Miracle-dz/UniFAR-FTUs huggingface dataset:
FILM THICKNESS DETERMINATION BY MATLAB ASSISTED ELLIPSOMETRY

Film thickness determination is important in lithography as the thickness of thin semiconductor or metal layer is critical in device fabrication. Ellipsometry, which is a nondestructive optical technique, recently has been used for characterizing the refractive indices and thickness of thin films and interfaces. It deals with the measurement of the changes in the polarization state of light upon reflection from a surface via two angles Ψ and ∆. In this work we developed a MATLAB subroutine to work seamlessly with the measured values of Ψ and ∆. to estimate film thickness numerically. A simulation model of single film on substrate was constructed and fitted with the measured data for the thickness estimation. The sum of squared error between the model and measured data, was use to evaluate the fitting. Results obtained were reliable as the thicknesses reported were calculated using values of Ψ and ∆ from multiple angles. On the whole, we have successfully determined thickness for various films with our developed MATLAB subroutine.
Spectrum Sensing in Cognitive Radios Based on Multiple Cyclic Frequencies

Cognitive radios sense the radio spectrum in order to find unused frequency bands and use them in an agile manner. Transmission by the primary user must be detected reliably even in the low signal-to-noise ratio (SNR) regime and in the face of shadowing and fading. Communication signals are typically cyclostationary, and have many periodic statistical properties related to the symbol rate, the coding and modulation schemes as well as the guard periods, for example. These properties can be exploited in designing a detector, and for distinguishing between the primary and secondary users' signals. In this paper, a generalized likelihood ratio test (GLRT) for detecting the presence of cyclostationarity using multiple cyclic frequencies is proposed. Distributed decision making is employed by combining the quantized local test statistics from many secondary users. User cooperation allows for mitigating the effects of shadowing and provides a larger footprint for the cognitive radio system. Simulation examples demonstrate the resulting performance gains in the low SNR regime and the benefits of cooperative detection.
Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain

The action of reading, understanding and combining words to create meaningful phrases comes naturally to most people. Still, the processes that govern semantic composition in the human brain are not well understood. In this thesis, we explore semantics (word meaning) and semantic composition (combining the meaning of multiple words) using two data sources: a large text corpus, and brain recordings of people reading adjective noun phrases. We show that these two very different data sources are both consistent, in that they contain overlapping information, and are complementary, in that they contain non-overlapping, but still congruent information. These disparate data sources can be used together to further the study of semantics and semantic composition as grounded in the brain, or more abstractly as represented in patterns of word usage in corpora. This thesis is supported by three experiments. Firstly, we extend a matrix factorization algorithm to learn an interpretable semantic space that respects the composition of words into noun phrases. We use the interpretability of the model to explore semantic composition as captured in the statistics of word usage in a large text corpus. Secondly, we build a joint model of semantics in corpora and the brain, which fuses brain imaging data with corpus data into one model of semantics. When compared to models that use only a single data source, we find that this joint model excels at a variety of tasks, from matching human judgements of semantics to predicting words from brain activity. Thirdly, we explore semantic composition in the brain through a new brain image dataset, collected with Magnetoencephalography while subjects read adjective-noun phrases. We learn several functions of the brain data that are capable of predicting semantic properties of the adjective, noun, and phrase. From the performance of these functions, we build a theory for the semantic composition of adjective noun phrases in the brain. This thesis asks a fundamentally different question than those asked in previous studies of adjective noun composition: where in the brain and when in time is phrasal meaning located? The answer to this question paints a unique picture of composition in the brain that is congruent with previous findings, but also sheds new light onto the neural processes governing semantic composition. Together, these contributions show that brain imaging data and corpus data can be used in concert to build better models of semantics. These more successful models provide a new understanding semantic composition, both in the brain and in a more abstract sense. Furthermore, this thesis demonstrates how machine learning techniques can be used to analyze and understand complicated data, like the neural activity captured in brain images.
Chinese Term Extraction Using Different Types of Relevance

This paper presents a new term extraction approach using relevance between term candidates calculated by a link analysis based method. Different types of relevance are used separately or jointly for term verification. The proposed approach requires no prior domain knowledge and no adaptation for new domains. Consequently, the method can be used in any domain corpus and it is especially useful for resource-limited domains. Evaluations conducted on two different domains for Chinese term extraction show significant improvements over existing techniques and also verify the efficiency and relative domain independent nature of the approach.
Malignant Lymphoma in Patients with Systemic Rheumatic Disease (Rheumatoid Arthritis, Systemic Lupus Erythematosus, Systemic Sclerosis, and Dermatomyositis): A Clinicopathologic Study of 24 Japanese Cases

We conducted clinicopathologic and immunohistochemical analyses of the prevalence of Epstein-Barr virus (EBV) among 24 patients with malignant lymphoma complicating systemic rheumatic diseases. (SRD) These 24 patients included 17 with rheumatoid arthritis (RA), 3 with systemic lupus erythematosus (SLE), 2 with systemic sclerosis (SS), and 2 with dermatomyositis (DM). There were 2 men and 22 women ranging in age from 30 to 86 years (mean: 64 years). The interval between the onset of rheumatic disease and that of malignant lymphomas ranged from 3 months to 35 years (mean: 142 months). The use of immunosuppressive drugs before the onset of malignant lymphoma was recorded in 15 patients. Among them, 5 patients received methotrexate (MTX) therapy. Malignant lymphomas were found at extranodal sites in 9 patients, and the disease was in the advanced stage in 17 patients. Histologic and immunohistochemical studies demonstrated that 18 cases (75%) were B-cell lymphoma (RA=12, SLE=2, SS=2, DM=2), 3 (12.5%) were peripheral T-cell lymphoma (RA=3), and 3 (12.5%) were classical Hodgkin lymphoma (RA=2, SLE=1). As in previous reports, there was an increased frequency of diffuse large B-cell lymphoma (50%) in the present series. Moreover, a majority of the diffuse large B-cell lymphomas exhibited activated B-cell phenotype. EBV-encoded small RNAs (Epstein-Barr early region [EBER]-) and/or LMP-1+tumor cells were identified in only 3 cases of classical Hodgkin lymphomas. Our findings suggested EBV-associated lymphoma comprised only a small fraction of all non-Hodgkin's lymphomas in the general SRD patient population.
Many Flows Asymptotics for SMART Scheduling Policies

Scheduling policies that favor small jobs have received growing attention due to their superior performance with respect to mean delay, e.g., Shortest Remaining Processing Time (SRPT) and Preemptive Shortest Job First (PSJF). In this paper, we study the delay distribution of a generalization of the class of scheduling policies called SMART (because policies in it have “SMAll Response Times”), which includes SRPT, PSJF, and a range of practical variants, in a discrete-time queueing system under the many sources large deviations regime. Our analysis of SMART in this regime (large number of flows and large capacity) hinges on a novel two-dimensional (2-D) queueing framework that employs virtual queues and total ordering of jobs. We prove that all SMART policies have the same asymptotic delay distribution as SRPT, i.e., the delay distribution has the same decay rate. In addition, we illustrate the improvements SMART policies make over First Come First Serve (FCFS) and Processor Sharing (PS). Our 2-D queueing technique is generalizable to other policies as well. As an example, we show how the Foreground-Background (FB) policy can be analyzed using a 2-D queueing framework. FB is a policy, not contained in SMART, which manages to bias towards small jobs without knowing which jobs are small in advance.
DSSR: Balancing semantics and speed requirements in packet trace replay

As new network services and middleboxes proliferate, it is important to have reliable means to test these services and devices, and a common practice to generate realistic testing traffic is through replaying previously recorded packet traces. However, existing trace replay tools are highly specialized for particular protocols and scenarios. In this paper, we present a general trace replay tool called DSSR. We show that in a directional trace replay, where outbound and inbound packets are simulated by two (logically) distinct sets of hosts, the timestamps of packets handled by the host(s) simulating external destinations must be adjusted to accurately re-create the effect of network latency. Interestingly, the timestamp adjustment can boost the replay speed. Moreover, we identify the range of timestamp adjustment that will guarantee to preserve the semantic orderings of packets pertaining to client-server protocol interactions. Therefore, our solution provides an effective tuning knob for a user to balance the speed and semantics requirements in a trace replay. Equally important, it requires no clock synchronization between the replaying hosts, as the hosts leverage the arrivals of incoming packets as a clocking mechanism for generating outgoing packets.
Secure Massive MIMO Transmission With an Active Eavesdropper

In this paper, we investigate secure and reliable transmission strategies for multi-cell multi-user massive multiple-input multiple-output systems with a multi-antenna active eavesdropper. We consider a time-division duplex system where uplink training is required and an active eavesdropper can attack the training phase to cause pilot contamination at the transmitter. This forces the precoder used in the subsequent downlink transmission phase to implicitly beamform toward the eavesdropper, thus increasing its received signal power. Assuming matched filter precoding and artificial noise (AN) generation at the transmitter, we derive an asymptotic achievable secrecy rate when the number of transmit antennas approaches infinity. For the case of a single-antenna active eavesdropper, we obtain a closed-form expression for the optimal power allocation policy for the transmit signal and the AN, and find the minimum transmit power required to ensure reliable secure communication. Furthermore, we show that the transmit antenna correlation diversity of the intended users and the eavesdropper can be exploited in order to improve the secrecy rate. In fact, under certain orthogonality conditions of the channel covariance matrices, the secrecy rate loss introduced by the eavesdropper can be completely mitigated.
TiO2—a prototypical memristive material

Redox-based memristive switching has been observed in many binary transition metal oxides and related compounds. Since, on the one hand, many recent reports utilize TiO2 for their studies of the memristive phenomenon and, on the other hand, there is a long history of the electronic structure and the crystallographic structure of TiO2 under the impact of reduction and oxidation processes, we selected this material as a prototypical material to provide deeper insight into the mechanisms behind memristive switching. In part I, we briefly outline the results of the historical and recent studies of electroforming and resistive switching of TiO2-based cells. We describe the (tiny) stoichiometrical range for TiO2 − x as a homogeneous compound, the aggregation of point defects (oxygen vacancies) into extended defects, and the formation of the various Magnéli phases. Furthermore, we discuss the driving forces for these solid-state reactions from the thermodynamical point of view. In part II, we provide new experimental details about the hierarchical transformation of TiO2 single crystals into Magnéli phases, and vice versa, under the influence of chemical, electrical and thermal gradients, on the basis of the macroscopic and nanoscopic measurements. Those include thermogravimetry, high-temperature x-ray diffraction (XRD), high-temperature conductivity measurements, as well as low-energy electron diffraction (LEED), x-ray photoelectron spectroscopy (XPS), and LC-AFM (atomic force microscope equipped with a conducting tip) studies. Conclusions are drawn concerning the relevant parameters that need to be controlled in order to tailor the memristive properties.
In-situ observations of catalytic surface reactions with soft x-rays under working conditions

Catalytic chemical reactions proceeding on solid surfaces are an important topic in fundamental science and industrial technologies such as energy conversion, pollution control and chemical synthesis. Complete understanding of the heterogeneous catalysis and improving its efficiency to an ultimate level are the eventual goals for many surface scientists. Soft x-ray is one of the prime probes to observe electronic and structural information of the target materials. Most studies in surface science using soft x-rays have been performed under ultra-high vacuum conditions due to the technical limitation, though the practical catalytic reactions proceed under ambient pressure conditions. However, recent developments of soft x-ray based techniques operating under ambient pressure conditions have opened a door to the in-situ observation of materials under realistic environments. The near-ambient-pressure x-ray photoelectron spectroscopy (NAP-XPS) using synchrotron radiation enables us to observe the chemical states of surfaces of condensed matters under the presence of gas(es) at elevated pressures, which has been hardly conducted with the conventional XPS technique. Furthermore, not only the NAP-XPS but also ambient-pressure compatible soft x-ray core-level spectroscopies, such as near-edge absorption fine structure (NEXAFS) and x-ray emission spectroscopy (XES), have been significantly contributing to the in-situ observations. In this review, first we introduce recent developments of in-situ observations using soft x-ray techniques and current status. Then we present recent new findings on catalytically active surfaces using soft x-ray techniques, particularly focusing on the NAP-XPS technique. Finally we give a perspective on the future direction of this emerging technique.
Behavior of compiled bases in compilation-based inference methods for min-based possibilistic networks

Min-based possibilistic networks are important graphical models for representing and analyzing uncertain information using the possibility theory framework. Diverse inference methods were developed for efficient computations in these models, we cite in particular, compilation-based inference, which consists in encoding the network into a CNF base and compiling this latter to efficiently compute the impact of an evidence on variables. This paper emphasizes on an experimental study between several compilation-based inference approaches in terms of CNF parameters, compiled bases parameters and inference time. The behavior of compiled bases is studied in depth for both of local structure and possibilistic local structure strategies.
Learning from missing data using selection bias in movie recommendation

Recommending items to users is a challenging task due to the large amount of missing information. In many cases, the data solely consist of ratings or tags voluntarily contributed by each user on a very limited subset of the available items, so that most of the data of potential interest is actually missing. Current approaches to recommendation usually assume that the unobserved data is missing at random. In this contribution, we provide statistical evidence that existing movie recommendation datasets reveal a significant positive association between the rating of items and the propensity to select these items. We propose a computationally efficient variational approach that makes it possible to exploit this selection bias so as to improve the estimation of ratings from small populations of users. Results obtained with this approach applied to neighborhood-based collaborative filtering illustrate its potential for improving the reliability of the recommendation.
A 40-GHz Fast-Locked All-Digital Phase-Locked Loop Using a Modified Bang-Bang Algorithm

A 40-GHz fast-locked all-digital phase-locked loop (ADPLL) using a modified bang-bang algorithm is presented. An inductor is used to extend the frequency tuning range of a 40-GHz digitally controlled oscillator. This ADPLL is fabricated by a 90-nm complementary metal-oxide-semiconductor process. The measured peak-to-peak jitter and the root-mean-square jitter are 2.622 ps and 303.632 fs, respectively, at 40 GHz. The measured locked times are 1.3 ms and 15 μs without and with the modified bang-bang algorithm, respectively.
Role of Hemolysin BL in the Pathogenesis of Extraintestinal Bacillus cereus Infection Assessed in an Endophthalmitis Model

ABSTRACT Bacillus cereus is a rare cause of serious human infection but, paradoxically, causes one of the most severe posttraumatic or endogenous infections of the eye, endophthalmitis, which frequently results in blindness. The virulence of B. cereus endophthalmitis historically has been attributed to toxin production. We therefore sought to examine the contribution of the dermonecrotic toxin, hemolysin BL, to the pathogenesis of B. cereus infection in an endophthalmitis system that is highly amenable to study. The pathogenesis of infection resulting from intravitreal injection of 102 CFU of either a clinical ocular isolate of B. cereus producing hemolysin BL (HBL+) or an isogenic mutant in this trait (HBL−) was assessed bacteriologically and by slit lamp biomicroscopy, electroretinography, histology, and inflammatory cell enumeration. Both HBL+ and HBL− strains evoked severe intraocular inflammatory responses as early as 12 h postinfection, with complete loss of retinal responsiveness by 12 h. The infections caused by both strains spread of the infection to adjacent tissues by 18 h. No significant differences in intraocular bacterial growth (P ≥ 0.21) or inflammatory changes (P ≥ 0.21) were observed in eyes infected with either HBL+ or HBL− strains during the course of infection. The level of retinal responsiveness was greater in HBL− infected eyes than in HBL+-infected eyes at 6 h only (P = 0.01). These results indicate that hemolysin BL makes no essential contribution to the severe and rapid course of infection in the endophthalmitis model.
Norepinephrine is required to promote wakefulness and for hypocretin-induced arousal in zebrafish

Pharmacological studies in mammals suggest that norepinephrine (NE) plays an important role in promoting arousal. However, the role of endogenous NE is unclear, with contradicting reports concerning the sleep phenotypes of mice lacking NE due to mutation of dopamine β-hydroxylase (dbh). To investigate NE function in an alternative vertebrate model, we generated dbh mutant zebrafish. In contrast to mice, these animals exhibit dramatically increased sleep. Surprisingly, despite an increase in sleep, dbh mutant zebrafish have a reduced arousal threshold. These phenotypes are also observed in zebrafish treated with small molecules that inhibit NE signaling, suggesting that they are caused by the lack of NE. Using genetic overexpression of hypocretin (Hcrt) and optogenetic activation of hcrt-expressing neurons, we also find that NE is important for Hcrt-induced arousal. These results establish a role for endogenous NE in promoting arousal and indicate that NE is a critical downstream effector of Hcrt neurons. DOI: http://dx.doi. org/10.7554/eLife.07000. 001
Millionaires and the millennium: New estimates of the forthcoming wealth transfer and the prospects for a golden age of philanthropy

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Exploiting Multi-User Diversity for Uplink Throughput in IEEE 802.11 WLANs

In multi-rate 802.11 networks, stations with better uplink conditions should access the shared channel more frequently than the ones with worse uplink conditions to enhance the aggregated throughput, which is referred to as multi-user diversity. In this letter, we propose Adaptive Basic Rate Set (ABRS) that uses the time gap between DIFS and EIFS for a prioritized channel access while minimizing starvation. ABRS requires the change in the firmware only at access points with no modification to the standard. Simulation results reveal that ABRS effectively prioritizes stations with higher bit rates; the aggregate uplink throughput is improved up to about 30-70% even in mobility scenarios.
A Quantitative Promoter Methylation Profile of Prostate Cancer

Purpose: Promoter hypermethylation is an alternative pathway for gene silencing in neoplastic cells and a promising cancer detection marker. Although quantitative methylation-specific PCR (QMSP) of the GSTP1 promoter has demonstrated near perfect specificity for cancer detection in prostate biopsies, we postulated that identification and characterization of additional methylation markers might further improve its high (80–90%) sensitivity. Experimental Design: We surveyed nine gene promoters (GSTP1, MGMT, p14/ARF, p16/CDKN2A, RASSF1A, APC, TIMP3, S100A2, and CRBP1) by QMSP in tissue DNA from 118 prostate carcinomas, 38 paired high-grade prostatic intraepithelial neoplasias (HGPIN), and 30 benign prostatic hyperplasias (BPH). The methylation levels were calculated and were correlated with clinical and pathologic indicators. Results: Only the methylation frequencies of GSTP1 and APC were significantly higher in prostate carcinoma compared with BPH (P < 0.001). Methylation levels of GSTP1, APC, RASSF1A, and CRBP1, differed significantly between prostate carcinoma and HGPIN, and/or HGPIN or BPH (P < 0.0001). With QMSP and empirically defined cutoff values, the combined use of GSTP1 and APC demonstrated a theoretical sensitivity of 98.3% for prostate carcinoma, with 100% specificity. Methylation levels were found to correlate with tumor grade (GSTP1 and APC) and stage (GSTP1, RASSF1A, and APC). Conclusions: Our data demonstrate the existence of a progressive increase of promoter methylation levels of several cancer-related genes in prostate carcinogenesis, providing additional markers to augment molecular detection of prostate carcinoma. Because methylation levels of GSTP1, APC, and RASSF1A are associated with advanced grade and stage, QMSP might augment the pathologic indicators currently used to predict tumor aggressiveness.