraction of a factored MDP that affords efficient planning. We then describe how to learn constraints to impose so the CAMP optimizes a trade-off between rewards and computational cost. Our experiments consider five planners across four domains, including robotic navigation among movable obstacles (NAMO), robotic task and motion planning for sequential manipulation, and classical planning. We find planning with learned CAMPs to consistently outperform baselines, including Stilman's NAMO-specific algorithm. Video: this https URL
PEARL MILLET PRODUCTION PRACTICES IN SEMI-ARID WEST AFRICA: A REVIEW

SUMMARY Pearl millet (Pennisetum glaucum L.) is an important grain crop for millions of poor farmers and consumers in the semi-arid region of West Africa. During the past 40 years, much research on pearl millet production practices and adoption in this region has been conducted, but an attempt to summarize these results has not been previously completed and these research results are not readily available to many West African scientists. This review was completed to address this need and integrate knowledge, and at the same time identify research needs for the future and extension priorities for semi-arid West African agro-ecological zones. Research has shown that selection of improved varieties and cropping systems, appropriate cultural practices, and recommended integrated soil, nutrient, residue and pest management can greatly increase grain and stover yields of pearl millet. However, adoption by farmers has been minimal due to limited profitability, high risk and labour demand, limited input supply, market availability and appropriate public policy. This review has 196 articles included as in-text citations (Table 1) compared to 149 articles in the reference list, indicating that only one in four articles integrated two or more topics in the research. The obvious conclusion is that most of the past research has not addressed the ‘system’ but rather one or two management practices. In addition, most studies have interpreted responses in terms of yield without addressing other important considerations for farmer adoption. Recent conservation agriculture research moves closer to addressing the larger integrative types of research needed. Such research is complex and requires sustained funding for field and laboratory activities, but also for computer simulation modelling and economic assessment.
Combating Misinformation in Bangladesh

There has been a growing interest within CSCW community in understanding the characteristics of misinformation propagated through computational media, and the devising techniques to address the associated challenges. However, most work in this area has been concentrated on the cases in the western world leaving a major portion of this problem unaddressed that is situated in the Global South. This paper aims to broaden the scope of this discourse by focusing on this problem in the context of Bangladesh, a country in the Global South. The spread of misinformation on Facebook in Bangladesh, a country with a population of over 163 million, has resulted in chaos, hate attacks, and killings. By interviewing journalists, fact-checkers, in addition to surveying the general public, we analyzed the current state of verifying misinformation in Bangladesh. Our findings show that most people in the 'news audience' want the news media to verify the authenticity of online information that they see online. However, the newspaper journalists say that fact-checking online information is not a part of their job, and it is also beyond their capacity given the amount of information being published online every day. We further find that the voluntary fact-checkers in Bangladesh are not equipped with sufficient infrastructural support to fill in this gap. We show how our findings are connected to some of the core concerns of CSCW community around social media, collaboration, infrastructural politics, and information inequality. From our analysis, we also suggest several pathways to increase the impact of fact-checking efforts through collaboration, technology design, and infrastructure development.
Towards understanding retrosynthesis by energy-based models

Retrosynthesis -- the process of identifying a set of reactants to synthesize a target molecule -- is of vital importance to material design and drug discovery. Existing machine learning approaches based on language models and graph neural networks have achieved encouraging results. In this paper, we propose a framework that unifies sequence- and graph-based methods as energy-based models (EBMs) with different energy functions. This unified perspective provides critical insights about EBM variants through a comprehensive assessment of performance. Additionally, we present a novel dual variant within the framework that performs consistent training over Bayesian forward- and backward-prediction by constraining the agreement between the two directions. This model improves state-of-the-art performance by 9.6% for template-free approaches where the reaction type is unknown.
Bid Prediction in Repeated Auctions with Learning

We consider the problem of bid prediction in repeated auctions and evaluate the performance of econometric methods for learning agents, using a dataset from a mainstream sponsored search auction marketplace. Sponsored search auctions are a billion dollar industry and the main source of revenue of several tech giants. A critical problem in optimizing such marketplaces is understanding how bidders will react to changes in the auction design. We propose the use of no-regret-based econometrics for bid prediction, modeling players as no-regret learners with respect to a utility function, unknown to the analyst. We propose econometric approaches to simultaneously learn the parameters of a player’s utility and her learning rule, and apply these methods to a real-world dataset from the BingAds sponsored search auction marketplace. We show that the no-regret econometric methods perform comparably to state-of-the-art time-series machine-learning methods when there is no co-variate shift, but significantly outperform machine-learning methods when there is a co-variate shift between the training and test periods. This portrays the importance of using structural econometric approaches for predicting how players will respond to changes in the market. Moreover, we show that among structural econometric methods, approaches based on no-regret learning outperform more traditional, equilibrium-based, econometric methods that assume that players continuously best respond to competition. Finally, we demonstrate how the prediction performance of the no-regret learning algorithms can be further improved by considering bidders who optimize a utility function with a visibility bias component.
Augmented signal processing in Liquid Argon Time Projection Chambers with a deep neural network

The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino detector technology widely used in recent and upcoming accelerator neutrino experiments. It features a low energy threshold and high spatial resolution that allow for comprehensive reconstruction of event topologies. In current-generation LArTPCs, the recorded data consist of digitized waveforms on wires produced by induced signal on wires of drifting ionization electrons, which can also be viewed as two-dimensional (2D) (time versus wire) projection images of charged-particle trajectories. For such an imaging detector, one critical step is the signal processing that reconstructs the original charge projections from the recorded 2D images. For the first time, we introduce a deep neural network in LArTPC signal processing to improve the signal region of interest detection. By combining domain knowledge (e.g., matching information from multiple wire planes) and deep learning, this method shows significant improvements over traditional methods. This work details the method, software tools, and performance evaluated with realistic detector simulations.
Spin polarization inducing diamagnetism, inverse magnetic catalysis and saturation behavior of charged pion spectra

We offer a unified picture to understand the noval properties of magnetized QCD matter revealed by lattice calculations, including diamagnetism at low temperature, the inverse magnetic catalysis around the critical temperature and the saturation behavior of charged pion spectra under magnetic fields. Motivated by the diamagnetic material caused by paired electrons in the atoms with zero net magnetic moment, we propose that the diamagnetism in magnetized QCD matter is caused by spin polarization condensation of quark antiquark with parallel spin pairing, which carries zero net magnetic moment and reduces the total net magnetic moment of the system, thus the system with spin polarization exhibits diamagnetism. The parallel spin pairing condensation naturally breaks chiral condensate, and induces the inverse magnetic catalysis around the critical temperature. It is noticed that the spin polarization operator is the same as the dynamical anomalous magnetic moment of quarks, its coupling with magnetic field causes Zeeman splitting of the dispersion relation of quarks thus changes the neutral pion and charged pion meson mass spectra under magnetic fields. The neutral pion mass decreases with magnetic field and charged pion mass show a nonlinear behavior, i.e., firstly linearly increases with the magnetic field and then saturates at strong magnetic field, which is consistent with the lattice result.
Inexact Newton Method for M-Tensor Equations

We first investigate properties of M-tensor equations. In particular, we show that if the constant term of the equation is nonnegative, then finding a nonnegative solution of the equation can be done by finding a positive solution of a lower dimensional M-tensor equation. We then propose an inexact Newton method to find a positive solution to the lower dimensional equation and establish its global convergence. We also show that the convergence rate of the method is quadratic. At last, we do numerical experiments to test the proposed Newton method. The results show that the proposed Newton method has a very good numerical performance.