Miracle-dz/UniFAR-FTUs huggingface dataset:
Object-oriented video prediction with pixel-level attention

It is really challenging to predict future video contents which can be partly achieved by learning from tons of videos automatically. Among the known video prediction approaches, pixel-level prediction is much harder than label-level prediction because of its dense characteristics. Unlike other researches viewing pixels as units to make pixel prediction, a novel prediction method is proposed in this work to learn the movement of visual objects from the previous frames via pixel-level attention and thus predict future video frames. The proposed method is able to promote video prediction performances by making object-oriented prediction instead of pixel-oriented in a long-term frame prediction manner, up to around one second. Two large real-world video prediction datasets are employed to carry out comparative experiments to demonstrate the effectiveness of the proposed method.
Quality Over Quantity: Advantages of Using Alpha-Synuclein Preformed Fibril Triggered Synucleinopathy to Model Idiopathic Parkinson’s Disease

Animal models have significantly advanced our understanding of Parkinson’s disease (PD). Alpha-synuclein (α-syn) has taken center stage due to its genetic connection to familial PD and localization to Lewy bodies, one pathological hallmark of PD. Animal models developed on the premise of elevated alpha-synuclein via germline manipulation or viral vector-mediated overexpression are used to investigate PD pathophysiology and vet novel therapeutics. While these models represented a step forward compared to their neurotoxicant model predecessors, they rely on overexpression of supraphysiological levels of α-syn to trigger toxicity. However, whereas SNCA-linked familial PD is associated with elevated α-syn, elevated α-syn is not associated with idiopathic PD. Therefore, the defining feature of the α-syn overexpression models may fail to appropriately model idiopathic PD. In the last several years a new model has been developed in which α-syn preformed fibrils are injected intrastriatally and trigger normal endogenous levels of α-syn to misfold and accumulate into Lewy body-like inclusions. Following a defined period of inclusion accumulation, distinct phases of neuroinflammation and progressive degeneration can be detected in the nigrostriatal system. In this perspective, we highlight the fact that levels of α-syn achieved in overexpression models generally exceed those observed in idiopathic and even SNCA multiplication-linked PD. This raises the possibility that supraphysiological α-syn expression may drive pathophysiological mechanisms not relevant to idiopathic PD. We argue in this perspective that synucleinopathy triggered to form within the context of normal α-syn expression represents a more faithful animal model of idiopathic PD when examining the role of neuroinflammation or the relationship between a-syn aggregation and toxicity.
MAFL: Multi-scale Adversarial Feature Learning for Saliency Detection

Previous saliency detection methods usually focus on extracting features to deal with the complex background in an image. However, these methods cannot effectively capture the semantic information of images. In recent years, Generative Adversarial Network (GAN) has become a prevalent research topic. Experiments show that GAN has ability to generate high quality images that look like natural images. Inspired by the effectiveness of GAN feature learning, we propose a novel multi-scale adversarial feature learning (MAFL) model for saliency detection. In particular, we model the complete framework of saliency detection is based on two deep CNN modules: the multi-scale G-network takes natural images as inputs and generates corresponding synthetic saliency map, and we designed a novel layer in D-network, namely a correlation layer, which is used to determine whether one image is a synthetic saliency map or ground-truth saliency map. Quantitative and qualitative experiments on three benchmark datasets demonstrate that our method outperforms seven state-of-the-art methods.
A modal analysis of staged computation

We show that a type system based on the intuitionistic modal logic S4 provides an expressive framework for specifying and analyzing computation stages in the context of functional languages. Our main technical result is a conservative embedding of Nielson & Nielson's two-level functional language in our language Mini-ML, thus proving that binding-time correctness is equivalent to modal correctness on this fragment. In addition Mini-ML can also express immediate evaluation and sharing of code across multiple stages, thus supporting run-time code generation as well as partial evaluation.
Multiphase Optimal and Non-Singular Power Flow by Successive Linear Approximations

We propose an optimal power flow method for multiphase power systems. The method works for both radial and meshed networks, and is able to accommodate different types of load/source connections such as wye, delta, and combination thereof. Moreover, the method generates a sequence of nonsingular points (i.e., associated with non-singular power-flow Jacobian) in the electrical state space and a corresponding sequence of power injections, which can be used in real-time control settings. Under certain conditions, the sequences attain the local minimum. The method has been tested using several typical networks, including the IEEE 37-bus and 123-bus test feeders.
Predicting Available Parking Slots on Critical and Regular Services by Exploiting a Range of Open Data

Looking for available parking slots has become a serious issue in contemporary urban mobility. The selection of suitable car parks could be influenced by multiple factors—e.g., the walking distance to destination, driving and waiting time, parking prices, availability, and accessibility—while the availability of unused parking slots might depend on parking location, events in the area, traffic flow, and weather conditions. This paper presents a set of metrics and techniques to predict the number of available parking slots in city garages with gates. With this aim, we have considered three different predictive techniques, while comparing different approaches. The comparison has been performed according to the data collected in a dozen of garages in the area of Florence by using Sii-Mobility National Research Project and Km4City infrastructure. The resulting solution has demonstrated that a Bayesian regularized neural network exploiting historical data, weather condition, and traffic flow data can offer a robust approach for the implementation of reliable and fast predictions of available slots in terms of flexibility and robustness to critical cases. The solution adopted in a Smart City Apps in the Florence area for sustainable mobility has been welcomed with broad appreciation or has been praised as successful.
Key-Skeleton Based Feedback Tool for Assisting Physical Activity

This paper presents an intuitive feedback tool able to implicitly guide motion with respect to a reference movement. Such a tool is important in multiple applications requiring assisting physical activities as in sports or rehabilitation. Our proposed approach is based on detecting key skeleton frames from a reference sequence of skeletons. The feedback is based on the 3D geometry analysis of the skeletons by taking into account the key-skeletons. Finally, the feedback is illustrated by a color-coded tool, which reflects the motion accuracy.
Efficient noise-tolerant learning from statistical queries

In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the class of “robust” learning algorithms in the most general way, we formalize a new but related model of learning from statistical queries. Intuitively, in this model a learning algorithm is forbidden to examine individual examples of the unknown target function, but is given acess to an oracle providing estimates of probabilities over the sample space of random examples. One of our main results shows that any class of functions learnable from statistical queries is in fact learnable with classification noise in Valiant's model, with a noise rate approaching the information-theoretic barrier of 1/2. We then demonstrate the generality of the statistical query model, showing that practically every class learnable in Valiant's model and its variants can also be learned in the new model (and thus can be learned in the presence of noise). A notable exception to this statement is the class of parity functions, which we prove is not learnable from statistical queries, and for which no noise-tolerant algorithm is known.
Topography of cancer-associated immune cells in human solid tumors

Lymphoid and myeloid cells are abundant in the tumor microenvironment, can be quantified by immunohistochemistry and shape the disease course of human solid tumors. Yet, there is no comprehensive understanding of spatial immune infiltration patterns (‘topography’) across cancer entities and across various immune cell types. In this study, we systematically measure the topography of multiple immune cell types in 965 histological tissue slides from N = 177 patients in a pan-cancer cohort. We provide a definition of inflamed (‘hot’), non-inflamed (‘cold’) and immune excluded patterns and investigate how these patterns differ between immune cell types and between cancer types. In an independent cohort of N = 287 colorectal cancer patients, we show that hot, cold and excluded topographies for effector lymphocytes (CD8) and tumor-associated macrophages (CD163) alone are not prognostic, but that a bivariate classification system can stratify patients. Our study adds evidence to consider immune topographies as biomarkers for patients with solid tumors.
Inhibition of HIF-1α Reduced Blood Brain Barrier Damage by Regulating MMP-2 and VEGF During Acute Cerebral Ischemia

Increase of blood brain barrier (BBB) permeability after acute ischemia stroke is a predictor to intracerebral hemorrhage transformation (HT) for tissue plasminogen activator (tPA) thrombolysis and post-endovascular treatment. Previous studies showed that 2-h ischemia induced damage of BBB integrity and matrix metalloproteinase-2 (MMP-2) made major contribution to this disruption. A recent study showed that blocking β2-adrenergic receptor (β2-AR) alleviated ischemia-induced BBB injury by reducing hypoxia-inducible factor-1 alpha (HIF-1α) level. In this study, we sought to investigate the interaction of HIF-1α with MMP-2 and vascular endothelial growth factor (VEGF) in BBB injury after acute ischemia stroke. Rat suture middle cerebral artery occlusion (MCAO) model was used to mimic ischemia condition. Our results showed that ischemia produced BBB damage and MMP-2/9 upregulation was colocalized with Rhodamine-dextran leakage. Pretreatment with YC-1, a HIF-1α inhibitor, alleviated 2-h ischemia-induced BBB injury significantly accompanied by decrease of MMP-2 upregulation. In addition, YC-1 also prevented VEGF-induced BBB damage. Of note, VEGF was shown to be colocalized with neurons but not astrocytes. Taken together, BBB damage was reduced by inhibition of interaction of HIF-1α with MMP-2 and VEGF during acute cerebral ischemia. These findings provide mechanisms underlying BBB damage after acute ischemia stroke and may help reduce thrombolysis- and post-endovascular treatment-related cerebral hemorrhage.
Learning geometric and photometric features from panoramic LiDAR scans for outdoor place categorization

ABSTRACT Semantic place categorization, which is one of the essential tasks for autonomous robots and vehicles, allows them to have capabilities of self-decision and navigation in unfamiliar environments. In particular, outdoor places are more difficult targets than indoor ones due to perceptual variations, such as dynamic illuminance over 24 hours and occlusions by cars and pedestrians. This paper presents a novel method of categorizing outdoor places using convolutional neural networks (CNNs), which take omnidirectional depth/reflectance images obtained by 3D LiDARs as the inputs. First, we construct a large-scale outdoor place dataset named Multi-modal Panoramic 3D Outdoor (MPO) comprising two types of point clouds captured by two different LiDARs. They are labeled with six outdoor place categories: coast, forest, indoor/outdoor parking, residential area, and urban area. Second, we provide CNNs for LiDAR-based outdoor place categorization and evaluate our approach with the MPO dataset. Our results on the MPO dataset outperform traditional approaches and show the effectiveness in which we use both depth and reflectance modalities. To analyze our trained deep networks, we visualize the learned features. GRAPHICAL ABSTRACT
Hardware Architecture Implemented on FPGA for Protecting Cryptographic Keys against Side-Channel Attacks

This paper presents a new hardware architecture designed for protecting the key of cryptographic algorithms against attacks by side-channel analysis (SCA). Unlike previous approaches already published, the fortress of the proposed architecture is based on revealing a false key. Such a false key is obtained when the leakage information, related to either the power consumption or the electromagnetic radiation (EM) emitted by the hardware device, is analysed by means of a classical statistical method. In fact, the trace of power consumption (or the EM) does not reveal any significant sign of protection in its behaviour or shape. Experimental results were obtained by using a Virtex 5 FPGA, on which a 128-bit version of the standard AES encryption algorithm was implemented. The architecture could easily be extrapolated to an ASIC device based on standard cell libraries. The system is capable of concealing the real key when various attacks are performed on the AES algorithm, using two statistical methods which are based on correlation, the Welch's t-test and the difference of means.
Serum-based microRNA biomarkers for major depression: MiR-16, miR-135a, and miR-1202

Background: Depression is a common medical condition with a high prevalence leading to emotional abnormality. Despite some drawbacks, depression currently diagnosed using a combination of patient interviews and self-report questionnaires. Recently, there is emerging emphasis to establish biomarkers to diagnosis and clinical management of depression. This case–control study was designed to develop microRNA (miRNA)-based serum biomarker for depression. Materials and Methods: In this study, 39 patients with depression and 36 healthy controls were enrolled. Serum miRNAs gene expression was measured using real-time polymerase chain reaction (PCR) analysis; finally, the data represent as the 2–ΔCt followed by further statistical analysis. Results: The serum level of miR-16 was significantly (P < 0.001) down-regulated (mean: 0.9123 and standard deviation [SD]: 0.06) in compared to normal individuals (mean: 1.6848 and SD: 0.09). The concentration of miR-135a was also catastrophically decreased (P < 0.001) in the patients (mean: 1.160 and SD: 0.07) in compared to control (mean: 1.819 and SD: 0.09). The relative miR-1202 expression levels were significantly lower (P < 0.001) in the patients (mean: 0.1755 and SD: 0.01) than in the healthy individuals (mean: 0.2939 and SD: 0.01). The receiver operating characteristic curve analysis indicated the obvious separation between patient and healthy control, with an AUC of 0.75 (95% confidence interval [CI] = 0.642–0.858, P < 0.001), 0.72 (95% CI = 0.607–0.834, P < 0.001), and 0.74 (95% CI = 0.630–0.861, P < 0.001) for miR-16, miR-135a, and miR-1202, respectively. The data suggest that these miRNAs have a potential to be used as a biomarker of depression with sensitivity 77.8% and specificity of 61.5% for miR-16, 94.4% and 41.0% for miR-135a as well as 86.1% and 61.5% for miR-1202, respectively (P < 0.001). Conclusion: Our findings showed that these miRNA can be used as a biomarker of depression diagnosis. MiR-135a and miR-1202 exhibited better sensitivity and specificity, respectively.
Anxiety and depression in adults with autism spectrum disorder: a systematic review and meta-analysis

Abstract Adults with autism spectrum disorder (ASD) are thought to be at disproportionate risk of developing mental health comorbidities, with anxiety and depression being considered most prominent amongst these. Yet, no systematic review has been carried out to date to examine rates of both anxiety and depression focusing specifically on adults with ASD. This systematic review and meta-analysis examined the rates of anxiety and depression in adults with ASD and the impact of factors such as assessment methods and presence of comorbid intellectual disability (ID) diagnosis on estimated prevalence rates. Electronic database searches for studies published between January 2000 and September 2017 identified a total of 35 studies, including 30 studies measuring anxiety (n = 26 070; mean age = 30.9, s.d. = 6.2 years) and 29 studies measuring depression (n = 26 117; mean age = 31.1, s.d. = 6.8 years). The pooled estimation of current and lifetime prevalence for adults with ASD were 27% and 42% for any anxiety disorder, and 23% and 37% for depressive disorder. Further analyses revealed that the use of questionnaire measures and the presence of ID may significantly influence estimates of prevalence. The current literature suffers from a high degree of heterogeneity in study method and an overreliance on clinical samples. These results highlight the importance of community-based studies and the identification and inclusion of well-characterized samples to reduce heterogeneity and bias in estimates of prevalence for comorbidity in adults with ASD and other populations with complex psychiatric presentations.
Clinical impact of the Sherlock 3CG® Tip Confirmation System for peripherally inserted central catheters

Objective This study was performed to evaluate the technical success rate and catheter tip malposition rate of peripherally inserted central catheter (PICC) placement using the Sherlock 3CG® Tip Confirmation System (TCS). Methods In total, 114 patients who underwent PICC insertion via the Sherlock 3CG® TCS from October 2017 to February 2018 were retrospectively evaluated. The primary endpoints were the technical success rate, malposition rate, and mean procedure time. The secondary endpoints were the sex-related difference in the malposition rate, radiologist’s experience level, and side of insertion. Technical success was defined as procedure completion using only the Sherlock 3CG® TCS without a guidewire or fluoroscopy guidance. In accordance with the North American guidelines, an adequate position was defined as the lower third of the superior vena cava and the cavoatrial junction. Results The technical success rate was 97% (111/114). The malposition rate was 16% (18/111), and four catheters were repositioned. There were no significant differences in the malposition rate between the sexes, radiologist’s level of experience, or side of insertion. Conclusions PICC placement using the Sherlock 3CG® TCS yielded a high technical success rate and low catheter tip malposition rate regardless of the radiologist’s level of experience or side of insertion.
Benchmarking Static Analysis Tools for Web Security

Static analysis tools are recurrently used by developers to search for vulnerabilities in the source code of web applications. However, distinct tools provide different results depending on factors such as the complexity of the code under analysis and the application scenario; thus, missing some of the vulnerabilities while reporting false problems. Benchmarks can be used to assess and compare different systems or components, however, existing benchmarks have strong representativeness limitations, disregarding the specificities of the environment, where the tools under benchmarking will be used. In this paper, we propose a benchmark for assessing and comparing static analysis tools in terms of their capability to detect security vulnerabilities. The benchmark considers four real-world development scenarios, including workloads composed of real web applications with different goals and constraints, ranging from low budget to high-end applications. Our benchmark was implemented and assessed experimentally using a set of 134 WordPress plugins, which served as the basis for the evaluation of five free PHP static analysis tools. Results clearly show that the best solution depends on the deployment scenario and class of vulnerability being detected; therefore, highlighting the importance of these aspects in the design of the benchmark and of future static analysis tools.
Histone deacetylase inhibitors prevent persistent hypersensitivity in an orofacial neuropathic pain model

Chronic orofacial pain is a significant health problem requiring identification of regulating processes. Involvement of epigenetic modifications that is reported for hindlimb neuropathic pain experimental models, however, is less well studied in cranial nerve pain models. Three independent observations reported here are the (1) epigenetic profile in mouse trigeminal ganglia (TG) after trigeminal inflammatory compression (TIC) nerve injury mouse model determined by gene expression microarray, (2) H3K9 acetylation pattern in TG by immunohistochemistry, and (3) efficacy of histone deacetylase (HDAC) inhibitors to attenuate development of hypersensitivity. After TIC injury, ipsilateral whisker pad mechanical sensitization develops by day 3 and persists well beyond day 21 in contrast to sham surgery. Global acetylation of H3K9 decreases at day 21 in ipsilateral TG . Thirty-four genes are significantly (p < 0.05) overexpressed in the ipsilateral TG by at least two-fold at either 3 or 21 days post-trigeminal inflammatory compression injury. The three genes most overexpressed three days post-trigeminal inflammatory compression nerve injury are nerve regeneration-associated gene ATF3, up 6.8-fold, and two of its regeneration-associated gene effector genes, Sprr1a and Gal, up 174- and 25-fold, respectively. Although transcription levels of 25 of 32 genes significantly overexpressed three days post-trigeminal inflammatory compression return to constitutive levels by day 21, these three regeneration-associated genes remain significantly overexpressed at the later time point. On day 21, when tissues are healed, other differentially expressed genes include 39 of the top 50 upregulated and downregulated genes. Remarkably, preemptive manipulation of gene expression with two HDAC inhibitors (HDACi's), suberanilohydroxamic acid (SAHA) and MS-275, reduces the magnitude and duration of whisker pad mechanical hypersensitivity and prevents the development of a persistent pain state. These findings suggest that trigeminal nerve injury leads to epigenetic modifications favoring overexpression of genes involved in nerve regeneration and that maintaining transcriptional homeostasis with epigenetic modifying drugs could help prevent the development of persistent pain.