Miracle-dz/UniFAR-FTUs huggingface dataset:
3D chip stack technology using through-chip interconnects

The current technology in micro-and nano-electronics is insufficient to meet future demands for several applications. Most state-of-the-art solutions rely on so-called embedded technologies, which are both expensive and complex. One solution to the problem of integrating mixed technologies is the concept of 3D stacking. Our approach implements an epitaxial etch-stop layer for thickness control of the thinning process. Using this etch-stop layer, we can create a precise alignment of back-side vias to the landing pads in the first metal layer of the active CMOS, resulting in small via diameters and high connection densities between individual-layers of the 3D stack. Furthermore, we can use other materials, like GaAs (gallium arsenide), in combination with an epitaxial lift-off process. We use a copper-tin soldering process based on the solid-liquid interdiffusion (solid) process to create the electrical and mechanical connection between the single chip layers. Using this process, we created true multilayer stacks and tested them with respect to the static electrical properties of ohmic contacts and interchip vias. We directly incorporated these results in the design of test circuits that create tests for stuck-at failures of the interchip connections after stack assembly. This article presents a technology overview of how to achieve the goal in a 3D fabrication process. It also shows measurements for characterizing interconnects.
Antitumor activity of the novel multi-kinase inhibitor EC-70124 in triple negative breast cancer

Disseminated triple negative breast cancer (TNBC) is an incurable disease with limited therapeutic options beyond chemotherapy. Therefore, identification of druggable vulnerabilities is an important aim. Protein kinases play a central role in cancer and particularly in TNBC. They are involved in many oncogenic functions including migration, proliferation, genetic stability or maintenance of stem-cell like properties. In this article we describe a novel multi-kinase inhibitor with antitumor activity in this cancer subtype. EC-70124 is a hybrid indolocarbazole analog obtained by combinatorial biosynthesis of Rebeccamycin and Staurosporine genes that showed antiproliferative effect and in vivo antitumoral activity. Biochemical experiments demonstrated the inhibition of the PI3K/mTOR and JAK/STAT pathways. EC-70124 mediated DNA damage leading to cell cycle arrest at the G2/M phase. Pathway analyses identified several deregulated functions including cell proliferation, migration, DNA damage, regulation of stem cell differentiation and reversion of the epithelial-mesenchymal transition (EMT) phenotype, among others. Combination studies showed a synergistic interaction of EC-70124 with docetaxel, and an enhanced activity in vivo. Furthermore, EC-70124 had a good pharmacokinetic profile. In conclusion these experiments demonstrate the antitumor activity of EC-70124 in TNBC paving the way for the future clinical development of this drug alone or in combination with chemotherapy.
ColFinder Collaborative Concurrency Bug Detection

Many concurrency bugs are extremely difficult to be detected by random test due to huge input space and huge interleaving space. The multicore technology trend worsens this problem. We propose an innovative, collaborative approach called ColFinder to detect concurrency bugs effectively and efficiently. ColFinder uses static analysis to identify potential buggy statements. With respect to these statements, ColFinder uses program slicing to cut the original programs into smaller programs. Finally, it uses dynamic active test to verify whether the potential buggy statements will trigger real bugs. We implement a prototype of ColFinder, and evaluate it with several real-world programs. It significantly improves the probability of bug manifestation, from 0.75% to 89%. Additionally, ColFinder makes the time of bug manifestation obviously reduced by program slicing, with an average of 33%.
Two-phase flow and heat transfer of a non azeotropic mixture inside a single microchannel.

In the recent years much attention has been paid to the use of fluorinated propene isomers for the substitution of high-GWP refrigerants in refrigeration and air conditioning applications. However, the HFOs (hydrofluoroolefins) cannot cover all the applications due to their thermodynamic properties. For the purpose to obtain a fluid that brings together the good heat transfer characteristics of HFCs, such as R410A, and the environmental friendly behavior of HFOs, in the latest literature it was attempted to blend R1234ze(E) with another refrigerant, R32. R32 has relatively low GWP and excellent thermodynamic characteristics. Therefore, a zeotropic mixture of R1234ze(E) and R32 can be used in the field of air-conditioning due to its mild impact on the environment. In this paper, a mixture of R1234ze(E) and R32 (0.5/0.5 by mass) is investigated. In particular the frictional pressure gradient and the local heat transfer coefficients during flow boiling and condensation of this mixture in a single microchannel with 0.96 mm diameter are measured. Tests are carried out in the experimental apparatus available at the Two Phase Heat Transfer Lab of the University of Padova. The heat transfer coefficients are compared against predicting models available in the literature. The new experimental data are also compared to heat transfer data of pure R1234ze(E) and R32. This allows to analyze the heat transfer penalization due to the mass transfer resistance of this zeotropic mixture. Pressure drop data are also used to assess predicting pressure gradient correlations.
Probability Models for the Splitting Algorithm in Wireless Access Networks with Multipacket Reception and Finite Nodes

In this paper, we propose an analytical approach for performance evaluation of the classic tree/stack splitting algorithm in an interference-dominating wireless access network with random traffic and finite nodes. In an interference-dominating wireless access network, a receiver could simultaneously receive multiple packets from a variety of transmitters, as long as the signal-to-interference-plus-noise ratio exceeds a predetermined threshold. We use discrete-time Markov chains and regenerative processes to derive the throughput curve, the packet blocking probability, the average system size, and the average packet delay. We show that the exact performance of the splitting algorithm depends on the total number of nodes in the network. We verify our numerical results by rigorous mathematical proof and computer simulations.
Concept-Level Multimodal Ranking of Flickr Photo Tags via Recall Based Weighting

Social media platforms allow users to annotate photos with tags that significantly facilitate an effective semantics understanding, search, and retrieval of photos. However, due to the manual, ambiguous, and personalized nature of user tagging, many tags of a photo are in a random order and even irrelevant to the visual content. Aiming to automatically compute tag relevance for a given photo, we propose a tag ranking scheme based on voting from photo neighbors derived from multimodal information. Specifically, we determine photo neighbors leveraging geo, visual, and semantics concepts derived from spatial information, visual content, and textual metadata, respectively. We leverage high-level features instead traditional low-level features to compute tag relevance. Experimental results on a representative set of 203,840 photos from the YFCC100M dataset confirm that above-mentioned multimodal concepts complement each other in computing tag relevance. Moreover, we explore the fusion of multimodal information to refine tag ranking leveraging recall based weighting. Experimental results on the representative set confirm that the proposed algorithm outperforms state-of-the-arts.
Testing Predicted Clusters A New Approach to This Powerful Research Tool, Illustrated Through a Questionnaire on Obsessive–Compulsive Disorder

Testing predicted clusters of questionnaire items can be a source of abundant feedback on the theory that is behind the prediction. Such testing is often performed by means of confirmatory factor analysis. However, that method offers insufficient feedback at the level of items, while its goodness-of-fit indices are notoriously unreliable. Richer and more precise feedback would be generated by a data-driven optimization of the predicted clusters (factors), allowing a comparison between predicted and empirical clusters (factors) at the level of items. Contrasting these two provides a basis for classifying the items into hits, false positives, and false negatives. This division greatly facilitates reinterpretation of the clusters (factors) and evaluation of the items. In addition, it offers a basis for two new measures of goodness of fit with respect to the correct assignment of items to clusters (indicators to factors). Application of this new approach to a questionnaire on Obsessive–Compulsive Disorder wil.
Executive Compensation: A New View from a Long-Term Perspective, 1936-2005

We analyze the long-run trends in executive compensation using a new panel dataset of top executives in large firms from 1936 to 2005. In sharp contrast to the well-known steep upward trajectory of pay of the past 30 years, the median real value of compensation was remarkably flat from the late 1940s to the mid-1970s, highlighting a weak relationship between compensation and aggregate firm size. While this correlation has changed considerably over the century, the cross-sectional relationship between pay and firm size has remained stable. Another surprising finding is that the sensitivity of changes in an executive's wealth to firm performance was not inconsequentially small for most of our sample period. Thus, recent years were not the first time when compensation arrangements served to align managerial incentives with those of shareholders. Overall, these trends pose a challenge to several common explanations for the recent surge in executive pay.
Rough Set Approach in Machine Learning: A Review

The Rough Set (RS) theory can be considered as a tool to reduce the input dimensionality and to deal with vagueness and uncertainty in datasets. Over the years, there has been a rapid growth in interest in rough set theory and its applications in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, data preprocessing, knowledge discovery, decision analysis, and expert systems. This paper discusses the basic concepts of rough set theory and point out some rough set-based research directions and applications. The discussion also includes a review of rough set theory in various machine learning techniques like clustering, feature selection and rule induction. General Terms Information and decision systems, (in)discernibility , approximation spaces, rough sets, rough membership functions, reducts, decision rules, dependencies of attributes. Clustering, Rule Induction, Feature Selection
Nitrite Reductase Genes (nirK andnirS) as Functional Markers To Investigate Diversity of Denitrifying Bacteria in Pacific Northwest Marine Sediment Communities

ABSTRACT Genetic heterogeneity of denitrifying bacteria in sediment samples from Puget Sound and two sites on the Washington continental margin was studied by PCR approaches amplifying nirK andnirS genes. These structurally different but functionally equivalent single-copy genes coding for nitrite reductases, a key enzyme of the denitrification process, were used as a molecular marker for denitrifying bacteria. nirS sequences could be amplified from samples of both sampling sites, whereas nirKsequences were detected only in samples from the Washington margin. To assess the underlying nir gene structure, PCR products of both genes were cloned and screened by restriction fragment length polymorphism (RFLP). Rarefraction analysis revealed a high level of diversity especially for nirS clones from Puget Sound and a slightly lower level of diversity for nirK andnirS clones from the Washington margin. One group dominated within nirK clones, but no dominance and only a few redundant clones were seen between sediment samples fornirS clones in both habitats. Hybridization and sequencing confirmed that all but one of the 228 putative nirS clones were nirS with levels of nucleotide identities as low as 45.3%. Phylogenetic analysis grouped nirS clones into three distinct subclusters within the nirS gene tree which corresponded to the two habitats from which they were obtained. These sequences had little relationship to any strain with knownnirS sequences or to isolates (mostly close relatives ofPseudomonas stutzeri) from the Washington margin sediment samples. nirK clones were more closely related to each other than were the nirS clones, with 78.6% and higher nucleotide identities; clones showing only weak hybridization signals were not related to known nirK sequences. AllnirK clones were also grouped into a distinct cluster which could not be placed with any strain with known nirKsequences. These findings show a very high diversity of nirsequences within small samples and that these novel nirclusters, some very divergent from known sequences, are not known in cultivated denitrifiers.
Forest-Based Semantic Role Labeling

Parsing plays an important role in semantic role labeling (SRL) because most SRL systems infer semantic relations from 1-best parses. Therefore, parsing errors inevitably lead to labeling mistakes. To alleviate this problem, we propose to use packed forest, which compactly encodes all parses for a sentence. We design an algorithm to exploit exponentially many parses to learn semantic relations efciently. Experimental results on the CoNLL-2005 shared task show that using forests achieves an absolute improvement of 1.2% in terms of F1 score over using 1-best parses and 0.6% over using 50-best parses.
Securing Wireless Sensor Networks with An Efficient B+ Tree-Based Key Management Scheme

Key management is an important issue in Wireless Sensor Networks (WSNs) for the secure communications among the nodes in the network. Based on the network settings and structure of the network, keys could be managed in various ways. Though there are quite a good number of key management schemes for WSNs, in this work, we present an enhanced heterogeneous tree based key management scheme for security of Wireless Sensor Networks (WSNs). Our scheme combines efficiently different key management techniques in each architecture level and also it has a dynamic key renewal process. Here, whenever a node is compromised, key renewal is done by one way hash functions and simple XOR operations. This combination gives the scheme good performance in terms of key storage overhead as well as resilience to the node capture attack. Our scheme shows relatively better performance compared to other existing heterogeneous schemes. Before presenting our scheme and its detailed analysis from multiple perspectives, we note down the related works to clarify this work’s background and motivation.
A non-periodic two scale asymptotic method to take account of rough topographies for 2-D elastic wave propagation

We propose a two scale asymptotic method to compute the effective effect of a free surface topography varying much faster than the minimum wavelength for 2-D P-SV elastic wave propagation. The topography variation is assumed to be non-periodic but with a deterministic description and, in this paper, the elastic body below the topography is assumed to be ho- mogeneous. Two asymptotic expansions are used, one in the boundary layer close to the free surface and one in the volume. Both expansions are matched appropriately up to the order 1 to provide an effective topography and an effective boundary condition. We show that the effective topography is not the averaged topography but it is a smooth free surface lying below the fast variations of the real topography. Moreover, the free boundary condition has to be modified to take account of the inertial effects of the fast variations of the topography above the effective topography. In other words, the wave is not propagating in the fast topography but below it and is slowed down by the weight of the fast topography. We present an iterative scheme allowing to find this effective topography for a given minimum wavelength. We do not attempt any mathematical proof of the proposed scheme, nevertheless, numerical tests show good results.
Credit Risk Analysis of Mortgage Loans: An Application to the Italian Market

The valuation of financial instruments in which both credit risk and interest rate risk are taken into account is an outstanding task for financial institutions. In this paper, we propose an affine-reduced model dealing with this topic. We show that this model offers analytical tractability as well as flexibility. We also show that the parameters of the model can be estimated via maximum likelihood in a straightforward way. To outline the procedure, we estimate the model on Italian data, using zero-coupon bond and historical default probabilities, as provided by the Bank of Italy.
The research of temperature compensation technology of high-temperature pressure sensor

Silicon piezoresistive pressure sensor is a kind of sensor with excellent performance, wide range of applications „ But its sensitive components make itself vulnerable to temperature, only by solving the problem of temperature compensation can improve the accuracy, stability and other important performance. The high temperature and high pressure silicon pressure sensor actual operating temperature range is (− 55 °C ∼ + 175 °C); its hardware compensated temperature range is (+ 25 °C ∼ + 80 °C) which is narrower than its actual operating temperature range, zero drift maximum reach 0.65%FS/10°C. These can not reach the using requirement. This article has analyzed the experimental data of this pressure sensor in the full operating temperature range. According to the characteristics of different temperature interval, the method of software compensation has been used to reduce zero temperature drift rate by an order of magnitude. Experiments prove that the piecewise polynomial fitting method which this article uses has good effect, and good applicability in compensating zero-point temperature drift and the original sensor unbalance, worth extending.
The effects of resilience and disability on participation and health-related quality of life among older Canadians with multiple sclerosis

This study investigated the characteristics older individuals with Multiple Sclerosis (MS). The focus was on examining high and low levels of resilience as well as the effect of resilience on health-related quality of life (HRQOL) and participation. Standardized measures were completed by 743 Canadian 55 years of age and older with MS. It was found that individuals with high levels of resilience were significantly older, reported lower disability, less impact of MS, and better mental health. The high resilience group reported being more physically active, participated more frequently in daily activities, had greater social support and perceived health, and were less financial stable than individuals with low levels of resilience. Resilience accounted for a significant proportion of variance in perceived health status (7.2%) and impact of MS (5.7%) considered as measures of HRQOL, and participation (3.1%). These findings inform future strategies to improve the lives of individuals aging with MS.
Streaming of scalable multimedia over Content Delivery Cloud

Content Delivery Cloud (CDC) extends Content Delivery Network (CDN) to provide elastic, scalable and low cost services to the customers. For multimedia streaming over CDC, caching the media content onto the edge server from storage cloud is commonly used to minimize the latency of content delivery. It is very important for CDN to balance between the resources being used (storage space, bandwidth, etc) and the performance achieved. Commercial CDNs (such as Akamai, Limelight, Amazon CloudFront) have their proprietary caching algorithms to deal with this issue. In this paper, we propose a method to further improve the efficiency of the caching system for scalable multimedia contents. Specifically, we notice that a scalable multimedia content can be flexibly truncated to lower bit rates on-the-fly based on the available network bandwidth between the edge server to the end users. Therefore, it may not be necessary to cache such a content at its highest quality/rate. Based on this observation, we show that edge server can decide an optimized truncation ratio for the cached scalable multimedia contents to balance between the quality of the media and the resource usage. The proposed optimized truncation algorithm is analyzed and its efficacy in improving the efficiency of the caching system is justified with simulation result.
Researching Client-Therapist Relationships: A Collaborative Study for Informing Therapy

This paper describes a research project conducted in Norway and Sweden that examined client-therapist relationships. A consultant (the author) conducted post-therapy interviews in which clients and their therapist(s) were asked to speak of their therapy experiences with each other in front of one another. Through such interviews, the therapists gained a better understanding and sensitivity to what clients want to talk about and how they want to talk about it. The research highlights how therapists can miss key clinical elements in therapy. Three case examples are provided.
Higher Intake of PUFAs Is Associated with Lower Total and Visceral Adiposity and Higher Lean Mass in a Racially Diverse Sample of Children.

BACKGROUND Polyunsaturated fatty acids (PUFAs) are associated with protection from obesity-related phenotypes in adults; however, the relation between reported intake of PUFAs with body-composition outcomes in children remains unknown. OBJECTIVE Our objective was to examine how self-reported intakes of PUFAs, including total, n-6 (ω-6), and n-3 (ω-3) PUFAs and ratios of n-6 to n-3 PUFAs and PUFAs to saturated fatty acids (SFAs), are associated with measures of adiposity and lean mass (LM) in children. We hypothesized that higher self-reported intakes of PUFAs and the ratio of PUFAs to SFAs would be positively associated with LM and negatively associated with total adiposity. METHODS Body composition and dietary intake were measured in a racially diverse sample of 311 children (39% European American, 34% African American, and 27% Hispanic American) aged 7-12 y. Body composition and abdominal fat distribution were measured by dual-energy X-ray absorptiometry and computed tomography scans, respectively. Self-reported dietary intakes (including total PUFAs, n-3 PUFAs, n-6 PUFAs, and SFAs) were assessed by using two 24-h recalls. Independent-sample t tests and multiple linear regression analyses were conducted. RESULTS Total PUFA intake was positively associated with LM (P = 0.049) and negatively associated with percentage of body fat (%BF; P = 0.033) and intra-abdominal adipose tissue (IAAT; P = 0.022). A higher ratio of PUFAs to SFAs was associated with higher LM (P = 0.030) and lower %BF (P = 0.028) and IAAT (P = 0.048). Intakes of n-3 and n-6 PUFAs were positively associated with LM (P = 0.017 and P = 0.021, respectively), and the ratio of n-6 to n-3 PUFAs was negatively associated with IAAT (P = 0.014). All results were independent of biological, environmental, and genetic covariates. CONCLUSIONS Our results show that a higher self-reported intake of PUFAs and a higher ratio of PUFAs to SFAs are positively associated with LM and negatively associated with visceral adiposity and %BF in a healthy cohort of racially diverse children aged 7-12 y. This trial was registered at clinicaltrials.gov as NCT00726778.