Miracle-dz/UniFAR-FTUs huggingface dataset:
Different inner retinal pathways mediate rod-cone input in irradiance detection for the pupillary light reflex and regulation of behavioral state in mice.

PURPOSE Detection of light in the eye contributes both to spatial awareness (form vision) and to responses that acclimate an animal to gross changes in light (irradiance detection). This dual role means that eye disease that disrupts form vision can also adversely affect physiology and behavioral state. The purpose of this study was to investigate how inner retinal circuitry mediating rod-cone photoreceptor input contributes to functionally distinct irradiance responses and whether that might account for phenotypic diversity in retinal disease. METHODS The sensitivity of the pupillary light reflex and negative masking (activity suppression by light) was measured in wild-type mice with intact inner retinal circuitry, Nob4 mice that lack ON-bipolar cell function, and rd1 mice that lack rods and cones and, therefore, have no input to ON or OFF bipolar cells. RESULTS An expected increase in sensitivity to negative masking with loss of photoreceptor input in rd1 was duplicated in Nob4 mice. In contrast, sensitivity of the pupillary light reflex was more severely reduced in rd1 than in Nob4 mice. CONCLUSIONS Absence of ON-bipolar cell-mediated rod-cone input can fully explain the phenotype of outer retina degeneration for negative masking but not for the pupillary light reflex. Therefore, inner retinal pathways mediating rod-cone input are different for negative masking and the pupillary light reflex.
Pathologic Characteristics of Cancers Detected in the Prostate Cancer Prevention Trial: Implications for Prostate Cancer Detection and Chemoprevention

The Prostate Cancer Prevention Trial (PCPT) showed a risk of prostate cancer at prostate-specific antigen (PSA) <4.0 ng/mL and that prostate cancer risk is reduced by finasteride. A major concern about early detection by PSA and prevention by finasteride is that they may involve biologically inconsequential tumors. We reviewed the pathologic characteristics of prostate biopsies from men in the placebo and finasteride groups of the PCPT. We examined tumor pathology characteristics stratified by level of PSA for men in the placebo group who underwent radical prostatectomy. Seventy-five percent of all cancers and 62% of Gleason score ≤6 cancers in the PCPT met the biopsy criteria for clinically significant tumors. Surrogate measures for tumor volume (number of cores positive, percent cores positive, linear extent, and bilaterality) and risk of perineural invasion were lower in men who received finasteride. The PSA-associated risks of insignificant cancer were 51.7% (PSA, 0-1.0 ng/mL), 33.7% (1.1-2.5 ng/mL), 17.8% (2.6-4.0 ng/mL), and 11.7% (4.1-10 ng/mL). Conversely, the risks of high-grade (Gleason score ≥7) tumors for the same PSA strata were 15.6%, 37.9%, 49.1%, and 52.4%, respectively. These data highlight the dilemma of PSA when used for screening: Lower cutoff levels increase detection of insignificant disease, but cure is more likely, whereas higher cutoff levels make detection of significant cancer more likely, but cure is less likely. Therefore, the effectiveness of finasteride in preventing prostate cancer, including Gleason score ≤6 cancer, with meaningful rates of significant disease in the PCPT suggests that cutoff values for PSA screening should be individualized and that men undergoing screening should be informed of the opportunity to reduce their risk of disease with finasteride.
Mutations in Bcl 9 and Pygo genes cause congenital heart defects by tissue-specific perturbation of Wnt / beta-catenin signaling

N.B.: When citing this work, cite the original publication. Cantù, C. , Felker, A. , Zimmerli, D. , Prummel, K. D. , Cabello, E. M. , Chiavacci, E. , Mendez-Acevedo, K. M. , Kirchgeorg, L. , Burger, S. , Ripoll, J., Valenta, T. , Hausmann, G. , Vilain, N. , Aguet, M. , Burger, A. , Panakova, D. , Basler, K. , Mosimann, C. , (2018), Mutations in Bcl9 and Pygo genes cause congenital heart defects by tissue-specific perturbation of Wnt/beta-catenin signaling, Genes & Development, 32(21-22), 1443-1458. https://doi.org/10.1101/gad.315531. 118
Mesoscopic reflectance angular domain spectroscopic imaging

Abstract. The advancement of angular domain imaging in mesoscopic reflectance multispectral imaging is reported. The key component is an angular filter array that performs the angular filtration of the back-scattered photons and generates image contrast due to the variances in tissue optical properties. The proposed modality enables multispectral imaging of subsurface features for samples too thick for transillumination angular domain spectroscopic imaging (ADSI) approaches. The validation was carried out with tissue-mimicking phantoms with multiple absorptive features embedded below the surface. Multispectral images in the range of 666 to 888 nm clearly revealed the location of the features with the background scattering levels up to 20 cm−1. The shape of the features was recoverable at depths of up to three to four times the transport mean free path. The spatial resolution was <1 mm and the field-of-view was larger than 2.5 cm×3.0 cm. Furthermore, the attenuation spectra of measured absorptive features were successfully extracted. Target detectability and imaging quality with different background scattering levels, target depths, and illumination focal depths were discussed, as well as the capability of ADSI in reflectance optical mesoscopic imaging and its potential applications.
Asynchronous "Events" are Better For Motion Estimation

Event-based camera is a bio-inspired vision sensor that records intensity changes (called event) asynchronously in each pixel. As an instance of event-based camera, Dynamic and Active-pixel Vision Sensor (DAVIS) combines a standard camera and an event-based camera. However, traditional models could not deal with the event stream asynchronously. To analyze the event stream asynchronously, most existing approaches accumulate events within a certain time interval and treat the accumulated events as a synchronous frame, which wastes the intensity change information and weakens the advantages of DAVIS. Therefore, in this paper, we present the first neural asynchronous approach to process event stream for event-based camera. Our method asynchronously extracts dynamic information from events by leveraging previous motion and critical features of gray-scale frames. To our best knowledge, this is the first neural asynchronous method to analyze event stream through a novel deep neural network. Extensive experiments demonstrate that our proposed model achieves remarkable improvements against the state-of-the-art baselines.
Large Sulfur Isotope Fractionation Does Not Require Disproportionation

In the absence of oxygenation, microbial activity can explain the magnitude of sulfur-isotope traces in sediments. The composition of sulfur isotopes in sedimentary sulfides and sulfates traces the sulfur cycle throughout Earth’s history. In particular, depletions of sulfur-34 (34S) in sulfide relative to sulfate exceeding 47 per mil (‰) often serve as a proxy for the disproportionation of intermediate sulfur species in addition to sulfate reduction. Here, we demonstrate that a pure, actively growing culture of a marine sulfate-reducing bacterium can deplete 34S by up to 66‰ during sulfate reduction alone and in the absence of an extracellular oxidative sulfur cycle. Therefore, similar magnitudes of sulfur isotope fractionation in sedimentary rocks do not unambiguously record the presence of other sulfur-based metabolisms or the stepwise oxygenation of Earth’s surface environment during the Proterozoic.
Cutaneous Feedback of Fingertip Deformation and Vibration for Palpation in Robotic Surgery

Despite its expected clinical benefits, current teleoperated surgical robots do not provide the surgeon with haptic feedback largely because grounded forces can destabilize the system's closed-loop controller. This paper presents an alternative approach that enables the surgeon to feel fingertip contact deformations and vibrations while guaranteeing the teleoperator's stability. We implemented our cutaneous feedback solution on an Intuitive Surgical da Vinci Standard robot by mounting a SynTouch BioTac tactile sensor to the distal end of a surgical instrument and a custom cutaneous display to the corresponding master controller. As the user probes the remote environment, the contact deformations, dc pressure, and ac pressure (vibrations) sensed by the BioTac are directly mapped to input commands for the cutaneous device's motors using a model-free algorithm based on look-up tables. The cutaneous display continually moves, tilts, and vibrates a flat plate at the operator's fingertip to optimally reproduce the tactile sensations experienced by the BioTac. We tested the proposed approach by having eighteen subjects use the augmented da Vinci robot to palpate a heart model with no haptic feedback, only deformation feedback, and deformation plus vibration feedback. Fingertip deformation feedback significantly improved palpation performance by reducing the task completion time, the pressure exerted on the heart model, and the subject's absolute error in detecting the orientation of the embedded plastic stick. Vibration feedback significantly improved palpation performance only for the seven subjects who dragged the BioTac across the model, rather than pressing straight into it.
Machine Learning For Distributed Acoustic Sensors, Classic versus Image and Deep Neural Networks Approach

Distributed Acoustic Sensing (DAS) using fiber optic cables is a promising new technology for pipeline monitoring and protection. In this work, we applied and compared two approaches for event detection using DAS: Classic machine learning approach and the approach based on image processing and deep learning. Although with both approaches acceptable performance can be achieved, the preliminary results show that image based deep learning is more promising approach, offering six times lower event detection delay and twelve times lower execution time.
Mechanisms of cell migration in the adult brain: modelling subventricular neurogenesis

Neurogenesis has been the subject of active research in recent years. Although the majority of neurons form during the embryonic period, neurogenesis continues in restricted regions of the mammalian brain well into adulthood. In rodent brains, neuronal migration is present in the rostral migratory stream (RMS), connecting the subventricular zone to the olfactory bulb (OB). The migration in the RMS is characterised by a lack of dispersion of neuroblasts into the surrounding tissues and a highly directed motion towards the OB. This study uses a simple mathematical model to investigate several theories of migration of neuroblasts through the RMS proposed in the literature, including chemo-attraction, chemorepulsion, general inhibition and the presence of a migration-inducing protein. Apart from the general inhibition model, all the models were able to provide results in good qualitative correspondence with the experimental observations.
Effects of Citalopram on Sutural and Calvarial Cell Processes

The use of selective serotonin reuptake inhibitors (SSRIs) for the treatment of depression during pregnancy is suggested to increase the incidence of craniofacial abnormalities including craniosynostosis. Little is known about this mechanism, however based on previous data we propose a mechanism that affects cell cycle. Excessive proliferation, and reduction in apoptosis may lead to hyperplasia within the suture that may allow for differentiation, bony infiltration, and fusion. Here we utilized in vivo and in vitro analysis to investigate this proposed phenomenon. For in vivo analysis we used C57BL–6 wild-type breeders treated with a clinical dose of citalopram during the third trimester of pregnancy to produce litters exposed to the SSRI citalopram in utero. At post-natal day 15 sutures were harvested from resulting pups and subjected to histomorphometric analysis for proliferation (PCNA) and apoptosis (TUNEL). For in vitro studies, we used mouse calvarial pre-osteoblast cells (MC3T3-E1) to assess proliferation (MTS), apoptosis (Caspase 3/7-activity), and gene expression after exposure to titrated doses of citalopram. In vivo analysis for PCNA suggested segregation of effect by location, with the sagittal suture, showing a statistically significant increase in proliferative response. The coronal suture was not similarly affected, however there was a decrease in apoptotic activity at the dural edge as compared to the periosteal edge. No differences in apoptosis by suture or area due to SSRI exposure were observed. In vitro results suggest citalopram exposure increased proliferation and proliferative gene expression, and decreased apoptosis of the MC3T3-E1 cells. Decreased apoptosis was not confirmed in vivo however, an increase in proliferation without a concomitant increase in apoptosis is still defined as hyperplasia. Thus prenatal SSRI exposure may exert a negative effect on post-natal growth through a hyperplasia effect at the cranial growth sites perhaps leading to clinically significant craniofacial abnormalities.
Design of A Low Power Dissipation Bandgap Voltage Reference Without Resistors

With the development of portable electronics, low power dissipation have become the main direction of current chip research, because Bandgap Reference (BGR) circuits are widely used in modern LSIs to generate a reference voltage on chips, So this paper presents a low power and low temperature coefficient bandgap voltage reference that employs the proportional to absolute temperature (PTAT) voltage generator circuit based on overdrive voltage differences. The BGR circuit consists of MOSFET and bipolar transistor without resistors. The simulated results are verified by Cadence Spectre.The simulated results based on the Magnachip 0. 18μm CMOS process demonstrate that the BGR’s temperature coefficient is 22.44ppm/°C at a temperature range from -40°C to 140°C when power voltage is 0.95V. The PSRR is -50.4023dB at 100Hz, -13.2811dB at 10MHz, and the power dissipation at room temperature is only 83nW.
Genetic algorithm for induction of finite automata with continuous and discrete output actions

In this paper, we describe a genetic algorithm for induction of finite automata with continuous and discrete output actions. Input data for the algorithm is a set of tests. Each test consists of two sequences: input events and output actions. In previous works output actions were discrete, i.e. selected from the finite set, in this work output actions can also be continuous, i.e. represented by real numbers. Only the structure of automaton transitions graph is evolved by the genetic algorithm. Values of output actions are found using transition labeling algorithm, which aim is to maximize the value of fitness function. New transition labeling algorithm is proposed. It also works with continuous output actions and is based on equations system solving. In case of proper selection of fitness function, equations in this system are linear and it can be solved by the Gaussian elimination method. The unmanned airplane performing the loop is considered as an example of the controlled object.
DEXTER - Data EXTraction & Entity Recognition for Low Resource Datasets

Extraction of key information such as named entities, key phrases, and numbers is critical for several banking and ﬁnan-cial processes. Banks and Financial Institutions resort to the use of automation tools to reduce the human effort required for these processes. Training a system to extract key data-points reliably and efﬁciently from text requires large labeled datasets. However, openly available datasets in the ﬁnancial sector have limited labeled data. In our paper, we address the issues in developing a data extraction system for low resource datasets. We experiment with a Bi-directional long short-term memory (Bi-LSTM) model which works well on low resource datasets. We introduce a novel domain-speciﬁc Bi-LSTM layer, which allows us to add domain-speciﬁc knowledge into the neural architecture. We observed that transfer learning from out-of-domain dataset boosts the accuracy on several extraction tasks. We create three new low resource ﬁ-nancial datasets and demonstrate that our model consistently achieves a high degree of accuracy on these datasets. Furthermore, our model outperforms the reported state of the art results on the Financial NER dataset and achieves F1 of 87.48. Our experiments consistently show that transfer learning combined with domain-speciﬁc knowledge engineering improves entity recognition in a low resource setting.
AirPincher: a handheld device for recognizing delicate mid-air hand gestures

We propose AirPincher, a handheld device for recognizing delicate mid-air hand gestures. AirPincher is designed to overcome disadvantages of the two kinds of existing hand gesture-aware techniques such as wearable sensor-based and external vision-based. The wearable sensor-based techniques cause cumbersomeness of wearing sensors every time and the external vision-based techniques incur performance dependence on distance between a user and a remote display. AirPincher allows a user to hold the device in one hand and to generate several delicate mid-air finger gestures. The gestures are captured by several sensors proximately embedded into AirPincher. These features help AirPincher avoid the aforementioned disadvantages of the existing techniques. It allows several delicate finger gestures, for example, rubbing a thumb against a middle finger, swiping with a thumb on an index finger, pinching with a thumb and an index finger, etc. Due to the inherent haptic feedback of these gestures, AirPincher eventually supports the eyes-free interaction. To validate AirPincher's feasibility, we implemented two use cases, i.e., controlling a pointing cursor and moving a virtual 3D object on the remote screen.
Study of Subvertical Fracturing Zones in Subsalt Carbonates of the Pre-Caspian Basin using Duplex Wave Migration

Fracture prediction methods are based on reflection amplitude, reflection curvature and its derivatives, coherency cube, spectral decomposition, ant-tracking technology, azimuthal anisotropy of Pwave velocity, and relatively recently introduced Duplex Wave Migration (DWM) amplitude cube analysis. This paper will report on the results of study a subsalt deposits located in the southwestern part of the Eastern Near-Slope zone of the Pre-Caspian Depression. The DWM amplitude maps will be shown to illustrate the success of this fracture prediction technique.
Revisiting the Double Checkpointing Algorithm

Fast check pointing algorithms require distributed access to stable storage. This paper revisits the approach base upon double check pointing, and compares the blocking algorithm of Zheng, Shi and Kalé, with the non-blocking algorithm of Ni, Meneses and Kalé, in terms of both performance and risk. We also extend their model proposed to assess the impact of the overhead associated to non-blocking communications. We then provide a new peer-to-peer check pointing algorithm, called the triple check pointing algorithm, that can work at constant memory, and achieves both higher efficiency and better risk handling than the double check pointing algorithm. We provide performance and risk models for all the evaluated protocols, and compare them through comprehensive simulations.
Image Segmentation Using Truncated Compound Normal with Gamma Mixture Model

In this paper, we formally present the truncated compound normal with gamma distribution model and define a mixture model(TCNGM) based on this as an extension work to the proposed compound normal with gamma mixture(CNGM) model introduced by us in our earlier work on image segmentation. We present update equations for this model for maximum likelihood estimation (MLE) procedure under Expectation Maximization (EM) framework, construct EM algorithm, and test the feasibility of the model to solve mixture density estimation problem in general and image segmentation in particular. We have found this model to be a competing one in the context of variations in data distributions within probabilistic framework.
Homography-based visual servoing of an aircraft for automatic approach and landing

This paper proposes the Euclidean homography matrix as visual feature in an image-based visual servoing scheme in order to control an aircraft along the approach and landing phase. With a trajectory defined in the image space by a sequence of equidistant key images along the glidepath, an interpolation in the homography space is also proposed in order to reduce the database size and ensure the required smoothness of the control task. In addition, a pan-tilt control was taken into account to respect the dynamics of the aircraft during manoeuvres and in the presence of wind perturbations. An optimal control design based on the linearized model of the aircraft dynamics is then consider to cancel the visual error function. To demonstrate the proposed concept, simulation results under realistic atmospheric disturbances are presented.
Bayesian Generative Active Deep Learning

© 36th International Conference on Machine Learning, ICML 2019. All rights reserved. Deep learning models have demonstrated outstanding performance in several problems, but their training process tends to require immense amounts of computational and human resources for training and labeling, constraining the types of problems that can be tackled. Therefore, the design of effective training methods that require small labeled training sets is an important research direction that will allow a more effective use of resources. Among current approaches designed to address this issue, two are particularly interesting: data augmentation and active learning. Data augmentation achieves this goal by artificially generating new training points, while active learning relies on the selection of the "most informative" subset of unlabeled training samples to be labelled by an oracle. Although successful in practice, data augmentation can waste computational resources because it indiscriminately generates samples that are not guaranteed to be informative, and active learning selects a small subset of informative samples (from a large un-annotated set) that may be insufficient for the training process. In this paper, we propose a Bayesian generative active deep learning approach that combines active learning with data augmentation - we provide theoretical and empirical evidence (MNIST, CIFAR-{10,100}, and SVHN) that our approach has more efficient training and better classification results than data augmentation and active learning.
Controlled interconversion of quantized spin wave modes via local magnetic fields

In the emerging field of magnonics, spin waves are considered for information processing and transmission at high frequencies. Towards this end, the manipulation of propagating spin waves in nanostructured waveguides for novel functionality has recently been attracting increasing focus of research. Excitations with uniform magnetic fields in such waveguides favors symmetric spin wave modes with odd quantization numbers. Interference between multiple odd spin wave modes leads to a periodic self-focusing effect of the propagating spin waves. Here we demonstrate, how antisymmetric spin wave modes with even quantization numbers can be induced by local magnetic fields in a well-controlled fashion. The resulting interference patterns are discussed within an analytical model and experimentally demonstrated using microfocused Brillouin light scattering ({\mu}-BLS).