Miracle-dz/UniFAR-FTUs huggingface dataset:
Application-Specific Wear Leveling for Extending Lifetime of Phase Change Memory in Embedded Systems

Phase change memory (PCM) has been proposed to replace NOR flash and DRAM in embedded systems because of its attractive features. However, the endurance of PCM greatly limits its adoption in embedded systems. As most embedded systems are application-oriented, we can tackle the endurance problem of PCM by exploring application-specific features such as fixed access patterns and update frequencies. In this paper, we propose an application-specific wear leveling technique, called Curling-PCM, to evenly distribute write activities across the whole PCM chip to improve the endurance of PCM in embedded systems. The basic idea is to exploit application-specific features in embedded systems and periodically move the hot region across the whole PCM chip. To reduce the overhead of moving the hot region and improve the performance of PCM-based embedded systems, a fine-grained partial wear leveling policy is proposed for Curling-PCM, by which only part of the hot region is moved during each request handling period. Experimental results show that Curling-PCM can effectively evenly distribute write traffic for a prime application of PCM in embedded systems. We expect this paper can serve as a first step toward the full exploration of application-specific features in PCM-based embedded systems.
The Induced Bounded-Degree Subgraph Problem and Stream Control in MIMO Networks

In this report, we consider maximal solutions to the induced bounded-degree subgraph problem and relate it to issues concerning stream control in multiple-input multiple-output (MIMO) networks. We present a new distributed algorithm that completes in logarithmic time with high probability and is guaranteed to complete in linear time. We conclude the report with simulation results that address the effectiveness of stream control and the relative impact of receiver overloading and flexible interference suppression.
Step-by-step growth of epitaxially aligned polythiophene by surface-confined reaction

One of the great challenges in surface chemistry is to assemble aromatic building blocks into ordered structures that are mechanically robust and electronically interlinked—i.e., are held together by covalent bonds. We demonstrate the surface-confined growth of ordered arrays of poly(3,4-ethylenedioxythiophene) (PEDOT) chains, by using the substrate (the 110 facet of copper) simultaneously as template and catalyst for polymerization. Copper acts as promoter for the Ullmann coupling reaction, whereas the inherent anisotropy of the fcc 110 facet confines growth to a single dimension. High resolution scanning tunneling microscopy performed under ultrahigh vacuum conditions allows us to simultaneously image PEDOT oligomers and the copper lattice with atomic resolution. Density functional theory calculations confirm an unexpected adsorption geometry of the PEDOT oligomers, which stand on the sulfur atom of the thiophene ring rather than lying flat. This polymerization approach can be extended to many other halogen-terminated molecules to produce epitaxially aligned conjugated polymers. Such systems might be of central importance to develop future electronic and optoelectronic devices with high quality active materials, besides representing model systems for basic science investigations.
A Novel Anti-Competitive Learning Neural Network Technique against Mining Knowledge from Databases

In this paper, we proposed an anti-competitive learning neural network scheme against mining of knowledge from databases. Neuron weights were trained by competitive learning in neural network and used with noise to harass the original database. The data mining process in anti-competitive learning will only allow data that contains unimportant knowledge to be mined. Experimental results showed that users can adjust neural weights to redirect harassment of the database to achieve the purpose of misleading illegal users and the mined data contained only unimportant knowledge.
Pathology quiz case: Plasmacytoid dendritic cell neoplasm

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare hematologic malignancy that possesses a heterogenous clinical and immunophenotypic presentation. The current case report describes an interesting and unique presentation of BPDCN as a primary paranasal sinus tumor without evidence of cutaneous or systemic involvement. As such, the report further contributes to the ongoing debate regarding the true putative origin of the neoplasm, as well as highlights the optimal diagnostic modalities, paramount importance of early diagnosis, and vast heterogeneity exhibited by this fascinating malignancy. The atypical presentation described here indicates the manifestations of BPDCN are more heterogenous than previously documented and thus can not be definitively ruled out in the absence of bone marrow, peripheral blood, or cutaneous involvement. Furthermore, atypical neoplastic presentations mandate flow cytometry and adjunctive immunohistochemistry for the definitive diagnosis of BPDCN, and early diagnosis of such neoplasms are critical for rapid initiation of treatment and improved outcomes.
Learning and Adaptation in Cognitive Radios Using Neural Networks

The estimation of the communication performance achievable with respect to environmental factors and configuration parameters plays a key role in the optimization process performed by a Cognitive Radio according to the original definition by Mitola [1]. In this paper we propose the use of Multilayered Feedforward Neural Networks as an effective technique for real-time characterization of the communication performance which is based on measurements carried out by the device and therefore offers some interesting learning capabilities.
Convolutional Neural Networks with Batch Normalization for Classifying Hi-hat, Snare, and Bass Percussion Sound Samples

After having revolutionized image and speech processing, convolutional neural networks (CNN) are now starting to become more and more successful in music information retrieval as well. We compare four CNN types for classifying a dataset of more than 3000 acoustic and synthesized samples of the most prominent drum set instruments (bass, snare, hi-hat). We use the Mel scale log magnitudes (MLS) as a representation for the input of the CNN. We compare the classification results of 1) a CNN (3 conv/max-pool layers and 2 fully connected layers) without drop-out and batch normalization vs. three variants, 2) with drop-out, 3) with batch normalization (BN), and 4) with both drop-out and BN. The CNNs with BN yield the best classification results (97% accuracy).
Improving Agile Software Practice

Software process improvement in small and agile organizations is often problematic, but achieving good SPI-assessments can still be necessary to stay in the marked or to meet demands of multinational owners. The traditional norm driven, centralized and control centered improvement approaches has been reported ineffective in small firms and a lot of research is done to find more suitable improvement approaches. Only very few of these alternative approaches breaks with the underlying values of a rational system development process that seems to fit so badly. But is it at all possible to stay agile and innovative, while striving for certification according to traditional rational SPI norms? This paper reports from a action research project in a small agile Danish firm (Techsoft) conducting a improvement initiative. The firm had just been met by the demand for a CMMI level-3 certification from their new multinational owners. In the project we experimented to reach a less centralized and control centered SPI approach trying to meet the agile culture of the firm both within diagnosing, improvement planning, process design and evaluation eventhough the goal was applying to the norm. After having chosen the improvement area; requirement management, a more formal culture assessment and comparisons with the culture of the CMM-norm helped guiding the design of the new processes and tools. The paper suggests a SPI -approach based on problem diagnosis instead of formal CMMI-assessment, culture analysis to support designing processes that balance the firm-culture with that of the CMM-model and achieving real change in practice by building commitment and engagement through participative SPI-planning and design.
Heart rate variability in normal and pathological sleep

Sleep is a physiological process involving different biological systems, from molecular to organ level; its integrity is essential for maintaining health and homeostasis in human beings. Although in the past sleep has been considered a state of quiet, experimental and clinical evidences suggest a noteworthy activation of different biological systems during sleep. A key role is played by the autonomic nervous system (ANS), whose modulation regulates cardiovascular functions during sleep onset and different sleep stages. Therefore, an interest on the evaluation of autonomic cardiovascular control in health and disease is growing by means of linear and non-linear heart rate variability (HRV) analyses. The application of classical tools for ANS analysis, such as HRV during physiological sleep, showed that the rapid eye movement (REM) stage is characterized by a likely sympathetic predominance associated with a vagal withdrawal, while the opposite trend is observed during non-REM sleep. More recently, the use of non-linear tools, such as entropy-derived indices, have provided new insight on the cardiac autonomic regulation, revealing for instance changes in the cardiovascular complexity during REM sleep, supporting the hypothesis of a reduced capability of the cardiovascular system to deal with stress challenges. Interestingly, different HRV tools have been applied to characterize autonomic cardiac control in different pathological conditions, from neurological sleep disorders to sleep disordered breathing (SDB). In summary, linear and non-linear analysis of HRV are reliable approaches to assess changes of autonomic cardiac modulation during sleep both in health and diseases. The use of these tools could provide important information of clinical and prognostic relevance.
Emotional agents: how personalities change behaviour

paper describes how personalities influence the behaviour of emotional agents. Two different emotional models are discussed, namely the OCC emotion model and the emotion-connectionist model. In both models personalities changes the behaviour on a different way. This paper also presents an implementation of a prototype to evaluate the performance of the different personalities in the emotion-connectionist model, whose testing results are discussed. Medium susceptible to all emotions showed to perform best.
Scalar Seismic-Wave Equation Modeling by a Multisymplectic Discrete Singular Convolution Differentiator Method

High-precision modeling of seismic-wave propagation in heterogeneous media is very important to seismological investigation. However, such modeling is one of the difficult problems in the seismological research fields. For developing methods of seismic inversion and high-resolution seismic-wave imaging, the modeling problem must be solved as perfectly as possible. Moreover, for long-term computations of seismic waves (e.g., Earth’s free-oscillations modeling and seismic noise-propagation modeling), the capability of seismic modeling methods for long-time simulations is in great demand. In this paper, an alternative method for accurately and efficiently modeling seismic wave fields is presented; it is based on a multisymplectic discrete singular convolution differentiator scheme (MDSCD). This approach uses optimization and truncation to form a localized operator, which preserves the fine structure of the wave field in complex media and avoids noncausal interaction when parameter discontinuities are present in the medium. The approach presented has a structure-preserving property, which is suitable for treating questions of high-precision or long-time numerical simulations. Our numerical results indicate that this method can suppress numerical dispersion and allow for research into long-time numerical simulations of wave fields. These numerical results also show that the MDSCD method can effectively capture the inner interface without any special treatment at the discontinuity.
Building a Chinese Dependency GraphBank

How to represent the structure of a sentence is a key issue in linguistic and NLP fields. Dependency Grammar (DG) has been widely used as it directly describes the relations between words in a sentence. However, it always follows the tree structure that does not fit the argument sharing phenomenon. On the other hand, the Semantic Role Labeling (SRL) annotation does not give a full structure for a sentence. In this paper, we combine DG and SRL together to build a unified dependency graph for Chinese sentences. Then we introduce the tagging sets for dependency graph as well as the toolkit designed for manual annotation. Annotation problems are discussed at the end.
Nemo-Like Kinase-Myocyte Enhancer Factor 2A Signaling Regulates Anterior Formation in Xenopus Development

ABSTRACT The development of anterior neural structure in Xenopus laevis requires the inhibition of bone morphogenic protein 4 and Wnt signaling. We previously reported that Nemo-like kinase (NLK) negatively regulates Wnt signaling via the phosphorylation of T-cell factor/lymphoid enhancer factor. However, the molecular events occurring downstream of NLK pathways in early neural development remain unclear. In the present study, we identified the transcription factor myocyte enhancer factor 2A (MEF2A) as a novel substrate for NLK. NLK regulates the function of Xenopus MEF2A (xMEF2A) via phosphorylation, and this modification can be inhibited by the depletion of endogenous NLK. In Xenopus embryos, the depletion of either NLK or MEF2A results in a severe defect in anterior development. The endogenous expression of anterior markers was blocked by the depletion of endogenous Xenopus NLK (xNLK) or xMEF2A but, notably, not by the depletion of other xMEF2 family proteins, xMEF2C and xMEF2D. Defects in head formation or the expression of the anterior marker genes caused by the depletion of endogenous xMEF2A could be eliminated by the expression of wild-type xMEF2A, but not xMEF2A containing mutated xNLK phosphorylation sites. Furthermore, the expression of xNLK-induced anterior markers was efficiently blocked by the depletion of endogenous xMEF2A in animal pole explants. These results show that NLK specifically regulates the MEF2A activity required for anterior formation in Xenopus development.
Efficient Inactivation of Symbiotic Nitrogen Fixation Related Genes in Lotus japonicus Using CRISPR-Cas9

The targeted genome editing technique, CRISPR/Cas9 system, has been widely used to modify genes of interest in a predictable and precise manner. In this study, we describe the CRISPR/Cas9-mediated efficient editing of representative SNF (symbiotic nitrogen fixation) related genes in the model legume Lotus japonicus via Agrobacterium-mediated stable or hairy root transformation. We first predicted nine endogenous U6 genes in Lotus and then demonstrated the efficacy of the LjU6-1 gene promoter in driving expression of single guide RNAs (sgRNAs) by using a split yellow fluorescence protein (YFP) reporter system to restore the fluorescence in Arabidopsis protoplasts. Next, we chose a customized sgRNA targeting SYMRK (symbiosis receptor-like kinase) loci and achieved ~35% mutagenic efficiency in 20 T0 transgenic plants, two of them containing biallelic homozygous mutations with a 2-bp deletion near the PAM region. We further designed two sgRNAs targeting three homologous leghemoglobin loci (LjLb1, LjLb2, LjLb3) for testing the possibility of generating multi-gene knockouts. 20 out of 70 hairy root transgenic plants exhibited white nodules, with at least two LjLbs disrupted in each plant. Compared with the constitutively active CaMV 35S promoter, the nodule-specific LjLb2 promoter was also effective in gene editing in nodules by hairy root transformation. Triple mutant knockout of LjLbs was also obtained by stable transformation using two sgRNAs. Collectively, these studies demonstrate that the CRISPR/Cas9 system should greatly facilitate functional analyses of SNF related genes in Lotus japonicus.
Development of osteoclasts from embryonic stem cells through a pathway that is c-fms but not c-kit dependent.

Osteoclasts are hematopoietic cells essential for bone resorption. To study the derivation of these interesting cells, we developed a stepwise culture system where stromal cells promote embryonic stem (ES) cells to differentiate into mature osteoclasts. Three phases to this differentiation process include (1) induction of hematopoiesis, along with the generation of osteoclast precursors, (2) expansion of these precursors, and (3) terminal differentiation into mature osteoclasts in the presence of 1alpha,25-dihydroxyvitamine D3 . Although the transition of ES cells to the hematopoietic lineage was not blocked by an antibody to c-fms, later phases were dependent on a signaling through this transmembrane receptor as indicated by the finding that anti-c-fms treatment of cells in the second and third phases reduced the number of osteoclasts produced by 75% and more than 99%, respectively. Blockade of signaling through another tyrosine kinase-type receptor, c-kit, did not affect any stages of osteoclastogenesis, although generation of other hemopoietic lineages was reduced to less than 10% of untreated. When small numbers of ES cells were directly cultured under conditions that promote osteoclast differentiation, tartrate-resistant acid phosphatase-positive multinucleated cells were observed at the edge but not inside of colonies. This suggests that some types of cell-cell interactions may inhibit development of mature osteoclasts. The culture system developed here provides an important tool for osteoclast biology.
A Comparison of Computational Efforts between Particle Swarm Optimization and Genetic Algorithm for Identification of Fuzzy Models

Fuzzy systems are rule-based systems that provide a framework for representing and processing information in a way that resembles human communication and reasoning process. Fuzzy modeling or fuzzy model identification is an arduous task, demanding the identification of many parameters that can be viewed as an optimization process. Evolutionary algorithms are well suited to the problem of fuzzy modeling because they are able to search complex and high dimensional search space while being able to avoid local minima (or maxima). The particle swarm optimization (PSO) algorithm, like other evolutionary algorithms, is a stochastic technique based on the metaphor of social interaction. PSO is similar to the genetic algorithm (GA) as these two evolutionary heuristics are population-based search methods. The main objective of this paper is to present the tremendous savings in computational efforts that can be achieved through the use of PSO algorithm in comparison to GA, when used for the identification of fuzzy models from the available input-output data. For realistic comparison, the training data, models complexity and some other common parameters that influence the computational efforts considerably are not changed. The real data from the rapid nickel-cadmium (Ni-Cd) battery charger developed has been used for the purpose of illustration and simulation purposes.
Real-time heart rate variability detection on sensor node

In this investigation we have prototyped a wireless ECG (electrocardiogram) sensor for real-time heart rate variability (HRV) detection, based on a commercially available sensor node. An ECG amplifier circuit is designed on a PCB board that is similar size to the sensor node so that it is easily attached to this node to achieve a minimum system size. In this implementation, the ECG is detected from both a patient simulator and a healthy subject body. The ECG signal is band-limited to 0.05Hz and 100Hz and amplified 1000 times before ADC conversion. We utilize the TinyOS 2.0. x open source operating system for providing data acquisition, computing and wireless communication services. A real-time HRV detection algorithm is implemented on the ECG sensor. The purpose is to reduce throughput of wireless data, and hence prolong battery life. Due to high sampling rate requirement (e.g 200Hz) of ECG detection, the HRV algorithms is required to complete within 5msec sample inter-arrival period in order to ensure no samples are lost. The digitized cardiac information is transmitted at 868 MHz ISM license free frequency band. A PC is used for displaying and storing the cardiac information.
Exchange relations on the dark web

ABSTRACT This article examines exchange relations on Silk Road, an anonymous online black market formerly located in a concealed portion of the Internet, the dark web. The federal court case of Ross William Ulbricht, Silk Road’s architect and executive operator, constitutes the core of my source material, along with Ulbricht’s online statements. I argue that Silk Road represented an aggressively capitalist mode of exchange, marked by an absence of state economic regulation, a lack of status codes, an ineffective reputation system, and a resulting deluge of blackmail, scam, coercion, and monopoly. Contrary to its founder’s vision of a libertarian utopia, the digital free market in contraband was plagued with fraudulent economic practices, underwritten by a market logic that exploited the site’s unique infrastructure. The salient principle of economic relationality on Silk Road was not cooperation and freedom but deception and intimidation.
Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification

This paper presents a novel three-stage blood vessel segmentation algorithm using fundus photographs. In the first stage, the green plane of a fundus image is preprocessed to extract a binary image after high-pass filtering, and another binary image from the morphologically reconstructed enhanced image for the vessel regions. Next, the regions common to both the binary images are extracted as the major vessels. In the second stage, all remaining pixels in the two binary images are classified using a Gaussian mixture model (GMM) classifier using a set of eight features that are extracted based on pixel neighborhood and first and second-order gradient images. In the third postprocessing stage, the major portions of the blood vessels are combined with the classified vessel pixels. The proposed algorithm is less dependent on training data, requires less segmentation time and achieves consistent vessel segmentation accuracy on normal images as well as images with pathology when compared to existing supervised segmentation methods. The proposed algorithm achieves a vessel segmentation accuracy of 95.2%, 95.15%, and 95.3% in an average of 3.1, 6.7, and 11.7 s on three public datasets DRIVE, STARE, and CHASE_DB1, respectively.
Sliced Ridgelet Transform for Image Denoising

Image denoising based on curvelet transforms gives better results than image denoising based on the standard ridgelet transforms. This paper introduces an approach for image denoising that is based on ridgelets computed in a localized manner and that is computationally less intensive than curvelets, but with similar denoising performance. In this localized ridgelet transform, denoted sliced ridgelet transform, the ridge function is divided into several slices of constant length. The projection of each slice at a certain angle is computed and followed by a one-dimensional wavelet transform, to produce the ridgelet coefficients for each slice. The denoising operation corresponds to a simple thresholding of these ridgelet coefficients. Finally, inverse wavelet transform and inverse Radon transform are applied to the denoised ridgelet coefficients, to reconstruct a denoised version of the input image