Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Technologies for Enterprise Networks

Optical networking technologies have been over the last two decades reshaping the entire telecom infrastructure networks around the world. As network bandwidth requirements increase, optical communication and networking technologies have been moving from their telecom origin into the enterprise. For example, today in data centers, all storage area networking is based on fiber interconnects with speeds ranging form 1 Gb/s to 10 Gb/s. As the transmission bandwidth requirements increase and the costs of the emerging optical technologies become more economical, the adoption and acceptance of these optical interconnects within enterprise networks will increase. This paper, which provides the framework for the different optical interconnect technologies in this special optical issue of the Intel Technology Journal, is organized as follows. First, a brief overview of the fiber optics interconnects technology evolution and its current application within the enterprise, is presented. Second, various interconnect evolution paths, such as, board-to-board, chip-to-chip, and on-chip interconnects, are discussed.
Emprical study of a two-step approach to estimate translation quality

We present a method to estimate the quality of automatic translations when reference translations are not available. Quality estimation is addressed as a two-step regression problem where multiple features are combined to predict a quality score. Given a set of features, we aim at automatically extracting the variables that better explain translation quality, and use them to predict the quality score. The soundness of our approach is assessed by the encouraging results obtained in an exhaustive experimentation with several feature sets. Moreover, the studied approach is highly-scalable allowing us to employ hundreds of features to predict translation quality.
c-kit ligand gene expression in normal and sublethally irradiated mice.

The c-kit ligand (KL; Steel factor, mast cell growth factor, stem cell factor) is a hematopoietic factor that has been shown to act as a potent cofactor for hematopoietic growth and differentiation in vitro. The in vivo effects of KL, however, have been variable. To study the hematopoietic role of KL in vivo, we evaluated KL gene expression in both normal mice and mice recovering from myelosuppressive radiation exposure using the reverse transcriptase-polymerase chain reaction (RT-PCR) technique. In a single RNA sample, we found that the RT-PCR technique has high precision (co-efficient of variation, 15.7%). Amplifications of serial 1:2 dilutions of template RNA precisely correlated with starting RNA concentrations at 20 cycles or at 25 cycles, depending on the level of expression. Amplification of individual normal bone marrow and spleen cell RNA showed basal expression in all normal bone marrows but irregular expression in normal spleens. On day 2 after a sublethal 7.75-Gy (0.4 Gy/min) 60Co irradiation, splenic KL gene expression increased approximately 2.5-fold (P = .011), and bone marrow expression increased 15-fold (P = .004). During a 28-day postirradiation recovery period, KL expression increased in bone marrow on days 2 through 7. Splenic expression during the same period was more variable. In conclusion, the KL gene is invariably expressed in normal murine spleens. Postirradiation, recovering bone marrow and spleen both express increased levels of KL mRNA at day 2 and continue to express increased levels for several days postexposure. These data support a role for KL in the endogenous recovery of hematopoiesis after hypoplastic injury.
Designing VM schedulers for embedded real-time applications

Virtual Machines (VMs) allow for platform-independent software development and their use in embedded systems is increasing. In particular, VMs are rewarding in the context of mixed-criticality applications to provide isolation between critical and non-critical tasks running on the same processor. In this paper, we study the design of a real-time system based on a VM monitor/hypervisor that supports multiple VMs/domains. Since each VM in the system runs several real-time tasks, scheduling the VMs leads to a hierarchical scheduling problem. So far, most published techniques for analyzing hierarchical scheduling deal with the schedulabil-ity problem, i.e., for a given hierarchical scheduler, testing whether a set of real-time tasks meet their deadlines. In this paper, we are rather concerned with the synthesis of hier-archical/VM schedulers; that is, how to design a scheduler such that all real-time tasks running on the different VMs meet their deadlines. We consider a setup where the tasks are scheduled on multiple VMs under fixed priorities according to the Deadline Monotonic (DM) policy. The VMs are scheduled under fixed priorities on a Rate Monotonic (RM) basis using one or more processors. A partitioned scheduling of VMs is considered, i.e., VMs are not allowed to migrate from one processor to the other. In this context, we propose a method for selecting optimum time slices and periods for each VM in the system. Our goal is to configure the VM scheduler such that not only all tasks are schedulable but also the minimum possible resources are used. Finally, to illustrate the proposed design technique, we present a case study based on automotive control applications.
Adaptive Latency-Aware Query Processing on Encrypted Data for the Internet of Things

The massive adoption of The Internet of Things (IoT) and the creation of a smart-world around us leads to several privacy and security concerns. There has been significant work in the past to address the privacy and confidentiality of IoT data such as: providing secure end-to-end channels for the transmission of IoT data, encrypting IoT data using optimized cryptographic schemes such as order-preserving and homomorphic encryption that impose a reasonable energy overhead while improving security. However, for data intense IoT applications, decrypting large data sets using cryptographic schemes is significantly expensive in terms of latency as seen by the end user of the application. In this paper, we propose an Adaptive Latency-Aware Query Processing over encrypted IoT data that aims to: (i) minimize query latency for data intense applications as seen by the end user and, (ii) at the same time maintain minimum energy consumption overhead, comparable to the current schemes as much as possible. We present two main contributions: (i) a novel Adaptive latency-aware algorithm which chops down a single large query into several iterations of small sized queries by adaptively computing the optimal query size (t) for each iteration, and (ii) a novel IoT architecture with server cache suitable for addressing the latency issue through parallel execution, while leaving the Cloud database unmodified. Both contributions together allow minimizing query latency while maintaining minimum energy overhead. We evaluated the effectiveness of the proposed adaptive algorithm for latency and energy performance. The results show that the proposed adaptive solution delivers significantly a better latency performance while being comparable to the existing solutions in terms of energy efficiency.
Whole-body Motion Planning with Simple Dynamics and Full Kinematics

Abstract : To plan dynamic, whole-body motions for robots one conventionally faces the choice between a complex, fullbody dynamic model containing every link and actuator of the robot, or a highly simplified model of the robot as a point mass. In this paper we explore a powerful middle ground between these extremes. We present an approach to generate whole-body motions using a simple dynamics model, which enforces that the linear and angular momentum of the robot be consistent with the external wrenches on the robot, and a full-body kinematics model that enforces rich geometric constraints, such as end-effector positioning or collision avoidance. We obtain a trajectory for the robot and profiles of contact wrenches by solving a nonlinear optimization problem (NLP). We further demonstrate that we can plan without pre-specifying the contact sequence by exploiting the complementarity conditions between contact forces and contact distance. We demonstrate that this algorithm is capable of generating highly-dynamic motion plans with examples of a humanoid robot negotiating obstacle course elements and gait optimization for a quadrupedal robot.
Between Help and Engineering: constraints on user task automation

In this paper we describe two projects relevant for automating user tasks. The first one, EUROHELP was aimed at tools and methods for developing Intelligent Help Systems (IHS). Experiences and accomplishments in this project shed light on understanding and monitoring task performance of users. The major bottleneck appears the lack of understanding of the content of user tasks, calling for research in ontologies rather than trying to come to grips with the common sense semantics of the workplace. A major part of the paper describes the CommonKADS projects in which a methodology and tools were developed for building KBS. CommonKADS has a long (> 10 years) and extensive history, and it is argued that it has survived several shifts in attention in problems about knowledge acquisition (KA), because it has focused on well defined, coherent specification languages. The need for such a core is signalled for current approaches in KA which provide operational components ((configurations of) mechanisms) to domain perts to develop small, but practical knowledge based systems.
Designing Robust GALS Circuits with Triple Modular Redundancy

In this paper we describe a new method for building fault-tolerant globally asynchronous locally synchronous (GALS) systems based on triple modular redundancy. In order to avoid a single point of failure, we present a redundant clocking scheme for providing independent clock signals to the triplicated GALS modules. Independent clocking, however, requires the use of an alternative recovery mechanism, which performs voting only at certain safe synchronization points. An implementation of an asynchronous state machine controlling this recovery process is given and thoroughly evaluated concerning its robustness against soft-errors. With the help of model checking the fault-resilience of the proposed circuit is formally verified. A case study, including a description for the design automation of GALS-based TMR systems, shows the viability of the new architecture. The resulting solution offers low area overheads and a competitive performance compared to standard TMR approaches.
Self-organization of wireless sensor networks based on severity of an emergency environment

Wireless sensor networks (WSNs) composed of large numbers of sensor nodes to monitor the environment and collect data. Self-organization of sensor nodes has been widely investigated to enhance the quality of service provided by WSNs. This paper proposes a Severity based Clustering Algorithm for Emergency (SCAE) for WSNs. The residual energy of nodes and severity of the emergency environment are the key parameters of SCAE. The severity of the emergency situation is estimated by using Dempster-Shafer theory. SCAE minimizes the communication loss due to node failures in an emergency environment. This is achieved by delaying the cluster head failures in the network and allocating less priority to non-cluster heads to select their cluster head which might get dropped from the network quickly. Communication failures due to node failures have been minimized successfully by considering the severity of the emergency. Additionally the simulation result shows that the SCAE prolong the network lifetime compared to some existing algorithms.
Intuitive page-turning interface of e-books on flexible e-paper based on user studies

In this paper we propose an intuitive page-turning and browsing interface of e-books on a flexible e-paper based on user studies. Our user studies showed various types of page-turning actions such as flipping, grasping, and sliding by different situations or users. We categorized these actions into three categories: turn, flip through, and leaf through the page(s). Based on this categorized model, we have developed a conceptual design and prototype of an interface for an e-book reader, which enables intuitive page-turning interactions using a simple architecture in both hardware and software design. The prototype has a flexible plastic sheet with bend sensors, which is attached to a small LCD monitor to physically unite the visual display with a tangible control interface based on the natural page-turning actions as used in reading a real book. The prototype handles all three page-turning actions observed in the user studies by interpreting the bend degree of the sheet.
RVMT-BIP : A Tool for the Runtime Verification of Multi-Threaded Component-Based Systems

RVMT-BIP is a tool for the runtime verification of multi-threaded componentbased systems described in the Behavior, Interaction, Priority (BIP) framework against linear-time and logic-independent properties. RVMT-BIP implements rigorous semantics-preserving transformations of BIP systems to instrument and inject a sound and concurrency-preserving global-state reconstructor for on-the-fly monitoring. Our experiments on several multi-threaded BIP systems show that RVMTBIP generally induces a cheap runtime overhead, especially when the system is highly concurrent.
NLPComp in TAC 2012 Entity Linking and Slot-Filling

The NLPComp team participated in two TAC-KBP2012 tasks: Regular Entity Linking and Regular Slot Filling. For the entity linking task, a three-step entity linking system is developed. In the ﬁrst step, a list of possible candidates are selected. Then the best candidate is identiﬁed to decide whether a link exists. In addition, a document clustering algorithm is used to group NIL queries. This system uses the Wikipedia anchor terms to enlarge the number of candidate instances. It then incorporates the topic modelling technique to select features of topical words. However, our system produces a poor answer coverage and the NIL detection system brings signiﬁcant loss in the ﬁnal F-score. For the slot ﬁlling task, we developed a system which combines rule-based approach and multiple instance learning technique. In rule-based slot ﬁlling, a number of trigger words are collected from the English Wikipedia and the frequency feature is used to select ﬁnal slot value(s). When extracting slot value(s) using the multiple instance learning technique, 2009 and 2010 KBP slot ﬁlling queries and manually annotated answers are used to create named entity pairs which exhibit a particular relation. Then bags of sentences containing named entity pair are extracted from the KBP source. Our system reaches the median level among all the participating systems.
Data Mirroring for Metro WDM Storage Area Networks

This paper presents a novel data mirroring method for storage area networks (SANs) in metropolitan area WDM (wavelength division multiplexing) ring network scenario. We describe our network architecture, the protocol, the network traffic and our SAN mirroring method. The WDM ring network with the SAN mirroring is analyzed next using simulation results for average node throughput, queuing delay and packet dropping probability.
TRAINING DEICTIC RELATIONS TO CHILDREN WITH DEVELOPMENTAL DELAYS THROUGH THE USE OF THE PEAK RELATIONAL TRAINING SYSTEM

Children with developmental delays often do not acquire perspective taking skills without training. These skills are imperative to the ability to relate to others socially and the development of appropriate social behavior. They may lack the ability to recognize that another person’s view may differ from their own, or that reality may differ from appearance. This study used deictic relational training to aid in the development of a ‘Theory of Mind’ and the acquisition of perspective taking skills. The PEAK relational training system was used in a special education classroom to train YOU and I relations, as well as YOU and I reversal, to two nine year old students with intellectual disability. Multiple exemplar training was then used to promote the generalization of these perspective taking skills to an in situ deceptive container task. One subject participated in the first two training phases, but was absent for the remainder of the study. The second subject successfully completed all training phases and was then able to correctly respond when asked to report what the perspective of another individual would be.
From Smart Objects to Smarter Workflows -- An Architectural Approach

To live up to its promised business impact, the emerging Internet of Things must be accompanied by new ways to decentralize and adapt business logic for smart objects. Smart object lifecycle management refers to the decentralized, event-based, context-sensitive execution and adaptation of processes by smart physical objects during their path through different domains and their interaction with other entities. These entities include the end-user or consumer, other objects, or web-based information services. In this paper, we propose an approach how events and reaction to those events can be virtually attached to certain situations in the lifecycle of physical smart objects. In addition, we present an smart object lifecycle architecture for enabling multiple stakeholders to provide the necessary event and processing information for specific domains.
nfn 2 dlp : A Normal Form Nested Programs Compiler ⋆

Normal Form Nested (NFN) programs have recently been introduced in order to allow for enriching the syntax of disjunctive logic programs und er the answer sets semantics. In particular, heads of rules can be disjunctions of conjunctions, while bodies can be conjunctions of disjunctions. Different to ma ny other proposals of this kind, NFN programs may contain variables, and a notion of safety has been defined for guaranteeing domain independence. M or over, previous results show that there is a polynomial translation from NFN program s to standard disjunctive logic programs (DLP). In this paper we present the tool nfn2dlp, a compiler for NFN programs, which implements an efficient translation from safe NFN programs to safe DLP p rograms. The answer sets of the original NFN program can be obtained fr om the answer sets of the transformed program (which in turn can be obtained b y using a DLP system) by a simple transformation. The system has been implemente d using the object-oriented programming language Ruby and Treetop, a lan guage for Parsing Expression Grammars (PEGs). It currently produces D LP programs in the format of DLV and can use DLV as a back-end.
A1CSA: An energy-efficient fast adder architecture for cell-based VLSI design

Energy-efficient fast adders are needed in the design of battery-powered portable devices. Although many fast adder architectures exist, most of them require transistor-level optimizations that prevent their synthesis in a standard-cell flow. This paper presents two energy-efficient Add-One Carry-Select Adders (A1CSA and A1CSAH) suited for standard-cells synthesis. Synthesis results showed that the A1CSA is the smallest fast adder requiring, on average, 22.2% less area than the Carry-Select Adder. They also showed that the A1CSAH is, on average, 10.8% faster and 3.4% more energy-efficient than the Carry-Lookahead Adder, thus corresponding to the best choice for high speed and high efficiency addition.
Communication Efficient Distributed Kernel Principal Component Analysis

Kernel Principal Component Analysis (KPCA) is a key machine learning algorithm for extracting nonlinear features from data. In the presence of a large volume of high dimensional data collected in a distributed fashion, it becomes very costly to communicate all of this data to a single data center and then perform kernel PCA. Can we perform kernel PCA on the entire dataset in a distributed and communication efficient fashion while maintaining provable and strong guarantees in solution quality? In this paper, we give an affirmative answer to the question by developing a communication efficient algorithm to perform kernel PCA in the distributed setting. The algorithm is a clever combination of subspace embedding and adaptive sampling techniques, and we show that the algorithm can take as input an arbitrary configuration of distributed datasets, and compute a set of global kernel principal components with relative error guarantees independent of the dimension of the feature space or the total number of data points. In particular, computing k principal components with relative error ε over s workers has communication cost Õ(spk/ε+sk2/ε3) words, where p is the average number of nonzero entries in each data point. Furthermore, we experimented the algorithm with large-scale real world datasets and showed that the algorithm produces a high quality kernel PCA solution while using significantly less communication than alternative approaches.
The relative importance of carbohydrate and nitrogen for the resprouting ability of Quercus crispula seedlings.

BACKGROUND AND AIMS Plants need some kind of stored resources to resprout after shoot destruction. The aim of this study was to determine the relative importance of carbohydrate and nitrogen (N) storage levels for their ability to resprout. METHODS A shoot clipping experiment was conducted on Quercus crispula seedlings, which were grown in a factorial experimental design, with two light levels (40% and 3% of full light) and three nutrient concentrations (low, medium and high). KEY RESULTS At the time of shoot clipping (the end of spring leaf expansion), seedlings exposed to 40% light had an average total non-structural carbohydrate (TNC) concentration of 17.0% in their roots compared with 4.9% in the roots of seedlings exposed to 3% light, and the average amount of TNC (TNC pools) in the roots was 203.8 mg and 20.0 mg at 40% light and 3% light, respectively. In contrast, root N concentration averaged 2.3% in the 3% light treatment compared with 1.2% in the 40% light treatment, and it increased with successive rises in nutrient concentrations at both light levels. Regardless of the nutrient status, at the 40% light level >80% of the seedlings resprouted after shoot clipping. Few seedlings, however, resprouted at the 3% light level, particularly in the medium- and high-nutrient treatments. Furthermore, both root TNC concentrations and TNC pools decreased after resprouting, but the amount of root N remained constant. CONCLUSIONS These results suggest that carbohydrate storage has a stronger influence on resprouting in Quercus crispula than N storage. However, the size of the resprouting shoot was positively correlated with the amount of both N and TNC in roots. The level of N storage is, therefore, also important for the growth of resprouting shoots.
Learning-Based Constraint Satisfaction With Sensing Restrictions

In this paper we consider graph-coloring problems, an important subset of general constraint satisfaction problems that arise in wireless resource allocation. We constructively establish the existence of fully decentralized learning-based algorithms that are able to find a proper coloring even in the presence of strong sensing restrictions, in particular sensing asymmetry of the type encountered when hidden terminals are present. Our main analytic contribution is to establish sufficient conditions on the sensing behavior to ensure that the solvers find satisfying assignments with probability one. These conditions take the form of connectivity requirements on the induced sensing graph. These requirements are mild, and we demonstrate that they are commonly satisfied in wireless allocation tasks. We argue that our results are of considerable practical importance in view of the prevalence of both communication and sensing restrictions in wireless resource allocation problems. The class of algorithms analyzed here requires no message-passing whatsoever between wireless devices, and we show that they continue to perform well even when devices are only able to carry out constrained sensing of the surrounding radio environment.