Miracle-dz/UniFAR-FTUs huggingface dataset:
Causal inference and constructed measures: towards a new model of measurement for psychosocial constructs.

Psychosocial constructs can only be assessed indirectly, and measures are typically formed by a combination of indicators that are thought to relate to the construct. Reflective and formative measurement models offer different conceptualizations of the relation between the indicators and what is often a univariate latent variable supposed to correspond in some way to the construct. In this paper, a causal interpretation is proposed of associations between constructed measures and various outcomes that is applicable to both reflective and formative models and is moreover applicable even if the usual assumptions of these models are violated such that the indicators have causal efficacy, rather than the supposed underlying latent. The interpretative approach is illustrated using associations between measures of social integration and cardiovascular disease incidence, concerning which neither the formative nor the reflective models seem adequate. It is argued that formative models misconstrue the relationship between the constructed measures and the underlying reality by which causal processes operate, but that reflective models misconstrue the nature of the underlying reality itself by typically presuming that the constituents of it that are causally efficacious are unidimensional. The ensuing problems arising from these misconstruals are discussed. An outline for a new model of the process of measure construction is put forward. Discussion is given to the practical implications of these observations and proposals for the provision of definitions, the selection of items, item-by-item analyses, the construction of measures, and the interpretation of the associations of these measures with subsequent outcomes.
Democratizing the Edge: A Pervasive Edge Computing Framework

The needs of emerging applications, such as augmented and virtual reality, federated machine learning, and autonomous driving, have motivated edge computing--the push of computation capabilities to the edge. Various edge computing architectures have emerged, including multi-access edge computing and edge-cloud, all with the premise of reducing communication latency and augmenting privacy. However, these architectures rely on static and pre-deployed infrastructure, falling short in harnessing the abundant resources at the network's edge. In this paper, we discuss the design of Pervasive Edge Computing (PEC)--a democratized edge computing framework, which enables end-user devices (e.g., smartphones, IoT devices, and vehicles) to dynamically participate in a large-scale computing ecosystem. Our vision of the democratized edge involves the real-time composition of services using available edge resources like data, software, and compute-hardware from multiple stakeholders. We discuss how the novel Named-Data Networking architecture can facilitate service deployment, discovery, invocation, and migration. We also discuss the economic models critical to the adoption of PEC and the outstanding challenges for its full realization.
Partial Recovery in the Graph Alignment Problem

Partially Recovering a Graph Alignment in the Correlated Erdös–Renyi Model Given two graphs, how can we partially recover a one-to-one mapping between nodes that maximizes edge overlap? This problem, known as the graph alignment problem, appears in settings such as social network deanonymization and cellular biology. In “Partial Recovery in the Graph Alignment Problem,” G. Hall and L. Massoulié consider a stylized mathematical model of problems of this type: they assume that the input graphs are generated via a probabilistic model, namely, the correlated Erdös–Renyi model with parameters (n, q, s). They provide both necessary and sufficient conditions on (n, q, s) under which partial recovery can be achieved. In particular, they show that partial recovery can be achieved in the nqs = Ɵ(1) regime under certain additional assumptions.
Enhancing surface production of negative ions using nitrogen doped diamond in a deuterium plasma

The production of negative ions is of significant interest for applications including mass spectrometry, particle acceleration, material surface processing, and neutral beam injection for magnetic confinement fusion. Methods to improve the efficiency of the surface production of negative ions, without the use of low work function metals, are of interest for mitigating the complex engineering challenges these materials introduce. In this study we investigate the production of negative ions by doping diamond with nitrogen. Negatively biased (−20 V or −130 V), nitrogen doped micro-crystalline diamond films are introduced to a low pressure deuterium plasma (helicon source operated in capacitive mode, 2 Pa, 26 W) and negative ion energy distribution functions are measured via mass spectrometry with respect to the surface temperature (30 °C to 750 °C) and dopant concentration. The results suggest that nitrogen doping has little influence on the yield when the sample is biased at −130 V, but when a relatively small bias voltage of −20 V is applied the yield is increased by a factor of 2 above that of un-doped diamond when its temperature reaches 550 °C. The doping of diamond with nitrogen is a new method for controlling the surface production of negative ions, which continues to be of significant interest for a wide variety of practical applications.
Approximation with Tensor Networks. Part II: Approximation Rates for Smoothness Classes

We study the approximation by tensor networks (TNs) of functions from classical smoothness classes. The considered approximation tool combines a tensorization of functions in $L^p([0,1))$, which allows to identify a univariate function with a multivariate function (or tensor), and the use of tree tensor networks (the tensor train format) for exploiting low-rank structures of multivariate functions. The resulting tool can be interpreted as a feed-forward neural network, with first layers implementing the tensorization, interpreted as a particular featuring step, followed by a sum-product network with sparse architecture. In part I of this work, we presented several approximation classes associated with different measures of complexity of tensor networks and studied their properties. In this work (part II), we show how classical approximation tools, such as polynomials or splines (with fixed or free knots), can be encoded as a tensor network with controlled complexity. We use this to derive direct (Jackson) inequalities for the approximation spaces of tensor networks. This is then utilized to show that Besov spaces are continuously embedded into these approximation spaces. In other words, we show that arbitrary Besov functions can be approximated with optimal or near to optimal rate. We also show that an arbitrary function in the approximation class possesses no Besov smoothness, unless one limits the depth of the tensor network.
DocVQA: A Dataset for VQA on Document Images

We present a new dataset for Visual Question Answering (VQA) on document images called DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images. Detailed analysis of the dataset in comparison with similar datasets for VQA and reading comprehension is presented. We report several baseline results by adopting existing VQA and reading comprehension models. Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy). The models need to improve specifically on questions where understanding structure of the document is crucial. The dataset, code and leaderboard are available at docvqa.org
Influence of O2 availability on NO and N2O release by nitrification and denitrification in soils

The availability of O2 is believed to be one of the main factors regulating nitrification and denitrification and the release of NO and N2O. The availability of O2 in soil is controlled by the O2 partial pressure in the gas phase and by the moisture content in the soil. Therefore, we investigated the influence of O2 partial pressures and soil moisture contents on the NO and N2O release in a sandy and a loamy silt and differentiated between nitrification and denitrification by selective inhibition of nitrification with 10 Pa acetylene. At 60% whc (maximum water holding capacity) NO and N2O release by denitrification increased with decreasing O2 partial pressure and reached a maximum under anoxic conditions. Under anoxic conditions NO and N2O were only released by denitrification. NO and N2O release by nitrification also increased with decreasing O2 partial pressure, but reached a maximum at 0.1–0.5% O2 and then decreased again. Nitrification was the main source of NO and N2O at O2 partial pressures higher than 0.1–0.5% O2. At lower O2 partial pressures denitrification was the main source of NO and N2O. With decreasing O2 partial pressure N2O release increased more than NO release, indicating that the N2O release was more sensitive against O2 than the NO release. At ambient O2 partial pressure (20.5% O2) NO and N2O release by denitrification increased with increasing soil moisture content. The maximum NO and N2O release was observed at soil moisture contents of 65–80% whc and 100% whc, respectively. NO and N2O release by nitrification also increased with increasing soil moisture content with a maximum at 45–55% whc and 90% whc, respectively. Nitrification was the main source of NO and N2O at soil moisture contents lower than 90% whc and 80% whc, respectively. Higher soil moisture contents favoured NO and N2O release by denitrification. Soil texture had also an effect on the release of NO and N2O. The coarse‐textured sandy silt released more NO than N2O compared with the fine‐textured loamy silt. At high soil moisture contents (80–100% whc) the fine‐textured soil showed a higher N2O release by denitrification than the coarse‐textured soil. We assume that the fine‐textured soil became anoxic at a lower soil moisture content than the coarse‐textured soil. In conclusion, the effects of O2 partial pressure, soil moisture and soil texture were consistent with the theory that denitrification increasingly contributes to the release of NO and in particular N2O when conditions for soil microorganisms become increasingly anoxic.
On elastic geodesic grids and their planar to spatial deployment

We propose a novel type of planar-to-spatial deployable structures that we call elastic geodesic grids. Our approach aims at the approximation of freeform surfaces with spatial grids of bent lamellas which can be deployed from a planar configuration using a simple kinematic mechanism. Such elastic structures are easy-to-fabricate and easy-to-deploy and approximate shapes which combine physics and aesthetics. We propose a solution based on networks of geodesic curves on target surfaces and we introduce a set of conditions and assumptions which can be closely met in practice. Our formulation allows for a purely geometric approach which avoids the necessity of numerical shape optimization by building on top of theoretical insights from differential geometry. We propose a solution for the design, computation, and physical simulation of elastic geodesic grids, and present several fabricated small-scale examples with varying complexity. Moreover, we provide an empirical proof of our method by comparing the results to laser-scans of the fabricated models. Our method is intended as a form-finding tool for elastic gridshells in architecture and other creative disciplines and should give the designer an easy-to-handle way for the exploration of such structures.
Deriving nutrient targets to prevent excessive cyanobacterial densities in U.S. lakes and reservoirs

1. High densities of cyanobacteria can interfere with the use of lakes and reservoirs for recreation and as sources for drinking water, and one approach for reducing the amount of cyanobacteria is to reduce nutrient concentrations in the waterbody. 2. An approach is described for deriving numeric targets for concentrations of total phosphorus (TP) and total nitrogen (TN) that are associated with a pre-specified probability of cyanobacterial biovolume that exceeds the recommended World Health Organization thresholds for recreation in the water. The analysis consisted of two phases. First, a divisive tree algorithm was used to identify groups of lakes in which the relationship between nutrients and cyanobacterial biovolume was similar. Second, hierarchical Bayesian models were used to estimate relationships between cyanobacterial biovolume, TP and TN, while partitioning the observed variance in biovolume into components associated with sampling variability, temporal variability, and among-lake differences. 3. The final model accounted for 91% of the variance in cyanobacterial biovolume among different lakes and was used to identify nutrient concentrations that maintain a low probability of excessively high cyanobacterial biovolumes. 4. When no classes of lakes were specified and the relationship between cyanobacterial biovolume and nutrient concentrations was modelled using a national data set, mean targets of 87 and 1100 lg L 1 were derived for TP and TN, respectively, to maintain cyanobacterial biovolume below moderate risk levels as defined by the World Health Organization. After classification, mean nutrient targets in lakes that were found to be most susceptible to high biovolumes of cyanobacteria (i.e. deep lakes) were 61 and 800 lg L 1 for TP and TN, while higher nutrient thresholds were observed for other classes of lakes.
Handling Variable-Dimensional Time Series with Graph Neural Networks

Several applications of Internet of Things (IoT) technology involve capturing data from multiple sensors resulting in multi-sensor time series. Existing neural networks based approaches for such multi-sensor or multivariate time series modeling assume fixed input dimension or number of sensors. Such approaches can struggle in the practical setting where different instances of the same device or equipment such as mobiles, wearables, engines, etc. come with different combinations of installed sensors. We consider training neural network models from such multi-sensor time series, where the time series have varying input dimensionality owing to availability or installation of a different subset of sensors at each source of time series. We propose a novel neural network architecture suitable for zero-shot transfer learning allowing robust inference for multivariate time series with previously unseen combination of available dimensions or sensors at test time. Such a combinatorial generalization is achieved by conditioning the layers of a core neural network-based time series model with a "conditioning vector" that carries information of the available combination of sensors for each time series. This conditioning vector is obtained by summarizing the set of learned "sensor embedding vectors" corresponding to the available sensors in a time series via a graph neural network. We evaluate the proposed approach on publicly available activity recognition and equipment prognostics datasets, and show that the proposed approach allows for better generalization in comparison to a deep gated recurrent neural network baseline.
Cyclic dynamics of cluster size and order causes microphase separation in active filament systems

The onset of polar flocking in active matter is discontinuous, akin to gas-liquid phase transitions, except that the steady state exhibits microphase separation into polar clusters. While these features have been observed in theoretical models and experiments, little is known about the underlying mesoscopic processes at the cluster level. Here we show that emergence and maintenance of polar order are governed by the interplay between the assembly and disassembly dynamics of clusters with varying size and degree of polar order. Using agent-based simulations of propelled filaments in a parameter regime relevant for actomyosin motility assays, we monitor the temporal evolution of cluster statistics and the transport processes of filaments between clusters. We find that, over a broad parameter range, the emergence of order is determined by nucleation and growth of polar clusters, where the nucleation threshold depends not only on the cluster size but also on its polar moment. Growth involves cluster self-replication, and polar order is established by cluster growth and fragmentation. Maintenance of the microphaseseparated, polar-ordered state results from a cyclic dynamics in cluster size and order, driven by an interplay between cluster nucleation, coagulation, fragmentation and evaporation of single filaments. These findings are corroborated by a kinetic model for the cluster dynamics that includes these elementary cluster-level processes. It consistently reproduces the cluster statistics as well as the cyclic turnover from disordered to ordered clusters and back. Such cyclic kinetic processes could represent a general mechanism for the maintenance of order in active matter systems.
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition

An important problem in machine auditory perception is to recognize and detect sound events. In this paper, we propose a sequential self-teaching approach to learning sounds. Our main proposition is that it is harder to learn sounds in adverse situations such as from weakly labeled and/or noisy labeled data, and in these situations a single stage of learning is not sufficient. Our proposal is a sequential stage-wise learning process that improves generalization capabilities of a given modeling system. We justify this method via technical results and on Audioset, the largest sound events dataset, our sequential learning approach can lead to up to 9% improvement in performance. A comprehensive evaluation also shows that the method leads to improved transferability of knowledge from previously trained models, thereby leading to improved generalization capabilities on transfer learning tasks.
Biosynthesis of the polyether antibiotic monensin-A. Results from the incorporations of labelled acetate and propionate as a probe of the carbon chain assembly processes

The incorporation of sodium [2-2H2]- and (S)-[2-2H1]-propionate into the polyether antibiotic monensin-A in cultures of Streptomyces cinnamonensis occurs with retention of label only at C-4 and C-6, whereas during the incorporation of sodium (R)-[2-2H1]propionate the deuterium label is lost to the medium. These results are consistent with the formation of (S)-methylmalonyl-CoA from the labelled propionate by carboxylation of propionyl-CoA with loss of the 2-pro-R hydrogen. The (S)-methylmalonyl-CoA is subsequently incorporated into the antibiotic by a decarboxylative condensation occurring with overall inversion. The incorporations of sodium [1-13C,2-2-H3]-and [2-13C,2-2H3]-acetates into monensin-A provide evidence for a pathway of metabolism leading to methylmalonyl-CoA that does not proceed via succinyl-CoA. Instead, acetyl-CoA may be processed via butyryl-CoA and isobutyryl-CoA, to afford (S)-methylmalonyl-CoA.
Fluorescent dye particles are good pollen analogs for hummingbird-pollinated Silene virginica (Caryophyllaceae)

We tested the utility of fluorescent dye particles as pollen analogs for hummingbird-pollinated Silene virginica (Caryophyllaceae) by comparing the movement of pollen and fluorescent dye across sequentially visited emasculated flowers. We found no differences in either the intercept or the slope of the regressions of the two particle types on floral visitation sequence. In addition, the presence of fluorescent powder on the stigmas of a flower was a good indicator of pollen transferred to that flower. Both pollen and dye particles were transported almost identical distances in the flower sequence. These data indicate that fluorescent powder is a good pollen analog for S. virginica. We compare our findings with previous studies examining the utility of fluorescent dye as a pollen analog. Keywords: pollen analog, fluorescent dye, pollen carry-over.
Research on the spreading of cuscuta l. (convolvulaceae) in the county of sibiu, romania: With 2 figures and 2 tables

The occurrence of Cuscuta L. species in the county of Sibiu has been studied during the years 1995–1999. For the seven Cuscuta species found in the area 64 new host plants have been identified. Zusammenfassung Untersuchungen zur Verbreitung von Cuscuta L. (Convolvulaceae) in der Provinz Sibiu, Rumanien Das Vorkommen von Arten der Gattung Cuscuta L. in der Provinz Sibiu wurde in den Jahren 1995–1999 untersucht. Fur die sieben nachgewiesenen Arten wurden 64 neue Wirtspflanzen bestimmt.
On Second Order q-Difference Equations Satisfied by Al-Salam–Carlitz I-Sobolev Type Polynomials of Higher Order

This contribution deals with the sequence {Un(a)(x;q,j)}n≥0 of monic polynomials in x, orthogonal with respect to a Sobolev-type inner product related to the Al-Salam–Carlitz I orthogonal polynomials, and involving an arbitrary number j of q-derivatives on the two boundaries of the corresponding orthogonality interval, for some fixed real number q∈(0,1). We provide several versions of the corresponding connection formulas, ladder operators, and several versions of the second order q-difference equations satisfied by polynomials in this sequence. As a novel contribution to the literature, we provide certain three term recurrence formula with rational coefficients satisfied by Un(a)(x;q,j), which paves the way to establish an appealing generalization of the so-called J-fractions to the framework of Sobolev-type orthogonality.
Modality-Agnostic Attention Fusion for visual search with text feedback

Image retrieval with natural language feedback offers the promise of catalog search based on fine-grained visual features that go beyond objects and binary attributes, facilitating real-world applications such as e-commerce. Our Modality-Agnostic Attention Fusion (MAAF) model combines image and text features and outperforms existing approaches on two visual search with modifying phrase datasets, Fashion IQ and CSS, and performs competitively on a dataset with only single-word modifications, Fashion200k. We also introduce two new challenging benchmarks adapted from Birds-to-Words and Spot-the-Diff, which provide new settings with rich language inputs, and we show that our approach without modification outperforms strong baselines. To better understand our model, we conduct detailed ablations on Fashion IQ and provide visualizations of the surprising phenomenon of words avoiding "attending" to the image region they refer to.
Up in the Clouds: Is Sustainable use of Tropical Montane Cloud Forests Possible in Malaysia?

Perched atop misty mountains, tropical montane cloud forests are one of Earth's most imperiled and neglected ecosystems. More than half of these forests occur in Southeast Asia; those in Malaysia are considered some of the best studied in the region. Malaysia has numerous mountains that are exceptionally rich in biodiversity and sustain numerous locally endemic species, but they are also threatened by expanding forestry, agriculture, infrastructure, and global warming. Malaysia serves as an excellent case study to illustrate the issues and challenges associated with tropical cloud forest conservation. We critically assess the current status of Malaysia's cloud forests—focusing on their biological uniqueness and prospects for long-term survival—and propose conservation strategies for agricultural, forestry, tourism, and policy sectors to help conserve these endangered ecosystems. It is our hope that decisionmakers around the region can use our review to evaluate and improve their national strategies related to cloud forest conservation.