Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges for an Ontology of Artificial Intelligence

Of primary importance in formulating a response to the increasing prevalence and power of artificial intelligence (AI) applications in society are questions of ontology. Questions such as: What "are" these systems? How are they to be regarded? How does an algorithm come to be regarded as an agent? We discuss three factors which hinder discussion and obscure attempts to form a clear ontology of AI: (1) the various and evolving definitions of AI, (2) the tendency for pre-existing technologies to be assimilated and regarded as "normal," and (3) the tendency of human beings to anthropomorphize. This list is not intended as exhaustive, nor is it seen to preclude entirely a clear ontology, however, these challenges are a necessary set of topics for consideration. Each of these factors is seen to present a 'moving target' for discussion, which poses a challenge for both technical specialists and non-practitioners of AI systems development (e.g., philosophers and theologians) to speak meaningfully given that the corpus of AI structures and capabilities evolves at a rapid pace. Finally, we present avenues for moving forward, including opportunities for collaborative synthesis for scholars in philosophy and science.
Persistent Malfunction of Glymphatic and Meningeal Lymphatic Drainage in a Mouse Model of Subarachnoid Hemorrhage

Subarachnoid hemorrhage (SAH) is a devastating cerebrovascular event that often is followed by permanent brain impairments. It is necessary to explore the pathogenesis of secondary pathological damages in order to find effective interventions for improving the prognosis of SAH. Blockage of brain lymphatic drainage has been shown to worsen cerebral ischemia and edema after acute SAH. However, whether or not there is persistent dysfunction of cerebral lymphatic drainage following SAH remains unclear. In this study, autologous blood was injected into the cisterna magna of mice to establish SAH model. One week after surgery, SAH mice showed decreases in fluorescent tracer drainage to the deep cervical lymph nodes (dcLNs) and influx into the brain parenchyma after injection into the cisterna magna. Moreover, SAH impaired polarization of astrocyte aquaporin-4 (AQP4) that is a functional marker of glymphatic clearance and resulted in accumulations of Tau proteins as well as CD3+, CD4+, and CD8+ cells in the brain. In addition, pathological changes, including microvascular spasm, activation of glial cells, neuroinflammation, and neuronal apoptosis were observed in the hippocampus of SAH mice. Present results demonstrate persistent malfunction of glymphatic and meningeal lymphatic drainage and related neuropathological damages after SAH. Targeting improvement of brain lymphatic clearance potentially serves as a new strategy for the treatment of SAH.
Protein Moonlighting Revealed by Noncatalytic Phenotypes of Yeast Enzymes

An increasing number of multifunctional proteins are being identified, but it is still unclear to what extent proteins moonlight beyond their annotated. A single gene can partake in several biological processes, and therefore gene deletions can lead to different—sometimes unexpected—phenotypes. However, it is not always clear whether such pleiotropy reflects the loss of a unique molecular activity involved in different processes or the loss of a multifunctional protein. Here, using Saccharomyces cerevisiae metabolism as a model, we systematically test the null hypothesis that enzyme phenotypes depend on a single annotated molecular function, namely their catalysis. We screened a set of carefully selected genes by quantifying the contribution of catalysis to gene deletion phenotypes under different environmental conditions. While most phenotypes were explained by loss of catalysis, slow growth was readily rescued by a catalytically inactive protein in about one-third of the enzymes tested. Such noncatalytic phenotypes were frequent in the Alt1 and Bat2 transaminases and in the isoleucine/valine biosynthetic enzymes Ilv1 and Ilv2, suggesting novel “moonlighting” activities in these proteins. Furthermore, differential genetic interaction profiles of gene deletion and catalytic mutants indicated that ILV1 is functionally associated with regulatory processes, specifically to chromatin modification. Our systematic study shows that gene loss phenotypes and their genetic interactions are frequently not driven by the loss of an annotated catalytic function, underscoring the moonlighting nature of cellular metabolism.
Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning

Deep neural networks (DNNs) are state-of-the-art solutions for many machine learning applications, and have been widely used on mobile devices. Running DNNs on resourceconstrained mobile devices often requires the help from edge servers via computation offloading. However, offloading through a bandwidth-limited wireless link is non-trivial due to the tight interplay between the computation resources on mobile devices and wireless resources. Existing studies have focused on cooperative inference where DNN models are partitioned at different neural network layers, and the two parts are executed at the mobile device and the edge server, respectively. Since the output data size of a DNN layer can be larger than that of the raw data, offloading intermediate data between layers can suffer from high transmission latency under limited wireless bandwidth. In this paper, we propose an efficient and flexible 2-step pruning framework for DNN partition between mobile devices and edge servers. In our framework, the DNN model only needs to be pruned once in the training phase where unimportant convolutional filters are removed iteratively. By limiting the pruning region, our framework can greatly reduce either the wireless transmission workload of the device or the total computation workload. A series of pruned models are generated in the training phase, from which the framework can automatically select to satisfy varying latency and accuracy requirements. Furthermore, coding for the intermediate data is added to provide extra transmission workload reduction. Our experiments show that the proposed framework can achieve up to 25.6X reduction on transmission workload, 6.01X acceleration on total computation and 4.81X reduction on end-to-end latency as compared to partitioning the original DNN model without pruning.
Gene Expression Profile of Olfactory Transduction Signaling in an Animal Model of Human Multiple Sclerosis

Olfactory dysfunction occurs in multiple sclerosis in humans, as well as in an animal model of experimental autoimmune encephalomyelitis (EAE). The aim of this study was to analyze differentially expressed genes (DEGs) in olfactory bulb of EAE-affected mice by next generation sequencing, with a particular focus on changes in olfaction-related signals. EAE was induced in C57BL/6 mice following immunization with myelin oligodendrocyte glycoprotein and adjuvant. Inflammatory lesions were identified in the olfactory bulbs as well as in the spinal cord of immunized mice. Analysis of DEGs in the olfactory bulb of EAE-affected mice revealed that 44 genes were upregulated (and which were primarily related to inflammatory mediators), while 519 genes were downregulated; among the latter, olfactory marker protein and stomatin-like 3, which have been linked to olfactory signal transduction, were significantly downregulated (log2 [fold change] >1 and p-value <0.05). These findings suggest that inflammation in the olfactory bulb of EAE-affected mice is associated with the downregulation of some olfactory signal transduction genes, particularly olfactory marker protein and stomatin-like 3, which may lead to olfactory dysfunction in an animal model of human multiple sclerosis.
Analysis on the Empirical Spectral Distribution of Large Sample Covariance Matrix and Applications for Large Antenna Array Processing

This paper addresses the asymptotic behavior of a particular type of information-plus-noise-type matrices, where the column and row numbers of the matrices are large and of the same order, while signals have diverged and the time delays of the channel are fixed. We prove that the empirical spectral distribution of the large dimension sample covariance matrix and a well-studied spiked central Wishart matrix converge to the same distribution. As an application, an asymptotic power function is presented for the generalized likelihood ratio statistics for testing the presence of the signal in large array signal processing.
Research on the pixel-based and object-oriented methods of urban feature extraction with GF-2 remote-sensing images

During the rapid urbanization construction of China, acquisition of urban geographic information and timely data updating are important and fundamental tasks for the refined management of cities. With the development of domestic remote sensing technology, the application of Gaofen-2 (GF-2) high-resolution remote sensing images can greatly improve the accuracy of information extraction. This paper introduces an approach using object-oriented classification methods for urban feature extraction based on GF-2 satellite data. A combination of spectral, spatial attributes and membership functions was employed for mapping the urban features of Qinhuai District, Nanjing. The data preprocessing is carried out by ENVI software, and the subsequent data is exported into the eCognition software for object-oriented classification and extraction of urban feature information. Finally, the obtained raster image classification results are vectorized using the ARCGIS software, and the vector graphics are stored in the library, which can be used for further analysis and modeling. Accuracy assessment was performed using ground truth data acquired by visual interpretation and from other reliable secondary data sources. Compared with the result of pixel-based supervised (neural net) classification, the developed object-oriented method can significantly improve extraction accuracy, and after manual interpretation, an overall accuracy of 95.44% can be achieved, with a Kappa coefficient of 0.9405, which objectively confirmed the superiority of the object-oriented method and the feasibility of the utilization of GF-2 satellite data.
Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning

There is a long history of using meta learning as representation learning, specifically for determining the relevance of inputs. In this paper, we examine an instance of meta-learning in which feature relevance is learned by adapting step size parameters of stochastic gradient descent---building on a variety of prior work in stochastic approximation, machine learning, and artificial neural networks. In particular, we focus on stochastic meta-descent introduced in the Incremental Delta-Bar-Delta (IDBD) algorithm for setting individual step sizes for each feature of a linear function approximator. Using IDBD, a feature with large or small step sizes will have a large or small impact on generalization from training examples. As a main contribution of this work, we extend IDBD to temporal-difference (TD) learning---a form of learning which is effective in sequential, non i.i. d. problems. We derive a variety of IDBD generalizations for TD learning, demonstrating that they are able to distinguish which features are relevant and which are not. We demonstrate that TD IDBD is effective at learning feature relevance in both an idealized gridworld and a real-world robotic prediction task.
Secret key distillation across a quantum wiretap channel under restricted eavesdropping

The theory of quantum cryptography aims to guarantee unconditional information-theoretic security against an omnipotent eavesdropper. In many practical scenarios, however, the assumption of an all-powerful adversary is excessive and can be relaxed considerably. In this paper we study secret key distillation across a lossy and noisy quantum wiretap channel between Alice and Bob, with a separately parameterized realistically lossy quantum channel to the eavesdropper Eve. We show that under such restricted eavesdropping, the key rates achievable can exceed the secret key distillation capacity against an unrestricted eavesdropper in the quantum wiretap channel. Further, we show upper bounds on the key rates based on the relative entropy of entanglement. This simple restricted eavesdropping model is widely applicable, e.g., to free-space quantum optical communication, where realistic collection of light by Eve is limited by the finite size of her optical aperture. Future work will include calculating bounds on the amount of light Eve can collect under various realistic scenarios.
The MicroRNA Family Both in Normal Development and in Different Diseases: The miR-17-92 Cluster

An increasing number of research studies over recent years have focused on the function of microRNA (miRNA) molecules which have unique characteristics in terms of structure and function. They represent a class of endogenous noncoding single-strand small molecules. An abundance of miRNA clusters has been found in the genomes of various organisms often located in a polycistron. The miR-17-92 family is among the most famous miRNAs and has been identified as an oncogene. The functions of this cluster, together with the seven individual molecules that it comprises, are most related to cancers, so it would not be surprising that they are considered to have involvement in the development of tumors. The miR-17-92 cluster is therefore expected not only to be a tumor marker, but also to perform an important role in the early diagnosis of those diseases and possibly also be a target for tumor biotherapy. The miR-17-92 cluster affects the development of disease by regulating many related cellular processes and multiple target genes. Interestingly, it also has important roles that cannot be ignored in disease of the nervous system and circulation and modulates the growth and development of bone. Therefore, it provides new opportunities for disease prevention, clinical diagnosis, prognosis, and targeted therapy. Here we review the role of the miR-17-92 cluster that has received little attention in relation to neurological diseases, cardiac diseases, and the development of bone and tumors.
Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images

We develop a system for modeling hand-object interactions in 3D from RGB images that show a hand which is holding a novel object from a known category. We design a Convolutional Neural Network (CNN) for Hand-held Object Pose and Shape estimation called HOPS-Net and utilize prior work to estimate the hand pose and configuration. We leverage the insight that information about the hand facilitates object pose and shape estimation by incorporating the hand into both training and inference of the object pose and shape as well as the refinement of the estimated pose. The network is trained on a large synthetic dataset of objects in interaction with a human hand. To bridge the gap between real and synthetic images, we employ an image-to-image translation model (Augmented CycleGAN) that generates realistically textured objects given a synthetic rendering. This provides a scalable way of generating annotated data for training HOPS-Net. Our quantitative experiments show that even noisy hand parameters significantly help object pose and shape estimation. The qualitative experiments show results of pose and shape estimation of objects held by a hand “in the wild”.
Mobile-App Analysis and Instrumentation Techniques Reimagined with DECREE

A large number of mobile-app analysis and instrumentation techniques have emerged in the past decade. However, those techniques' components are difficult to extract and reuse outside their original tools, their evaluation results are hard to reproduce, and the tools themselves are hard to compare. This paper introduces DECREE, an infrastructure intended to guide such techniques to be reproducible, practical, reusable, and easy to adopt in practice. DECREE allows researchers and developers to easily discover existing solutions to their needs, enables unbiased and reproducible evaluation, and supports easy construction and execution of replication studies. The paper describes DECREE's three modules and its potential to fundamentally alter how research is conducted in this area.
Semi- and Weakly Supervised Directional Bootstrapping Model for Automated Skin Lesion Segmentation

—Automated skin lesion segmentation on dermoscopy images is an essential and challenging task in the computer-aided diagnosis of skin cancer. Despite their prevalence and relatively good performance, deep learning based segmentation methods re-quire a myriad number of training images with pixel-level dense annotation, which is hard to obtain due to the efforts and costs related to dermoscopy images acquisition and annotation. In this paper, we propose the semi- and weakly supervised directional bootstrapping (SWSDB) model for skin lesion segmentation, which consists of three deep convolutional neural networks: a coarse segmentation network (coarse-SN), a dilated classiﬁcation network (dilated-CN) and an enhanced segmentation network (enhanced-SN). Both the coarse-SN and enhanced-SN are trained using the images with pixel-level annotation, and the dilated-CN is trained using the images with image-level class labels. The coarse-SN generates rough segmentation masks that provide a prior bootstrapping for the dilated-CN and help it produce accurate lesion localization maps. The maps are then fed into the enhancedSN to transfer the localization information learned from image-level labels to the enhanced-SN to generate segmentation results. Furthermore, we introduce a hybrid loss that is the weighted sum of a dice loss and a rank loss to the coarse-SN and enhanced-SN, ensuring both networks’ good compatibility for the data with imbalanced classes and imbalanced hard-easy pixels. We evaluated the proposed SWSDB model on the ISIC- 2017 challenge dataset and PH2 dataset and achieved a Jaccard index of 80.4% and 89.4%, respectively, setting a new record in skin lesion segmentation.
Mesogenin 1 is a master regulator of paraxial presomitic mesoderm differentiation

Neuromesodermal (NM) stem cells generate neural and paraxial presomitic mesoderm (PSM) cells, which are the respective progenitors of the spinal cord and musculoskeleton of the trunk and tail. The Wnt-regulated basic helix-loop-helix (bHLH) transcription factor mesogenin 1 (Msgn1) has been implicated as a cooperative regulator working in concert with T-box genes to control PSM formation in zebrafish, although the mechanism is unknown. We show here that, in mice, Msgn1 alone controls PSM differentiation by directly activating the transcriptional programs that define PSM identity, epithelial-mesenchymal transition, motility and segmentation. Forced expression of Msgn1 in NM stem cells in vivo reduced the contribution of their progeny to the neural tube, and dramatically expanded the unsegmented mesenchymal PSM while blocking somitogenesis and notochord differentiation. Expression of Msgn1 was sufficient to partially rescue PSM differentiation in Wnt3a−/− embryos, demonstrating that Msgn1 functions downstream of Wnt3a as the master regulator of PSM differentiation. Our data provide new insights into how cell fate decisions are imposed by the expression of a single transcriptional regulator.
Antidepressant Effect of Tetragonia tetragonoides (Pall.) Kuntze Extract on Serotonin Turnover

Tetragonia tetragonoides (Pall.) Kuntze (TTK) is a groundcover found along coastal areas of the Korean peninsula. TTK is traditionally used to improve women's health and treat gastrointestinal diseases. Use of herbal medicines in the treatment of mood disorders has recently been suggested as an alternative therapeutic strategy. In the present study, we determined that consumption of TTK extract ameliorated progression of depressive-like symptoms in ovariectomized (OVX) rats and further examined the mechanisms involved, i.e., synthesis, release, and reuptake(s) of serotonin (also known as 5-HT). We assessed the mRNA expression levels of tryptophan hydroxylases (TPH-1 and TPH-2) and serotonin transporter (SERT) as well as the reuptake activity of serotonin in RBL-2H3 cells. We also determined whether or not TTK extract regulates the serum level of serotonin and improves depressive-like symptoms in 0.5, 1, and 2% TTK-fed OVX female rats in a forced swimming test. Our results show that the mRNA levels of TPH-1 and SERT were significantly reduced, whereas the mRNA level of TPH-2 was dose-dependently elevated by TTK (50 and 100 μg/mL) in RBL-2H3 cells. TTK significantly inhibited LPS- (lipopolysaccharide-) induced serotonin uptake in RBL-2H3 cells in a dose-dependent manner. The serum level(s) of serotonin was elevated by 1% and 2% TTK treatment in OVX female rats. Moreover, immobility time in the forced swimming test was reduced by 1% and 2% TTK treatment but not altered by 0.5% TTK treatment in OVX female rats. Taken together, these results indicate that TTK may significantly inhibit depressive-like symptoms due to upregulation of serotonin level(s) and regulation of serotonin reuptake activity. Thus, TTK may exert beneficial effects on depression during pre- or/and postmenopausal periods via modulation of serotonin synthesis and metabolism.
Why are male Chinese smokers unwilling to quit? A multicentre cross-sectional study on smoking rationalisation and intention to quit

Objective We aimed to describe the rationalisation beliefs endorsed by Chinese male smokers and to examine the association between rationalisation and the intention to quit. Setting Questionnaires were conducted among male smokers in three cities (Shanghai, Nanning and Mudanjiang) which represent different geographical locations, economic development levels and legislative status of tobacco control in China. Design and participants It was a multicentre cross-sectional survey involved a total of 3710 male smokers over 18 years. Outcome measures Primary outcomes were intention to quit, smoking rationalisation scores and sub scores in six dimensions. Smoking rationalisation was assessed using a newly developed Chinese rationalisation scale. Multivariable logistic regression was performed to examine the relationship between rationalisation and intention to quit. Results On average, smokers scored 3.3 out of 5 on the smoking rationalisation scale. With a one point increase in total rationalisation scale, the odds for intention to quit in the next 6 months decreased by 48% (OR=0.52, 95% CI: 0.44 to 0.61; p<0.001). Separate logistic regressions for six subscales of rationalisation shown consistent inverse associations with intention to quit (all p values <0.001). Believing that smoking was socially acceptable was the strongest predictor (OR=0.62, 95% CI: 0.55 to 0.71; p<0.001). Conclusions Rationalisation beliefs could be important barriers to smoking cessation. Some beliefs have stronger association with quit intention than others. Eroding rationalisation beliefs endorsed by smokers is a potential strategy for smoking cessation intervention.