Miracle-dz/UniFAR-FTUs huggingface dataset:
ResKD: Residual-Guided Knowledge Distillation

Knowledge distillation, aimed at transferring the knowledge from a heavy teacher network to a lightweight student network, has emerged as a promising technique for compressing neural networks. However, due to the capacity gap between the heavy teacher and the lightweight student, there still exists a significant performance gap between them. In this article, we see knowledge distillation in a fresh light, using the knowledge gap, or the residual, between a teacher and a student as guidance to train a much more lightweight student, called a res-student. We combine the student and the res-student into a new student, where the res-student rectifies the errors of the former student. Such a residual-guided process can be repeated until the user strikes the balance between accuracy and cost. At inference time, we propose a sample-adaptive strategy to decide which res-students are not necessary for each sample, which can save computational cost. Experimental results show that we achieve competitive performance with 18.04%, 23.14%, 53.59%, and 56.86% of the teachers’ computational costs on the CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet datasets. Finally, we do thorough theoretical and empirical analysis for our method.
Organochlorine Compounds in Common Carp (Cyprinus carpio) from Patagonia Argentina

The Negro River is the most important hydrological system of Patagonia, Argentina, drains a watershed of 140,000 km2. Its main economic activities are agriculture and industry, representing potential sources of organochlorine compounds (OCCs): organochlorine pesticides (OCPs) and PCBs, respectively. These pollutants are persistent, ubiquitous and hydrophobics and are accumulated in the lipid fraction of biota. Fish are excellent biomonitors of pollution because incorporate COCs from water and through diet, and represent different trophic levels. The aims of the present study were to investigate OCCs levels in wild common carp, Cyprinus carpio (Cyprynidae) focusing on: a) the distribution pattern of OCPs and PCBs in gills, gonads, liver, muscle, and stomach content, b) the potential of common carp as indicator of aquatic OCCs pollution, and c) the evaluation of residues in gills and stomach content to discuss different input pathways of OCCs. HCHs, Chlordanes, DDTs, Endosulfan, Heptachlors, Drines and PCBs were quantified by GC-ECD. The distribution of OCCs (ng.g–1 lipid) was gills> gonads> liver> muscle> stomach content. Endosulfan accounted for 64% of OCCs in gills while, DDTs, PCBs and Chlordanes predominated in the stomach content suggesting the incorporation of more hydrophilic pollutants from the water column and those most persistent and hydrophobic from the diet.
Interactive Extractive Search over Biomedical Corpora

We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean keyword queries. In contrast to previous attempts to dependency-based search, we introduce a light-weight query language that does not require the user to know the details of the underlying linguistic representations, and instead to query the corpus by providing an example sentence coupled with simple markup. Search is performed at an interactive speed due to efficient linguistic graph-indexing and retrieval engine. This allows for rapid exploration, development and refinement of user queries. We demonstrate the system using example workflows over two corpora: the PubMed corpus including 14,446,243 PubMed abstracts and the CORD-19 dataset, a collection of over 45,000 research papers focused on COVID-19 research. The system is publicly available at https://allenai.github. io/spike
Neural Network Calculator for Designing Trojan Detectors

This work presents a web-based interactive neural network (NN) calculator and a NN inefficiency measurement that has been investigated for the purpose of detecting trojans embedded in NN models. This NN Calculator is designed on top of TensorFlow Playground with in-memory storage of data and NN coefficients. Its been extended with additional analytical, visualization, and output operations performed on training datasets and NN architectures. The analytical capabilities include a novel measurement of NN inefficiency using modified Kullback-Liebler (KL) divergence applied to histograms of NN model states, as well as a quantification of the sensitivity to variables related to data and NNs. Both NN Calculator and KL divergence are used to devise a trojan detector approach for a variety of trojan embeddings. Experimental results document desirable properties of the KL divergence measurement with respect to NN architectures and dataset perturbations, as well as inferences about embedded trojans.
An efficient k‐modes algorithm for clustering categorical datasets

Mining clusters from data is an important endeavor in many applications. The k‐means method is a popular, efficient, and distribution‐free approach for clustering numerical‐valued data, but does not apply for categorical‐valued observations. The k‐modes method addresses this lacuna by replacing the Euclidean with the Hamming distance and the means with the modes in the k‐means objective function. We provide a novel, computationally efficient implementation of k‐modes, called Optimal Transfer Quick Transfer (OTQT). We prove that OTQT finds updates to improve the objective function that are undetectable to existing k‐modes algorithms. Although slightly slower per iteration due to algorithmic complexity, OTQT is always more accurate and almost always faster (and only barely slower on some datasets) to the final optimum. Thus, we recommend OTQT as the preferred, default algorithm for k‐modes optimization.
CM-Cellulase Activity in Sou as affected by Addition of Organic Materials, Temperature, Storage and drying and wetting Cycles

The increase in carboxymethyl-cellulase activity (C.A.) resulting from the addition of different organic materials to calcareous, sandy loam and clay soils was studied. The effect of temperature, storage, as well as drying and wetting cycles on C.A. were also investigated. More C.A. was noticed when the 3 different soils were amended with clover straw than when cotton stalks or farmyard manure were added. Incubating these soils at temperatures from 4 °C up to 70 °C caused a sharp decrease in C. A. which was affected by the time of incubation. This reduction in C.A. varied from 23 to 84 % in the 3 soils used. Considerable losses in C.A. were also noticed when these soils were stored for 28 days either in the refrigerator (40–45 %) or at room temperature (54–71 %). Drying the samples amended with different organic materials caused a tremendous decrease in C.A. ranging from 14 % to 60 %, while rewetting enhanced the regain of C.A. but not to its original level. Die Beeinflussung der CM-Cellulase-Aktivitat in Boden durch Zugabe organischer Substanzen sowie Temperatur, Lagerungsdauer und Wechsel von Befeuchtung und Austrocknung Das Verhalten der Carboxymethyl-Cellulase-Aktivitat (C.A.) nach Zugabe verschiedener organischer Substanzen wurde an drei Boden untersucht. Es handelte sich dabei um einen Kalkboden, einen Tonboden und einen sandigen Lehmboden. Weiter wurde der Einflus der Lagertemperatur, der Lagerungsdauer und der Einflus wiederholter Befeuchtung und Austrocknung auf die C.A. untersucht. Bei den mit Kleeheu versetzten Boden konnte eine hohere C.A. festgestellt werden, als bei den mit Baumwollstengeln oder Stalldung behandelten Varianten. Die Inkubation der Boden bei ansteigenden Temperaturen zwischen 4 und 70 °C fuhrte zu einer rapiden Abnahme der C.A. Dieser Effekt wurde durch die Dauer der Inkubation noch verstarkt. Die Abnahme der C.A. variierte zwischen 23 und 84 %. Eine betrachtliche Abnahme der C.A. konnte auch festgestellt werden, wenn die Bodenproben wahrend 28 Tagen im Kuhlschrank (Abnahme 4 bis 45 %) oder bei Zimmertemperatur (Abnahme 54 bis 71 %) gelagert wurden. Die Trocknung der mit verschiedenen organischen Substanzen behandelten Boden verursachte einen starken Abfall der CA. , der zwischen 14 und 60 % lag. Bei Wiederbefeuchtung stieg die C.A. erneut an, die Ausgangs-Aktivitat wurde jedoch nicht wieder erreicht.
Analytical mechanics allows novel vistas on mathematical epidemic dynamics modeling

This contribution aims to shed light on mathematical epidemic dynamics modelling from the viewpoint of analytical mechanics. To set the stage, it recasts the basic SIR model of mathematical epidemic dynamics in an analytical mechanics setting. Thereby, it considers two possible re-parameterizations of the basic SIR model. On the one hand, it is proposed to re-scale time, while on the other hand, to transform the coordinates, i.e.\ the independent variables. In both cases, Hamilton's equations in terms of a suited Hamiltonian as well as Hamilton's principle in terms of a suited Lagrangian are considered in minimal and extended phase and state space coordinates, respectively. The corresponding Legendre transformations relating the various options for the Hamiltonians and Lagrangians are detailed. Ultimately, this contribution expands on a multitude of novel vistas on mathematical epidemic dynamics modelling that emerge from the analytical mechanics viewpoint. As result, it is believed that interesting and relevant new research avenues open up when exploiting in depth the analogies between analytical mechanics and mathematical epidemic dynamics modelling.
An operational architecture for privacy-by-design in public service applications

Governments around the world are trying to build large data registries for effective delivery of a variety of public services. However, these efforts are often undermined due to serious concerns over privacy risks associated with collection and processing of personally identifiable information. While a rich set of special-purpose privacy-preserving techniques exist in computer science, they are unable to provide end-to-end protection in alignment with legal principles in the absence of an overarching operational architecture to ensure purpose limitation and protection against insider attacks. This either leads to weak privacy protection in large designs, or adoption of overly defensive strategies to protect privacy by compromising on utility. In this paper, we present an operational architecture for privacy-by-design based on independent regulatory oversight stipulated by most data protection regimes, regulated access control, purpose limitation and data minimisation. We briefly discuss the feasibility of implementing our architecture based on existing techniques. We also present some sample case studies of privacy-preserving design sketches of challenging public service applications.
Varietal Preference of Lipaphis erysimi (Kaltenbach) on Three Species of Brassica

Context: Lipaphis erysimi have different type of preference to settle on different varieties of mustard. Pest preferential movement is important for ecofriendly pest control. Objectives: The aim of this study was to determine the L. erysimi resistance varieties of mustard to get more yields by lower cost and avoid most aphid infesting varieties for higher cost Materials and Methods: Two criteria were used to determine the varietal preference of L. erysimi , viz. (i) dispersal of aphids by cutting the base of host plants and enforce to settle on different varieties of three species of Brassica under free choice conditions, and (ii) suitability of those varieties based on the aphid infestation in the field crop condition at same season. Results: The preferential movement of L. erysimi on ten varieties of Brassica showed significant differences (P<0.01). The variety, Bari sharisa-6 showed highest (90.00 ± 7.21) aphid settlement followed by the variety, Rai-5 (89.00 ± 5.69). The variety, Bari sharisa-10 showed lowest (10.00 ± 0.58) aphid settlement followed by the variety, Bari sharisa-11 (19.00 ± 3.61). Naturally infested aphid population on first sowing date was recorded highest (2.1 ± 0.79) on the variety Bari Sarisha-8, followed by Bari Sarisha-6 (1.93 ± 0.88). The lowest (0.19 ± 0.09) number of aphids recorded on Bari Sarisha-12. In second sowing date, Bari Sarisha-7 showed highest (19.5 ± 6.51) aphid population, followed by Bari Sarisha-8 (15.42 ± 5.72). The lowest (1.2 ± 0.38) aphid population found on the variety, Bari Sarisha-11. In third sowing date, highest (14.69 ± 5.69) number of aphids was recorded on the variety, Bari Sarisha-6, followed by the variety, Sonali-75 (14.48±4.74). Lowest number (6.06 ± 2.30) of aphids was found on the variety, Bari Sarisha-12. In the fourth sowing date, highest (15.54 ± 5.72) aphid population recorded on the variety, Bari Sarisha-8, followed by Bari Sarisha-7 (14.39 ± 5.70); and the lowest (4.41 ± 1.75) number of aphids recorded on the variety, Bari Sarisha-9. Key words: Lipaphis erysimi ; varietal preference; Brassica ; Sarisha; Rai. DOI: 10.3329/jbs.v17i0. 7123 J. bio-sci. 17: 145-148, 2009
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret

Recently, model-free reinforcement learning has attracted research attention due to its simplicity, memory and computation efficiency, and the flexibility to combine with function approximation. In this paper, we propose Exploration Enhanced Q-learning (EE-QL), a model-free algorithm for infinite-horizon average-reward Markov Decision Processes (MDPs) that achieves regret bound of $O(\sqrt{T})$ for the general class of weakly communicating MDPs, where $T$ is the number of interactions. EE-QL assumes that an online concentrating approximation of the optimal average reward is available. This is the first model-free learning algorithm that achieves $O(\sqrt T)$ regret without the ergodic assumption, and matches the lower bound in terms of $T$ except for logarithmic factors. Experiments show that the proposed algorithm performs as well as the best known model-based algorithms.
Randomized Policy Learning for Continuous State and Action MDPs

Deep reinforcement learning methods have achieved state-of-the-art results in a variety of challenging, high-dimensional domains ranging from video games to locomotion. The key to success has been the use of deep neural networks used to approximate the policy and value function. Yet, substantial tuning of weights is required for good results. We instead use randomized function approximation. Such networks are not only cheaper than training fully connected networks but also improve the numerical performance. We present \texttt{RANDPOL}, a generalized policy iteration algorithm for MDPs with continuous state and action spaces. Both the policy and value functions are represented with randomized networks. We also give finite time guarantees on the performance of the algorithm. Then we show the numerical performance on challenging environments and compare them with deep neural network based algorithms.
Traffic Flow Forecast of Road Networks With Recurrent Neural Networks

The interest in developing smart cities has increased dramatically in recent years. In this context an intelligent transportation system depicts a major topic. The forecast of traffic flow is indispensable for an efficient intelligent transportation system. The traffic flow forecast is a difficult task, due to its stochastic and non linear nature. Besides classical statistical methods, neural networks are a promising possibility to predict future traffic flow. In our work, this prediction is performed with various recurrent neural networks. These are trained on measurements of induction loops, which are placed in intersections of the city Hagen. We utilized data from beginning of January to the end of July in 2018. Each model incorporates sequences of the measured traffic flow from all sensors and predicts the future traffic flow for each sensor simultaneously. A variety of model architectures, forecast horizons and input data were investigated. Most often the vector output model with gated recurrent units achieved the smallest error on the test set over all considered prediction scenarios. Due to the small amount of data, generalization of the trained models is limited.
A recoverable cost of brood parasitism during the nestling stage of the American robin (Turdus migratorius): implications for the evolution of egg rejection behaviors in a host of the brown-headed cowbird (Molothrus ater)

Hosts of brood parasitic birds face reduced reproductive success as a direct consequence of rearing parasitic young. The most commonly evolved host behavior to combat costly parasitism is the rejection of foreign eggs. Despite consistent patterns of reduced nesting success in broods parasitized by brown-headed cowbirds (Molothrus ater), most of its host species do not reject foreign eggs. Paradoxically, where it is present, egg rejection is more common among cowbird hosts with larger body sizes, yet cowbird chicks are less likely to outcompete larger host nest mates. This raises a fundamental question whether egg rejection, even in the minority of cowbird hosts, has specifically evolved in response to costly brood parasitism. Here we tested predictions associated with this hypothesis in an egg-rejecter host, the American robin (Turdus migratorius), by assessing whether experimental cowbird parasitism causes reduced nesting success. We cross-fostered cowbird and control, host chicks into robin nests, testing for experimental effects of chick species, brood size, and hatching asynchrony; unmanipulated nests served as additional controls. Rearing a foreign chick in the brood reduced the reproductive output of host robins, however, we detected no effect of parasitizing host nests with a cowbird versus robin chick. Cowbird chicks were significantly less likely to fledge than cross-fostered robin chick controls. These experiments reveal that parasitism itself can exert a cost on robin hosts during the nestling stage, representing a recoverable cost of cowbird parasitism that can be avoided by the host through rejecting parasite eggs prior to hatching. These results support the assumption that foreign egg rejection is an evolved host response to brood parasitism in this system, despite the poor survival rate of cowbird chicks in robin broods.
The Allometry of Cranial Morphology and Gape Size in Red-Bellied Mudsnakes (Farancia abacura)

Abstract: For snakes, maximum gape size can determine the size and shape of consumable prey. Small body size may accentuate the limitations of gape; however, some evidence indicates that juvenile snakes may compensate for small body size by exhibiting negative gape allometry. In this study, we tested the hypothesis that Red-bellied Mudsnakes (Farancia abacura) display negative allometry for relative gape size. We measured five external head dimensions for each of 10 specimens and directly measured maximum gape circumference using either a ring or bracelet mandrel. Soft tissue was removed from each skull, after which we measured nine osteological features of the cranium. Maximum gape circumference exhibited negative allometry with snout–vent length, as did all cranial dimensions except quadrate length, which was isometric. All cranial bones exhibited positive allometry with skull length except for parietal width, which was isometric with skull length. Based upon a model of the external characteristics using Akaike Information Criterion, jaw length and head width were the strongest predictors of gape circumference. Supratemporal length, skull length, and mandible length were the three highest ranked osteological dimensions for predicting maximum gape. These model ranks differ from those of similar studies conducted using different snake species, indicating that the morphological determinants of gape circumference may be highly diverse among lineages.
Integration through transients approach to the μ(I) rheology.

This work generalizes the granular integration through transients formalism introduced by Kranz et al. [Phys. Rev. Lett. 121, 148002 (2018)10.1103/PhysRevLett.121. 148002] to the determination of the pressure. We focus on the Bagnold regime and provide theoretical support to the empirical μ(I) rheology laws that have been successfully applied in many granular flow problems. In particular, we confirm that the interparticle friction is irrelevant in the regime where the μ(I) laws apply.
A critical revision of the churchill snoutfish, genus Petrocephalus Marcusen, 1854 (Actinopterygii: Teleostei: Mormyridae), from southern and eastern Africa, with the recognition of Petrocephalus tanensis, and the description of five new species

We morphologically and genetically studied the southern African electric fish Petrocephalus catostoma, or churchill, and its six nominal species, five of which by synonymization (three valid subspecies). We reinstate the synonymized species, and recognize Petrocephalus tanensis (Whitehead and Greenwood, 1959) from the Tana River in Kenya, also using electric organ discharges. The Okavango delta (Botswana) is inhabited by Petrocephalus okavangensis sp. nov. and Petrocephalus magnitrunci sp. nov., and the Namibian Cunene River by Petrocephalus magnoculis sp. nov. We recognize Petrocephalus petersi sp. nov. for the Lower Zambezi River (Mozambique), and Petrocephalus longicapitis sp. nov. for the Upper Zambezi River (Namibia). The Lufubu River in Northern Zambia is inhabited by Petrocephalus longianalis sp. nov. For the southern churchill, Petrocephalus wesselsi Kramer and Van der Bank, 2000, we confirm intraspecific and interspecific differentiation. Sequence data from mitochondrial DNA confirm differentiation of two new western and two eastern species, forming mutual sister groups.
SHADOWCAST: Controlling Network Properties to Explain Graph Generation

We introduce the problem of explaining graph generation, formulated as controlling the generative process to produce graphs of explainable desired structures. By directing this generative process, we can measure and explain the observed outcomes. We propose SHADOWCAST, a controllable generative model capable of mimicking real-world networks and directing the generation, as a novel approach to this problem. The proposed model is based on a conditional generative adversarial network for graph data. We design it with the capability to maintain generation control by managing the conditions. Comprehensive experiments on three real-world network datasets demonstrate our model's competitive performance in the graph generation task. Furthermore, we direct SHADOWCAST to generate explainable graphs of different structures to show its effective controllability. As the first work to pose the problem of explaining generated graphs by controlling the generation, SHADOWCAST paves the way for future research in this exciting area.
ASSESSING DENSITY DEPENDENCE USING INTRA-ANNUAL COUNTS

If causes of population fluctuation vary temporally, then tests that search for density dependence in long time series cannot distinguish, at any given time, how much population change is due to density-dependent or density-independent factors. For some species' dynamics, however, it is possible to determine over a restricted period whether density-dependent factors are involved in reducing population densities. I use a new Return Trajectory Likelihood Ratio Test (RTLRT) to determine whether a population perturbed to high density (e.g., by periodic recruitment) exhibits density dependence by returning to a positive asymptote (or leveling density, L) or is density independent and, through proportional hazards, approaches zero exponentially. In a series of simulations, declines after recruitment were mimicked by creating sequences that changed due to a combination of density-dependent and density-independent factors. Until the sequence dropped below L, losses due to each mortality type were combined; after .