Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance metrics and empirical results of a PUF cryptographic key generation ASIC

We describe a PUF design with integrated error correction that is robust to various layout implementations and achieves excellent and consistent results in each of the following four areas: Randomness, Uniqueness, Bias and Stability. 133 PUF devices in 0.13 μm technology encompassing seven circuit layout implementations were tested. The PUF-based key generation design achieved less than 0.58 ppm failure rates with 50%+ stability safety margin. 1.75M error correction blocks ran error-free under worst-case V/T corners (±10% V, 125°C/-65°C) and under voltage extremes of ±20% V. All PUF devices demonstrated excellent NIST-random behavior (99 cumulative percentile), a criterion used to qualify random sources for use as keying material for cryptographic-grade applications.
The Author-Topic Model and the author prediction

The author-topic model is a generative model for documents that extends Latent Dirichilet Allocation to include authorship information, which is proposed by Michal Rosen-Zvi et al. The model connects each author to a multinomial distribution over topics and associated each topic with a words’ multinomial distribution. A document with multiple authors is modeled as a distribution over topics that are a mixture of the distributions associated with the authors. In this project, I re-implement the model to a collection of about 250 NIPS conference papers (be chosen randomly from a collection of about 1700 NIPS papers). Exact inference is intractable for these datasets and I use Gibbs sampling to estimate the topic and author distributions. The tagging results with different topic numbers are given. After getting the distribution values, I present a new method that apply maximum likelihood estimate to do author prediction on about other 100 papers of which the authors are in the same set as the training papers. The precision of prediction is given. Key word: author-topic model; Gibbs sampling; multinomial distribution; tagging; author prediction;
Intrinsic variability in Pv, RRP size, Ca2+ channel repertoire, and presynaptic potentiation in individual synaptic boutons

The strength of individual synaptic contacts is considered a key modulator of information flow across circuits. Presynaptically the strength can be parsed into two key parameters: the size of the readily releasable pool (RRP) and the probability that a vesicle in that pool will undergo exocytosis when an action potential fires (Pv). How these variables are controlled and the degree to which they vary across individual nerve terminals is crucial to understand synaptic plasticity within neural circuits. Here we report robust measurements of these parameters in rat hippocampal neurons and their variability across populations of individual synapses. We explore the diversity of presynaptic Ca2+ channel repertoires and evaluate their effect on synaptic strength at single boutons. Finally, we study the degree to which synapses can be differentially modified by a known potentiator of presynaptic function, forskolin. Our experiments revealed that both Pv and RRP spanned a large range, even for synapses made by the same axon, demonstrating that presynaptic efficacy is governed locally at the single synapse level. Synapses varied greatly in their dependence on N or P/Q type Ca2+ channels for neurotransmission, but there was no association between specific channel repertoires and synaptic efficacy. Increasing cAMP concentration using forskolin enhanced synaptic transmission in a Ca2+-independent manner that was inversely related with a synapse's initial Pv, and independent of its RRP size. We propose a simple model based on the relationship between Pv and calcium entry that can account for the variable potentiation of synapses based on initial probability of vesicle fusion.
Recruitment of Glycosyl Hydrolase Proteins in a Cone Snail Venomous Arsenal: Further Insights into Biomolecular Features of Conus Venoms

Cone snail venoms are considered an untapped reservoir of extremely diverse peptides, named conopeptides, displaying a wide array of pharmacological activities. We report here for the first time, the presence of high molecular weight compounds that participate in the envenomation cocktail used by these marine snails. Using a combination of proteomic and transcriptomic approaches, we identified glycosyl hydrolase proteins, of the hyaluronidase type (Hyal), from the dissected and injectable venoms (“injectable venom” stands for the venom variety obtained by milking of the snails. This is in contrast to the “dissected venom”, which was obtained from dissected snails by extraction of the venom glands) of a fish-hunting cone snail, Conus consors (Pionoconus clade). The major Hyal isoform, Conohyal-Cn1, is expressed as a mixture of numerous glycosylated proteins in the 50 kDa molecular mass range, as observed in 2D gel and mass spectrometry analyses. Further proteomic analysis and venom duct mRNA sequencing allowed full sequence determination. Additionally, unambiguous segment location of at least three glycosylation sites could be determined, with glycans corresponding to multiple hexose (Hex) and N-acetylhexosamine (HexNAc) moieties. With respect to other known Hyals, Conohyal-Cn1 clearly belongs to the hydrolase-type of Hyals, with strictly conserved consensus catalytic donor and positioning residues. Potent biological activity of the native Conohyals could be confirmed in degrading hyaluronic acid. A similar Hyal sequence was also found in the venom duct transcriptome of C. adamsonii (Textilia clade), implying a possible widespread recruitment of this enzyme family in fish-hunting cone snail venoms. These results provide the first detailed Hyal sequence characterized from a cone snail venom, and to a larger extent in the Mollusca phylum, thus extending our knowledge on this protein family and its evolutionary selection in marine snail venoms.
Optimal Design of Multiple Q-shells experiments for Diffusion MRI

Recent advances in diffusion MRI make use of the diffusion signal sampled on the whole Q-space, rather than on a single sphere. While much effort has been done to design uniform sampling schemes for single shell experiment, it is yet not clear how to build a strategy to sample the diffusion signal in the whole Fourier domain. In this article, we propose a method to generate acquisition schemes for multiple Q-shells experiment in diffusion MRI. The acquisition protocols we design are incremental, which means they remain approximately optimal when truncated before the acquisition is complete. Our method is fast, incremental, and we can generate diffusion gradients schemes for any number of acquisitions, any number of shells, and any number of points per shell. The samples arranged on different shells do not share the same directions. The method is tested for Spherical Polar Fourier reconstruction of the diffusion signal, and based on Monte-Carlo simulations, several preferred acquisition parameters are identified.
PADRE : A Policy Architecture for building Data REplication systems

This paper presents Padre, a new policy architecture for developing data replication systems. Padre simplifies design and implementation by embodying the right abstractions for replication in widely distributed systems. In particular, Padre cleanly separates the problem of building a replication system into the subproblems of specifying liveness policy and specifying safety policy, and it identifies a small set of primitives that are sufficient to specify sophisticated systems. As a result, building a replication system is reduced to writing a few liveness rules in a domain-specific language to trigger communication among nodes and specifying safety predicates that define when the system must block requests. We demonstrate the flexibility and simplicity of Padre by constructing a dozen substantial and diverse systems, each using just a few dozen system-specific policy rules. We demonstrate the agility that Padre enables by adding new features to several systems, yielding significant performance improvements; each addition required fewer than ten additional rules and took less than a day.
An FPGA implementation of speech recognition with weighted finite state transducers

In this paper we present a hardware architecture for large vocabulary continuous speech recognition that conducts a search over a weighted finite state transducer (WFST) network. A pipelined architecture is proposed to fully utilize the memory bandwidth. A hash table is used to manage small sized working sets efficiently. We also applied a parallelization technique that increases the traversal speed by 17%. The recognition system is fully functional on an FPGA, which runs at 100MHz. The experimental result on the Wall Street Journal 5,000 vocabulary task shows that the recognition speed of the system is 5.3× faster than real-time.
Coping with Diverse Semantic Models when Routing Ubiquitous Computing Information

Routing of contextual information within ubiquitous computing environments is a key challenge that must be tackled for such environments to be successful. It is accepted that such routing systems need to cope with mobile and volatile sources and destinations of contextual information, and semantic-based publish subscribe systems have been proposed as a means to support this. However such systems typically assume a common semantic model underpinning the routing which limits their ability to cope with heterogeneity. In contrast, the authors have developed a semantic-based publish subscribe system that is unique in allowing several semantic models to support routing. It is known from previous work that the number of semantic models in memory directly impacts on performance of routing, and that different loading strategies are needed, the selection of which is influenced by: the characteristics of the semantic model itself, the applications using the system and the network environment. This paper however just focuses on the experiments that have been undertaken to determine the key semantic model characteristics that may influence the selection of which loading strategy to use.
Multi-resolution sub-band features and models for HMM-based phonetic modelling

HMM acoustic models are typically trained on a single set of cepstral features extracted over the full bandwidth of mel-spaced filterbank energies. In this paper, multi-resolution sub-band transformations of the log energy spectra are introduced based on the conjecture that additional cues for phonetic discrimination may exist in the local spectral correlates not captured by the full-band analysis. In this approach the discriminative contribution from sub-band features is considered to supplement rather than substitute for full-band features. HMMs trained on concatenated multi-resolution cepstral features are investigated, along with models based on linearly combined independent multi-resolution streams, in which the sub-band and full-band streams represent different resolutions of the same signal. For the stream-based models, discriminative training of the linear combination weights to a minimum classification error criteria is also applied. Both the concatenated feature and the independent stream modelling configurations are demonstrated to outperform traditional full-band cepstra for HMM-based acoustic phonetic modelling on the TIMIT database. Experiments on context-independent modelling achieve a best increase on the core test set from an accuracy of 62.3% for full-band models to a 67.5% accuracy for discriminately weighted multi-resolution sub-band modelling. A triphone accuracy of 73.9% achieved on the core test set improves notably on full-band cepstra and compares well with results previously published on this task.
Performance Analysis of OpenAirInterface System Emulation

With the rapid growth of mobile networks, the radio access network becomes more and more costly to deploy, operate, maintain and upgrade. The most effective answer to this problem lies in the centralization and virtualization of the eNodeBs. This solution is known as Cloud RAN and is one of the key topics in the development of fifth generation networks. Within this context Open Air Interface is a promising emulation tool that can be used for prototyping innovative scheduling algorithms, making the most of the new architecture. In this work we first describe the emulation environment of OpenAirInterface and its scheduling framework and we use it to implement two MAC schedulers. Moreover we validate the above schedulers and we perform a thorough profiling of Open Air Interface, in terms of both memory occupancy and execution time. Our results show that OpenAirInterface can be effectively used for prototyping scheduling algorithms in emulated LTE networks.
All for one and one for all: Theoretical models, sociological theory, and mathematical sociology

ABSTRACT I make the argument that mathematical sociology uniquely contributes to sociological theory through the theoretical models it develops to bridge the gap between the ideas of sociological theorists and data relevant to their empirical evaluation. My work on intergroup association and social integration, the fruit of a long-time collaboration with Fararo and more recently with Karpiński, is used throughout to illustrate my points.
Differentially Expressed Genes during Contrasting Growth Stages of Artemisia annua for Artemisinin Content

Artemisia annua is the source of antimalarial phytomolecule, artemisinin. It is mainly produced and stored in the glandular secretory trichomes present in the leaves of the plant. Since, the artemisinin biosynthesis steps are yet to be worked out, in this investigation a microarray chip was strategized for the first time to shortlist the differentially expressing genes at a stage of plant producing highest artemisinin compared to the stage with no artemisinin. As the target of this study was to analyze differential gene expression associated with contrasting artemisinin content in planta and a genotype having zero/negligible artemisinin content was unavailable, it was decided to compare different stages of the same genotype with contrasting artemisinin content (seedling - negligible artemisinin, mature leaf - high artemisinin). The SCAR-marked artemisinin-rich (∼1.2%) Indian variety ‘CIM-Arogya’ was used in the present study to determine optimal plant stage and leaf ontogenic level for artemisinin content. A representative EST dataset from leaf trichome at the stage of maximal artemisinin biosynthesis was established. The high utility small scale custom microarray chip of A. annua containing all the significant artemisinin biosynthesis-related genes, the established EST dataset, gene sequences isolated in-house and strategically selected candidates from the A. annua Unigene database (NCBI) was employed to compare the gene expression profiles of two stages. The expression data was validated through semiquantitative and quantitative RT-PCR followed by putative annotations through bioinformatics-based approaches. Many candidates having probable role in artemisinin metabolism were identified and described with scope for further functional characterization.
A varactor model including avalanche noise source for VCOs phase noise simulation

This work studies an important area of oscillator operation — VCO biased at varactor breakdown. In this operation, the oscillator's phase noise is primarily determined by avalanche noise generated in the varactor, i.e., the phase noise can be modulated by the incremental shot noise generated at varactor breakdown. A simple experimental diode model was optimized for modeling varactor-breakdown current and an empirical shot noise model based on Hine's theory presents good agreement with noise power measurements of varactors. The developed varactor noise-model has successfully been used to simulate the phase noise of a single-ended Colpitts InGaP HBT VCO over and beyond the varactor's breakdown voltage. The VCO presents a phase noise below −100dBc/Hz for varactor voltages lower than breakdown voltage. Beyond the breakdown the phase noise gradually increases to −60 dBc/Hz@100kHz with a centre frequency deviating less than 0.6% from a 7.38GHz carrier.
Complex event processing over uncertain data

In recent years, there has been a growing need for active systems that can react automatically to events. Some events are generated externally and deliver data across distributed systems, while others are materialized by the active system itself. Event materialization is hampered by uncertainty that may be attributed to unreliable data sources and networks, or the inability to determine with certainty whether an event has actually occurred. Two main obstacles exist when designing a solution to the problem of event materialization with uncertainty. First, event materialization should be performed efficiently, at times under a heavy load of incoming events from various sources. The second challenge involves the generation of a correct probability space, given uncertain events. We present a solution to both problems by introducing an efficient mechanism for event materialization under uncertainty. A model for representing materialized events is presented and two algorithms for correctly specifying the probability space of an event history are given. The first provides an accurate, albeit expensive method based on the construction of a Bayesian network. The second is a Monte Carlo sampling algorithm that heuristically assesses materialized event probabilities. We experimented with both the Bayesian network and the sampling algorithms, showing the latter to be scalable under an increasing rate of explicit event delivery and an increasing number of uncertain rules (while the former is not). Finally, our sampling algorithm accurately and efficiently estimates the probability space.
CloudSEC: A Cloud Architecture for Composing Collaborative Security Services

Massive Internet invasions implemented through the distributed platform fabricated by rapid diffusion of malwares, has become a significant issue in network security. We argue that the notion of “Collaborative Security” is an emerging trend in resisting distributed attacks originated from malware. Therefore, this paper proposes a new architecture: CloudSEC, for composing collaborative security-related services in clouds, such as correlated intrusion analysis, anti-spam, anti-DDOS, automated malware detection and containment. CloudSEC is modeled as a dynamic peer-to-peer overlay hierarchy with three types of top-down architectural components. Based on, this architecture, both data distribution and task scheduling overlays can be simultaneously implemented in a loosely coupled fashion, which can efficiently retrieve data resources from heterogeneous network security facilities, and harness distributed collection of computational resources to process data-intensive tasks. Hence, CloudSEC endues the network security infrastructure with the capability of dynamic adaptation and collaboration on an inter-organizational scale. The results of preliminary evaluation demonstrate that, CloudSEC not only delivers a sample service of distributed intrusion correlation with high scalability and robustness, but also achieves remarkable effectiveness in data sharing and task scheduling.
IS-IS Cryptographic Authentication

This document describes the authentication of Intermediate System to Intermediate System (IS-IS) Protocol Data Units (PDUs) using the Hashed Message Authentication Codes - Message Digest 5 (HMAC-MD5) algorithm as found in RFC 2104. IS-IS is specified in International Standards Organization (ISO) 10589, with extensions to support Internet Protocol version 4 (IPv4) described in RFC 1195. The base specification includes an authentication mechanism that allows for multiple authentication algorithms. The base specification only specifies the algorithm for cleartext passwords. This document replaces RFC 3567. This document proposes an extension to that specification that allows the use of the HMAC-MD5 authentication algorithm to be used in conjunction with the existing authentication mechanisms.
Vocalist Gender Recognition in Recorded Popular Music

ABSTRACTWe introduce the task of vocalist gender recognition inpopular music and evaluate the beneﬁt of Non-NegativeMatrix Factorization based enhancement of melodic com-ponents to this aim. The underlying automatic separationof drum beats is described in detail, and the obtained sig-niﬁcant gain by its use is veriﬁed in extensive test-runs ona novel database of 1.5 days of MP3 coded popular songsbased on transcriptions of the Karaoke-game UltraStar. Asclassiﬁers serve Support Vector Machines and Hidden NaiveBayes. Overall, the suggested methods lead to fully auto-matic recognition of the pre-dominant vocalist gender at87.31% accuracy on song level for artists unkown to thesystem in originally recorded music.1. INTRODUCTIONDetermination of the gender of the (main) vocalist(s) is anastonishingly untouched task in the ﬁeld of Music Informa-tion Retrieval (MIR): while there is a substantial body ofliterature dealing with gender in spoken language, e.g. toimprove automatic speech recognition systems by switch-ing or adapting acoustic models (e.g. [1]) or accordinglyto improve emotion recognition systems (e.g. [22]), onlysome works consider singer identiﬁcation (on artiﬁcial sig-nals) [3,10]. However, explicit recognition of the gender ofthe main performing vocal artist in original audio record-ings of e.g. contemporary popular music has apparently notbeen addressed in MIR research, yet, which is to overcome,as like genre, mood or style, it can be an important featurefor organizing and querying music collections, for exampleto ﬁnd a song whose artist’s name is unknown to the user, orfor recommendation systems in on-line stores. In addition,it might be considered interesting as mid-level attribute forother MIR tasks as audio mood classiﬁcation [14] or tran-scription of the sung lyrics with gender-adapted models –shown to be beneﬁcial in [9]. Apart from ﬁnding suitable features and an appropriateclassiﬁcation method, as is the pre-concern for gender iden-
RlmCD-mediated U747 methylation promotes efficient G748 methylation by methyltransferase RlmAII in 23S rRNA in Streptococcus pneumoniae; interplay between two rRNA methylations responsible for telithromycin susceptibility

Adenine at position 752 in a loop of helix 35 from positions 745 to 752 in domain II of 23S rRNA is involved in binding to the ribosome of telithromycin (TEL), a member of ketolides. Methylation of guanine at position 748 by the intrinsic methyltransferase RlmAII enhances binding of telithromycin (TEL) to A752 in Streptococcus pneumoniae. We have found that another intrinsic methylation of the adjacent uridine at position 747 enhances G748 methylation by RlmAII, rendering TEL susceptibility. U747 and another nucleotide, U1939, were methylated by the dual-specific methyltransferase RlmCD encoded by SP_1029 in S. pneumoniae. Inactivation of RlmCD reduced N1-methylated level of G748 by RlmAII in vivo, leading to TEL resistance when the nucleotide A2058, located in domain V of 23S rRNA, was dimethylated by the dimethyltransferase Erm(B). In vitro methylation of rRNA showed that RlmAII activity was significantly enhanced by RlmCD-mediated pre-methylation of 23S rRNA. These results suggest that RlmCD-mediated U747 methylation promotes efficient G748 methylation by RlmAII, thereby facilitating TEL binding to the ribosome.