Miracle-dz/UniFAR-FTUs huggingface dataset:
VCP/p97 increases BMP signaling by accelerating ubiquitin ligase Smurf1 degradation

The bone morphogenetic protein (BMP)–Smad signaling pathway plays a crucial role in the control of bone homeostasis by regulating osteoblast activity. It is known that the ubiquitin ligase Smad ubiquitination regulatory factor (Smurf)1 is a master negative regulator of BMP signaling, but how its stability and activity are regulated remains poorly understood. Our study showed that valosin‐containing protein/p97, the mutations of which lead to rare forms of Paget's disease of bone (PDB)‐like syndrome—such as inclusion body myopathy (IBM) associated with Paget's disease of bone and frontotemporal dementia (IBM‐PFD)—together with its adaptor nuclear protein localization (NPL)4, specifically interact with Smurfl and deliver the ubiquitinated Smurfl for degradation. Depletion of either p97 or NPL4 resulted in the elevation of Smurfl protein level and decreased BMP signaling accordingly. Mechanically, a typical proline, glutamic acid, serine, and threonine motif specifically existing in Smurfl is necessary for its recognition and degradation by p97, and this process is dependent on p97 ATPase activity. More importantly, compared with p97 WT, PDB‐associated mutation of p97 (mainly A232E) harboring the higher ATPase activity of p97 further promoted Smurfl degradation, thus increasing BMP signaling activity. Our findings first establish a link between p97 and Smurfl, providing an in‐depth understanding of how Smurfl is regulated, as well as the mechanism of p97‐related bone diseases. —Li, H. , Cui, Y. , Wei, J., Liu, C. , Chen, Y. , Cui, C. ‐P., Li, L. , Zhang, X. , Zhang, L. VCP/p97 increases BMP signaling by accelerating ubiquitin ligase Smurf1 degradation. FASEB J. 33, 2928–2943 (2019). www.fasebj. org
Don't Unroll Adjoint: Differentiating SSA-Form Programs

This paper presents reverse-mode algorithmic differentiation (AD) based on source code transformation, in particular of the Static Single Assignment (SSA) form used by modern compilers. The approach can support control flow, nesting, mutation, recursion, data structures, higher-order functions, and other language constructs, and the output is given to an existing compiler to produce highly efficient differentiated code. Our implementation is a new AD tool for the Julia language, called Zygote, which presents high-level dynamic semantics while transparently compiling adjoint code under the hood. We discuss the benefits of this approach to both the usability and performance of AD tools.
Investigations of Large-Scale Voltage-Dependent Loads for Damping Inter-Area Oscillations: Mechanism and Robust Decentralized Control

To uncover the mechanism by which voltage-dependent loads (VDLs) influence electromechanical dynamics, this paper mathematically deduces the damping torque induced by VDLs based on a single machine infinite bus system. Moreover, by continuously regulating the terminal voltage of a typical large-scale VDL, i.e., the aluminum electrolysis load, this paper proposes a novel load damping control architecture to dynamically modulate the power consumption of controlled loads so as to eliminate interarea oscillations in power systems. Specifically, the decentralized load damping controllers (LDCs) used in this architecture are designed by a proposed multistage mixed ${{{H}}_{2}} / {{{H}}_\infty }$ control approach to guarantee robustness against uncertainties (e.g., tie-line outages and wind generation fluctuations) as well as to ensure reasonable control efforts of controlled VDLs. Simulation results on the modified New England and New York interconnected system validate the method for analysis of VDLs’ effects on interarea oscillations and show the proposed load damping control strategy can satisfactorily damp interarea oscillations over multiple operating points.
Domain Specific Learning for Sentiment Classification and Activity Recognition

A deep neural network, while avoiding its complex process of feature selection, requires sufficient training samples to learn those connection weights of adjacent layers. However, in many real applications, not enough training samples are available in all cases. This paper suggests a universal-to-domain-specific learning method based on recurrent neural network for cross-domain sentiment classifi- cation and activity recognition. In the situation of having only a small amount of training samples that is in available, the structure of its network model can be adjusted flexibly according to the needs of a target domain classification or recognition. Where there are two points of our concern as follows: 1) the fine-tune and regular constraints can increase its training efficiency by updating in a small local area, namely, sharing these parameters between input and hidden layers with a target domain and 2) then, a linear output network moves on its implementing amelioration from subtlety as an exploration or exploitation in order to mitigate the phenomenon of over-fitting. Aiming at an actual situation, this domain-specific learning model with a slide window of instances and features is designed and implemented for a good long-short term memory. Finally, the two strategies are applied into IMDB reviews, Amazon product reviews, and human activities recognition collected by the built-in gyroscope sensors data, and the experimental results verify their validity.
Accelerating Venture Milestone Achievement: Examining the Impact of Resource Acquisition Timing

This study examines how resource acquisition timing affects a venture's ability to accelerate milestone achievement. We examine acquisition timing decisions for three types of resources: material resources, facilities and equipment resources, and human resources. Two venture milestones are examined: accomplishment of the first sale and achievement of positive cash flows. We theorize that resource acquisition timing can impact the degree of resource availability delays and the rate of resource-driven cash outflows/inflows, which, in turn, determine the time to first sale and the time to positive cash flows, respectively. Our results demonstrate that decisions concerning resource acquisition timing could have contradictory effects vis-à-vis shortening the time to first sale versus shortening the time to positive cash flows. These findings highlight the difficult tradeoffs that ventures have to navigate when making resource acquisition decisions, which have implications for multiple milestones across a venture's lifecycle. Finally, the paper demonstrates how technological novelty moderates, and, in fact, radically alters, the relationship between the timing of resource acquisitions and the time to venture milestone achievement.
Vertical and Horizontal DNA Differential Methylation Analysis for Predicting Breast Cancer

DNA methylation plays an important role for initiation and development of human cancers; therefore, it is used as a biological marker for early detection of cancer. A huge number of features for each sample and a low number of the available samples are two main problems of this field. This paper presents novel vertical, horizontal, and cascaded DNA methylation feature analysis methods in promoter regions. Vertical analysis processes each feature across all normal or cancer samples to get indicators about the methylation level. The generated values are used to select a subset of features within a given threshold. This set undergoes a horizontal analysis process where we group many features into a window that is used to yield a single value. Hence, the original sample size goes through two reduction steps: the first one is an unsupervised feature selection via the vertical analysis of the features and the second one is a feature extraction via the horizontal process of the selected features. For evaluation and comparison, we used traditional feature selection methods and SVD to compare them with the proposed approaches and found that the proposed approaches outperform all other approaches with a good margin. The results of vertical analysis or horizontal analysis alone are better than traditional approaches. Moreover, the results are improved more when combining both types of analysis. With only 97 features, the proposed combined approach is 99.16% accurate while the best traditional classification is only 98.16% accurate with 31195 features. The combined approach achieved 8.8% to 54.3% improvement percentages compared to all other approaches in terms of a mean absolute error and a root-mean-square error. This indicates that the cascaded approach is far better than the previous approaches. Moreover, the combined approach improves the system accuracy and reduces space and processing complexities of the system.
LENTA: Longitudinal Exploration for Network Traffic Analysis

In this work, we present LENTA (Longitudinal Exploration for Network Traffic Analysis), a system that supports the network analysts to easily identify traffic generated by services and applications running on the web, being them benign or possibly malicious. First, LENTA simplifies analysts' job by letting them observe few hundreds of clusters instead of the original hundred thousands of single URLs. Second, it implements a self-learning methodology, where a semi-supervised approach lets the system grow its knowledge, which is used in turn to automatically associate traffic to previously observed services and identify new traffic generated by possibly suspicious applications. This lets the analysts easily observe changes in the traffic, like the birth of new services, or unexpected activities. We follow a data driven approach, running LENTA on real data. Traffic is analyzed in batches of 24-hour worth of traffic. We show that LENTA allows the analyst to easily understand which services are running on their network, highlights malicious traffic and changes over time, greatly simplifying the view and understanding of the traffic.
Innovative Big Data Analytics: A System for Document Management

This paper shows the solutions developed in the Project "Sistema Innovativo Big Data Analytics" named SIBDA (The needs of the three involved companies are described, which led to define an overall framework in the field of Big Data through application cases. The functional and technological requirements of an integrated Big Data Architecture are given. In particular, the criteria for selecting the solutions for Document Management for one company (Microdata Service) are described. The resulting Enterprise Content Management (ECM) system architecture and the overall system architecture are given. SIBDA stands for Sistema Innovativo Big Data Analytics, a project funded by Regione Lombardia within "Accordi di Competitività", involving three ICT companies (Mail Up s.p.a, Microdata Service and LineaCom), belonging to the CRIT Consortium, Cremona, and Politecnico di Milano.
Distributed Learning over Unreliable Networks

Most of today's distributed machine learning systems assume {\em reliable networks}: whenever two machines exchange information (e.g., gradients or models), the network should guarantee the delivery of the message. At the same time, recent work exhibits the impressive tolerance of machine learning algorithms to errors or noise arising from relaxed communication or synchronization. In this paper, we connect these two trends, and consider the following question: {\em Can we design machine learning systems that are tolerant to network unreliability during training? } With this motivation, we focus on a theoretical problem of independent interest---given a standard distributed parameter server architecture, if every communication between the worker and the server has a non-zero probability $p$ of being dropped, does there exist an algorithm that still converges, and at what speed? The technical contribution of this paper is a novel theoretical analysis proving that distributed learning over unreliable network can achieve comparable convergence rate to centralized or distributed learning over reliable networks. Further, we prove that the influence of the packet drop rate diminishes with the growth of the number of \textcolor{black}{parameter servers}. We map this theoretical result onto a real-world scenario, training deep neural networks over an unreliable network layer, and conduct network simulation to validate the system improvement by allowing the networks to be unreliable.
Fast QTBT Partition Algorithm for Intra Frame Coding through Convolutional Neural Network

The latest Joint Video Exploration Team employs quad-tree plus binary-tree (QTBT) block partitioning structure, which can improve coding performance significantly than High Efficiency Video Coding with hugely increased encoding complexity. To address this issue, we propose a novel fast QTBT partition method through a convolutional neural network (CNN). Specifically, the proposed algorithm uses CNN to predict the QTBT partition depth range of $32\times32$ block directly according to the inherent texture richness of the image, rather than to judge split or not at each depth level. For training optimization, we introduce a misclassification penalty term combined with L2 HingeLoss function, which can further boost the classification accuracy. Experimental results demonstrate the effectiveness of our proposed method; our rate-distortion maintaining setting can achieve 42.33% complexity reduction with just 0.69% bitrate increase. Our low complexity setting can achieve 62.08% complexity reduction with 2.04% bitrate increase.
A Compact 31.47 fJ/Conversion Subthreshold Level Shifter With Wide Conversion Range in 65 nm MTCMOS

This paper presents an energy-efficient level shifter design that is capable of converting an extremely low input voltage to the supply voltage level. Featuring a core area as compact as $32.99~\mu \text{m}^{2}$ , the proposed design comprises a front-end current mirror and an output cross-coupled structure. Concretely, the current mirror is used to boost the complementary input signals to a proper level, with the operations of pull-up and pull-down networks well-balanced. The cross-coupled structure is used at the second stage to achieve a full-swing output. In addition, multi-threshold CMOS transistors are employed and optimized to elevate the circuit performance. The prototype was fabricated using a commercial 65-nm CMOS process. Experiments show that a 90-mV low input voltage can be successfully converted to 1.2 V. The energy consumed per conversion is reported to be 31.47 fJ for converting a 0.2-V input to 1.2 V at 10 MHz, and the corresponding propagation delay is measured to be 23.98 ns.
Influenza A virus neuraminidase protein interacts with Hsp90, to stabilize itself and enhance cell survival

Neuraminidase protein (NA) of influenza A virus (IAV) is popularly known for its sialidase function to assist in the release of progeny virus. However, involvement of NA in other stages of the IAV life cycle also indicates its multifunctional nature and necessity to interact with other host proteins. Here, we report a host protein—heat shock protein 90 (Hsp90), as a novel interacting partner of IAV NA. A classical yeast two‐hybrid screen was conducted to identify a new host interacting partner for NA and the interaction was further validated by coimmunoprecipitation from cells, transiently expressing both proteins and also from IAV‐infected cells. Confocal imaging showed that both proteins colocalized in the cytoplasm in transfected host cells. Interestingly, increased levels of NA in the presence of Hsp90 was observed, which tends to decrease if adenosine triphosphatase activity of Hsp90 is inhibited using 17‐N‐allylamino‐17‐demethoxygeldanamycin (17AAG). This establishes viral NA as a client protein of host chaperone Hsp90 contributing toward NA's stability via the NA‐Hsp90 interaction. This is the first report showing the interaction of NA with Hsp90 and its role in stabilizing viral NA thus preventing it from degradation. Enhanced cell survival in the presence of this interaction was also observed, thus suggesting the requirement of stable viral NA, post‐IAV infection, for efficient virus production in infected mammalian cells.
Valuation of Energy Storage Operation in an AC Power Flow Model

High integration of renewable sources in power systems requires additional assets that can sustain reduced controllability and increased variability of these sources. Energy storage has emerged as a flexible asset that increases flexibility and ensures a more economic and secure power system operation. This paper integrates large-scale battery energy storage in power system under different levels of wind power penetration. As opposed to many models already available, we use the full AC model approach that accurately represents power system operation, but at a cost of high computational burden. The proposed model is applied to the IEEE 24-bus test case modeled in GAMS environment and solved with. The power system operation is simulated with and without battery energy storage to show its contribution to the reduction of power system operating costs.
I Know What You Did Last Summer: New Persistent Tracking Mechanisms in the Wild

As the usage of the Web increases, so do the threats an everyday user faces. One of the most pervasive threats a Web user faces is tracking, which enables an entity to gain unauthorized access to the user’s personal data. Through the years, many client storage technologies, such as cookies, have been used for this purpose and have been extensively studied in the literature. The focus of this paper is on three newer client storage mechanisms, namely, Web Storage, Web SQL Database, and Indexed Database API. Initially, a large-scale analysis of their usage on the Web is conducted to appraise their usage in the wild. Then, this paper examines the extent that they are used for tracking purposes. The results suggest that Web Storage is the most used among the three technologies. More importantly, to the best of our knowledge, this paper is the first to suggest Web tracking as the main use case of these technologies. Motivated by these results, this paper examines whether popular desktop and mobile browsers protect their users from tracking mechanisms that use Web Storage, Web SQL Database, and Indexed Database. Our results uncover many cases where the relevant security controls are ineffective, thus making it virtually impossible for certain users to avoid tracking.
Wiki message linking

WIKI MESSAGE LINKING (WML) is a piece of server software that allows registered users to freely create and edit web page content using any web browser. It is a free communication tool which will be used as a group communication tool and repository of user's work. Members can edit their works online and the group leader can review the works and correct the works directly if necessary from the WML. And furthermore, WML supports HTML markup.
Spectrum Management Scheme in Fog IoT Networks

With the rapid growth of communication devices in number and diversity, there is now increasing interest in the Internet of Things (IoT). There are various challenges, in terms of the spectral efficiency, data rates, average cell throughput, data latency, and connectivity density, on largescale IoT networks. To deal with those challenges, we explore the novel IoT network architecture and efficient spectrum management schemes to unlock 5G spectrum potential. In this article, we introduce an overview of the design principles for IoT architecture in relation to adaptability, performance, reliability, and scalability. Based on all of the design principles, a logical architecture is proposed, where both fog computing and network functions virtualization are taken into consideration for fog IoT networks. Furthermore, we provide two channel access schemes, as well as two spectrum allocation schemes based on the multi-armed bandit algorithm to increase spectrum efficiency and maximize economic revenues in unlicensed spectrum. Simulation results verify that the proposed spectrum management schemes significantly improve IoT network performance. Finally, several challenges in IoT networks are discussed, including network architecture, network technology, network security, and economic revenue.
Analysis of Cu-Graphene Interconnects

Due to its ultrathin feature, graphene has been recently proposed as diffusion barrier layer for Cu wires. This paper is geared toward developing an equivalent single-conductor (ESC) transmission-line (TL) model for analysis of Cu-graphene interconnects, i.e., Cu wires encapsulated with graphene barriers. Based on the ESC TL model, electrical performances of Cu-graphene interconnects are examined and evaluated. It is shown that the time delay and temperature rise can be reduced by replacing the conventional diffusion barriers in the Cu/low-k interconnect with the graphene barriers.
Symmetric Block-Wise Concatenated BCH Codes for NAND Flash Memories

This paper introduces a high rate error-correcting coding scheme called symmetric block-wise concatenated Bose–Chaudhuri–Hocquenghem (symmetric BC-BCH) codes tailored for storage devices with hard-decision outputs, e.g., storage devices based on NAND flash memory. It will be shown that a careful integration of the symmetry and 2-D block-wise concatenation is especially beneficial to achieve improvements of error-rate performance when an iterative hard-decision-based decoding (IHDD) is assumed. The claim is substantiated by proving that the proposed symmetric concatenation is optimal in terms of error-rate performance in the low error-rate regime over other 2-D block-wise concatenations. Besides, this paper proposes a novel way to design constituent codes, which enables us to enjoy advantages of primitive BCH codes and to efficiently break stopping sets associated with the IHDD in the low error-rate regime. We consider error-control systems made up of a symmetric BC-BCH code, the IHDD, and simple auxiliary decoders specifically targeting to break stopping sets caused in the IHDD. It will be shown that the auxiliary decoders significantly improve error-rate performance at a negligible amount of extra complexity. Performance comparisons are also carried out between error-control systems with the proposed and other coding schemes such as BCH codes, quasi-primitive BC-BCH codes, and low-density parity-check codes.
Neuropolypathology as a result of severe traumatic brain injury?

A history of brain trauma has long been acknowledged as increasing an individual's risk of developing dementia in later life. The underlying mechanisms that belie this pre-disposition are, however, very poorly understood. Here, we report a clinical-neuropathological correlation of a man who presented at the age of 66 with a progressive complex atypical dementia with early and prominent neurobehavioral symptoms. His neurological condition continued to decline up to his death at the age of 74. During the compilation of his clinical history, it was established that the subject had experienced a single severe traumatic brain injury (TBI) aged 12 years in 1954 resulting in loss of consciousness, hospitalization, and coma for a number of days after which he was deemed to have recovered. Following post-mortem neuropathological analysis, numerous distinct neuropathologies were observed in various brain regions and these included i) widespread Braak stage VI neurofibrillary tangle formation, ii) widespread α-synuclein positive Lewy bodies and Lewy neurites and iii) diffuse amyloid plaques and severe cerebral amyloid angiopathy (CAA). Added to this, a comprehensive analysis of blood-brain barrier (BBB) integrity, known to be disrupted during and after TBI, showed iv) distinct BBB breakdown with extravasated IgG and activated microglia present. This report represents an interesting documented case of neuropolypathology that may be associated with prior history of severe TBI. We propose one testable theory that a history of brain trauma may be a potential trigger for late onset dementia due to damage and unresolved functioning of the cerebral microvasculature.