Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence from intrinsic shapes for two families of elliptical galaxies

Bright elliptical galaxies have a markedly different distribution of Hubble types than faint ellipticals; the division occurs near M_B=-20 and bright ellipticals are rounder on average. The Hubble types of galaxies in both groups are narrowly clustered, around E1.5 in the case of the bright galaxies and around E3 for the fainter ones. The Hubble-type distribution of the faint ellipticals is consistent with oblate symmetry, but the oblate hypothesis fails for the bright ellipticals. However a distribution of triaxial intrinsic shapes can successfully reproduce the apparent shape data for either group. The distribution of intrinsic, short-to-long axis ratios is peaked around 0.75 for bright galaxies and 0.65 for faint galaxies. Our results provide further evidence that elliptical galaxies should be divided into two, morphologically distinct families.
Video object cut in complex scenes

Extracting the moving interesting objects accurately in complex scenes is one of the most challenging problems in computer vision. In this paper, we introduce a novel approach to cutout interesting moving objects with userpsilas interaction, which partitions pre-segmentation regions into foreground and background while preserving temporal coherence. First frame of video is divided into several pre-segmentation regions, which are used as nodes of directed graph. After graph is constructed, user draws several simple lines as seeds in background and foreground in image. Graph cuts algorithm is used to divide the image into two parts: background and foreground by the seed pixels. When next frame comes in, Bhattacharyya distance and spatial adjacent condition are employed to measure the similarity between pre-segmentation regions in current frame and regions of prior segmentation result in prior frame. Then we modify the weight of graph link, cut object out. If the result is goods, next frame is dealt with. Otherwise, there exit some errors, user can draw the seed line again to segment interesting object accurately. Experiments show that our approach achieves nice performance.
Potential-based Shaping in Model-based Reinforcement Learning

Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have high value, this approach can decrease experience complexity--the number of trials needed to find near-optimal behavior. An orthogonal way of decreasing experience complexity is to use a model-based learning approach, building and exploiting an explicit transition model. In this paper, we show how potential-based shaping can be redefined to work in the model-based setting to produce an algorithm that shares the benefits of both ideas.
A functional trait perspective on plant invasion.

BACKGROUND AND AIMS Global environmental change will affect non-native plant invasions, with profound potential impacts on native plant populations, communities and ecosystems. In this context, we review plant functional traits, particularly those that drive invader abundance (invasiveness) and impacts, as well as the integration of these traits across multiple ecological scales, and as a basis for restoration and management. SCOPE We review the concepts and terminology surrounding functional traits and how functional traits influence processes at the individual level. We explore how phenotypic plasticity may lead to rapid evolution of novel traits facilitating invasiveness in changing environments and then 'scale up' to evaluate the relative importance of demographic traits and their links to invasion rates. We then suggest a functional trait framework for assessing per capita effects and, ultimately, impacts of invasive plants on plant communities and ecosystems. Lastly, we focus on the role of functional trait-based approaches in invasive species management and restoration in the context of rapid, global environmental change. CONCLUSIONS To understand how the abundance and impacts of invasive plants will respond to rapid environmental changes it is essential to link trait-based responses of invaders to changes in community and ecosystem properties. To do so requires a comprehensive effort that considers dynamic environmental controls and a targeted approach to understand key functional traits driving both invader abundance and impacts. If we are to predict future invasions, manage those at hand and use restoration technology to mitigate invasive species impacts, future research must focus on functional traits that promote invasiveness and invader impacts under changing conditions, and integrate major factors driving invasions from individual to ecosystem levels.
Distortionary, Non-Distortionary, and Distributional Properties of Environmental Taxes: Extension of Sandmo's Observation

We find a limited parallel between lump-sum taxes and environmental taxes. Corollary 2, which extends Sandmo's observation, shows that appropriated corrective revenues have the same non-distortionary effects as lump-sum taxes, the result reducing to the original observation when the appropriated corrective revenues meet the revenue need and achieve first-best efficiency with other taxes set at zero. Corollary 1 finds that when the corrective part of environmental taxes is used as marginal damage compensation, the non-corrective part is distortionary and symmetric with ordinary proportional labor taxes in second-best equilibria. The extension of Sandmoi¯s observation suggests that environmental taxes may be among the least distortionary taxes in the tax system.
In Vivo Diagnostic Imaging Using Micro-CT: Sequential and Comparative Evaluation of Rodent Models for Hepatic/Brain Ischemia and Stroke

Background There is an increasing need for animal disease models for pathophysiological research and efficient drug screening. However, one of the technical barriers to the effective use of the models is the difficulty of non-invasive and sequential monitoring of the same animals. Micro-CT is a powerful tool for serial diagnostic imaging of animal models. However, soft tissue contrast resolution, particularly in the brain, is insufficient for detailed analysis, unlike the current applications of CT in the clinical arena. We address the soft tissue contrast resolution issue in this report. Methodology We performed contrast-enhanced CT (CECT) on mouse models of experimental cerebral infarction and hepatic ischemia. Pathological changes in each lesion were quantified for two weeks by measuring the lesion volume or the ratio of high attenuation area (%HAA), indicative of increased vascular permeability. We also compared brain images of stroke rats and ischemic mice acquired with micro-CT to those acquired with 11.7-T micro-MRI. Histopathological analysis was performed to confirm the diagnosis by CECT. Principal Findings In the models of cerebral infarction, vascular permeability was increased from three days through one week after surgical initiation, which was also confirmed by Evans blue dye leakage. Measurement of volume and %HAA of the liver lesions demonstrated differences in the recovery process between mice with distinct genetic backgrounds. Comparison of CT and MR images acquired from the same stroke rats or ischemic mice indicated that accuracy of volumetric measurement, as well as spatial and contrast resolutions of CT images, was comparable to that obtained with MRI. The imaging results were also consistent with the histological data. Conclusions This study demonstrates that the CECT scanning method is useful in rodents for both quantitative and qualitative evaluations of pathologic lesions in tissues/organs including the brain, and is also suitable for longitudinal observation of the same animals.
Unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis

Computer-assisted surgery (CAS) aims to provide the surgeon with the right type of assistance at the right moment. Such assistance systems are especially relevant in laparoscopic surgery, where CAS can alleviate some of the drawbacks that surgeons incur. For many assistance functions, e.g. displaying the location of a tumor at the appropriate time or suggesting what instruments to prepare next, analyzing the surgical workflow is a prerequisite. Since laparoscopic interventions are performed via endoscope, the video signal is an obvious sensor modality to rely on for workflow analysis. Image-based workflow analysis tasks in laparoscopy, such as phase recognition, skill assessment, video indexing or automatic annotation, require a temporal distinction between video frames. Generally computer vision based methods that generalize from previously seen data are used. For training such methods, large amounts of annotated data are necessary. Annotating surgical data requires expert knowledge, therefore collecting a sufficient amount of data is difficult, time-consuming and not always feasible. In this paper, we address this problem by presenting an unsupervised method for training a convolutional neural network (CNN) to differentiate between laparoscopic video frames on a temporal basis. We extract video frames at regular intervals from 324 unlabeled laparoscopic interventions, resulting in a dataset of approximately 2.2 million images. From this dataset, we extract image pairs from the same video and train a CNN to determine their temporal order. To solve this problem, the CNN has to extract features that are relevant for comprehending laparoscopic workflow. Furthermore, we demonstrate that such a CNN can be adapted for surgical workflow segmentation. We performed image-based workflow segmentation on a publicly available dataset of 7 cholecystectomies and 9 colorectal interventions.
The Incremental Design of Parallel Compiler Intermediate Representations using SPIRE

SPIRE is the first incremental methodology for designing the intermediate representations (IR) of compilers that target parallel programming languages. Its core philosophy is to extend in a systematic manner the IRs found in the compilation frameworks of sequential languages. Avoiding the often-used ad-hoc approach of encoding all parallel constructs as “fake” function calls, SPIRE enables the leveraging of current compilers for sequential languages to address both control and data parallel constructs while preserving as much as possible the correctness of existing analyses for sequential code. This upgrading process is formalized as an “intermediate representation transformer” at the syntactic and semantic levels. We show this can be done via the addition of only three global parallel traits on top of any sequential IR, namely execution, synchronization and data distribution, precisely defined via a formal semantics and rewriting rules. We use the sequential intermediate representation of PIPS, a comprehensive source-to-source compilation platform, as a usecase for our approach. We introduce SPIRE parallel primitives, extend PIPS intermediate representation and show how example code snippets from the OpenCL, Cilk, OpenMP, X10, HabaneroJava, CAF and Chapel parallel programming languages and MPI and OpenSHMEM libraries can be represented this way. A formal definition of SPIRE operational semantics is provided, built on top of the one used for the sequential intermediate representation. We assess the generality of our proposal by showing how different sequential IRs, namely the LLVM and WHIRL IRs of the widely used LLVM and Open64-based OpenUH compilers, can be systematically extended to handle parallelism using the SPIRE methodology. Experimentally, SPIRE has been implemented in PIPS, and the resulting parallel IR used successfully to perform OpenMP and MPI parallel code generation as part of the PIPS parallelization process; it is also being implemented within OpenUH to validate its use in the parsing of the explicitly-parallel languages CAF and OpenMP. [Copyright notice will appear here once ’preprint’ option is removed. ]
An approach for secure wireless communication using friendly noise

As the need of highly secure data transmission in wireless communication has increased rapidly, physical layer security gains a lots of attention recently. The transmission of confidential data between two legitimate users in the existence of the passive eavesdropper with quasi-static Rayleigh channel is considered. A new technique is proposed in which a friendly noise is incorporated with confidential data bearing signal such that it preserves the integrity of the signal and does not degrade the legitimate link. This friendly noise acts as jamming signal for eavesdropper. The analytical expression for secrecy outage probability of the proposed technique for both single and multiple antennas eavesdropper is derived. The secrecy performance of proposed technique is compared with the traditional direct transmission and the cooperative jamming schemes. The asymptotic nature of the outage probability is also studied for the different parameters. Numerical results are also presented to verify the analytical expressions of proposed model, which show that the proposed technique is better than the traditional direct transmission and the cooperative jamming schemes.
An Analysis of the Synchrotron Spectrum in the Fanaroff-Riley Type I Galaxy 3C 449

We studied the evolution of relativistic electrons in the Fanaroff-Riley type I (FR I) galaxy 3C 449, by observing with the VLA at three frequencies (330, 1445, and 4835 MHz) and in all four configurations for a primary resolution of 3. ″6. The conventional model for this type of source is that the relativistic electrons are accelerated in the core and subsequently lose energy as the jet flows outward, expands, and bends downstream. We find that 3C 449 is more complicated than that, with evidence for a different type of structure, more reminiscent of higher luminosity FR II galaxies. We introduce a new analysis tool—the spectral tomography gallery—and find evidence for two spectral components that we label the “flat jet” and the “sheath. ” The flat jet remains fairly well collimated throughout the inner 5', with the sheath accounting for most of the observed widening. The flat jet shows little steepening with distance from the core. However, the sheath has a steeper spectral index than the jet. If the same spectral shape is appropriate for both components, there may be an evolutionary connection between the particles in the flat jet and those in the sheath. Another possibility is that the sheath has a steeper injection index than the flat jet. This may mean that the sheath and flat jet are separate electron populations. In either case, the standard aging analyses would give incorrect answers because of confusion between the separate spectral components.
Optimal Resource Provisioning Approach based on Cost Modeling for Spark Applications in Public Clouds

Efficient resource provisioning is required when running Spark applications in public clouds. However, how to optimize resource provisioning to minimize the time and/or monetary cost for a specific application remains an intractable problem since resource provisioning may differ from application to application and even be affected by the amount of input data. Existing resource settings heavily rely on random selection or previous deployer experience, frequently leading to low-quality resource provisioning. Therefore, there is an urgent need to propose an approach towards optimal resource provisioning for Spark applications in public clouds. This is a PhD proposal, where an approach based on time and monetary cost modeling is presented for cloud resource provisioning optimization under two typical constrained scenarios. The approach systematically drives resource provisioning for a specific Spark application, which may save a significant amount of time and money, compared to randomly selected settings.
Microfabricated tissue gauges to measure and manipulate forces from 3D microtissues

Physical forces generated by cells drive morphologic changes during development and can feedback to regulate cellular phenotypes. Because these phenomena typically occur within a 3-dimensional (3D) matrix in vivo, we used microelectromechanical systems (MEMS) technology to generate arrays of microtissues consisting of cells encapsulated within 3D micropatterned matrices. Microcantilevers were used to simultaneously constrain the remodeling of a collagen gel and to report forces generated during this process. By concurrently measuring forces and observing matrix remodeling at cellular length scales, we report an initial correlation and later decoupling between cellular contractile forces and changes in tissue morphology. Independently varying the mechanical stiffness of the cantilevers and collagen matrix revealed that cellular forces increased with boundary or matrix rigidity whereas levels of cytoskeletal and extracellular matrix (ECM) proteins correlated with levels of mechanical stress. By mapping these relationships between cellular and matrix mechanics, cellular forces, and protein expression onto a bio-chemo-mechanical model of microtissue contractility, we demonstrate how intratissue gradients of mechanical stress can emerge from collective cellular contractility and finally, how such gradients can be used to engineer protein composition and organization within a 3D tissue. Together, these findings highlight a complex and dynamic relationship between cellular forces, ECM remodeling, and cellular phenotype and describe a system to study and apply this relationship within engineered 3D microtissues.
Inferring users' online activities through traffic analysis

Traffic analysis may threaten user privacy, even if the traffic is encrypted. In this paper, we use IEEE 802.11 wireless local area networks (WLANs) as an example to show that inferring users' online activities accurately by traffic analysis without the administrator's privilege is possible during very short periods (e.g., a few seconds). The online activities we investigated include web browsing, chatting, online gaming, downloading, uploading and video watching, etc. We implement a hierarchical classification system based on machine learning algorithms to discover what a user is doing on his/her computer. Furthermore, we conduct experiments in different network environments (e.g., at home, on university campus, and in public areas) with different application scenarios to evaluate the performance of the classification system. Results show that our system can distinguish different online applications on the accuracy of about 80% in 5 seconds and over 90% accuracy if the eavesdropping lasts for 1 minute.
Heterogeneous anthropomorphic phantoms with realistic dielectric properties for microwave breast imaging experiments

We present a technique for fabricating realistic breast phantoms for microwave imaging experiments. Using oil‐in‐gelatin dispersions that mimic breast tissue dielectric properties at microwave frequencies, we constructed four heterogeneous phantoms spanning the full range of volumetric breast densities. We performed CT scans and dielectric properties measurements to characterize each phantom. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26128
An Improved Retransmission Steganography and Its Detection Algorithm

Network steganography (referred to as network covert channel) is a covert communication technique that uses the redundancies of network protocol to transfer secret information. The retransmission steganography (RSTEG) is first proposed by Szczypiorski in 2009. It embeds secret information in the payload field of the retransmission packets which are produced intended by the communicators. However, this algorithm ignores the checksum fields of the original packets and the retransmission packets are different, which will decrease its stealthy. In this paper, an improved RSTEG algorithm (IRSTEG) is proposed first. This improved algorithm makes the checksum field of the original and the retransmission packets same. This will make the IRSTEG stealthier than RSTEG. Then, the payload sampling comparison detection method is proposed. At last, the experiments are given about the IRSTEG and its detection algorithm.
Wind turbine bearing failure detection using generator stator current homopolar component ensemble empirical mode decomposition

Failure detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a failure detection techniques based on the homopolar component of the generator stator current and attempts to highlight the use of the Ensemble Empirical Mode Decomposition (EEMD) as a tool for failure detection in wind turbine generators for stationary and non stationary cases.
Waiting time analysis of BMAP vacation queue and its application to IEEE 802.16e sleep mode

The paper deals with the continuous-time BMAP/G/1 queue with multiple vacations and with its application to IEEE 802.16e sleep mode. The lengths of the vacation periods have general distribution and they depend on the number of preceding vacations (dependent multiple vacation). We obtain new formulas for the vector Laplace-Stieljes transform of the stationary virtual waiting time and for its first two moments in case of First-Come First-Serve scheduling. Finally the application of this vacation model to IEEE 802.16e sleep mode mechanism is demonstrated.
Bilateral human-robot control for semi-autonomous UAV navigation

This paper proposes a semi-autonomous bilateral control architecture for unmanned aerial vehicles. During autonomous navigation, a human operator is allowed to assist the autonomous controller of the vehicle by actively changing its navigation parameters to assist it in critical situations, such as navigating through narrow paths. The overall goal of the controller is to combine the stability and precision of an autonomous control with the cognitive abilities of a human operator, only when strictly required for the accomplishment of a task. The control architecture has been validated through simulations and experiments.
Dynamic Autonomic Network Management: Evaluating the Architectural Challenges of Autonomic Management for Mobile Ubiquitous Access

As technology rapidly improves there is more mo- bile and portable devices available on the market, making the prospects of ubiquitous access to Information Communications Technology (ICT) services a bigger better reality every day. The major hurdle which is the ICT skills shortage can be solved by using autonomic management of the devices on the network and end user equipment. Network and application service providers competing to retain the customer base in order to maintain a guaranteed and healthy income, need to improve network man- agement and stick to service level agreements. This can easily be achieved through enabling network components to automati- cally configure and optimize their settings, operations and per- formance. Autonomic network and device management has great advantages including, reduction of human error, reduction on the dependency of the scarce and expensive human skill and much faster introduction of applications, new services and tech- nology, saving the critical and scarce time. However, due to ar- chitectural differences major problems arise when a mobile node traverses heterogeneous networks and systems that em- ploy different management paradigms different aspects for simi- lar processes such as Call Admission Control (CAC) mecha- nisms, Quality of Service (QoS) issues and Security. Index Terms—Self-configuration, Self-optimization, Ubiqui- tous access, Mobility, Heterogeneous Networks