Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Feature Processing for Robust Human Activity Recognition on a Novel Multi-Modal Dataset

Human Activity Recognition (HAR) is a key building block of many emerging applications such as intelligent mobility, sports analytics, ambient-assisted living and human-robot interaction. With robust HAR, systems will become more human-aware, leading towards much safer and empathetic autonomous systems. While human pose detection has made significant progress with the dawn of deep convolutional neural networks (CNNs), the state-of-the-art research has almost exclusively focused on a single sensing modality, especially video. However, in safety critical applications it is imperative to utilize multiple sensor modalities for robust operation. To exploit the benefits of state-of-the-art machine learning techniques for HAR, it is extremely important to have multimodal datasets. In this paper, we present a novel, multi-modal sensor dataset that encompasses nine indoor activities, performed by 16 participants, and captured by four types of sensors that are commonly used in indoor applications and autonomous vehicles. This multimodal dataset is the first of its kind to be made openly available and can be exploited for many applications that require HAR, including sports analytics, healthcare assistance and indoor intelligent mobility. We propose a novel data preprocessing algorithm to enable adaptive feature extraction from the dataset to be utilized by different machine learning algorithms. Through rigorous experimental evaluations, this paper reviews the performance of machine learning approaches to posture recognition, and analyses the robustness of the algorithms. When performing HAR with the RGB-Depth data from our new dataset, machine learning algorithms such as a deep neural network reached a mean accuracy of up to 96.8% for classification across all stationary and dynamic activities
On Relativistic f-Divergences

This paper provides a more rigorous look at Relativistic Generative Adversarial Networks (RGANs). We prove that the objective function of the discriminator is a statistical divergence for any concave function $f$ with minimal properties ($f(0)=0$, $f'(0) \neq 0$, $\sup_x f(x)>0$). We also devise a few variants of relativistic $f$-divergences. Wasserstein GAN was originally justified by the idea that the Wasserstein distance (WD) is most sensible because it is weak (i.e., it induces a weak topology). We show that the WD is weaker than $f$-divergences which are weaker than relativistic $f$-divergences. Given the good performance of RGANs, this suggests that WGAN does not performs well primarily because of the weak metric, but rather because of regularization and the use of a relativistic discriminator. We also take a closer look at estimators of relativistic $f$-divergences. We introduce the minimum-variance unbiased estimator (MVUE) for Relativistic paired GANs (RpGANs; originally called RGANs which could bring confusion) and show that it does not perform better. Furthermore, we show that the estimator of Relativistic average GANs (RaGANs) is only asymptotically unbiased, but that the finite-sample bias is small. Removing this bias does not improve performance.
A Deep Learning based Approach to Automated Android App Testing

Automated input generators are widely used for large-scale dynamic analysis and testing of mobile apps. Such input generators must constantly choose which UI element to interact with and how to interact with it, in order to achieve high coverage with a limited time budget. Currently, most input generators adopt pseudo-random or brute-force searching strategies, which may take very long to find the correct combination of inputs that can drive the app into new and important states. In this paper, we propose Humanoid, a deep learning-based approach to automated Android app testing. Our insight is that if we can learn from human-generated interaction traces, it is possible to generate human-like test inputs based on the visual information in the current UI state and the latest state transitions. We design and implement a deep neural network model to learn how end-users would interact with an app (specifically, which UI elements to interact with and how), and show that we can successfully generate human-like inputs for any new UI based on the learned model. We then apply the model to automated testing of Android apps and demonstrate that it is able to reach higher coverage, and faster as well, than the state-of-the-art test input generators.
Factorized Machine Self-Confidence for Decision-Making Agents.

Algorithmic assurances from advanced autonomous systems assist human users in understanding, trusting, and using such systems appropriately. Designing these systems with the capacity of assessing their own capabilities is one approach to creating an algorithmic assurance. The idea of `machine self-confidence' is introduced for autonomous systems. Using a factorization based framework for self-confidence assessment, one component of self-confidence, called `solver-quality', is discussed in the context of Markov decision processes for autonomous systems. Markov decision processes underlie much of the theory of reinforcement learning, and are commonly used for planning and decision making under uncertainty in robotics and autonomous systems. A `solver quality' metric is formally defined in the context of decision making algorithms based on Markov decision processes. A method for assessing solver quality is then derived, drawing inspiration from empirical hardness models. Finally, numerical experiments for an unmanned autonomous vehicle navigation problem under different solver, parameter, and environment conditions indicate that the self-confidence metric exhibits the desired properties. Discussion of results, and avenues for future investigation are included.
Generalized Deduplication: Bounds, Convergence, and Asymptotic Properties

We study a generalization of deduplication, which enables lossless deduplication of highly similar data and show that classic deduplication with fixed chunk length is a special case. We provide bounds on the expected length of coded sequences for generalized deduplication and show that the coding has asymptotic near-entropy cost under the proposed source model. More importantly, we show that generalized deduplication allows for multiple orders of magnitude faster convergence than classic deduplication. This means that generalized deduplication can provide compression benefits much earlier than classic deduplication, which is key in practical systems. Numerical examples demonstrate our results, showing that our lower bounds are achievable, and illustrating the potential gain of using the generalization over classic deduplication. In fact, we show that even for a simple case of generalized deduplication, the gain in convergence speed is linear with the size of the data chunks.
Collaborative Execution of Deep Neural Networks on Internet of Things Devices

With recent advancements in deep neural networks (DNNs), we are able to solve traditionally challenging problems. Since DNNs are compute intensive, consumers, to deploy a service, need to rely on expensive and scarce compute resources in the cloud. This approach, in addition to its dependability on high-quality network infrastructure and data centers, raises new privacy concerns. These challenges may limit DNN-based applications, so many researchers have tried optimize DNNs for local and in-edge execution. However, inadequate power and computing resources of edge devices along with small number of requests limits current optimizations applicability, such as batch processing. In this paper, we propose an approach that utilizes aggregated existing computing power of Internet of Things (IoT) devices surrounding an environment by creating a collaborative network. In this approach, IoT devices cooperate to conduct single-batch inferencing in real time. While exploiting several new model-parallelism methods and their distribution characteristics, our approach enhances the collaborative network by creating a balanced and distributed processing pipeline. We have illustrated our work using many Raspberry Pis with studying DNN models such as AlexNet, VGG16, Xception, and C3D.
Children’s Learning From Interactive eBooks: Simple Irrelevant Features Are Not Necessarily Worse Than Relevant Ones

The purpose of this study was to investigate experimentally the extent to which children’s novel word learning and story comprehension differs for non-interactive eBooks and interactive eBooks with simple relevant or irrelevant interactive features that advance the narrative. An original story with novel word-object pairs was read to preschoolers (3–5 years old, N = 103) using one of the three eBook formats: non-interactive control, interactive-relevant, interactive-irrelevant. The book formats differed only in the manner in which the story advanced from one page to the next: children observed the experimenter turn the page (non-interactive), children touched a relevant image on the screen (relevant-interactive), or children touched an irrelevant image on the screen (irrelevant-interactive). Novel word learning and story comprehension were assessed with post-tests in which children picked target objects from an array and sorted story events into their original sequence, respectively. Findings indicate that word learning and story comprehension were similar across all three books, suggesting that simple interactive features – whether relevant or irrelevant to the story – had little impact on preschoolers’ learning in this controlled experiment. Thus, simple interactivity that does not disrupt the story also does not hinder ongoing story comprehension.
Targeting Polyamine Oxidase to Prevent Excitotoxicity-Induced Retinal Neurodegeneration

Dysfunction of retinal neurons is a major cause of vision impairment in blinding diseases that affect children and adults worldwide. Cellular damage resulting from polyamine catabolism has been demonstrated to be a major player in many neurodegenerative conditions. We have previously shown that inhibition of polyamine oxidase (PAO) using MDL 72527 significantly reduced retinal neurodegeneration and cell death signaling pathways in hyperoxia-mediated retinopathy. In the present study, we investigated the impact of PAO inhibition in limiting retinal neurodegeneration in a model of NMDA (N-Methyl-D-aspartate)-induced excitotoxicity. Adult mice (8–10 weeks old) were given intravitreal injections (20 nmoles) of NMDA or NMLA (N-Methyl-L-aspartate, control). Intraperitoneal injection of MDL 72527 (40 mg/kg body weight/day) or vehicle (normal saline) was given 24 h before NMDA or NMLA treatment and continued until the animals were sacrificed (varied from 1 to 7 days). Analyses of retinal ganglion cell (RGC) layer cell survival was performed on retinal flatmounts. Retinal cryostat sections were prepared for immunostaining, TUNEL assay and retinal thickness measurements. Fresh frozen retinal samples were used for Western blotting analysis. A marked decrease in the neuronal survival in the RGC layer was observed in NMDA treated retinas compared to their NMLA treated controls, as studied by NeuN immunostaining of retinal flatmounts. Treatment with MDL 72527 significantly improved survival of NeuN positive cells in the NMDA treated retinas. Excitotoxicity induced neurodegeneration was also demonstrated by reduced levels of synaptophysin and degeneration of inner retinal neurons in NMDA treated retinas compared to controls. TUNEL labeling studies showed increased cell death in the NMDA treated retinas. However, treatment with MDL 72527 markedly reduced these changes. Analysis of signaling pathways during excitotoxic injury revealed the downregulation of pro-survival signaling molecules p-ERK and p-Akt, and the upregulation of a pro-apoptotic molecule BID, which were normalized with PAO inhibition. Our data demonstrate that inhibition of polyamine oxidase blocks NMDA-induced retinal neurodegeneration and promotes cell survival, thus offering a new therapeutic target for retinal neurodegenerative disease conditions.
Functional Analysis of NtZIP4B and Zn Status-Dependent Expression Pattern of Tobacco ZIP Genes

Tobacco is frequently considered as a plant useful for phytoremediation of metal-contaminated soil, despite the mechanisms for regulation of uptake and accumulation being largely unknown. Here we cloned and characterized a new tobacco Zn and Cd transporter NtZIP4B from the ZIP family (ZRT-IRT-Like proteins). It complemented the Zn-uptake defective yeast mutant zrt1zrt2, and rendered the wild type DY1457 yeast more sensitive to Cd. Bioinformatic analysis and transient expression of the NtZIP4B-GFP fusion protein in tobacco leaves indicated its localization to the plasma membrane. Real-time q-PCR based analysis showed that it is expressed in all vegetative organs with the highest level in leaves. The Zn status determined transcript abundance; NtZIP4B was upregulated by Zn-deficiency and downregulated by Zn excess. At the tissue level, in roots NtZIP4B is expressed in the vasculature of the middle part of the roots and in surrounding tissues including the root epidermis; in leaves primarily in the vasculature. Bioinformatic analysis identified two copies of ZIP4 in tobacco, NtZIP4A and NtZIP4B with 97.57% homology at the amino acid level, with the same expression pattern for both, indicating a high degree of functional redundancy. Moreover, the present study provides new insights into the coordinated function of NtZIP1, NtZIP2, NtZIP4, NtZIP5, NtZIP8, NtIRT1, and NtIRT1-like in response to low-to-high Zn status. Leaves were the major site of NtZIP4, NtZIP5, and NtZIP8 expression, and roots for NtZIP1, NtZIP2, NtIRT1, and NtIRT1-like. Contrasting expression level in the apical and basal root parts indicates distinct roles in root-specific processes likely contributing to the regulation of Zn root-to-shoot translocation. In summary, new insight into the role of ZIP genes in Zn homeostasis pointing to their overlapping and complementary functions, offers opportunities for strategies to modify Zn and Cd root/shoot partition in tobacco.
Morphology Controllable Synthesis of NiO/NiFe2O4 Hetero-Structures for Ultrafast Lithium-Ion Battery

Rational design of high performance anode material with outstanding rate capability and cycling stability is of great importance for lithium ion batteries (LIBs). Herein, a series of NiO/NiFe2O4 hetero-structures with adjustable porosity, particle size, and shell/internal structure have been synthesized via a controllable annealing process. The optimized NiO/NiFe2O4 (S-NFO) is hierarchical hollow nanocube that is composed of ~5 nm subunits and high porosity. When being applied as anode for LIBs, the S-NFO exhibits high rate capability and excellent cycle stability, which remains high capacity of 1,052 mAh g−1 after 300 cycles at 5.0 A g−1 and even 344 mAh g−1 after 2,000 cycles at 20 A g−1. Such impressive electrochemical performance of S-NFO is mainly due to three reasons. One is high porosity of its hierarchical hollow shell, which not only promotes the penetration of electrolyte, but also accommodates the volume change during cycling. Another is the small particle size of its subunits, which can effectively shorten the electron/ion diffusion distance and provide more active sites for Li+ storage. Besides, the hetero-interfaces between NiO and NiFe2O4 also contribute toitsfast charge transport.
CSF1R Stimulation Promotes Increased Neuroprotection by CD11c+ Microglia in EAE

Microglia are resident immune cells of the central nervous system. Their development and maintenance depend on stimulation of Colony Stimulating Factor-1 receptor (CSF1R). Microglia play an important role in neurodevelopment and a population of microglia that expresses the complement receptor CD11c is critical for primary myelination. This population is virtually absent in the healthy adult brain but increases dramatically upon neuroinflammatory conditions, and these microglia are suggested to play a protective role in central nervous system (CNS) diseases. To date, the molecular trigger for their expansion is unknown. Here we showed that stimulation of CSF1R by either of its ligands, CSF1 and interleukin (IL)-34, can induce expansion of CD11c+ microglia. In addition, such stimulation resulted in amelioration of EAE symptoms and decreased demyelination. Treatment with CSF1R ligands also induced expression of the chemokine CCL2, and we showed that experimental overexpression of CCL2 in the brain led to a dramatic increase of CD11c+ microglia, independent of CCR2. Moreover, this led to elevated CSF1 expression, suggesting a positive feedback loop between CSF1R and CCL2. These data provide new insights to microglia biology and open new perspectives for modulating microglial activity in neuroinflammatory diseases such as multiple sclerosis.
Precuneus Dysfunction in Parkinson’s Disease With Mild Cognitive Impairment

Background: Mild cognitive impairment (MCI) frequently occurs in Parkinson’s disease (PD). Neurovascular changes interact with neurodegenerative processes in PD. However, the deficits of cerebral blood flow (CBF) perfusion and the associated functional connectivity (FC) in PD patients with MCI (PD-MCI) remain unclear. Purpose: This study aimed to explore the specific neurovascular perfusion alterations in PD-MCI compared to PD with normal cognition (PD-NC) and healthy controls (HCs), and to further examine the resultant whole brain FC changes in the abnormal perfusion regions. Methods: Relative CBF (rCBF) was calculated using arterial spin labeling (ASL) in 54 patients with PD (27 patients with PD-NC and 27 patients with PD-MCI) and 25 HCs matched for age and gender ratio, who also underwent the structural MRI, resting-state functional MRI (rs-fMRI) and neuropsychological examinations. The gray matter (GM) changes in PD patients were analyzed using voxel-based morphometry (VBM). The alterations in rCBF perfusion and FC among groups were then analyzed respectively. Additionally, correlations between these alterations and neuropsychological performances were further examined. Results: Compared to HC, left caudate atrophy was detected in patients with PD. In comparison to both PD-NC and HC, patients with PD-MCI specifically exhibited hypoperfusion in the parietal memory network (PMN) in the precuneus (PCu) and decreased PCu-FC in the right striatum. Moreover, PCu perfusion and PCu-FC strengths in the right striatum were positively associated with memory performance in PD-MCI. Conclusions: These findings suggest that the posterior PMN dysfunction underlies memory deficits in PD-MCI.
Correlated MRI and Ultramicroscopy (MR-UM) of Brain Tumors Reveals Vast Heterogeneity of Tumor Infiltration and Neoangiogenesis in Preclinical Models and Human Disease

Diffuse tumor infiltration into the adjacent parenchyma is an effective dissemination mechanism of brain tumors. We have previously developed correlated high field magnetic resonance imaging and ultramicroscopy (MR-UM) to study neonangiogenesis in a glioma model. In the present study we used MR-UM to investigate tumor infiltration and neoangiogenesis in a translational approach. We compare infiltration and neoangiogenesis patterns in four brain tumor models and the human disease: whereas the U87MG glioma model resembles brain metastases with an encapsulated growth and extensive neoangiogenesis, S24 experimental gliomas mimic IDH1 wildtype glioblastomas, exhibiting infiltration into the adjacent parenchyma and along white matter tracts to the contralateral hemisphere. MR-UM resolves tumor infiltration and neoangiogenesis longitudinally based on the expression of fluorescent proteins, intravital dyes or endogenous contrasts. Our study demonstrates the huge morphological diversity of brain tumor models regarding their infiltrative and neoangiogenic capacities and further establishes MR-UM as a platform for translational neuroimaging.
Computed Tomography: Return on Investment and Regional Disparity Factor Analysis

The number of computed tomography (CT) systems in operation in Japan is approximately 4.3 times higher than that of the OECD average. However, CT systems are expensive, and thus, a heavy financial burden for hospital management. We calculate the annual net profits from CT introduction in Japan for single-slice CT (SSCT), multi-slice CT (MSCT), number of hospital beds, and prefecture. We also analyze the factors that affect CT profitability. First, the annual income per CT in operation is estimated for 2011. Second, the annual costs per CT are calculated as the sum of depreciation, maintenance, and labor costs. Finally, the annual net profits per CT are estimated for SSCT and MSCT, the number of hospital beds, and prefecture. A correlation analysis between the annual net profits, population, and number of physicians per CT equipment is used to determine the determinants of the net CT profits by prefecture. Our results show that, for hospitals with fewer than 100 beds, the annual net CT profits are higher for SSCT than MSCT, and vice versa for hospitals with at least 100 beds. Both SSCT and MSCT increased profits as the number of hospital beds increased. The annual net CT profits per prefecture are USD −12,105 for SSCT and USD 87,233 for MSCT, on average. The annual net profits per prefecture and population per CT show positive correlations with both SSCT and MSCT, as do the annual net profits per prefecture and number of physicians per CT. Thus, choosing high-performance MSCT is advantageous in terms of profitability in facilities with at least 100 beds. Additionally, CT profitability presumably affects the balance between the number of introduced CTs, population per CT, and number of physicians per CT.
Choice of Alternative Polyadenylation Sites, Mediated by the RNA-Binding Protein Elavl3, Plays a Role in Differentiation of Inhibitory Neuronal Progenitors

Alternative polyadenylation (APA) is a widespread mechanism involving about half of the expressed genes, resulting in varying lengths of the 3′ untranslated region (3′UTR). Variations in length and sequence of the 3′UTR may underlie changes of post-transcriptional processing, localization, miRNA targeting and stability of mRNAs. During embryonic development a large array of mRNAs exhibit APA, with a prevalence of the longer 3′UTR versions in differentiating cells. Little is known about polyA+ site usage during differentiation of mammalian neural progenitors. Here we exploit a model of adherent neural stem (ANS) cells, which homogeneously and efficiently differentiate into GABAergic neurons. RNAseq data shows a global trend towards lengthening of the 3′UTRs during differentiation. Enriched expression of the longer 3′UTR variants of Pes1 and Gng2 was detected in the mouse brain in areas of cortical and subcortical neuronal differentiation, respectively, by two-probes fluorescent in situ hybridization (FISH). Among the coding genes upregulated during differentiation of ANS cells we found Elavl3, a neural-specific RNA-binding protein homologous to Drosophila Elav. In the insect, Elav regulates polyA+ site choice while interacting with paused Pol-II promoters. We tested the role of Elavl3 in ANS cells, by silencing Elavl3 and observed consistent changes in 3′UTR length and delayed neuronal differentiation. These results indicate that choice of the polyA+ site and lengthening of 3′UTRs is a possible additional mechanism of posttranscriptional RNA modification involved in neuronal differentiation.