Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network

Abstract. Segmentation of the levator hiatus in ultrasound allows the extraction of biometrics, which are of importance for pelvic floor disorder assessment. We present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a two-dimensional image extracted from a three-dimensional ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalizing activation function, which for the first time has been applied in medical imaging with CNN. SELU has important advantages such as being parameter-free and mini-batch independent, which may help to overcome memory constraints during training. A dataset with 91 images from 35 patients during Valsalva, contraction, and rest, all labeled by three operators, is used for training and evaluation in a leave-one-patient-out cross validation. Results show a median Dice similarity coefficient of 0.90 with an interquartile range of 0.08, with equivalent performance to the three operators (with a Williams’ index of 1.03), and outperforming a U-Net architecture without the need for batch normalization. We conclude that the proposed fully automatic method achieved equivalent accuracy in segmenting the pelvic floor levator hiatus compared to a previous semiautomatic approach.
Indexed Types in Object-Oriented Programming

Dependent type systems allow semantic properties to be expressed in types that carry important information about program values. The type systems in mainstream languages such as Java are effective but have a limited expressive power. We propose to extend a simple Java-like language with indexed types, a form of dependent types deﬁned on index expressions that can statically detect many programming errors. Index types take the form of type annotations in the gener-ics style, so as to express semantic properties in a fashion familiar to object-oriented programmers. For example, Polynomial ⟨ 3 ⟩ is an instance of Polynomial ⟨ nat degree ⟩ , where degree has type nat , which is the type of all polynomials of some degree. Expressions in index types currently represent integer values only. Index types may be associated with class deﬁnitions, and may be used to constrain base types in ﬁelds or local variables, in arguments to methods or in return types. As opposed to conventional types, indexed types may change over a program lifetime. We discuss introducing indirection into type environments to provide support to type check references to mutable objects.
Tissue-type plasminogen activator is a neuroprotectant in the central nervous system

Tissue-type plasminogen activator (tPA) is a serine proteinase found not only in the intravascular space but also in a well-defined sub-set of neurons in the brain. tPA is rapidly released from neurons after either exposure to hypoxia or hypoglycemia in vitro, or the induction of cerebral ischemia in vivo. It has been proposed that tPA has a neurotoxic effect in the ischemic brain. However, recent evidence indicate that once released into the synaptic cleft tPA activates specific cell signaling pathways that promote the detection and adaptation to metabolic stress. More specifically, the non-proteolytic interaction of tPA with N-methyl-D-aspartate receptors (NMDARs) and a member of the low-density lipoprotein receptor (LDLR) family in dendritic spines activates the mammalian target of rapamycin (mTOR) pathway that adapts cellular processes to the availability of energy and metabolic resources. TPA-induced mTOR activation in neurons leads to hypoxia-inducible factor 1α (HIF-1α) accumulation, HIF-1α-induced expression and membrane recruitment of the neuronal transporter of glucose GLUT3, and GLUT3-mediated uptake of glucose. These and other data discussed in this Review suggest that the postulated neurotoxic effect of tPA needs to be reconsidered and instead indicate the emergence of a new paradigm: that tPA is an endogenous neuroprotectant in the central nervous system (CNS).
Insulin-like Growth Factor I-mediated Protection from Rapamycin-induced Apoptosis Is Independent of Ras-Erk 1-Erk 2 and Phosphatidylinositol 3-Kinase-Akt Signaling Pathways 1

The mTOR inhibitor rapamycin induces G1 cell cycle accumulation and p53-independent apoptosis of the human rhabdomyosarcoma cell line Rh1. Insulin-like growth factor I (IGF-I) and insulin, but not epidermal growth factor or platelet-derived growth factor, completely prevented apoptosis of this cell line. Because the Ras-Erk1-Erk2 and phosphatidylinositol 3 -kinase (PI3K)-Akt pathways are implicated in the survival of various cancer cells, we determined whether protection from rapamycininduced apoptosis by IGF-I requires one or both of these pathways. Despite the blocking of Ras-Erk signaling by the addition of PD 98059 (a MEK1 inhibitor) or by the overexpression of dominant-negative RasN17, IGF-I completely prevented rapamycin-induced death. Inhibition of Ras signaling did not prevent Akt activation by IGF-I. To determine the role of the PI3K-Akt pathway in rescuing cells from apoptosis caused by rapamycin, cells expressing dominant-negative Akt were tested. This mutant protein inhibited IGF-I-induced phosphorylation of Akt and blocked phosphorylation of glycogen synthase kinase 3. The prevention of rapamycin-induced apoptosis by IGF-I was not inhibited by expression of dominant-negative Akt either alone or under conditions in which LY 294002 inhibited PI3K signaling. Furthermore, IGF-I prevented rapamycin-induced apoptosis when the Ras-Erk1-Erk2 and PI3K-Akt pathways were blocked simultaneously. Similar experiments in a second rhabdomyosarcoma cell line, Rh30, using pharmacological inhibitors of PI3K or MEK1, alone or in combination, failed to block IGF-I rescue from rapamycin-induced apoptosis. Therefore, we conclude that a novel pathway(s) is responsible for the IGF-I-mediated protection against rapamycin-induced apoptosis in these rhabdomyosarcoma cells.
Scalable forwarding tables for supporting flexible policies in enterprise networks

With increasing demands for more flexible services, the routing policies in enterprise network becomes much richer. This has placed a heavy burden to the current router forwarding plane to support the increasing number of policies, primarily due to the limited capacity in TCAM. This hinders the development of new network services. In this paper, we present the design and implementation of a new forwarding table structure. It separates the functions of TCAM and SRAM and maximally utilizes the large & flexible SRAM. We progressively design a set of schemes, to maintain correctness, compress storage, and achieve line-card speeds. We also design incremental update algorithms that bring less accesses to memory. We present implementation designs and evaluate our scheme with a real implementation on a commercial router using real data sets. Our design does not require new devices. The evaluation results show that the performance of our forwarding tables is promising.
Using Ontologies to Identify Patients with Diabetes in Electronic Health Records

This paper describes a work in progress that explores the applicability of ontologies to solve problems in the medical domain. We investigate whether it is feasible to use ontologies and ontology-based data access (OBDA) to automate common clinical tasks faced by general practitioners (GPs), which are labor-intensive and error prone in terms of relevant information retrieved from electronic health records (EHRs). Our study aims to improve the selection of diabetes patients for clinical trials or medical research. The biggest impediment to automating such clinical tasks is the essential bridging of the semantic gaps between existing patient data in EHRs, such as reasons for visit, chronic conditions and diagnoses, pathology tests and prescriptions stored in general practice EHRs (GPEHR), and the ways which medical researchers or GPs interpret those records. Our current understanding is that automated identification of diabetes patients can be specified systematically as a solution supported by semantic retrieval. We detail the challenges to building a realistic case study, which consists of solving issues related to conceptualization of data and domain context, integration of different datasets, ontology creation based on the SNOMED CT-AU® standard, mapping between existing data and ontology, and the challenge of data fitness for research use. Our prototype is based on data which scale to thirteen years of approximately 100,000 anonymous patient records from four general practices in south western Sydney.
Neurocognition and Cognitive Biases in Schizophrenia

Abstract Individuals with schizophrenia have been found to exhibit a number of information processing biases that may play a role in the development and exacerbation of symptoms and may impair overall functioning. However, little is known about the factors that are associated with these cognitive biases. Recently, researchers have begun to consider whether neurocognitive deficits, common in schizophrenia, may be risk factors for the development of cognitive biases. In the present study, we assessed neurocognition (verbal learning, delayed verbal recall memory, and verbal recognition memory) and cognitive biases (knowledge corruption and impaired cognitive insight) in 72 individuals with schizophrenia or schizoaffective disorder. As hypothesized, poorer delayed verbal recall memory was associated with increased knowledge corruption. Contrary to expectations, verbal learning and verbal memory were not associated with cognitive insight. These findings suggest that an inadequate recall memory system may put patients with schizophrenia at greater risk for cognitive distortions.
Tumor Specific Epigenetic Silencing of Corticotropin Releasing Hormone -Binding Protein in Renal Cell Carcinoma: Association of Hypermethylation and Metastasis

The relevance of Corticotropin Releasing Hormone (CRH)-system in human malignancies is a question of growing interest. Here we investigated hypermethylation and epigenetic silencing of the CRH-Binding Protein (CRHBP) gene in clear cell renal cell cancer (ccRCC). Relative methylation of the CRHBP CpG island (CGI) was determined in 17 tumor cell lines as well as 86 ccRCC samples and 66 paired normal tissues using pyrosequencing and quantitative methylation specific PCR of bisulfite converted DNA. Results were statistically compared with relative mRNA expression levels of CRHBP and clinicopathological parameters of patients. Re-expression of CRHBP following 5-aza-2´-deoxycytidine treatment was investigated by quantitative mRNA expression analysis. Real-time impedance analysis was applied for analysis of invasiveness of renal tumor cells following si-RNA knockdown of CRHBP expression or ectopic expression of CRHBP. We found the CRHBP CGI to be frequently methylated in tumor cell lines of renal, prostatic, and bladder cancer. Comparison of methylation in normal and paired renal cancer tissue specimens revealed hypermethylation of the CRHBP CGI in tumors (p<1*10−12). DNA methylation and decreased mRNA expression were correlated (R = 0.83, p<1*10−12). Tumor cell lines showed 5-aza-2´-deoxycytidine dependent reduction of methylation and re-expression of CRHBP was associated with altered cellular invasiveness of renal cancer cells in real-time impedance invasion assays. Hypermethylation and inverse relationship with mRNA expression were validated in silico using the TCGA network data. We describe for the first time tumor specific epigenetic silencing of CRHBP and statistical association with aggressive tumors thus suggesting the CRH system to contribute to the development of kidney cancer.
Anisotropic Image Sharpening via Well-Posed Sobolev Gradient Flows

We study well-posed perturbations of formally ill-posed diffusion equations which are used in image processing, such as the Perona–Malik equation. Our perturbation technique is to consider the diffusion equations as $L^2$ gradient flows on integral functionals and then modify the inner product from $L^2$ to a Sobolev inner product. We show that the functional differential equations obtained in this way are well-posed in both the forward and backward directions. We then show how to design a well-posed image sharpening algorithm via Sobolev gradient ascent on a Perona–Malik type functional. We provide full numerical implementation details and show experimental results on natural images which suggest that this method outperforms previous work by the authors as well as other sharpening algorithms such as the shock filter.
In vivo characterization of the connectivity and subcomponents of the human globus pallidus

Projections from the substantia nigra and striatum traverse through the pallidum on the way to their targets. To date, in vivo characterization of these pathways remains elusive. Here we used high angular resolution diffusion imaging (N=138) to study the characteristics and structural subcompartments of the human pallidum. Our results show that the diffusion orientation distribution at the pallidum is asymmetrically oriented in a dorsolateral direction, consistent with the orientation of underlying fiber systems. Furthermore, compared to the outer pallidal segment, the internal segment has more peaks in the orientation distribution function and stronger anisotropy in the primary fiber direction, consistent with known cellular differences between the underlying nuclei. These differences in orientation, complexity, and degree of anisotropy are sufficiently robust to automatically segment the pallidal nuclei using diffusion properties. Thus the gray matter diffusion signal can be useful as an in vivo measure of the collective nigrostriatal and striatonigral pathways.
Power Awareness in Network Design and Routing

Exponential bandwidth scaling has been a fundamental driver of the growth and popularity of the Internet. However, increases in bandwidth have been accompanied by increases in power consumption, and despite sustained system design efforts to address power demand, significant technological challenges remain that threaten to slow future bandwidth growth. In this paper we describe the power and associated heat management challenges in today's routers. We advocate a broad approach to addressing this problem that includes making power-awareness a primary objective in the design and configuration of networks, and in the design and implementation of network protocols. We support our arguments by providing a case study of power demands of two standard router platforms that enables us to create a generic model for router power consumption. We apply this model in a set of target network configurations and use mixed integer optimization techniques to investigate power consumption, performance and robustness in static network design and in dynamic routing. Our results indicate the potential for significant power savings in operational networks by including power-awareness.
Building Effective Queries In Natural Language Information Retrieval

In this paper we report on our natural language information retrieval (NLIR) project as related to the recently concluded 5th Text Retrieval Conference (TREC-5). The main thrust of this project is to use natural language processing techniques to enhance the effectiveness of full-text document retrieval. One of our goals was to demonstrate that robust if relatively shallow NLP can help to derive a better representation of text documents for statistical search. Recently, we have turned our attention away from text representation issues and more towards query development problems. While our NLIR system still performs extensive natural language processing in order to extract phrasal and other indexing terms, our focus has shifted to the problems of building effective search queries. Specifically, we are interested in query construction that uses words, sentences, and entire passages to expand initial topic specifications in an attempt to cover their various angles, aspects and contexts. Based on our earlier results indicating that NLP is more effective with long, descriptive queries, we allowed for long passages from related documents to be liberally imported into the queries. This method appears to have produced a dramatic improvement in the performance of two different statistical search engines that we tested (Cornell's SMART and NIST's Prise) boosting the average precision by at least 40%. In this paper we discuss both manual and automatic procedures for query expansion within a new stream-based information retrieval model.
Example-based Creation of Digital Imagery

In modern computer graphics, the collective creativity of digital artists far outstrips the capabilities of available tools. In the drive to catch up, more traditional simulation-based and procedural tools are being supplemented by example-based methods, which take advantage of the cornucopia of available data as guidance. This thesis is an anthology of articles published in various journals in the period of 2013 to 2015, which present three such novel methods. The first is an example-based painting approach which focuses on proper preservation of feature-rich boundaries between textures. The second is a video stylization approach which allows fine control over the amount of temporal noise in the output, and the third is another texture painting approach which focuses on handling shape-specific features.
HMM-based Speech Synthesis Using an Acoustic Glottal Source Model

A major cause of degradation of speech quality in HMMbased speech synthesis is the use of a simple delta pulse signal to generate the excitation of voiced speech. This paper describes a new approach to using an acoustic glottal source model in HMM-based synthesisers. The goal is to improve speech quality and parametric flexibility to better model and transform voice characteristics.
DCT sign-based robust image hashing

This paper proposes a robust image hashing technique based on the sign of coefficients in the Discrete Cosine Transforms (DCT) domain. The idea consists of exploiting the energy compaction property of the DCT and its ability to carry information of edges and texture in DCT sign values. It has been shown that the DCT sign-based hashing technique offers an excellent robustness against signal processing operations and geometric attacks and outperforms the conventional DCT-based image hashing technique.
Speeding up the brain: when spatial facilitation translates into latency shortening

Waves of activity following a focal stimulation are reliably observed to spread across the cortical tissue. The origin of these waves remains unclear and the underlying mechanisms and function are still debated. In this study, we ask whether waves of activity modulate the magnetoencephalography (MEG) signals recorded in humans during visual stimulation with Gabor patches sequentially flashed along a vertical path, eliciting a perception of vertical apparent motion. Building upon the functional properties of long-rang horizontal connections, proposed to contribute to spreading activity, we specifically probe the amplitude and latency of MEG responses as a function of Gabor contrast and orientation. The results indicate that in the left hemisphere the response amplitude is enhanced and the half height response latency is shortened for co-aligned Gabor as compared to misaligned Gabor patches at a low but not at a high contrast. Building upon these findings, we develop a biologically plausible computational model that performs a “spike time alignment” of the responses to elongated contours with varying contrast, endowing them with a phase advance relative to misaligned contours.
Performance analysis of call assignment and carrier packing schemes for TDMA systems

In IS-136 and GSM time-division multiple-access (TDMA) systems that employ dynamic channel allocation, it is important to minimize the number of active carriers for a given load. This paper studies the carrier packing properties of both call assignment and carrier repacking schemes, where the average number of active carriers and the average number of intra-sector (repack induced) handoffs per call are the relevant metrics. We show that the simple to implement sequential trunk hunt (STH) call assignment scheme is almost as good as the optimal intelligent channel assignment (ICA) scheme and that the MaxPack repacking scheme is optimal for both of these TDMA standards among all call assignment and repacking schemes. The call assignment and repacking schemes considered are shown to have either closed-form or efficient numerical recursive solutions for the North American IS-136 standard, and in most cases we obtain the underlying state space distributions. A mixture of analytic and simulation results is also presented for the GSM standard. Numerical results show that most of the packing efficiency can be achieved simply by a good call assignment scheme such as ICA and that the additional gain from optimal repacking is generally in the 10-15% range for typical IS-136 and GSM configurations.
Thread Lock Section-Aware Scheduling on Asymmetric Single-ISA Multi-Core

As thread level parallelism in applications has continued to expand, so has research in chip multi-core processors. As more and more applications become multi-threaded we expect to find a growing number of threads executing on a machine. As a consequence, the operating system will require increasingly larger amounts of CPU time to schedule these threads efficiently. Instead of perpetuating the trend of performing more complex thread scheduling in the operating system, we propose a scheduling mechanism that can be efficiently implemented in hardware as well. Our approach of identifying multi-threaded application bottlenecks such as thread synchronization sections complements the Fairness-aware Scheduler method. It achieves an average speed up of 11.5 percent (geometric mean) compared to the state-of-the-art Fairness-aware Scheduler.
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays

We discuss a multiple-play multi-armed bandit (MAB) problem in which several arms are selected at each round. Recently, Thompson sampling (TS), a randomized algorithm with a Bayesian spirit, has attracted much attention for its empirically excellent performance, and it is revealed to have an optimal regret bound in the standard single-play MAB problem. In this paper, we propose the multiple-play Thompson sampling (MP-TS) algorithm, an extension of TS to the multiple-play MAB problem, and discuss its regret analysis. We prove that MP-TS for binary rewards has the optimal regret upper bound that matches the regret lower bound provided by Anantharam et al. (1987). Therefore, MP-TS is the first computationally efficient algorithm with optimal regret. A set of computer simulations was also conducted, which compared MP-TS with state-of-the-art algorithms. We also propose a modification of MP-TS, which is shown to have better empirical performance.