Miracle-dz/UniFAR-FTUs huggingface dataset:
Solder joint formation simulation and finite element analysis

The solder joint formation during reflow and related subsequent solder joint reliability of surface mounted electronic devices are critical issues in the field of electronic packaging. Solder joint reflow and reliability are highly dependent on joint configuration which are governed by bond pad size, alloy material, solder paste volume, leadframe geometry, and leadframe/pad alignment, etc. The objective of this work is to develop numerical models and methods to: (1) simulate the solder joint formation during the reflow process; (2) determine the stress/strain distribution within the joint; and (3) further predict the reliability of the solder joints. The solder joint formation process during the solidification stage can be simulated using the Surface Evolver program developed by University of Minnesota. The thermomechanical force/displacement (or stress/strain) analysis can be carried out using ANSYS, a general purpose Finite Element Analysis program. The effort also includes the linking of the two programs. Examples of applications of this work include study of components falling off during second (facing down) reflow and thermal stress analysis (due to CTE mismatch) of PQFP mounted on FR4 PWB.
IFI16 Expression Is Related to Selected Transcription Factors during B-Cell Differentiation

The interferon-inducible DNA sensor IFI16 is involved in the modulation of cellular survival, proliferation, and differentiation. In the hematopoietic system, IFI16 is consistently expressed in the CD34+ stem cells and in peripheral blood lymphocytes; however, little is known regarding its regulation during maturation of B- and T-cells. We explored the role of IFI16 in normal B-cell subsets by analysing its expression and relationship with the major transcription factors involved in germinal center (GC) development and plasma-cell (PC) maturation. IFI16 mRNA was differentially expressed in B-cell subsets with significant decrease in IFI16 mRNA in GC and PCs with respect to naïve and memory subsets. IFI16 mRNA expression is inversely correlated with a few master regulators of B-cell differentiation such as BCL6, XBP1, POU2AF1, and BLIMP1. In contrast, IFI16 expression positively correlated with STAT3, REL, SPIB, RELA, RELB, IRF4, STAT5B, and STAT5A. ARACNE algorithm indicated a direct regulation of IFI16 by BCL6, STAT5B, and RELB, whereas the relationship between IFI16 and the other factors is modulated by intermediate factors. In addition, analysis of the CD40 signaling pathway showed that IFI16 gene expression directly correlated with NF-κB activation, indicating that IFI16 could be considered an upstream modulator of NF-κB in human B-cells.
Analyses of the Association between Discourse Relation and Sentiment Polarity with a Chinese Human-Annotated Corpus

Discourse relation may entail sentiment information. In this work, we annotate both discourse relation and sentiment information on a moderate-sized Chinese corpus extracted from the ClueWeb09. Based on the annotation, we investigate the association between the relation type and the sentiment polarity in Chinese and interpret the data from various aspects. Finally, we highlight some language phenomena and give some remarks.
Program Autotuning as a Service: Opportunities and Challenges

Program autotuning is becoming an increasingly valuable tool for improving performance portability across diverse target architectures, exploring trade-offs between several criteria, or meeting quality of service requirements. Recent work on general autotuning frameworks enabled rapid development of domain-specific autotuners reusing common libraries of parameter types and search techniques. In this work we explore the use of such frameworks to develop general-purpose online services for program autotuning using the Software as a Service model. Beyond the common benefits of this model, the proposed approach opens up a number of unique opportunities, such as collecting performance data and utilizing it to improve further runs, or enabling remote online autotuning. However, the proposed autotuning as a service approach also brings in several challenges, such as accessing target systems, dealing with measurement latency, and supporting execution of user-provided code. This paper presents the first step towards implementing the proposed approach and addressing these challenges. We describe an implementation of generic autotuning service that can be used for tuning arbitrary programs on user-provided computing systems. The service is based on OpenTuner autotuning framework and runs on Everest platform that enables rapid development of computational web services. In contrast to OpenTuner, the service doesn't require installation of the framework, allows users to avoid writing code and supports efficient parallel execution of measurement tasks across multiple machines. The performance of the service is evaluated by using it for tuning synthetic and real programs.
Calibration-Free Eye Gaze Direction Detection with Gaussian Processes

In this paper we present a solution for eye gaze detection from a wireless head mounted camera designed for children aged between 6 months and 18 months. Due to the constraints of working with very young children, the system does not seek to be as accurate as other state-of-the-art eye trackers, however it requires no calibration process from the wearer. Gaussian Process Regression and Support Vector Machines are used to analyse the raw pixel data from the video input and return an estimate of the child's gaze direction. A confidence map is used to determine the accuracy the system can expect for each coordinate on the image. The best accuracy so far obtained by the system is 2.34$^{circ}$ on adult subjects, tests with children remain to be done.
Threshold Voltage Design and Performance Assessment of Hetero-Channel SRAM Cells

Optimized threshold voltage (Vt) design to enhance the variation immunity of high-performance (super-threshold) and low-voltage (near-/sub-threshold) 6 T SRAM cells is presented. For low-voltage SRAM cells operating at low Vdd, low-Vt design shows smaller variability, while the design tradeoff between performance and leakage should be considered. For high-performance SRAM cells operating at high Vdd, ultra-thin-body SOI SRAM cells with high-Vt design show smaller variability while sacrificing performance compared with the low-Vt design. Our study indicates that hetero-channel SRAM cells enable high-Vt design and exhibit improved Read/Write stability and performance, and maintain comparable RSNM variations for the high-performance SRAM applications.
Morphologic Alteration of Metastatic Neuroblastic Tumor in Bone Marrow after Chemotherapy

Background The aim of this study is to evaluate the histologic features of metastatic neuroblastic tumors (NTs) in bone marrow (BM) before and after chemotherapy in comparison with those of primary NTs. Methods A total of 294 biopsies from 48 children diagnosed with NTs with BM metastasis were examined. There were 48 primary neoplasm biopsies, 48 BM biopsies before chemotherapy, 36 primary neoplasm excisional biopsies after chemotherapy, and 162 BM biopsies after chemotherapy. Results Metastatic NTs in BM before chemotherapy were composed of undifferentiated and/or differentiating neuroblasts, but had neither ganglion cells nor Schwannian stroma. Metastatic foci of BM after chemotherapy were found to have differentiated into ganglion cells or Schwannian stroma, which became more prominent after further cycles of chemotherapy. Persistence of NTs or tumor cell types in BM after treatment did not show statistically significant correlation to patients' outcome. However, three out of five patients who newly developed poorly differentiated neuroblasts in BM after treatment expired due to disease progression. Conclusions Metastatic NTs in BM initially consist of undifferentiated or differentiating neuroblasts regardless of the primary tumor subtype, and become differentiated after chemotherapy. Newly appearing poorly differentiated neuroblasts after treatment might be an indicator for poor prognosis.
Developmental expression of BMP4/ALK3/SMAD5 signaling pathway in the mouse testis: a potential role of BMP4 in spermatogonia differentiation

It is well established that the c-kit gene plays an essential role in the proliferation of differentiating spermatogonia in prepuberal mice. However, the mechanisms that regulate the onset of spermatogenesis, i.e. differentiation of spermatogonial stem cells and c-kit expression, are poorly understood. Here we identify a novel signal transduction system in mouse prepuberal testis regulating this developmental event, involving bone morphogenetic protein 4 (BMP4) and its transduction machinery. BMP4 is produced by Sertoli cells very early in the postnatal life and is successively down regulated in peri-puberal Sertoli cells. Its receptor Alk3 and the R-Smad Smad5 are specifically expressed both in proliferating primordial germ cells and in postnatal spermatogonia. BMP4 stimulation of cultured spermatogonia induces Smad4/5 nuclear translocation and the formation of a DNA-binding complex with the transcriptional coactivator p300/CBP. In vitro exposure of undifferentiated spermatogonia to BMP4 exerts both mitogenic and differentiative effects, inducing [3H]thymidine incorporation and Kit expression. As a result of the latter event, Kit-negative spermatogonia acquire sensitivity to Stem Cell Factor.
A New Step for Artificial Creatures

In this article, we present our research on the evolution of morphology and behavior of complex creatures in virtual environments. We propose to study the evolution of creatures faced with different situations from crawling and walking to more complex activities like climbing and skating. Our creatures use solid 3D blocks and graphtals like Karl Sims' creatures and a new type of controller based on classifier system. The results constitute a new step toward creatures adapted to more complex environments
Allometry and spatial scales of foraging in mammalian herbivores.

Herbivores forage in spatially complex habitats. Due to allometry and scale-dependent foraging, herbivores are hypothesized to perceive and respond to heterogeneity of resources at scales relative to their body sizes. This hypothesis has not been manipulatively tested for animals with only moderate differences in body size and similar food niches. We compared short-term spatial foraging behavior of two herbivores (sheep and cattle) with similar dietary niche but differing body size. Although intake rates scaled allometrically with body mass (mass(0.75)), spatial foraging strategies substantially differed, with cattle exhibiting a coarser-grained use of the 'foodscape. ' Selectivity by cattle (and not sheep) for their preferred food was more restricted when patches were smaller (< 10 m(2)). We conclude that differences in spatial scales of selection offers a plausible mechanism by which species can coexist on shared resources that exhibit multiple scales of spatial heterogeneity.
Semi-Relaxation Supervised Hashing for Cross-Modal Retrieval

Recently, some cross-modal hashing methods have been devised for cross-modal search task. Essentially, given a similarity matrix, most of these methods tackle a discrete optimization problem by separating it into two stages, i.e., first relaxing the binary constraints and finding a solution of the relaxed optimization problem, then quantizing the solution to obtain the binary codes. This scheme will generate large quantization error. Some discrete optimization methods have been proposed to tackle this; however, the generation of the binary codes is independent of the features in the original space, which makes it not robust to noise. To consider these problems, in this paper, we propose a novel supervised cross-modal hashing method---Semi-Relaxation Supervised Hashing (SRSH). It can learn the hash functions and the binary codes simultaneously. At the same time, to tackle the optimization problem, it relaxes a part of binary constraints, instead of all of them, by introducing an intermediate representation variable. By doing this, the quantization error can be reduced and the optimization problem can also be easily solved by an iterative algorithm proposed in this paper. Extensive experimental results on three benchmark datasets demonstrate that SRSH can obtain competitive results and outperform state-of-the-art unsupervised and supervised cross-modal hashing methods.
Learning to represent haptic feedback for partially-observable tasks

The sense of touch, being the earliest sensory system to develop in a human body [1], plays a critical part of our daily interaction with the environment. In order to successfully complete a task, many manipulation interactions require incorporating haptic feedback. However, manually designing a feedback mechanism can be extremely challenging. In this work, we consider manipulation tasks that need to incorporate tactile sensor feedback in order to modify a provided nominal plan. To incorporate partial observation, we present a new framework that models the task as a partially observable Markov decision process (POMDP) and learns an appropriate representation of haptic feedback which can serve as the state for a POMDP model. The model, that is parametrized by deep recurrent neural networks, utilizes variational Bayes methods to optimize the approximate posterior. Finally, we build on deep Q-learning to be able to select the optimal action in each state without access to a simulator. We test our model on a PR2 robot for multiple tasks of turning a knob until it clicks.
Wasserstein Learning of Deep Generative Point Process Models

Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world phenomena. Currently, they are often characterized via intensity function which limits model's expressiveness due to unrealistic assumptions on its parametric form used in practice. Furthermore, they are learned via maximum likelihood approach which is prone to failure in multi-modal distributions of sequences. In this paper, we propose an intensity-free approach for point processes modeling that transforms nuisance processes to a target one. Furthermore, we train the model using a likelihood-free leveraging Wasserstein distance between point processes. Experiments on various synthetic and real-world data substantiate the superiority of the proposed point process model over conventional ones.
Regular rewriting of active XML and unambiguity

We consider here the exchange of Active XML (AXML) data, i.e., XML documents where some of the data is given explicitly while other parts are given only intensionally as calls to Web services. Peers exchanging AXML data agree on a data exchange schema that specifies in particular which parts of the data are allowed to be intensional. Before sending a document, a peer may need to rewrite it to match the agreed data exchange schema, by calling some of the services and materializing their data. Previous works showed that the rewriting problem is undecidable in the general case and of high complexity in some restricted cases. We argue here that this difficulty is somewhat artificial. Indeed, we study what we believe to be a more adequate, from a practical view point, rewriting problem that is (1) in the spirit of standard 1-unambiguity constraints imposed on XML schema and (2) can be solved by a single pass over the document with a computational device not stronger than a finite state automation. Following previous works, we focus on the core of the problem, i.e., on the problem on words. The results may be extended to (A)XML trees in a straightforward manner.
A secure group solution for multi-agent ec system

Mobile agent technology applied into EC is facing many problems. Security should be the first concern. But in large scale multi-agent systems, the most challenging problems encountered are locating and communicating of these autoprocessing agents. Some approaches have used several different methods to solve these problems. But unfortunately, they were not satisfied either for their complexity, poor performance or their lack of applicability. In our approach, we propose a secure group communication solution toward the security, locating and communication problems. We differentiated the ”Agent-Based” Multicast concept from the traditional ”Host-Based” Multicast. We then described the requirements for providing secure group services and introduce the key distribution method. A cryptographic method is complemented for further protect the agents. Finally, we analyzed the security and performance issues and conclude our solution.
Towards addressing group selfishness of cluster-based collaborative spectrum sensing in cognitive radio networks

Collaborative spectrum sensing has been recognized as a promising way to ameliorate the sensing performance in cognitive radio networks. Unfortunately, it also introduces some system overhead to users, and as a result some selfish secondary users might be unwilling to contribute to collaborative spectrum sensing. In this paper, we propose a new selfishness model in cluster-based collaborative spectrum sensing, which is referred to Overclaim Selfishness (OS). An OS group may gain benefit by sharing nominally equal but actually much less sensing reports than it declares. To deal with this problem, we propose an Overclaim Selfishness Detection Scheme (OSDS) to detect the potential OS groups. We find that a single secondary user tends to have one special type of sensing reports correlated with his physical location, thus the cluster number estimated by OSDS should be no much less than the number of users the group contains. Further, we adopt an incentive scheme to stimulate rational groups to behave honestly. Finally, a real world experiment is adopted to demonstrate the effectiveness of our proposed scheme OSDS.
Categorizing software applications for maintenance

Software repositories hold applications that are often categorized to improve the effectiveness of various maintenance tasks. Properly categorized applications allow stakeholders to identify requirements related to their applications and predict maintenance problems in software projects. Unfortunately, for different legal and organizational reasons the source code is often not available, thus making it difficult to automatically categorize binary executables of software applications. In this paper, we propose a novel approach in which we use Application Programming Interface (API) calls from third-party libraries as attributes for automatic categorization of software applications that use these API calls. API calls can be extracted from source code and more importantly, from the byte-code of applications, thus making automatic categorization approaches applicable to closed source repositories. We evaluate our approach along with other machine learning algorithms for software categorization on two large Java repositories: an open-source repository containing 3,286 projects and a closed-source one with 745 applications. Our contribution is twofold: not only do we propose a new approach that makes it possible to categorize software projects without any source code using a small number of API calls as attributes, but also we carried out the first comprehensive empirical evaluation of automatic categorization approaches.
LTE link level performance evaluation using stochastic channel models for V2X communication

This paper presents an evaluation of the Long Term Evolution (LTE) link level performance for vehicular communication. For this, doubly-selective stochastic channel models originally derived for IEEE 802.11p networks are adapted according to the LTE system characteristics. These modified channel models are implemented into a LTE link level simulator. By comparing the simulation results with results obtained using classical cellular channel models it is shown that a more detailed modeling of the channel dynamics typical for vehicular communications is of great importance. Moreover, the comparison with the physical layer performance of an IEEE 802.11p system shows good potential to utilize LTE for vehicular applications or as part of a hybrid communication system.
Cloning and expression of the genes specifying Shiga-like toxin production in Escherichia coli H19

Some strains of Escherichia coli produce a protein which is cytotoxic for Vero cell and HeLa cell monolayers. This toxin is very similar to the toxin of Shigella dysenteriae 1 and has been named verotoxin or E. coli Shiga-like toxin. It has been shown that toxin conversion is due to a group of bacteriophages, one of which has been designated H-19B. In this study we report hybridization experiments showing that part of the H-19B genome is homologous to phage lambda. We have cloned a 1.7-kilobase BalI-BglII fragment from the genome of H-19B into pUC18. The recombinant plasmid confers the ability to produce high levels of Shiga-like toxin on transformed E. coli cells. We demonstrate using an in vitro transcription/translation system that the cloned fragment specifies the two verotoxin subunit peptides which have masses of 31 and 5.5 kilodaltons. The identity of peptides was confirmed by immunoprecipitation with verotoxin antiserum and protein A-Sepharose beads.
Efficient Load-Aware Routing Scheme for Wireless Mesh Networks

This paper proposes a load-aware routing scheme for wireless mesh networks (WMNs). In a WMN, the traffic load tends to be unevenly distributed over the network. In this situation, the load-aware routing scheme can balance the load, and consequently, enhance the overall network capacity. We design a routing scheme which maximizes the utility, i.e., the degree of user satisfaction, by using the dual decomposition method. The structure of this method makes it possible to implement the proposed routing scheme in a fully distributed way. With the proposed scheme, a WMN is divided into multiple clusters for load control. A cluster head estimates traffic load in its cluster. As the estimated load gets higher, the cluster head increases the routing metrics of the routes passing through the cluster. Based on the routing metrics, user traffic takes a detour to avoid overloaded areas, and as a result, the WMN achieves global load balancing. We present the numerical results showing that the proposed scheme effectively balances the traffic load and outperforms the routing algorithm using the expected transmission time (ETT) as a routing metric.