Miracle-dz/UniFAR-FTUs huggingface dataset:
THE STUDY OF GLIAL SCAR FORMATION AFTER BRAIN ISCHEMIA USING IN VITRO STRATEGIES For the degree of Master in Science (M.Sc.) INTERNATIONAL MASTER PROGRAM IN BIOMEDICAL SCIENCES (IMBS)

Reactive gliosis is a generic response to Central Nervous System (CNS) injury mediated by astrocytes and microglia. Following ischemic damage to the CNS parenchyma, the injured area becomes surrounded by a dense astroglial cell layer known as glial scar. Glial scar formation has been recognized for many decades as a major impediment for neuronal reconnection and a serious obstacle for functional recovery. However, more recent studies have shown that scar limits the area of damage, preventing the diffusion of blood-derived activated immune cells into the CNS that could cause a generalized proinflammatoryneurodegenerative response. In spite that it has been morphologically recognized for many years since Ramon y Cajal times, to study the biochemical signaling cascades involved in glial scar formation has been difficult mostly because of the in vivo nature of the process. In this context, we studied here the mechanisms of glial scar assembly/disassembly in vitro to identify potential pharmacological targets for therapeutic interventions. To achieve this goal we will use the classical 2-Dimensional (2D) astroglial cultures, but we will also develop 3-dimensional (3D) astroglial cultures by using nanotube matrixes to attempt to better reproduce the in vivo situation. The results of this thesis showed that meningeal macrophages or ischemia-activated macrophages induce astroglial retraction and formation of scar-like structures in vitro. Scar-forming astrocytes over-express GFAP, S100B and TLR24. Using the NF-B antagonist BAY-11-7082 we demonstrated that scar formation and its density is partially NF-B dependent. Finally, in 3D astroglial culture grown on hydromatrix nanotubes, we showed that DAMPs can induce astroglial polarization but not the formation of the glial scar in vitro. We conclude that TLR/ NF-B pathway is probably implicated in the glial scar formation or stabilization and that DAMPs and macrophages are necessary for the formation of glial scars in vitro. Raja Sekhar Mannava IMBS 4
Reconsidering the Contents and Function of the rDzogs chen Classifications of Sems phyogs and Sems sde

he following paper seeks to revisit some of the key rubrics under which much of the received oral tradition literature, or bKa’ ma, of the Great Perfection (rdzogs pa chen po) became subsumed. Specifically, I am concerned with the, so-called, Mind Series (sems sde) texts, which are traditionally believed to have been imported into Tibet by the monk-translator Pa gor Bai ro tsa na during the latter part of the rule of King Khri srong lde’u btsan. However, as I shall discuss, this material seems to have only been referred to as such centuries later, in texts we can reasonably date to around the 11thth century, at the earliest. Furthermore, those very texts which delineate this classification belong to the literature of a competing trend of the Great Perfection, namely the Pith Instruction Series (man ngag sde). Which begs the question: what did those who practiced and propagated the Mind Series teachings call them before somebody else started calling them the Mind Series? And so, therein lies one of the major obstacles that we must face when attempting to come to terms with the Mind Series, in that much of the picture we get of it is colored by the hindsight of later authors. A case in point, when the famed fourteenth century scholar Klong chen rab ‘byams (1308–1364) wrote the Seven Troves (mdzod bdun), his
Comparative Study on various Losses for Vehicle Re-identification

In this paper, we tackle the problem of vehicle reidentification, which has extensive applications in traffic analysis such as anomaly detection, congestion pricing and tolling. While previous methods extract visual features from the images and then use spatio-temporal regularization to further refine the results, our method focuses on extracting purely visual features from vehicle images and then further employs a re-ranking technique to improve results. We evaluate the proposed pipeline on the VeRi and CityFlow (NVIDIA AI City Challenge 2019) datasets. Experiments show that our pipeline achieves state of the art performance on the VeRi dataset. We also perform extensive analysis on each step of the pipeline and demonstrate how they increase overall performance.
COMPACT RECURRENT NEURAL NETWORK BASED ON TENSOR-TRAIN FOR POLYPHONIC MUSIC MODELING

This paper introduces a novel compression method for recurrent neural networks (RNNs) based on Tensor Train (TT) format. The objective in this work are to reduce the number of parameters in RNN and maintain their expressive power. The key of our approach is to represent the dense matrices weight parameter in the simple RNN and Gated Recurrent Unit (GRU) RNN architectures as the ndimensional tensor in TT-format. To evaluate our proposed models, we compare it with uncompressed RNN on polyphonic sequence prediction tasks. Our proposed TT-format RNN are able to preserve the performance while reducing the number of RNN parameters significantly up to 80 times smaller.
Efficient kNN Computation for Multiple Query Points on Road Network

Finding k nearest neighbours to one query point on road network is the typical kNN problem, and attracts lots of research interests in recent years with the development of mobile technologies. In this paper, we study a variant of the kNN problem, namely mkNN, which returns k objects for m query points. We firstly propose a baseline algorithm, which solves the mkNN problem in naive way. We then propose an optimized algorithm, which avoids the redundant computation of the baseline algorithm by organizing objects using index to firstly get candidate objects, such that to reduce the number of visited nodes of the given road network. Our third algorithm further improves the performance by using a tighter threshold to greatly reduce the number of visited nodes. We make detailed comparison between the three algorithms and show their benefits w.r. t. the mkNN problem.
Maximizing the Processing Rate for Streaming Applications in Apache Storm

A key aspect in improving the performance of distributed real-time stream processing platforms lies in tasks mapping strategy to the underlying network. In this paper, the problem of scheduling streaming applications on a heterogeneous Apache Storm environment is investigated and the Maximum Tuple Processing Rate (MTPR) algorithm is designed and presented. MTPR uses dynamic programming technique to efficiently schedule Storm topology jobs based on the computing and data transfer requirements and the capacity of the underlying cluster resources. Our proposed approach maximizes the system throughput by identifying and minimizing the time incurred on the bottleneck along the critical path extracted from the job topology. The performance and the scalability of the proposed scheduler have been demonstrated by our simulation results in comparison with the default Storm scheduler.
Hadoop-based storage architecture for mass MP3 files

MP3 as the de facto standard for digital music,the number of files is quite large and user access requirements rapidly grow up.How to effectively store and manage vast amounts of MP3 files to provide good user experience has become a matter of bigger concern.The emergence of Hadoop provides us with new ideas.However,because Hadoop itself is not suitable for handling massive small files,this paper presented a Hadoop-based storage architecture for massive MP3 files,fullly using the MP3 file's rich metadata.The classification algorithm by pre-processing module would merged small files into sequence file,and the introduction of efficient indexing mechanism served as a good solution to the problem of small files.The experimental results show that the approach can achieve a better performance.
A user study to compare two conversational assistants designed for people with hearing impairments

Participating in conversations can be difficult for people with hearing loss, especially in acoustically challenging environments. We studied the preferences the hearing impaired have for a personal conversation assistant based on automatic speech recognition (ASR) technology. We created two prototypes which were evaluated by hearing impaired test users. This paper qualitatively compares the two based on the feedback obtained from the tests. The first prototype was a proof-of-concept system running real-time ASR on a laptop. The second prototype was developed for a mobile device with the recognizer running on a separate server. In the mobile device, augmented reality (AR) was used to help the hearing impaired observe gestures and lip movements of the speaker simultaneously with the transcriptions. Several testers found the systems useful enough to use in their daily lives, with majority preferring the mobile AR version. The biggest concern of the testers was the accuracy of the transcriptions and the lack of speaker identification.
Decadal fates and impacts of nitrogen additions on temperate forest carbon storage: a data–model comparison

Abstract. To accurately capture the impacts of nitrogen (N) on the land carbon (C) sink in Earth system models, model responses to both N limitation and ecosystem N additions (e.g., from atmospheric N deposition and fertilizer) need to be evaluated. The response of the land C sink to N additions depends on the fate of these additions: that is, how much of the added N is lost from the ecosystem through N loss pathways or recovered and used to increase C storage in plants and soils. Here, we evaluate the C–N dynamics of the latest version of a global land model, the Community Land Model version 5 (CLM5), and how they vary when ecosystems have large N inputs and losses (i.e., an open N cycle) or small N inputs and losses (i.e., a closed N cycle). This comparison allows us to identify potential improvements to CLM5 that would apply to simulated N cycles along the open-to-closed spectrum. We also compare the short- (< 3 years) and longer-term (5–17 years) N fates in CLM5 against observations from 13 long-term 15N tracer addition experiments at eight temperate forest sites. Simulations using both open and closed N cycles overestimated plant N recovery following N additions. In particular, the model configuration with a closed N cycle simulated that plants acquired more than twice the amount of added N recovered in 15N tracer studies on short timescales (CLM5: 46±12 %; observations: 18±12 %; mean across sites ±1 standard deviation) and almost twice as much on longer timescales (CLM5: 23±6 %; observations: 13±5 %). Soil N recoveries in simulations with closed N cycles were closer to observations in the short term (CLM5: 40±10 %; observations: 54±22 %) but smaller than observations in the long term (CLM5: 59±15 %; observations: 69±18 %). Simulations with open N cycles estimated similar patterns in plant and soil N recovery, except that soil N recovery was also smaller than observations in the short term. In both open and closed sets of simulations, soil N recoveries in CLM5 occurred from the cycling of N through plants rather than through direct immobilization in the soil, as is often indicated by tracer studies. Although CLM5 greatly overestimated plant N recovery, the simulated increase in C stocks to recovered N was not much larger than estimated by observations, largely because the model's assumed C:N ratio for wood was nearly half that suggested by measurements at the field sites. Overall, results suggest that simulating accurate ecosystem responses to changes in N additions requires increasing soil competition for N relative to plants and examining model assumptions of C:N stoichiometry, which should also improve model estimates of other terrestrial C–N processes and interactions.
AI City Challenge 2019 - City-Scale Video Analytics for Smart Transportation

Understanding large-scale video trafﬁc big data is the new frontier of today’s AI smart transportation advancement. The AI City Challenge 2019 is the third sequel of a yearly event that draws signiﬁcantly growing attention and participation. This paper presents works contributed to the three Challenges Tracks. In Track 1 City-Scale Multi-Camera Vehicle Tracking, we developed a new multi-camera fusion method by extending the state-of-the-art single-camera tracking-by-detection with site calibrations. Our approach jointly optimizes the matching of vehicle image features and geometrical factors including trajectory continuity, vehicle moving directions and travel duration across views, to effectively fuse tracks and identify vehicles across 40+ cameras in a city-wide scale. In Track 2 City-Scale Multi-Camera Vehicle Re-Identiﬁcation, we pro-pose a Pyramid Granularity Attentive Model (PGAM) for ReID by improving the recent Region-Aware deep Model (RAM) with a pyramid design and training strategy improvements. In Track 3 Trafﬁc Anomaly Detection, we improved the 2nd-best method from AIC2018 with reﬁned event recognizers of stalled vehicles with back-tracking to accurately locate event occurrence. The proposed methods achieve compelling performance in the leaderboard among 80+ world-wide participant teams.
Prospective Study of Core Needle Biopsy on Bone Lesion

1 Post Graduate Final Year, Osmania Medical College, 5-1-876, Turrebaz Khan Rd, Troop Bazaar, Koti, Hyderabad, Telangana, India 2 Professor, Department of Pathology, Osmania Medical College, 5-1-876, Turrebaz Khan Rd, Troop Bazaar, Koti, Hyderabad, Telangana, India 3 Professor & HOD, Department of Pathology, Osmania Medical College, 5-1-876, Turrebaz Khan Rd, Troop Bazaar, Koti, Hyderabad, Telangana, India
Absolute dating of aeolian sediments in relationship to the development of the city of Ghent : first results

Today's physical landscape in the Ghent city area (E Flanders, Belgium) is the result of human activity. Previous studies that aimed at understanding the city in its former environmental context focused on reconstructing the hydrographical network. The role of Late Quaternary aeolian processes and landforms has not been considered. Incited by a reappraisal of the evidence in historical records, our study aims at contributing to an improved understanding of the significance of this windblown (micro) relief. In this paper, we report on results from optical dating of two aeolian sequences (near the "Emile Braunplein" and the "Hoogpoort") in the historical city centre. Optical dates of similar to 11 ka - similar to 15 ka indicate deposition during the Late Glacial, and allow refining the Quaternary geological map of the area. Anthropogenic activity on this sediment is supported by a cart mark observed in the upper part of the sediments and post-sedimentary reworking of the top of the "Hoogpoort" sequence. This confirms that Pleistocene aeolian formed elevations were present and at least as relevant to Ghent city's settlement history as a local Tertiary outlier (the "Blandijnberg") and the regional riverine environment.
Automatic Malware Analysis: An Emulator Based Approach

Malicious software (i.e., malware) has become a severe threat to interconnected computer systems for decades and has caused billions of dollars damages each year. A large volume of new malware samples are discovered daily. Even worse, malware is rapidly evolving becoming more sophisticated and evasive to strike against current malware analysis and defense systems. Automatic Malware Analysis presents a virtualized malware analysis framework that addresses common challenges in malware analysis. In regards to this new analysis framework, a series of analysis techniques for automatic malware analysis is developed. These techniques capture intrinsic characteristics of malware, and are well suited for dealing with new malware samples and attack mechanisms.
Efficient Management of Community Question Answering Sites using Improved Spectral Clustering

Community Question-Answering(CQA) sites are the major platform where posts are generated by peers in the form of questions and answers for information seeking in online environments. In general, multiple posts are created by different users on a particular topic or subject. Large number of posts raises the difficulties in information management of these sites. A number of approaches are suggested in recent research work for efficient management of data for CQA sites. Many of the existing approaches have suggested use of clustering techniques for managing the CQA sites, but ignored the tagging data (user tags) of the posts. In this paper, an improved spectral clustering technique is derived based on similarity measures for text processing (SMTP) and utilized for clustering the posts considering the tagging data available on CQA sites. A specialized data structure, namely, folksonomy is developed for clustering using the relationship between tags, posts and users.The proposed method is developed in two stages. In first stage, the folksonomy relation is created and post similarity graph is built with the help of tag frequency-inverse post frequency. In the second stage, the spectral clustering algorithm is applied to the post similarity matrix to group the similar posts. The post clusters are generated as the output of the proposed algorithm where the post information management can be made with the help of user tags. The experimental results show that the improvedspectral clustering algorithm outperforms the other considered clustering algo Efficient Management of Community Question Answering Sites using Improved Spectral Clustering Abhishek Kumar Singh1, Naresh Kumar Nagwani2, Sudhakar Pandey3 1Ph.D. Scholar, Computer Science and Engineering National institute of Technology Raipur India aksingh.phd2014. cs@nitrr.ac. in 2Assistant Professor, Computer Science and Engineering National institute of Technology Raipur India nknagwani.cs@nitrr.ac. in 3Assistant Professor, Information Technology National institute of Technology Raipur India spandey.it@nitrr.ac. in rithms with a huge margin and it will be helpful for information management of the CQA sites. The improved spectral clustering algorithm will be useful for post pre-processing, faster information retrieval, duplicate posts identification and posts management from clusters. Subject Categories and Descriptors: [H.3. 4 Systems and Software] Question-answering [H.3. 3 Information Search and Retrieval]: Clustering General Terms: Text Processing, Question Answering, folksonomy, Clustering Algorithm
A Computational Framework for Norm-Aware Reasoning in Autonomous Systems

Autonomous agents are increasingly deployed in complex social environments where they not only have to reason about their domain goals but also about the norms that can impose constraints on task performance. To accomplish this, robots must be able to reason not only on how to perform their tasks, but also incorporate societal values, social norms and legal rules so they can gain human acceptability and trust. The intelligent trade-offs that these systems must make between domain and normative constraints is the key to any system being socially responsible and acceptable. Integrating task planning with norm aware reasoning is a challenging problem due to the curse of dimensionality associated with product spaces of the domain state variables and norm-related variables. In this work, we propose a Modular Normative Markov Decision Process (MNMDP) framework that is shown to have orders of magnitude increase in performance compared to previous approaches. The MNMDP framework applies normative reasoning considering only the norms that are activated in appropriate contexts, rather than considering the full set of norms, thus significantly reducing computational complexity. Since norms are both context-dependent as well as context-sensitive, we must model context in an expressive, scalable and compact manner in order to find the activation and deactivation conditions for norms. To this end, we propose a generalizable context modeling approach to understand norm activations in social environments combining the expressiveness of propositional logic with the compactness of decision trees. We show how we can combine our context model with our MNMDP framework to support norm understanding as well as norm enforcement for real systems. We discuss the inferences obtained from the human experiments data that we conducted confirming the complexity of the relationship between contexts and norms. We demonstrate the effectiveness of our approach through scenarios in simulated social environments in which agents using the framework display normaware behavior. We also show the significant computational improvements that we obtain when using our proposed approach for computationally modeling social interactions.
Progress on Flexible Oxide-Based Thin Film Transistors

Oxide semiconductors have drawn much attention in recent years due to their outstanding electrical performance, such as relatively high carrier mobility, good uniformity, low process temperature, optical transparency, low cost and especially flexibility. Flexible oxide-based thin film transistors (TFTs) are one of the hottest research topics for next-generation displays, radiofrequency identification (RFID) tags, sensors, and integrated circuits in the wearable field. The carrier transport mechanism of oxide semiconductor materials and typical device configurations of TFTs are firstly described in this invited review. Then, we describe the research progress on flexible oxide-based TFTs, including representative TFTs fabricated on different kinds of flexible substrates, the mechanical stress effect on TFTs and optimized methods to reduce this effect. Finally, an outlook for the future development of oxide-based TFTs is given.
Noise Identification in a Mach 0 . 85 Jet using Modified Low-Dimensional Techniques

The principle objective of this study is to identify the noise producing events in the compressible turbulent shear layer of a Mach 0.85, 5.08cm diameter, cold (300K) axisymmetric jet that are responsible for the far field acoustic noise. The unique character of this experiment utilizes non-intrusive instruments to estimate the velocity field at bandwidths comparable to that of the dominant events, along several downstream positions in z (slices of the velocity field are made in the x, y plane), while simultaneously sampling the far field acoustics. This is achieved by using a modified form of the Complimentary technique of Bonnet et. al. (1994)[1], to effectively estimate these low-dimensional turbulent events. Thus the jet’s turbulent velocity field is reconstructed using only the more energetic modes, thereby revealing high strain, short duration events, as was seen in the investigations by Citriniti & George (2001)[2] in the incompressible axisymmetric jet and by Taylor et. al. (2000)[3] in the compressible axisymmetric jet. These events are thought to be important mechanisms for the sound generation process from the large scale structures in the flow.
Performance Analysis of Reciprocity Calibration in Massive MIMO

Time Division Duplexing (TDD) Massive MIMO (MaMIMO) relies on channel reciprocity to derive the channel state information at the transmit side (CSIT) from the uplink (UL) channel estimates. This reciprocity is impacted by the transmit and receive front ends which are non-reciprocal and hence a calibration is required to obtain the downlink (DL) channels from the UL channel estimates. The Cramer-Rao Bound (CRB) for reciprocity calibration and an optimal algorithm were derived in [1], [2] recently. In this work, we extend this and derive a generalized CRB that applies to both coherent and non-coherent calibration schemes (introduced in [1]). More importantly, we provide original mean squared error analysis for the commonly used Least-Squares (LS) estimator under different parameter constraints, and relate it to the CRB. Finally, we compare some antenna grouping strategies for calibration based on their CRB.