Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Particle Film Application on Leaf Gas Exchange, Water Relations, Nut Yield, and Insect Populations in Mature Pecan Trees

Trials were conducted during summer months of 2002 and 2003 to evaluate the effects of a kaolin-based particle film (Surround WP, Engelhard Corp.) on gas exchange, nut quality, casebearer density and population of natural enemies (insects and arachnids) on pecan (Carya illinoinensis 'Pawnee') trees. Film application was repeated for seven (2002) or nine (2003) times during the growing season. In both years, treated trees showed lower leaf temperature (up to 4 degrees C) than untreated trees. During the warmest hours of the day, kaolin-treated leaves were generally 0 to 2 degrees C cooler than air temperatures, compared to 4 to 6 degrees C for control leaves. Leaf net assimilation rate, stomatal conductance and stem water potential were not affected by film application. Nut size and quality did not differ between the two treatments. Shellout (percentage of nut consisting of kernel) was not affected by treatment and averaged about 55%. Crop grade distribution (fancy, choice, standard, and damaged) was also similar among treatments in both years. In both years, numbers of green lacewing eggs was less on kaolin-treated compared to control leaves. The density of common natural enemies (lady beetles, green lacewings, spiders) of pecan pests did not differ between treatments. The average number of developing nuts damaged by pecan nut casebearer (Acrobasis nuxvorella Neunzig) was significantly higher in kaolin-sprayed trees (24.2%) compared to control trees sprayed with conventional insecticides (9.3%). The results suggest that kaolin-based particle film may not be a viable alternative to conventional methods of controlling pecan pests. Also, under adequate irrigation conditions, carbon assimilation, water relations and productivity may not benefit from kaolin particle film application.
Selective predisposition of Nile tilapia ( Oreochromis niloticus L.) to bacterial and parasitic infection- evidence from the crater lakes Babogaya and Hora-Arsedi,

Mass mortalities of fish sporadically occur throughout the globe and often caused by single or multiple factors posing difficulties on the search for plausible explanations. Following the selective mass mortality of Oreochromis niloticus in two of the Bishoftu Crater Lakes: Babogaya, and Hora-Arsedi between April and May of 2013, we carried out a snapshot survey on the limnology of Lakes Babogaya and Hora-Arsedi including the histopathological and bacteriological examinations of O. niloticus fish. Complete mixing of the lake water and the subsequent anoxic condition was primarily hypothesized as the likely cause. However, vertical, profile of water temperatures and dissolved oxygen concentrations revealed that both lakes were stratified and the epilimnion was well oxygenated (> 8.9 mg L -1 ). All key water quality parameters were within the optimum range for O. niloticus culture except ammonia which was well above the lethal level (2.1 mg L -1 ) in Babogaya and stressive sublethal level of 0.1mg L -1 in Hora-Arsedi. Live fish samples showed clinical symptoms such as caudal skin lesion and injured gills. Postmortem examinations revealed hemorrhagic shock, liver and kidney damage with Aeromonas sp. and parasitic infections detected. Therefore we concluded that direct ammonia toxicity have caused the mortalities in Lake Babogaya, whilst extended sublethal ammonia exposure with subsequent stress and gill damage predisposed the fish to bacterial and parasitic infection that led to mass mortalities in Lake Hora-Arsedi.
Modelling the impact of the recycling fee on indust rial dynamics

This paper investigates the impact of a recycling fee on the dynamic of firms' environmental innovation strategies, waste manageme nt and on environmental variables. We address this issue by developing an evolutionary si mulation model to describe the behaviour of business firms as interacting with that of consumer s and recyclers. Our experiments show that the effect of the fee depends mainly on the design of t he instrument and the way it is implemented. The model dynamics show that an individual fee, pro po tional to the recyclability of each product, paid by the consumers would be the most effective i nstrument.
Novel min-max reformulations of Linear Inverse Problems

In this article, we dwell into the class of so-called ill-posed Linear Inverse Problems (LIP) which simply refers to the task of recovering the entire signal from its relatively few random linear measurements. Such problems arise in a variety of settings with applications ranging from medical image processing, recommender systems, etc. We propose a slightly generalized version of the error constrained linear inverse problem and obtain a novel and equivalent convex-concave min-max reformulation by providing an exposition to its convex geometry. Saddle points of the min-max problem are completely characterized in terms of a solution to the LIP, and vice versa. Applying simple saddle point seeking ascend-descent type algorithms to solve the min-max problems provides novel and simple algorithms to find a solution to the LIP. Moreover, the reformulation of an LIP as the min-max problem provided in this article is crucial in developing methods to solve the dictionary learning problem with almost sure recovery constraints.
Choosing a sampling frequency for ECG QRS detection using convolutional networks

Automated QRS detection methods depend on the ECG data which is sampled at a certain frequency, irrespective of filter-based traditional methods or convolutional network (CNN) based deep learning methods. These methods require a selection of the sampling frequency at which they operate in the very first place. While working with data from two different datasets, which are sampled at different frequencies, often, data from both the datasets may need to resample at a common target frequency, which may be the frequency of either of the datasets or could be a different one. However, choosing data sampled at a certain frequency may have an impact on the model's generalisation capacity, and complexity. There exist some studies that investigate the effects of ECG sample frequencies on traditional filter-based methods, however, an extensive study of the effect of ECG sample frequency on deep learning-based models (convolutional networks), exploring their generalisability and complexity is yet to be explored. This experimental research investigates the impact of six different sample frequencies (50, 100, 250, 500, 1000, and 2000Hz) on four different convolutional network-based models' generalisability and complexity in order to form a basis to decide on an appropriate sample frequency for the QRS detection task for a particular performance requirement. Intra-database tests report an accuracy improvement no more than approximately 0.6\% from 100Hz to 250Hz and the shorter interquartile range for those two frequencies for all CNN-based models. The findings reveal that convolutional network-based deep learning models are capable of scoring higher levels of detection accuracies on ECG signals sampled at frequencies as low as 100Hz or 250Hz while maintaining lower model complexity (number of trainable parameters and training time).
Design and Implementation of SMARTHO - A Network Initiated Handover mechanism in NG-RAN, on P4-based Xilinx NetFPGA switches

This report deals with the design of handover schemes for radio access networks (RAN) in 5G networks, using programmable data plane switches. In 5G networks, the NG-RAN architecture splits the Base Band Unit (BBU) into Central and Distributed Units (CU and DU). This structure has created a mid-haul Network, connecting CUs and DUs. The recent advancements in dataplane programmability can be used to enhance system performance. We show how P4 switches can be used to parse the packets between DU, CU, and Back Haul (Core Network) for potential system improvements. In particular, we consider the scenario of mobile handover. The proposed protocol is called SMARTHO, illustrating a smart handover. Programming Protocol-Independent Packet Processors (P4) is a programming language designed to support specification and programming the forwarding plane behavior of network switches/routers. In SMARTHO, we use P4 Switches to intervene in the handover process for fixed-path mobile users. A resource pre-allocation scheme that reserves resources before the UE reaches a future cell, is proposed. The solution is implemented using a P4-based switch introduced between the CU and the DU. The P4 switch is used to spoof the behavior of User Equipment (UE) and perform the resource allocation in advance. The proposed SMARTHO framework is implemented in the mininet emulation environment and in a reconfigurable hardware environment using NetFPGA-SUME boards. The emulation results show a handover response time improvement of 18% for a tandem of two HOs and 25% for a tandem of three HOs. For testbed implementation, we used NetFPGA-SUME boards as P4 switches. The handover time was measured to be approximately 50~milliseconds in the experiments conducted.
Meta-Semi: A Meta-learning Approach for Semi-supervised Learning

Deep learning based semi-supervised learning (SSL) algorithms have led to promising results in recent years. However, they tend to introduce multiple tunable hyper-parameters, making them less practical in real SSL scenarios where the labeled data is scarce for extensive hyper-parameter search. In this paper, we propose a novel meta-learning based SSL algorithm (Meta-Semi) that requires tuning only one additional hyper-parameter, compared with a standard supervised deep learning algorithm, to achieve competitive performance under various conditions of SSL. We start by defining a meta optimization problem that minimizes the loss on labeled data through dynamically reweighting the loss on unlabeled samples, which are associated with soft pseudo labels during training. As the meta problem is computationally intensive to solve directly, we propose an efficient algorithm to dynamically obtain the approximate solutions. We show theoretically that Meta-Semi converges to the stationary point of the loss function on labeled data under mild conditions. Empirically, Meta-Semi outperforms state-of-the-art SSL algorithms significantly on the challenging semi-supervised CIFAR-100 and STL-10 tasks, and achieves competitive performance on CIFAR-10 and SVHN.
SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes

We present SplitFusion, a novel dense RGB-D SLAM framework that simultaneously performs tracking and dense reconstruction for both rigid and non-rigid components of the scene. SplitFusion first adopts deep learning based semantic instant segmentation technique to split the scene into rigid or non-rigid surfaces. The split surfaces are independently tracked via rigid or non-rigid ICP and reconstructed through incremental depth map fusion. Experimental results show that the proposed approach can provide not only accurate environment maps but also well-reconstructed non-rigid targets, e.g., the moving humans.
Splintering with distributions: A stochastic decoy scheme for private computation

Performing computations while maintaining privacy is an important problem in todays distributed machine learning solutions. Consider the following two set ups between a client and a server, where in setup i) the client has a public data vector $\mathbf{x}$, the server has a large private database of data vectors $\mathcal{B}$ and the client wants to find the inner products $\langle \mathbf{x,y_k} \rangle, \forall \mathbf{y_k} \in \mathcal{B}$. The client does not want the server to learn $\mathbf{x}$ while the server does not want the client to learn the records in its database. This is in contrast to another setup ii) where the client would like to perform an operation solely on its data, such as computation of a matrix inverse on its data matrix $\mathbf{M}$, but would like to use the superior computing ability of the server to do so without having to leak $\mathbf{M}$ to the server. \par We present a stochastic scheme for splitting the client data into privatized shares that are transmitted to the server in such settings. The server performs the requested operations on these shares instead of on the raw client data at the server. The obtained intermediate results are sent back to the client where they are assembled by the client to obtain the final result.
Dalek -- a deep-learning emulator for TARDIS

Supernova spectral time series contain a wealth of information about the progenitor and explosion process of these energetic events. The modeling of these data requires the exploration of very high dimensional posterior probabilities with expensive radiative transfer codes. Even modest parametrizations of supernovae contain more than ten parameters and a detailed exploration demands at least several million function evaluations. Physically realistic models require at least tens of CPU minutes per evaluation putting a detailed reconstruction of the explosion out of reach of traditional methodology. The advent of widely available libraries for the training of neural networks combined with their ability to approximate almost arbitrary functions with high precision allows for a new approach to this problem. Instead of evaluating the radiative transfer model itself, one can build a neural network proxy trained on the simulations but evaluating orders of magnitude faster. Such a framework is called an emulator or surrogate model. In this work, we present an emulator for the TARDIS supernova radiative transfer code applied to Type Ia supernova spectra. We show that we can train an emulator for this problem given a modest training set of a hundred thousand spectra (easily calculable on modern supercomputers). The results show an accuracy on the percent level (that are dominated by the Monte Carlo nature of TARDIS and not the emulator) with a speedup of several orders of magnitude. This method has a much broader set of applications and is not limited to the presented problem.
Stochastic Stein Discrepancies

Stein discrepancies (SDs) monitor convergence and non-convergence in approximate inference when exact integration and sampling are intractable. However, the computation of a Stein discrepancy can be prohibitive if the Stein operator - often a sum over likelihood terms or potentials - is expensive to evaluate. To address this deficiency, we show that stochastic Stein discrepancies (SSDs) based on subsampled approximations of the Stein operator inherit the convergence control properties of standard SDs with probability 1. In our experiments with biased Markov chain Monte Carlo (MCMC) hyperparameter tuning, approximate MCMC sampler selection, and stochastic Stein variational gradient descent, SSDs deliver comparable inferences to standard SDs with orders of magnitude fewer likelihood evaluations.
Complex Human Action Recognition in Live Videos Using Hybrid FR-DL Method

Automated human action recognition is one of the most attractive and practical research fields in computer vision, in spite of its high computational costs. In such systems, the human action labelling is based on the appearance and patterns of the motions in the video sequences; however, the conventional methodologies and classic neural networks cannot use temporal information for action recognition prediction in the upcoming frames in a video sequence. On the other hand, the computational cost of the preprocessing stage is high. In this paper, we address challenges of the preprocessing phase, by an automated selection of representative frames among the input sequences. Furthermore, we extract the key features of the representative frame rather than the entire features. We propose a hybrid technique using background subtraction and HOG, followed by application of a deep neural network and skeletal modelling method. The combination of a CNN and the LSTM recursive network is considered for feature selection and maintaining the previous information, and finally, a Softmax-KNN classifier is used for labelling human activities. We name our model as "Feature Reduction \& Deep Learning" based action recognition method, or FR-DL in short. To evaluate the proposed method, we use the UCF dataset for the benchmarking which is widely-used among researchers in action recognition research. The dataset includes 101 complicated activities in the wild. Experimental results show a significant improvement in terms of accuracy and speed in comparison with six state-of-the-art articles.
Detecting Opportunities for Differential Maintenance of Extracted Views

Semi-structured and unstructured data management is challenging, but many of the problems encountered are analogous to problems already addressed in the relational context. In the area of information extraction, for example, the shift from engineering ad hoc, application-specific extraction rules towards using expressive languages such as CPSL and AQL creates opportunities to propose solutions that can be applied to a wide range of extraction programs. In this work, we focus on extracted view maintenance, a problem that is well-motivated and thoroughly addressed in the relational setting. In particular, we formalize and address the problem of keeping extracted relations consistent with source documents that can be arbitrarily updated. We formally characterize three classes of document updates, namely those that are irrelevant, autonomously computable, and pseudo-irrelevant with respect to a given extractor. Finally, we propose algorithms to detect pseudo-irrelevant document updates with respect to extractors that are expressed as document spanners, a model of information extraction inspired by SystemT.
Detection of the magnetar XTE J1810−197 at 150 and 260 GHz with the NIKA2 kinetic inductance detector camera

Context. The investigation of pulsars between millimetre and optical wavelengths is challenging due to the faintness of the pulsar signals and the relative low sensitivity of the available facilities compared to 100 m class telescopes operating in the centimetre band. The kinetic inductance detector (KID) technology offers large instantaneous bandwidths and a high sensitivity that can help to increase the ability of existing observatories at short wavelengths substantially to detect pulsars and transient emission. Aims. To investigate whether pulsars can be detected with KIDs, we observed the anomalous X-ray pulsar XTE J1810−197 with the New IRAM KIDs Array-2 (NIKA2) camera installed at the IRAM 30 m telescope in Spain. Methods. Several short observations of XTE J1810−197 were made on 2019 March 25 under good weather conditions to verify the stability of the KIDs and to try to detect the expected broadband pulsations from the neutron star. Results. We detected the pulsations from the pulsar with NIKA2 at its two operating frequency bands, 150 and 260 GHz (λ = 2.0 and 1.15 mm, respectively). This is the first time that a pulsar is detected with a receiver based on KID technology in the millimetre band. In addition, this is the first report of short millimetre emission from XTE J1810−197 after its reactivation in December 2018, and it is the first time that the source is detected at 260 GHz, which gives us new insights into the radio emission process of the star. Conclusions. We demonstrate that KIDs can fulfil the technical requirements for detecting pulsed emission from neutron stars in the millimetre band. We show that the magnetar XTE J1810−197 is again emitting strong pulsations in the short millimetre band.
Colostrum production of Alentejano and Large-White × Landrace sows: consumption, passive immunity and mortality of piglets

Colostrum production and composition of 45 Alentejano (AL) and 45 crossed (Large–White × Landrace, LL) sows together with consumption and passive immunization of piglets, were evaluated. Piglets were weighed at birth, 24h, and 21 and 28 days of age. Colostrum was collected, chemically analysed and immunoglobulin G (IgG) determined. On piglets, serum IgG concentrations were determined at 2d and 28d of age. Alentejano piglets consumed 19% less colostrum than LL piglets (267 vs 328 g; p <0.001), but when adjusted for birth weight, the difference between genotypes was not significant ( p =0.891). Alentejano sows produced less colostrum (1985±139 g vs 3761±139 g, p <0.001) than LL sows, even after adjustment for litter birth weight. IgG concentrations were higher in AL colostrum at 12 h and 36 h after the onset of farrowing. They were higher in AL than in LL piglets ( p =0.025) and higher at 2d than at 28d of age ( p< 0.001). Mortality rate of piglets until d21 post–farrowing was higher in AL genotype (27% vs 16.1%, p <0.001). In both genotypes main factors influencing piglet mortality were birth weight and colostrum intake (g/kg birth weight). Higher mortality in AL than in LL genotype was attributable to the higher proportion (28.7% vs 11.6%, p <0.001) of light piglets. It is concluded that for similar birth weight, both AL and LL piglets have a similar ability to ingest colostrum and that AL sows are less able to produce colostrum than LL sows. IgG concentrations of colostrum and of piglets serum were higher in AL than in LL genotype.
Counterfactual Data Augmentation using Locally Factored Dynamics

Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses. Though the subprocesses are not independent, their interactions are often sparse, and the dynamics at any given time step can often be decomposed into locally independent causal mechanisms. Such local causal structures can be leveraged to improve the sample efficiency of sequence prediction and off-policy reinforcement learning. We formalize this by introducing local causal models (LCMs), which are induced from a global causal model by conditioning on a subset of the state space. We propose an approach to inferring these structures given an object-oriented state representation, as well as a novel algorithm for model-free Counterfactual Data Augmentation (CoDA). CoDA uses local structures and an experience replay to generate counterfactual experiences that are causally valid in the global model. We find that CoDA significantly improves the performance of RL agents in locally factored tasks, including the batch-constrained and goal-conditioned settings.
Depthwise Separable Convolutions Versus Recurrent Neural Networks for Monaural Singing Voice Separation

Recent approaches for music source separation are almost exclusively based on deep neural networks, mostly employing recurrent neural networks (RNNs). Although RNNs are in many cases superior than other types of deep neural networks for sequence processing, they are known to have specific difficulties in training and parallelization, especially for the typically long sequences encountered in music source separation. In this paper we present a use-case of replacing RNNs with depth-wise separable (DWS) convolutions, which are a lightweight and faster variant of the typical convolutions. We focus on singing voice separation, employing an RNN architecture, and we replace the RNNs with DWS convolutions (DWS-CNNs). We conduct an ablation study and examine the effect of the number of channels and layers of DWS-CNNs on the source separation performance, by utilizing the standard metrics of signal-to-artifacts, signal-to-interference, and signal-to-distortion ratio. Our results show that by replacing RNNs with DWS-CNNs yields an improvement of 1.20, 0.06, 0.37 dB, respectively, while using only 20.57% of the amount of parameters of the RNN architecture.