Miracle-dz/UniFAR-FTUs huggingface dataset:
Historical influence of predation pressure on escape by Podarcis lizards in the Balearic Islands

Island tameness (reduced escape behaviour on islands where prey have experienced prolonged relaxation of predation pressure) is known in several taxa, although the relationships between recent predation pressure and escape on islands are poorly known. We investigated escape by numerous populations exposed to differing predation pressure of two sister species of Podarcis lizards in the Balearic Islands. Our main findings are that flight initiation distance was greater in Podarcis pityusensis than Podarcis lilfordi and increased as predation pressure increased in P. pityusensis. Island tameness led to extinction of P. lilfordi on Menorca and Mallorca following anthropogenic introduction of predators; this species is extant only on nearby islets. The lack of relationship between recent predation pressure and flight initiation distance in P. lilfordi indicates that the historically acquired deficit in the ability to adjust escape behaviour to predation pressure still exists. Podarcis pityusensis, which was exposed to greater natural predation pressure before human introduction of predators, survives on Ibiza and Formentera, as well as on islets. Retention of the ability to respond to predation pressure is consistent with our finding that flight initiation distance increases as predation pressure increases among current populations. © 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, ••, ••–••.
Crowd Counting using Deep Recurrent Spatial-Aware Network

Crowd counting from unconstrained scene images is a crucial task in many real-world applications like urban surveillance and management, but it is greatly challenged by the camera’s perspective that causes huge appearance variations in people’s scales and rotations. Conventional methods address such challenges by resorting to fixed multi-scale architectures that are often unable to cover the largely varied scales while ignoring the rotation variations. In this paper, we propose a unified neural network framework, named Deep Recurrent Spatial-Aware Network, which adaptively addresses the two issues in a learnable spatial transform module with a region-wise refinement process. Specifically, our framework incorporates a Recurrent Spatial-Aware Refinement (RSAR) module iteratively conducting two components: i) a Spatial Transformer Network that dynamically locates an attentional region from the crowd density map and transforms it to the suitable scale and rotation for optimal crowd estimation; ii) a Local Refinement Network that refines the density map of the attended region with residual learning. Extensive experiments on four challenging benchmarks show the effectiveness of our approach. Specifically, comparing with the existing best-performing methods, we achieve an improvement of 12\% on the largest dataset WorldExpo’10 and 22.8\% on the most challenging dataset UCF\_CC\_50
Optimal Process Mining for Large and Complex Event Logs

This paper addresses the problem of process discovery from large and complex event logs. We depart from the existing literature and formulate the problem of optimal process discovery. A formal mathematical programming model is given based on a novel hierarchical structuration of the event logs. Desired properties of event trace score functions are described, and the properties of optimal process models are proved. A combination of Monte Carlo optimization and tabu search is proposed to overcome the complexity related to the huge size of the event logs and the combinatorial solution space. Numerical results show that our approach is suitable for large event logs and that it performs better than the state-of-the-art approaches. We also demonstrate the applicability of our method on a real case study in health care. This paper illustrates the benefits of combining techniques from the operational research and the process mining fields. Note to Practitioners—Though directly applicable to general business process discovery, this paper is motivated by our collaboration with the company HEVA (Lyon, France) and health practitioners on patient care pathway discovery. The French hospitalization database that contains hospitalization history of all patients is used for this purpose and our goal is to determine the most meaningful process model of the patient hospitalization history. The hierarchical event structure of this paper provides a natural way of representing relationship between hospitalization events and is readily obtainable from the classical ICD-10 codes. The formal mathematical model and our optimization algorithm allow the end users to best balance between the faithfulness of the process model and its complexity. A case study of cardiovascular patients is presented to show the capability of the proposed approach to clearly capture the major patient pathways before and after the implementation of defibrillators. The results of this paper are highly valuable for doctors and public health decision makers, as crucial information is provided on patient care pathways for any selected cohort.
PORCA: Modeling and Planning for Autonomous Driving Among Many Pedestrians

This letter presents a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is Pedestrian Optimal Reciprocal Collision Avoidance, a pedestrian motion prediction model that accounts for both a pedestrian's global navigation intention and local interactions with the vehicle and other pedestrians. Unfortunately, the autonomous vehicle does not know the pedestrians’ intentions a priori and requires a planning algorithm that hedges against the uncertainty in pedestrian intentions. Our planning system combines a Partially Observable Markov Decision Process algorithm with the pedestrian motion model and runs in real time. Experiments show that it enables a robot scooter to drive safely, efficiently, and smoothly in a crowd with a density of nearly one person per square meter.
The Effect of Breed, Gender, and Acid Stimulation in Dog Saliva Proteome

Saliva gained interest as a potential noninvasive source of biomarkers in humans and that interest starts to be extended also to other animal species. For this purpose, the knowledge of the salivary proteome in healthy conditions and the factors that affect it and how they affect it are necessary. The aim of the present study was to assess the effect that gender and breed have in saliva proteome and the changes in it induced by stimulation with acid. Saliva from 4 different purebred dogs (Portuguese Podengo, Greyhound, Rafeiro Alentejano, and Beagle) of both genders was collected without and after stimulation with lemon juice. SDS-PAGE and two-dimensional gel electrophoresis (2-DE) profiles were compared and the proteins of interest in-gel digested and identified by mass spectrometry. Acid stimulation decreased total protein concentration and the relative amounts of some protein bands/spots. Gender appeared to have minimal effect in saliva proteome, whereas the influence of breed varies. Beagles and Portuguese Podengos were the two breeds with higher differences. In conclusion, stimulation procedures and dog breed should be considered in data analysis when using salivary proteins for diagnostic purposes.
Design of fault-tolerant neuromorphic computing systems

Neuromorphic computing is rapidly becoming mainstream, and Resistive Random Access Memory (RRAM) and RRAM-based computing systems (RCS) provide a promising hardware implementation of neuromorphic computing. This emerging computing system helps us to realize vector-matrix multiplications in a time complexity of 0(1), and it improves energy efficiency dramatically. However, due to the immature fabrication process, RCS is susceptible to defects; the resulting errors lead to a significant accuracy drop in neuromorphic computing applications. In order to take advantage of RCS in practical applications, fault-tolerant design is necessary. We present a survey of fault-tolerant designs for RRAM-based neuromorphic computing systems. We first describe RRAM-based crossbars and their role in neuromorphic computing systems. Following this, we classify fault models into different categories, and review the test solutions. Subsequently, the framework of fault-tolerant design for RCS is presented, which contains an online testing phase and a fault-tolerant training phase. The techniques proposed for these two phases are classified and explained to highlight their similarities and differences. The methods reviewed in this survey represent recent trends in fault-tolerant designs of RCS, and are expected motivate further research in this field.
A New Benchmark and Progress Toward Improved Weakly Supervised Learning

Knowledge Matters: Importance of Prior Information for Optimization [7], by Gulcehre et. al., sought to establish the limits of current black-box, deep learning techniques by posing problems which are difficult to learn without engineering knowledge into the model or training procedure. In our work, we completely solve the previous Knowledge Matters problem using a generic model, pose a more difficult and scalable problem, All-Pairs, and advance this new problem by introducing a new learned, spatially-varying histogram model called TypeNet which outperforms conventional models on the problem. We present results on All-Pairs where our model achieves 100% test accuracy while the best ResNet models achieve 79% accuracy. In addition, our model is more than an order of magnitude smaller than Resnet-34. The challenge of solving larger-scale All-Pairs problems with high accuracy is presented to the community for investigation.
Punctuation Prediction Model for Conversational Speech

An ASR system usually does not predict any punctuation or capitalization. Lack of punctuation causes problems in result presentation and confuses both the human reader andoff-the-shelf natural language processing algorithms. To overcome these limitations, we train two variants of Deep Neural Network (DNN) sequence labelling models - a Bidirectional Long Short-Term Memory (BLSTM) and a Convolutional Neural Network (CNN), to predict the punctuation. The models are trained on the Fisher corpus which includes punctuation annotation. In our experiments, we combine time-aligned and punctuated Fisher corpus transcripts using a sequence alignment algorithm. The neural networks are trained on Common Web Crawl GloVe embedding of the words in Fisher transcripts aligned with conversation side indicators and word time infomation. The CNNs yield a better precision and BLSTMs tend to have better recall. While BLSTMs make fewer mistakes overall, the punctuation predicted by the CNN is more accurate - especially in the case of question marks. Our results constitute significant evidence that the distribution of words in time, as well as pre-trained embeddings, can be useful in the punctuation prediction task.
Performance evaluation of CQIC and TCP BBR in mobile network

Original TCP design is not agile enough to fit the rapidly varying mobile network bandwidth. In this paper, we revisit the Channel Quality Indicator Control (CQIC) congestion control algorithm and adapt its implementation from 3G/QUIC semantic to LTE/TCP with detailed congestion reaction gestures. We then compare its performance with that of the brand new TCP congestion control BBR — yet already widely deployed. We also identify the reason for performance degradation of high data rate downloads in LTE Acknowledged Mode (AM, which is turned on by default). Furthermore, impacts of the commonly encountered DelAck (delayed acknowlegment) feature are analyzed for both congestion control algorithms e.g. BBR and CQIC.
Real-Time Adaptive Histogram Min-Max Bucket (HMMB) Model for Background Subtraction

This paper proposes an efficient real-time background subtraction algorithm, which is essential in many computer vision applications. Initially, histograms of the intensity values for each channel of a pixel position in a set of training framesare constructed. A background model, histogram min-max bucket, is constructed from the minimum and maximum values of contiguous non-zero frequencies of the temporal intensity histogram. A novel feature of this algorithm is the use of a single sliding window to update the system adaptively, capturing the proper background even under sudden and/or gradual illumination changes in the scene. We incorporate both local and global information with the help of the current and the previously visited pixel values for binary classification of a pixel into foreground and background. This algorithm is compared with several state-of-the-art techniques and experimental studies show that the proposed method outperforms all these methods in terms of accurate binary classification.
Pre-surgical Brain Mapping: To Rest or Not to Rest?

Preoperativemapping of brain functions is themost common clinical application of functionalMRI (fMRI) (1). The pre-surgical localization of eloquent areas has a positive impact for maximizing the extent of resection while reducing intra-operative mapping time (2) and improving patient outcome (3). In the pre-surgical setting, the typical fMRI approach employs conventional tasks which require patients to execute simple tasks in the scanner (4–8). This task-based fMRI (tb-fMRI) approach is well established and widely used in clinical routine, but has limitations: patients must be able to perform the tasks appropriately, implementation has a cost, and trained personnel is needed to select the proper task and instruct the patient. One straightforward alternative to task-based fMRI is resting-state fMRI (rs-fMRI). This technique allows the study of spontaneous, low-frequency fluctuations that occur throughout the brain and has been widely used to characterize the healthy brain and neurological diseases. With rs-fMRI it is possible to identify a number of networks, or components, which are strongly functionally connected at rest and highly reproducible across subjects and sessions (9–11). Some of them show similar topographies to tb-fMRI networks, that is regions activated during cognitive functions, visual or sensory-motor tasks (12, 13). Measures of functional connectivity allow the study of brain functional reorganization and neuronal changes associated with brain disease (14). Alterations of rs-fMRI networks have been identified in many neurological and psychiatric disorders, even in absence of structural modifications, and in some studies have been shown to correlate with disease progression and severity (15). Rs-fMRI has been applied also in the preoperative setting to overcome the limitations of task-based fMRI, including the need of active patient participation (16). rs-fMRI has the additional advantage that acquisition is brief (∼6–10min) and can be easily managed by MRI technicians. Initial studies to validate the technique show good concordance with the gold standard intraoperative electro-cortical stimulation (ECS) (17–20). The most commonly identified networks for pre-surgical planning are the sensori-motor component, encompassing preand post-central cortex with the supplementary motor area, and the language network including Broca and Wernicke’s areas, often corresponding to the fronto-parietal network (21–23).
Placement selection of millimeter wave FMCW radar for indoor fall detection

Indoor fall detection based on radar technology has recently drawn more and more attention because of the advantages of privacy protection, all-day working and non-intrusive properties. Numerous studies have used Micro-Doppler information to extract fall signatures. However, mounting position of a radar sensor has a great influence on the Doppler response and fall signature extraction. In this work, we demonstrate the effects of radar placements on micro-Doppler and range intensity. For this purpose, we first use an approximation rigid-motion model to analyze the process of a fall. Then time-frequency distribution and range-time intensity are used as the input data for feature extraction. A millimeter wave frequency modulated continuous wave (FMCW) radar with 3-GHz bandwidth is mounted on three positions to detect the action of human fall. Experimental results show that well selected placement of the radar sensor can provide improved feature extraction results both from micro-Doppler and range-time intensity.
Chromosomal Changes and Form-Formation, Subspeciation in the Wideranged Euroasian Species Zootoca vivipara (Evolution, Biogeography)

The wideranged Euroasian species Zootoca vivipara (family Lacertidae) is a rare species, possessing transpalearctic distribution from the Pyrenees up to the archipelago of the Sea of Japan. A further very important feature is that Zootoca vivipara has two reproductive modes in different populations. Primitive oviparous populations inhabit western Europe (the Pyrenees region and the south-eastern central Europe), whereas advanced viviparous populations occur in the other part of the distribution range. Karyological investigations of Zootoca vivipara from many geographically distant populations have shown that the species is characterized by differences in the diploid number [2n = 36! 36 in both sexes or 36 (male)! 35 (female)]; in the system of sex chromosomes (ZW or Z1 Z2 W) and in the types and structure of W sex chromosome (Kupriyanova, 1990, 1997; Odierna et al., 1993). Morphological differentiation of the populations is poorly pronounced because only four subspecies are recognized: Z. v. vivipara, Z. v. carniolica, Z. v. sakhalinensis (nomen nudum), and Z. v. pannonica. Modern cytogenetical studies have revealed five structures of karyotypes among oviand viviparous populations of Z. vivipara from different geographic ranges.
Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. CLE images can be distorted by motion artifacts, fluorescence signals out of detector dynamic range, or may be obscured by red blood cells, and thus interpreted as nondiagnostic (ND). However, just a single CLE image with a detectable pathognomonic histological tissue signature can suffice for intraoperative diagnosis. Dealing with the abundance of images from CLE is not unlike sifting through a myriad of genes, proteins, or other structural or metabolic markers to find something of commonality or uniqueness in cancer that might indicate a potential treatment scheme or target. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/ND, glioma/nonglioma, tumor/injury/normal categories, and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow, and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.
Enhanced Biased ASK Modulation Performance for SWIPT With AWGN Channel and Dual-Purpose Hardware

A lot of small sensors are deployed in Internet of Things applications. Simultaneous wireless information and power transfer (SWIPT) is a solution to avoid a large amount of batteries, while exchanging information with the sensors. Combining both energy harvesting and information receiving in dual-purpose hardware lowers power consumption while also decreasing sensor size. Biased amplitude shift keying (ASK) is introduced as a solution for the SWIPT modulation technique. In particular, the design of enhanced biased ASK modulation in case of an additive white Gaussian noise channel is analyzed in this paper. Novel formulas for symbol error rate are derived, while improvements of the modulation scheme are proposed based on measurements, taking rectifier nonlinearity into account and resulting in reducing the transmitted power by 14 dB for fixed error rate.
Discrete Similariton and Dissipative Soliton Modelocking for Energy Scaling Ultrafast Thin-Disk Laser Oscillators

Since their first demonstration, modelocked thin-disk lasers have consistently surpassed other modelocked oscillator technologies in terms of achievable pulse energy and average power by several orders of magnitude. Surprisingly, state-of-the-art results using this technology have so far only been achieved in modelocking regimes where soliton pulse shaping is dominant (i.e., soliton modelocking with semiconductor saturable absorber mirrors or Kerr lens modelocking), in which only small nonlinear phase shifts are tolerable, ultimately limiting pulse energy scaling. Inspired by the staggering success of novel modelocking regimes applied to overcome these limitations in modelocked fiber lasers, namely the similariton (self-similarly evolving pulses) and dissipative soliton regimes, here, we explore these nonlinearity-resistant regimes for the next generation of high-energy modelocked thin-disk lasers, whereby millijoule pulse energies appear to be realistic targets. In this goal, we propose two possible implementations. The first is based on a passive multipass cell and designed to support dissipative solitons in an all-normal dispersion cavity. The second incorporates an active multipass cell and is designed to support similaritons. Our numerical investigations indicate that this is a very promising path to increase the pulse energy achievable directly from modelocked oscillators toward the millijoule level while additionally simplifying their implementation by eliminating the need for operation in cumbersome vacuum chambers.
Differences in Thermal Biology Between Two Morphologically Distinct Populations of Iberian Wall Lizards Inhabiting Different Environments

Populations should adapt to the climate at their respective localities. Here, we examined differences in thermal biology between two populations of Podarcis hispanica lizards from areas with different climates. Lizards from the cold, northern mountains attained lower field body temperatures than lizards form the warm, southern plains. However, the larger body size and darker coloration of northern lizards resulted in slower cooling rates, which may increase efficiency of thermoregulation. Northern populations selected higher temperatures in a thermal gradient, possibly as an adaptation to heat up before conditions changed, as their mountain environment is unpredictable. Finally, lizards from both populations had similar temperature-dependent locomotor performance curves, although southern lizards were relatively faster considering their smaller body size. We suggest that environmental differences may lead to differences in morphology and locally adapted thermal biology in lizards that might allow maximizing thermoregulation in each local climatic conditions.