Avoiding Conflicts Dynamically in Direct Mapped Caches with Minimal Hardware Support

The memory system is often the weakest link in the performance of today\u27s computers. Cache design has received increasing attention in recent years as increases in CPU performance continues to outpace decreases in memory latency. Bershad et al. proposed a hardware modification called the Cache Miss Lookaside buffer which attempts to dynamically identify data which is conflicting in the cache and remap pages to avoid future conflicts. In a follow-up paper, Bershad et al. tried to modify this idea to work with standard hardware but had less success than with their dedicated hardware. In this thesis, we focus on a modification of these ideas, using less complicated hardware and focusing more on sampling policies. The hardware support is reduced to a buffer of recent cache misses and a cache miss counter. Because determination of remapping candidates is moved to software, sampling policies are studied to reduce overhead which will most likely fall on the OS. Our results show that sampling can be highly effective in identifying conflicts that should be remapped. Finally, we show that the theoretical performance of such a system can compare favorably with more costly higher associativity caches

Differentiating Leadership Styles and Behaviors of Teacher-Leaders

The rapidly changing landscape of education necessitates that schools build the capacity to swiftly pivot to new modalities, curriculum, and operations models. Much of the onus for positively responding to these changes falls to teacher-leaders. In many schools, teacher leaders are untrained and designated by virtue of their seniority. The authors posit that differences exist in the leadership styles and behaviors of professional educators depending on their role, status, and professional learning journey. Findings from the present study affirm the existence of some of these differences and, thus, could inform future efforts to designate and train teachers for leadership roles based off of these attributes

Precursor processes of human self-initiated action

A gradual buildup of electrical potential over motor areas precedes self-initiated movements. Recently, such "readiness potentials" (RPs) were attributed to stochastic fluctuations in neural activity. We developed a new experimental paradigm that operationalised self-initiated actions as endogenous 'skip' responses while waiting for target stimuli in a perceptual decision task. We compared these to a block of trials where participants could not choose when to skip, but were instead instructed to skip. Frequency and timing of motor action were therefore balanced across blocks, so that conditions differed only in how the timing of skip decisions was generated. We reasoned that across-trial variability of EEG could carry as much information about the source of skip decisions as the mean RP. EEG variability decreased more markedly prior to self-initiated compared to externally-triggered skip actions. This convergence suggests a consistent preparatory process prior to self-initiated action. A leaky stochastic accumulator model could reproduce this convergence given the additional assumption of a systematic decrease in input noise prior to self-initiated actions. Our results may provide a novel neurophysiological perspective on the topical debate regarding whether self-initiated actions arise from a deterministic neurocognitive process, or from neural stochasticity. We suggest that the key precursor of self-initiated action may manifest as a reduction in neural noise

Competing with stationary prediction strategies

In this paper we introduce the class of stationary prediction strategies and construct a prediction algorithm that asymptotically performs as well as the best continuous stationary strategy. We make mild compactness assumptions but no stochastic assumptions about the environment. In particular, no assumption of stationarity is made about the environment, and the stationarity of the considered strategies only means that they do not depend explicitly on time; we argue that it is natural to consider only stationary strategies even for highly non-stationary environments.Comment: 20 page

Modulation of morphology and glycan composition of mucins in farmed guinea fowl (Numida meleagris) intestine by the multi-strain probiotic slab51®

Probiotics have become highly recognized as supplements for poultry.Since gut health can be considered synonymous withanimal health, the effects of probiotic Slab51® on the morphology and the glycan composition of guineafowlintestine were examined. The probiotics were added in drinking water (2 × 1011 UFC/L) throughout the grow-out cycle.Birds were individually weighed andslaughtered after four months. Samples from the duodenum, ileum and caecum were collected and processed for morphological, morphometric, conventional and lectin glycohisto-chemical studies.The results were analyzed for statistical significance by Student’s t test. Compared with control samples, probiotic group revealed (1) significant increase in villus height (p < 0.001 in duodenum and ileum; p < 0.05 in caecum), crypt depth (p < 0.001 in duodenum and caecum;p < 0.05 in ileum) and goblet cells (GCs) per villus (p < 0.001) in all investigated tracts; (2) increase in galac-toseβl,3N-acetylgalacyosamine(Galβl,3GalNAc)terminating O-glycans and αl,2-fucosylated glycans secretory GCs in the duodenum; (3) increase in α2,6-sialoglycans and high-mannose N-linked glycans secretory GCs but reduction in GCs-secreting sulfoglycans in the ileum; (4) increase in Galβl,3GalNAc and high-mannose N-linked glycans secretory GCs and decrease in GCs-producing sulfomucins in the caecum; (5) increase in the numbers of crypt cells containing sulfate and non-sulfated acidic glycans. Overall, dietary Slab51® induces morphological and region-specific changes in glycoprotein composition of guinea fowl intestine, promoting gut health

Differential surface glycoprofile of buffalo bull spermatozoa during mating and non-mating periods

The buffalo has a seasonal reproduction activity with mating and non-mating periods occurring from late autumn to winter and from late spring to beginning of autumn, respectively. Sperm glycocalyx plays an important role in reproduction as it is the first interface between sperm and environment. Semen quality is poorer during non-mating periods, so we aimed to evaluate if there were also seasonal differences in the surface glycosylation pattern of mating period spermatozoa (MPS) compared with non-mating period spermatozoa (NMPS). The complexity of carbohydrate structures makes their analysis challenging, and recently the high-throughput microarray approach is now providing a new tool into the evaluation of cell glycosylation status. We adopted a novel procedure in which spermatozoa was spotted on microarray slides, incubated with a panel of 12 biotinylated lectins and Cy3-conjugated streptavidin, and then signal intensity was detected using a microarray scanner. Both MPS and NMPS microarrays reacted with all the lectins and revealed that the expression of (i) O-glycans with NeuNAcα2-3Galβ1,3(±NeuNAcα2-6)GalNAc, Galβ1,3GalNAc and GalNAcα1,3(l-Fucα1,2)Galβ1,3/4GlcNAcβ1 was not season dependent; (ii) O-linked glycans terminating with GalNAc, asialo N-linked glycans terminating with Galβ1,4GlcNAc, GlcNAc, as well as α1,6 and α1,2-linked fucosylated oligosaccharides was predominant in MPS; (iii) high mannose- and biantennary complex types N-glycans terminating with α2,6 sialic acids and terminal galactose were lower in MPS. Overall, this innovative cell microarray method was able to identify specific glycosylation changes that occur on buffalo bull sperm surface during the mating and non-mating periods

Accurate Profiling of Microbial Communities from Massively Parallel Sequencing using Convex Optimization

We describe the Microbial Community Reconstruction ({\bf MCR}) Problem, which is fundamental for microbiome analysis. In this problem, the goal is to reconstruct the identity and frequency of species comprising a microbial community, using short sequence reads from Massively Parallel Sequencing (MPS) data obtained for specified genomic regions. We formulate the problem mathematically as a convex optimization problem and provide sufficient conditions for identifiability, namely the ability to reconstruct species identity and frequency correctly when the data size (number of reads) grows to infinity. We discuss different metrics for assessing the quality of the reconstructed solution, including a novel phylogenetically-aware metric based on the Mahalanobis distance, and give upper-bounds on the reconstruction error for a finite number of reads under different metrics. We propose a scalable divide-and-conquer algorithm for the problem using convex optimization, which enables us to handle large problems (with $\sim10^6$ species). We show using numerical simulations that for realistic scenarios, where the microbial communities are sparse, our algorithm gives solutions with high accuracy, both in terms of obtaining accurate frequency, and in terms of species phylogenetic resolution.Comment: To appear in SPIRE 1

Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis

Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses. To address this, we propose a signed graph regularized deep neural network with adversarial augmentation, named \textsc{DiagNet}. Firstly, we use adversarial learning to generate positive and negative mass-contained mammograms for each mass class. After that, a signed similarity graph is built upon the expanded data to further highlight the discrimination. Finally, a deep convolutional neural network is trained by jointly optimizing the signed graph regularization and classification loss. Experiments show that the \textsc{DiagNet} framework outperforms the state-of-the-art in breast mass diagnosis in mammography.Comment: To appear in MICCAI October 201

NAST: a multiple sequence alignment server for comparative analysis of 16S rRNA genes

Microbiologists conducting surveys of bacterial and archaeal diversity often require comparative alignments of thousands of 16S rRNA genes collected from a sample. The computational resources and bioinformatics expertise required to construct such an alignment has inhibited high-throughput analysis. It was hypothesized that an online tool could be developed to efficiently align thousands of 16S rRNA genes via the NAST (Nearest Alignment Space Termination) algorithm for creating multiple sequence alignments (MSA). The tool was implemented with a web-interface at . Each user-submitted sequence is compared with Greengenes' ‘Core Set’, comprising ∼10 000 aligned non-chimeric sequences representative of the currently recognized diversity among bacteria and archaea. User sequences are oriented and paired with their closest match in the Core Set to serve as a template for inserting gap characters. Non-16S data (sequence from vector or surrounding genomic regions) are conveniently removed in the returned alignment. From the resulting MSA, distance matrices can be calculated for diversity estimates and organisms can be classified by taxonomy. The ability to align and categorize large sequence sets using a simple interface has enabled researchers with various experience levels to obtain bacterial and archaeal community profiles