Miracle-dz/UniFAR-FTUs huggingface dataset:
Secondhand homes: Woodpecker cavity location and structure influences secondary nester’s success

Understanding how ecosystem engineers influence other organisms has long been a goal of ecologists. Woodpeckers select nesting sites with high food availability and will excavate and then abandon multiple cavities through their lifetime. These cavities are crucial to secondary cavity nesting birds (SCB) that are otherwise limited by the availability of naturally occurring cavities. Our study examined the role food resources have on the nest site location and home range size of woodpeckers, and the respective influence woodpeckers and the construction of cavities have on the nesting success of SCB. Using five years of avian point count data to locate golden-fronted woodpeckers (GFWO: Melanerpes aurifrons), we correlated insect availability with GFWO home range size and determined differences in insect availability between GFWO occupied and unoccupied sites, while recording nesting success (success: ≥ 1 fledgling) for the GFWO and common SCB in south Texas: Black-crested Titmouse (Baeolophus atricristatus), Ash-throated Flycatcher (Myiarchus cinerascens), Brown-crested Flycatcher (Myiarchus tyrannulus), and Bewick’s Wren (Thryomanes bewickii). We used model averaging to fit species-specific logistic regression models to predict nest success based on cavity metrics across all species. Sites occupied by GFWO had a higher biomass of insects in orders Coleoptera, Hymenoptera, and Orthoptera than unoccupied sites, and there was a negative correlation between the availability of these insect orders and home-range size. GFWO had increased nest success in trees with increased vegetation cover and lower levels of decay, while SCB had higher levels of nesting success in abandoned GFWO cavities opposed to naturally occurring ones, and in trees with low decay. Our results suggest that SCB may be drawn to nest in abandoned woodpecker cavities where they have higher rates of nest success compared to natural cavities. Additionally, the prevalence for GFWO to excavate cavities in trees with lower levels of decay contradicts previous literature and may indicate a novel temperature trade-off, with live trees requiring more energy to excavate, but providing more protection from high breeding season temperatures in arid and semi-arid areas.
An extended catalogue of tandem alternative splice sites in human tissue transcriptomes

Tandem alternative splice sites (TASS) is a special class of alternative splicing events that are characterized by a close tandem arrangement of splice sites. Most TASS lack functional characterization and are believed to arise from splicing noise. Based on the RNA-seq data from the Genotype Tissue Expression project, we present an extended catalogue of TASS in healthy human tissues and analyze their tissue-specific expression. The expression of TASS is usually dominated by one major splice site (maSS), while the expression of minor splice sites (miSS) is at least an order of magnitude lower. Among 73k miSS with sufficient read support, 12k (17%) are significantly expressed above the expected noise level, and among them 2k are expressed tissue-specifically. We found significant correlations between tissue-specific expression of RNA-binding proteins (RBP) and tissue-specific expression of miSS that is consistent with miSS response to RBP inactivation by shRNA. In combination with RBP profiling by eCLIP, this allowed prediction of novel cases of tissue-specific splicing regulation including a miSS in QKI mRNA that is likely regulated by PTBP1. According to the structural annotation of the human proteome, tissue-specific miSS are enriched within disordered regions, and indels induced by miSS are enriched with short linear motifs and post-translational modification sites. Nonetheless, more than 15% of tissue-specific miSS affect structured protein regions and may adjust protein-protein interactions or modify the stability of the protein core. The significantly expressed miSS evolve under the same selection pressure as maSS, while other miSS lack signatures of evolutionary selection and conservation. Using mixture models, we estimated that not more than 10% of maSS and not more than 50% of significantly expressed miSS are noisy, while the proportion of noisy splice sites among not significantly expressed miSS is above 70%.
Brainwave Controlled Automated Wheelchair

we have seen manypeople across the globe born with physical disabilities. Some of them are born with disabilities which make them difficult for the movement and they need someone’s assistance for their movement, finding someone who can assist them all the time is very difficult. So to overcome this difficulty Wheelchairs using few technologies have been developed in the past to overcome this problem but, completely paralyzed patients feel it difficult to use the technology like joystick, electromyography arm, voice controlled wheelchair etc. So, in this paper to overcome the limitations of previously existing technologies we have used Electroencephalogram (EEG) signals to operate the wheelchair and this technology is called brain computer interface (BCI) in which the human brain interacts with the computer to perform a particular task. Here eye blinking is used to control the robot as it generates the significant pulse in EEG signals. The first objective of this project is to provide mobility for the completely paralyzed patients. The second objective is to use the EEG signal to control the wheelchair. Hence this robot is programmed in such a way that it takes the EEG signal generated by the eye blinking as commands in a short period of time. In addition to this, the ultrasonic sensors are used to detect any obstacles and to stop the robot thereby ensuring the safety for the users. Keywords—Arduino, Electro encephalogram, Brain Computer Interface.
Asymptotic Behavior and Stationary Distribution of a Nonlinear Stochastic Epidemic Model with Relapse and Cure

In this paper, by introducing environmental perturbation, we extend an epidemic model with graded cure, relapse, and nonlinear incidence rate from a deterministic framework to a stochastic differential one. The existence and uniqueness of positive solution for the stochastic system is verified. Using the Lyapunov function method, we estimate the distance between stochastic solutions and the corresponding deterministic system in the time mean sense. Under some acceptable conditions, the solution of the stochastic system oscillates in the vicinity of the disease-free equilibrium if the basic reproductive number , while the random solution oscillates near the endemic equilibrium, and the system has a unique stationary distribution if . Moreover, numerical simulation is conducted to support our theoretical results.
Large tridactyl dinosaur tracks from the Early Jurassic of southern Gondwana – uppermost Elliot Formation, Upper Moyeni, Lesotho

A new ichnosite in southwest Lesotho (Upper Moyeni; Quthing District) is located within the uppermost part of the highly fossiliferous Elliot Formation, ~35 m below the conformably overlying Clarens Formation and ~65 m above the world-renowned Lower Moyeni ichnosite. While the Lower Moyeni site preserves diverse Early Jurassic ichnofossils, the ichnites at the Upper Moyeni comprise one vertebrate burrow and ~50 tridactyl tracks with footprint lengths between 15 and 51 cm. Many of the tracks preserve digital pad impressions, claw marks and displacement rims, all related to substrate conditions. The morphometric parameters of the Upper Moyeni tracks are consistent with Grallator , Eubrontes and Kayentapus . Several larger tracks with footprint lengths > 40 cm are Kayentapus -like and Eubrontes -like, and are comparable to previously described very large theropods tracks with lengths > 50 cm from the uppermost Elliot and Clarens formations. On the basis of sedimentological and ichnological evidence, the Upper Moyeni ichnofossils were formed in a palaeolandscape with small rivers and shallow lakes by burrowing tetrapods and a variety of bipedal dinosaurs (theropods), some of which were up to 7–8 m in body length. The Upper Moyeni tracks, together with the other very large tracks from coeval locations in southern Africa, collectively highlight the tendency towards increasing diversity in size of tridactyl tracks and by extension theropod trackmaker body size body size, which runs in tandem with the increasing diversity of non-sauropod, sauropodomorph body fossils in the Sinemurian-Pliensbachian of southern Gondwana.
A Note on C 2 Interpreted over Finite Data-Words

We consider the satisﬁability problem for the two-variable fragment of ﬁrst-order logic extended with counting quantiﬁers, interpreted over ﬁnite words with data, denoted here with C 2 [ ≤ , succ , ∼ , π bin ]. In our scenario, we allow for using arbitrary many uninterpreted binary predicates from π bin , two navigational predicates ≤ and succ over word positions as well as a data-equality predicate ∼ . We prove that the obtained logic is undecidable, which contrasts with the decidability of the logic without counting by Montanari, Pazzaglia and Sala [27]. We supplement our results with decidability for several sub-fragments of C 2 [ ≤ , succ , ∼ , π bin ], e . g . without binary predicates, without successor succ , or under the assumption that the total number of positions carrying the same data value in a data-word is bounded by an a priori given constant.
Dissipation Function: Nonequilibrium Physics and Dynamical Systems

An exact response theory has recently been developed within the field of Nonequilibrium Molecular Dynamics. Its main ingredient is known as the Dissipation Function, Ω. This quantity determines nonequilbrium properties like thermodynamic potentials do with equilibrium states. In particular, Ω can be used to determine the exact response of particle systems obeying classical mechanical laws, subjected to perturbations of arbitrary size. Under certain conditions, it can also be used to express the response of a single system, in contrast to the standard response theory, which concerns ensembles of identical systems. The dimensions of Ω are those of a rate, hence Ω can be associated with the entropy production rate, provided local thermodynamic equilibrium holds. When this is not the case for a particle system, or generic dynamical systems are considered, Ω can equally be defined, and it yields formal, thermodynamic-like, relations. While such relations may have no physical content, they may still constitute interesting characterizations of the relevant dynamics. Moreover, such a formal approach turns physically relevant, because it allows a deeper analysis of Ω and of response theory than possible in case of fully fledged physical models. Here, we investigate the relation between linear and exact response, pointing out conditions for the validity of the response theory, as well as difficulties and opportunities for the physical interpretation of certain formal results.
Effect of Revisitation Surveys on Detection of Wetland Birds with Different Diel Vocalization Patterns

Reliable species distribution data are important for valid scientific conclusions and effective conservation planning. Mismatch between survey timing and animal behaviors that influence detection may result in false absences that can lead to poorly informed management decisions. Birds exhibit a diversity of diel vocalization patterns, but many largescale multispecies surveys are based on the songbird dawn chorus, indicating the potential for bias to detect birds with other diel vocalization patterns. In this study, we quantified bias in point counts and morning acoustic recordings to measure the number of occupied sites detected for a set of dawn chorusing birds (songbirds) and irregularly vocalizing wetland birds (waterfowl) relative to estimates obtained from 10-min acoustic recordings conducted hourly throughout 24-h periods for three consecutive days. Furthermore, we investigated which revisitation schedule—same day or different day sampling, as well as increased sampling effort—best minimized false-negative detections for songbirds and waterfowl. Morning surveys significantly underestimated the number of occupied sites for 10 of 13 species. No differences were found between same-day and between-day revisitation schedules with identical sampling effort, regardless of whether birds exhibited a dawn chorus or irregular vocalization patterns. Detection improved with increased sampling effort. Subsampled recordings captured the majority of occupied sites for songbirds (up to 87% of occupied sites detected), but less so for waterfowl (up to 60% of occupied sites detected). Accurate detection for irregularly vocalizing species such as waterfowl will require more intensive sampling effort (likely throughout 24-h periods) when using acoustic recordings.
Knowledge Distillation for Model-Agnostic Meta-Learning

Recently, model-agnostic meta-learning (MAML) and its variants have drawn much attention in few-shot learning. In this paper, we investigate how to improve the performance of a portable MAML network so that it can be used in handheld devices, such as small robots, mobile phones, and laptops. We propose a novel approach named portable model-agnostic meta-learning (P-MAML), where valuable knowledge is distilled from a teacher MAML network to a portable student MAML. Moreover, data augmentation and ResNet architecture are employed in the teacher MAML network so as to avoid overﬁtting and enhance efﬁciency. To the best of our knowledge, this is the ﬁrst work to consider a portable meta-learning model through knowledge distillation (KD) to learn a good initialization. Extensive experimental results on three real datasets show that our P-MAML algorithm greatly enhances the accuracy through KD from the teacher network. As shown, P-MAML with KD improves the performance of one-shot learning as high as 10% in comparison to that without KD.
Rewritability Results for OMQs with Closed Predicates

The semantics of Description Logic (DL) ontologies adopts the open-world assumption (OWA) from classical ﬁrst-order logic (FOL), which makes them well suited to reason about incomplete knowledge. However, there are many applications where incomplete and complete data co-exist, and the standard OWA, which assumes that all data is incomplete, allows to draw less inferences than desired. Adding closed-predicates to ontology-mediated queries (OMQs) is an elegant and powerful way to reach better conclusions leveraging partial completeness, but unfortunately, it is well-known that OMQs with closed predicates are coNP-hard in practically every non-trivial setting, and hence they are not FOL-rewritable. We recall here a small selection of recent re-sults on rewritability of OMQs with closed predicates: two polynomial rewritings into extensions of Datalog for restricted classes of conjunctive queries mediated by ontologies in very expressive DLs like ALCHOI and ALCHOIF , and a recent approach that identiﬁes an FOL-rewritable family of OMQs that includes full conjunctive queries and DL-Lite ontologies, while restricting in a novel yet non-trivial way the use of closed predicates.
A Graph-Based Measurement for Text Imbalance Classification

Imbalanced text classiﬁcation, as practical and essential text classiﬁcation, is the task to learn labels or categories for imbalanced text data. Existing imbalanced text classiﬁcation approaches are mostly based on the Imbalance Ratio (i.e. ratio of sizes between categories). Recently, some researchers veriﬁed that the imbalance ratio severely affects the performance of classiﬁers when intrinsic characteristics of data such as class overlapping and small disjuncts occur. However, since the distribution of real-world data is unknown, it is difﬁcult to describe above intrinsic characteristics directly. In this paper, we transform the unknown distribution of data into a graph model and present a graph-based imbalance index named G IR to predict the impact of imbalanced text data on classiﬁcation performance. Firstly, we introduce an environmental factor that makes the imbalance index sensitive to the intrinsic characteristics of data. Secondly, we propose a graph-based method to calculate this environmental factor. Finally, we use the imbalance index to analyze the performances of imbalanced learning methods and the impact of imbalanced data on text classiﬁers. The experimental results evaluated on both synthetic data sets and real-world data sets demonstrate the effectiveness of our approach.
A Multilayered Informative Random Walk for Attributed Social Network Embedding

Network representation learning (also known as Graph embedding) is a technique to map the nodes of a network to a lower dimensional vector space. Random walk based representation techniques are found to be efﬁcient as they can easily preserve different orders of proximities between the nodes in the embedding space. Most of the social networks now-a-days have some content (or attributes) associated with each node. These attributes can provide complementary information along with the link structure of the network. But in a real life network, the information carried by the link structure and that by the attributes vary signiﬁcantly over the nodes. Most of the existing unsupervised attributed network embedding algorithms do not distinguish between the link structure and the attributes of a node depending on their informativeness. In this work, we propose an unsupervised node embedding technique that exploits both the structure and attributes by intelligently prioritizing one of them, in the random walk, for each node separately. We convert the network into a multi-layered graph and pro-pose a novel random walk based on the informativeness of a node in different layers. This uniﬁed approach is simple and computationally fast, yet able to use the content as a complement to structure and vice-versa. Experimental evaluations on four real world publicly available datasets show the merit of our approach (up to 168.75% improvement) compared to the state-of-the-art algorithms in the domain. We make the source code available to download.
CIDetector: Semi-Supervised Method for Multi-Topic Confidential Information Detection

Conﬁdential information ﬁrewalling with text classiﬁer is to identify the text containing conﬁdential information whose publication might be harmful to national security, business trade, or personal life. Traditional methods, e.g., listing a set of suspicious keywords together with regular-expression based ﬁlter, fail to solve the multi-topic phenomenon, i.e., one text containing the conﬁdential information with different topics. In this paper, we propose a semi-supervised method, CIDetector, for multi-topic conﬁdential information detection. We introduce coarse conﬁdential polarity as prior knowledge into word embeddings, which can regularize the distribution of words to have a clear task classiﬁcation boundary. Then we introduce a multi-attention network classiﬁer to extract task-related features and model dependencies between features for multi-topic classiﬁcation. Experiments are conducted by real-world data from WikiLeaks and demonstrated the superiority of our proposed method.
Online Learning to Accelerate Neural Network Inference with Traveling Classifiers

Deep neural networks trained on millions of instances can recognize a wide variety of patterns. It is common to use these pre-trained deep networks in applications where the domain speciﬁc training data is not readily available. Once a pre-trained network is deployed to such applications, some of the information contained in the network may be irrelevant due to the difference between the training set and the application data distributions. As a result, parts of the neural network become redundant and slow down inference. This redundancy is unknown until the model is deployed and input data is received. Therefore, it can only be identiﬁed and avoided in real-time. Existing works on neural network acceleration can not exploit such redundancy during ofﬂine training when the domain-speciﬁc datasets are unavailable. In this paper, we study online learning to accelerate neural network inference. We propose traveling classiﬁers that continuously learn from the activations of two consecutive network layers to accelerate inference in real-time. Traveling classiﬁers model class conditional probabilities to generate early predictions and bypass unnecessary computation of network layers. The classi-ﬁers also adaptively switch the layers they learn from by measuring the feature space differences between the activations. This traveling mechanism automatically adjusts the aggressiveness of the acceleration without sacriﬁcing prediction accuracy. We demonstrate the performance of the proposed algorithm on the ImageNet dataset [10] using the state-of-the-art ResNet-50, ResNet-152 [18] and VGG-16 [38] architectures. Experiments demonstrate that our method signiﬁ-cantly outperforms baseline approaches.
On the Effectiveness of Social Proof Recommendations in Markets with Multiple Products

The social proof marketing strategy assumes that the marketer provides a novel product for free to some users of a social network and then promptly recommends the product to other users, by informing them that a number of their friends are already using it. In this paper we study this popular marketing strategy in scenarios where the new product enters in markets where two old products are already competing. We show that if customers tend to adopt the product that is the most popular one (over the three alternative products) among their friends, then this marketing strategy allows to maximize the diffusion of the new product only on a narrow class of networks. Moreover, even if we focus on this narrow class of networks, computing the best order of the recommendations is computationally intractable. Instead, if customers are less prone to change their mind, that is, if they are willing to adopt some product only when an absolute majority of their friends has already agreed on it, then the marketing strategy always works well and, furthermore, an optimal order of recommendations can be computed in polynomial time.
Trust-Based Attack and Defense in Wireless Sensor Networks: A Survey

As a key component of the information sensing and aggregating for big data, cloud computing, and Internet of Things (IoT), the information security in wireless sensor network (WSN) is critical. Due to constrained resources of sensor node, WSN is becoming a vulnerable target to many security attacks. Compared to external attacks, it is more difficult to defend against internal attacks. The former can be defended by using encryption and authentication schemes. However, this is invalid for the latter, which can obtain all keys of the network. The studies have proved that the trust management technology is one of effective approaches for detecting and defending against internal attacks. Hence, it is necessary to investigate and review the attack and defense with trust management. In this paper, the state-of-the-art trust management schemes are deeply investigated for WSN. Moreover, their advantages and disadvantages are symmetrically compared and analyzed in defending against internal attacks. The future directions of trust management are further provided. Finally, the conclusions and prospects are given.
COLOR PENCIL SKETCH IMAGE GENERATION BASED ON FILTERING AND LINEAR INTERPOLATION

In this paper, we present a method to automatically generate a color pencil sketch image from a photo. First the image is converted into a sketch using a gradient estimation and then the color pencil sketch is produced by linear interpolation with original image and the sketched image. The experimental results show that the final image has a visual aspect of a color pencil sketch like image