Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal variation in the abundance of South American sea lions Otaria flavescens (Shaw, 1800) in Chañaral Island, Reserva Nacional Pingüino de Humboldt, Chile

Desde enero de 2007 y hasta marzo de 2008 se analizo la variacion de la abundancia del lobo marino comun Otaria flavescens en la lobera reproductiva Isla Chanaral, de acuerdo al sexo, clase de edad y estacion reproductiva. El mayor numero de lobos marinos en la lobera se registro durante el periodo reproductivo. Esta variacion se debio principalmente a la fluctuacion del numero de hembras, ya que esta clase de edad fue la mas importante en la colonia y mostro un aumento en su abundancia durante este periodo. No se registraron fluctuaciones estacionales en el numero de machos adultos, machos subadultos y juveniles. La proporcion de sexos durante la estacion reproductiva estuvo fuertemente sesgada hacia las hembras, mientras que fue cercana a 1:1 durante el resto del ano. Este estudio demuestra que la variacion en la abundancia y composicion por sexo y clase de edad del lobo marino comun en Isla Chanaral se relaciona principalmente con la actividad reproductiva.
CLINICAL PATTERN OF IDIOPATHIC PULMONARY FIBROSIS: A RETROSPECTIVE STUDY

Background: IPF is the most common form of interstitial lung disease. IPF is a clinico-pathological syndrome characterized by cough, exertional dyspnea, basilar crackles, a restrictive pattern on pulmonary function test (PFT) and honey comb pattern on HRCT. Since there are no exact data on IPF in Iranian patients and also the controversy that exist in this regard we decided to study the IPF cases in regard to epidemiologic, clinical features, radiologic manifestations and diagnosis in Masih Daneshvari Hospital during 1998-2001. Materials and Methods: This study was a descriptive retrospective study on files of IPF patients in Masih Daneshvari Hospital during 1998-2001. Although the total number of patients was 98, only 50 cases that had clinical and pathological findings compatible with IPF were included in the study. Results: Twenty-seven (54%) were males and 23 (46%) were females. Mean age was 56.25±15.86 yrs. The most common clinical signs and symptoms were dyspnea (100%), cough (90%) and crackle (90%). HRCT findings were abnormal in all patients. Eighteen percent were smokers and the most common occupational exposure was through agriculture. 82.2% of patients had restrictive pattern on PFT. Seventy percent of patients had transbronchial lung biopsy (TBLB), 26% had open lung biopsy (OLB) and 4% had video-assisted thoracoscopy (VATS). Conclusion: Age of patients with IPF in our study is one decade lower than in Western countries. HRCT and TBLB assist significantly in the diagnosis of IPF and limiting the surgical procedures to only a limited number of cases. (Tanaffos 2006; 5(2): 27-32)
Neurofibromin-deficient Schwann cells secrete a potent migratory stimulus for Nf1+/– mast cells

The NF1 tumor suppressor gene encodes a GTPase-activating protein called neurofibromin that negatively regulates Ras signaling. Mutations in NF1 cause neurofibromatosis type 1 (NF1). The development of neurofibromas, which are complex tumors composed of multiple cell types, is a hallmark of NF1. Somatic inactivation of murine Nf1 in Schwann cells is necessary, but not sufficient, to initiate neurofibroma formation. Neurofibromas occur with high penetrance in mice in which Nf1 is ablated in Schwann cells in the context of a heterozygous mutant (Nf1+/-) microenvironment. Mast cells infiltrate neurofibromas, where they secrete proteins that can remodel the ECM and initiate angiogenesis. Thus, identification of mechanisms responsible for mast cell migration to tumor microenvironments is important for understanding tumorigenesis and for designing potential therapies. Here, we show that homozygous Nf1 mutant (Nf1-/-) Schwann cells secrete Kit ligand (KitL), which stimulates mast cell migration, and that Nf1+/- mast cells are hypermotile in response to KitL. Furthermore, we link hyperactivation of the Ras-class IA-PI3K-Rac2 pathway to increased Nf1+/- mast cell migration. Thus, these studies identify a novel interaction between Nf1-/- Schwann cells and Nf1+/- mast cells that is likely to be important in neurofibroma formation.
Assessment of Nursing Workload and Related Factors in Intensive Care Units Using the Nursing Activities Score

Background and purpose: Suitable workload of nurses is one of the main factors in patient care safety and quality assurance. Thus, measuring nurses' workload in ICUs is really important. The aim of this study was to determine the degree of nursing workload in ICU and associated factors using Nursing Activity Scale (NAS). Materials and methods: A descriptive-analytical study was performed in 285 ICU inpatients in educational Hospitals affiliated to Mazandaran University of Medical Sciences. The patients' demographic data and medical data were collected through a checklist during three months. NAS was applied to evaluate the nursing workload. Results: The sample consisted of 162 (56.8%) male and 123(43.2%) female. The mean age of patients was 42 ± 4.2. Among the subjects 164 (57.5%) had medication and 121(42.5%) were on surgical treatments. Most of the patients (48.4%) were admitted from operating rooms. The mean scores of NAS were 121.56 ± 40.49, 105.95 ± 34.98, and 99.57 ± 26.07, in morning, afternoon and evening shifts, respectively. Conclusion: The study showed a high degree of nursing workload in ICU in three working shifts. Using NAS is recommended for determining the ratio of the nurses' activities to their numbers in order to provide high quality patient care in ICUs via allocating adequate number of nursing staff.
Ultrastructural study of the hypothalamus in mice chronically treated with manganese

Manganese (Mn) is an essential metal that is an integral part of some metalloproteins and acts as a cofactor of several enzymes. Mn is able to cross the blood-brain barrier and enter the nervous system. It has a low toxicity but exposure to high concentrations or for prolonged periods of time produce neurological disorders in humans that initially cause hallucinations and compulsive behaviour followed by stiffness, muscle weakness, ataxia, memory loss and a tremor resembling Parkinson’ s disease. This study assessed the ultrastructural alterations produced in the hypothalamus of male albino mice injected intraperitoneally with MnCl2 (5 mg Mn/Kg/day) and a control group injected with NaCl 0.9% (0.1 mL) daily for 9 weeks. The animals were sacrificed by cervical dislocation. The hypothalamus was extracted and subsequently processed to be observed on the conventional transmission electron microscope at 2, 4, 6 and 9 weeks of treatment. After 2 weeks it was observed a slight disruption of the Golgi apparatus and the myelin fibers. After 4 weeks the disorganization was accentuated and dilatation of the endoplasmic reticulum (ER) and alterations of mitochondria were observed. After 6 weeks the normal pattern of the myelin sheath was lost. After 9 weeks of treatment it was found swollen mitochondria with lost of crystae, a marked dilatation of rough and smooth endoplasmic reticulum and dendrites with a high degree of swelling. These results suggest that the neurotoxic effect of Mn increases as time of exposure passes and produces ultrastructural alterations of nerve cells in the hypothalamus.
Exigência de lisina para aves de reposição de 0 a 6 semanas de idade

The objective of this work was to estimate the requirement of lysine from 13 to 20 weeks and to evaluate the posterior effect on the performance of two egg-type strains. Three hundred egg-white pullets from 13 to 20 weeks of age and three hundred egg-brown pullets from 13 to 20 weeks of age were allotted to a randomised block design with five treatments and four replicates. A basal diet with 14.0% of crude protein and 2900 kcal metabolizable energy/kg was supplemented with L-lysine HCl to obtain 0.39, 0.42, 0.45, 0.48, and 0.51% of total lysine in the diet. During the laying period, all hens received the same diet with 16.5 CP, 2822 kcal ME/kg, 3.81% Ca+2 and 0.755% of lysine. The pullets requirement estimates of lysine based on weight gain from 13 to 20 weeks were 0.48% or a daily intake of 354 mg for white-egg, and 0.49% or a daily intake of 365 mg for brown-egg. Egg production and mass and egg dozen conversions of two strains were quadraticly affected. Egg mass of white-egg was affected by quadratic manner. During the final growing phase, the brown-egg pullets showed better weight gain than white-egg pullets. During the egg production phase, the brown-egg pullets showed a lower feed intake and better egg mass conversion than white-egg pullets.
Hardware Versus Software Fault Injection of Modern Undervolted SRAMs

To improve power efficiency, researchers are experimenting with dynamically adjusting the supply voltage of systems below the nominal operating points. However, production systems are typically not allowed to function on voltage settings that is below the reliable limit. Consequently, existing software fault tolerance studies are based on fault models, which inject faults on random fault locations using fault injection techniques. In this work we study whether random fault injection is accurate to simulate the behavior of undervolted SRAMs. Our study extends the Gem5 simulator to support fault injection on the caches of the simulated system. The fault injection framework uses fault maps, which describe the faulty bits of SRAMs, as inputs. To compare random fault injection and hardware guided fault injection, we use two types of fault maps. The first type of maps are created through undervolting real SRAMs and observing the location of the erroneous bits, whereas the second type of maps are created by corrupting random bits of the SRAMs. During our study we corrupt the L1-Dcache of the simulated system and we monitor the behavior of the two types of fault maps on the resiliency of six benchmarks. The difference among the resiliency of a benchmark when tested with the different fault maps can be up to 24%.
Federated Learning with Personalization Layers

The emerging paradigm of federated learning strives to enable collaborative training of machine learning models on the network edge without centrally aggregating raw data and hence, improving data privacy. This sharply deviates from traditional machine learning and necessitates the design of algorithms robust to various sources of heterogeneity. Specifically, statistical heterogeneity of data across user devices can severely degrade the performance of standard federated averaging for traditional machine learning applications like personalization with deep learning. This paper pro-posesFedPer, a base + personalization layer approach for federated training of deep feedforward neural networks, which can combat the ill-effects of statistical heterogeneity. We demonstrate effectiveness ofFedPerfor non-identical data partitions ofCIFARdatasetsand on a personalized image aesthetics dataset from Flickr.
Detecting mechanical loosening of total hip replacement implant from plain radiograph using deep convolutional neural network

Plain radiography is widely used to detect mechanical loosening of total hip replacement (THR) implants. Currently, radiographs are assessed manually by medical professionals, which may be prone to poor inter and intra observer reliability and low accuracy. Furthermore, manual detection of mechanical loosening of THR implants requires experienced clinicians who might not always be readily available, potentially resulting in delayed diagnosis. In this study, we present a novel, fully automatic and interpretable approach to detect mechanical loosening of THR implants from plain radiographs using deep convolutional neural network (CNN). We trained a CNN on 40 patients anteroposterior hip x rays using five fold cross validation and compared its performance with a high volume board certified orthopaedic surgeon (AFC). To increase the confidence in the machine outcome, we also implemented saliency maps to visualize where the CNN looked at to make a diagnosis. CNN outperformed the orthopaedic surgeon in diagnosing mechanical loosening of THR implants achieving significantly higher sensitively (0.94) than the orthopaedic surgeon (0.53) with the same specificity (0.96). The saliency maps showed that the CNN looked at clinically relevant features to make a diagnosis. Such CNNs can be used for automatic radiologic assessment of mechanical loosening of THR implants to supplement the practitioners decision making process, increasing their diagnostic accuracy, and freeing them to engage in more patient centric care.
Modeling German Verb Argument Structures: LSTMs vs. Humans

LSTMs have proven very successful at language modeling. However, it remains unclear to what extent they are able to capture complex morphosyntactic structures. In this paper, we examine whether LSTMs are sensitive to verb argument structures. We introduce a German grammaticality dataset in which ungrammatical sentences are constructed by manipulating case assignments (eg substituting nominative by accusative or dative). We find that LSTMs are better than chance in detecting incorrect argument structures and slightly worse than humans tested on the same dataset. Surprisingly, LSTMs are contaminated by heuristics not found in humans like a preference toward nominative noun phrases. In other respects they show human-similar results like biases for particular orders of case assignments.
Skeleton based Activity Recognition by Fusing Part-wise Spatio-temporal and Attention Driven Residues

There exist a wide range of intra class variations of the same actions and inter class similarity among the actions, at the same time, which makes the action recognition in videos very challenging. In this paper, we present a novel skeleton-based part-wise Spatiotemporal CNN RIAC Network-based 3D human action recognition framework to visualise the action dynamics in part wise manner and utilise each part for action recognition by applying weighted late fusion mechanism. Part wise skeleton based motion dynamics helps to highlight local features of the skeleton which is performed by partitioning the complete skeleton in five parts such as Head to Spine, Left Leg, Right Leg, Left Hand, Right Hand. The RIAFNet architecture is greatly inspired by the InceptionV4 architecture which unified the ResNet and Inception based Spatio-temporal feature representation concept and achieving the highest top-1 accuracy till date. To extract and learn salient features for action recognition, attention driven residues are used which enhance the performance of residual components for effective 3D skeleton-based Spatio-temporal action representation. The robustness of the proposed framework is evaluated by performing extensive experiments on three challenging datasets such as UT Kinect Action 3D, Florence 3D action Dataset, and MSR Daily Action3D datasets, which consistently demonstrate the superiority of our method
On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks

The gradient noise (GN) in the stochastic gradient descent (SGD) algorithm is often considered to be Gaussian in the large data regime by assuming that the \emph{classical} central limit theorem (CLT) kicks in. This assumption is often made for mathematical convenience, since it enables SGD to be analyzed as a stochastic differential equation (SDE) driven by a Brownian motion. We argue that the Gaussianity assumption might fail to hold in deep learning settings and hence render the Brownian motion-based analyses inappropriate. Inspired by non-Gaussian natural phenomena, we consider the GN in a more general context and invoke the \emph{generalized} CLT, which suggests that the GN converges to a \emph{heavy-tailed} $\alpha$-stable random vector, where \emph{tail-index} $\alpha$ determines the heavy-tailedness of the distribution. Accordingly, we propose to analyze SGD as a discretization of an SDE driven by a Levy motion. Such SDEs can incur `jumps', which force the SDE and its discretization \emph{transition} from narrow minima to wider minima, as proven by existing metastability theory and the extensions that we proved recently. In this study, under the $\alpha$-stable GN assumption, we further establish an explicit connection between the convergence rate of SGD to a local minimum and the tail-index $\alpha$. To validate the $\alpha$-stable assumption, we conduct experiments on common deep learning scenarios and show that in all settings, the GN is highly non-Gaussian and admits heavy-tails. We investigate the tail behavior in varying network architectures and sizes, loss functions, and datasets. Our results open up a different perspective and shed more light on the belief that SGD prefers wide minima.
Pilot Hi survey of Planck Galactic Cold Clumps with FAST

We present a pilot Hi survey of 17 Planck Galactic Cold Clumps (PGCCs) with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Hi Narrow Self-Absorption (HINSA) is an effective method to detect cold Hi being mixed with molecular hydrogen H2 and improves our understanding of the atomic to molecular transition in the interstellar medium. HINSA was found in 58% PGCCs that we observed. The column density of HINSA was found to have an intermediate correlation with that of 13CO, following (N(HINSA)) = (0.52 ± 0.26) (N13CO) + (10 ± 4.1). Hi abundance relative to total hydrogen [Hi]/[H] has an average value of 4.4 × 10–3, which is about 2.8 times of the average value of previous HINSA surveys toward molecular clouds. For clouds with total column density NH > 5 × 1020 cm–2, an inverse correlation between HINSA abundance and total hydrogen column density is found, confirming the depletion of cold Hi gas during molecular gas formation in more massive clouds. Non-thermal line width of 13CO is about 0-0.5 km s−1 larger than that of HINSA. One possible explanation of narrower non-thermal width of HINSA is that HINSA region is smaller than that of 13CO. Based on an analytic model of H2 formation and H2 dissociation by cosmic ray, we found the cloud ages to be within 106.7 – 107.0 yr for five sources.
One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation

Recent advances in the sparse neural network literature have made it possible to prune many large feed forward and convolutional networks with only a small quantity of data. Yet, these same techniques often falter when applied to the problem of recovering sparse recurrent networks. These failures are quantitative: when pruned with recent techniques, RNNs typically obtain worse performance than they do under a simple random pruning scheme. The failures are also qualitative: the distribution of active weights in a pruned LSTM or GRU network tend to be concentrated in specific neurons and gates, and not well dispersed across the entire architecture. We seek to rectify both the quantitative and qualitative issues with recurrent network pruning by introducing a new recurrent pruning objective derived from the spectrum of the recurrent Jacobian. Our objective is data efficient (requiring only 64 data points to prune the network), easy to implement, and produces 95% sparse GRUs that significantly improve on existing baselines. We evaluate on sequential MNIST, Billion Words, and Wikitext.
Speaker detection in the wild: Lessons learned from JSALT 2019

This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios. The main focus was to tackle a wide range of conditions that go from meetings to wild speech. We describe the research threads we explored and a set of modules that was successful for these scenarios. The ultimate goal was to explore speaker detection; but our first finding was that an effective diarization improves detection, and not having a diarization stage impoverishes the performance. All the different configurations of our research agree on this fact and follow a main backbone that includes diarization as a previous stage. With this backbone, we analyzed the following problems: voice activity detection, how to deal with noisy signals, domain mismatch, how to improve the clustering; and the overall impact of previous stages in the final speaker detection. In this paper, we show partial results for speaker diarizarion to have a better understanding of the problem and we present the final results for speaker detection.
On the optimality of kernels for high-dimensional clustering

This paper studies the optimality of kernel methods in high-dimensional data clustering. Recent works have studied the large sample performance of kernel clustering in the high-dimensional regime, where Euclidean distance becomes less informative. However, it is unknown whether popular methods, such as kernel k-means, are optimal in this regime. We consider the problem of high-dimensional Gaussian clustering and show that, with the exponential kernel function, the sufficient conditions for partial recovery of clusters using the NP-hard kernel k-means objective matches the known information-theoretic limit up to a factor of $\sqrt{2}$ for large $k$. It also exactly matches the known upper bounds for the non-kernel setting. We also show that a semi-definite relaxation of the kernel k-means procedure matches up to constant factors, the spectral threshold, below which no polynomial-time algorithm is known to succeed. This is the first work that provides such optimality guarantees for the kernel k-means as well as its convex relaxation. Our proofs demonstrate the utility of the less known polynomial concentration results for random variables with exponentially decaying tails in a higher-order analysis of kernel methods.
A novel framework for joint sparse clustering and alignment of functional data

We propose a novel framework for sparse functional clustering that also embeds an alignment step. Sparse functional clustering means finding a grouping structure while jointly detecting the parts of the curves' domains where their grouping structure shows the most. Misalignment is a well-known issue in functional data analysis, that can heavily affect functional clustering results if not properly handled. Therefore, we develop a sparse functional clustering procedure that accounts for the possible curve misalignment: the coherence of the functional measure used in the clustering step to the class where the warping functions are chosen is ensured, and the well-posedness of the sparse clustering problem is proved. A possible implementing algorithm is also proposed, that jointly performs all these tasks: clustering, alignment, and domain selection. The method is tested on simulated data in various realistic situations, and its application to the Berkeley Growth Study data and to the AneuRisk65 data set is discussed.
Canonical Coordinates and Natural Equations for Minimal Time-like Surfaces in $R^4_2$

We apply the complex analysis over the double numbers $D$ to study the minimal time-like surfaces in $R^4_2$. A minimal time-like surface which is free of degenerate points is said to be of general type. We divide the minimal time-like surfaces of general type into three types and prove that these surfaces admit special geometric (canonical) parameters. Then the geometry of the minimal time-like surfaces of general type is determined by the Gauss curvature $K$ and the curvature of the normal connection $\varkappa$, satisfying the system of natural equations for these surfaces. We prove the following: If $(K, \varkappa), \, K^2- \varkappa^2 > 0 $ is a solution to the system of natural equations, then there exists exactly one minimal time-like surface of the first type and exactly one minimal time-like surface of the second type with invariants $(K, \varkappa)$; if $(K, \varkappa),\, K^2- \varkappa^2 < 0 $ is a solution to the system of natural equations, then there exists exactly one minimal time-like surface of the third type with invariants $(K, \varkappa)$.