Miracle-dz/UniFAR-FTUs huggingface dataset:
Formal Verification of MSSP

MSSP is a new execution paradigm that achieves high performance by removing correctness constraints from the critical path. A collection of concurrently executing slave processors, which are not on the critical path, check the operation of a single master processor, whose execution is on the critical path, but is fast because it need not be correct. This report formally verifies that such an execution model works, in the sense that it correctly achieves a sequential execution of the application code. We describe abstract models of both the sequential and MSSP execution paradigms, and distill from these the fundamental aspects of functionality that are needed to establish their equivalence. The verification itself is an iterative process. We begin with a number of high-level assumptions, which we use to prove some very basic results, and then successively refine our formalisms to argue that our initial assumptions are indeed reasonable. In formally reasoning about MSSP, we achieve a number of goals. First, and most importantly, we demonstrate that MSSP is indeed equivalent to sequential execution. Second, we derive an abstract model of MSSP execution, which permits us to distill the fundamental properties of an MSSP machine that are necessary for correct operation. Having thus enumerated those properties, we facilitate reasoning about the impacts on correctness (and hence the feasibility) of new design ideas. Finally, we show that operation of the master—which is not guaranteed to be correct—does not compromise overall correctness. We thus demonstrate that an architecture in which performance and correctness are pursued as distinct design goals is indeed viable, at least from the point of view of maintaining correctness irrespective of the activity of the performance sub-component.
Restoration of a Mediterranean forest after a fire: bioremediation and rhizoremediation field-scale trial

Forest fires pose a serious threat to countries in the Mediterranean basin, often razing large areas of land each year. After fires, soils are more likely to erode and resilience is inhibited in part by the toxic aromatic hydrocarbons produced during the combustion of cellulose and lignins. In this study, we explored the use of bioremediation and rhizoremediation techniques for soil restoration in a field‐scale trial in a protected Mediterranean ecosystem after a controlled fire. Our bioremediation strategy combined the use of Pseudomonas putida strains, indigenous culturable microbes and annual grasses. After 8 months of monitoring soil quality parameters, including the removal of monoaromatic and polycyclic aromatic hydrocarbons as well as vegetation cover, we found that the site had returned to pre‐fire status. Microbial population analysis revealed that fires induced changes in the indigenous microbiota and that rhizoremediation favours the recovery of soil microbiota in time. The results obtained in this study indicate that the rhizoremediation strategy could be presented as a viable and cost‐effective alternative for the treatment of ecosystems affected by fires.
Reducing computational complexity of coded caching by partitioning users into groups: poster

In this paper, we show how to partition K users in a coded caching domain into L groups to reduce the complexity from O(2K) to [EQUATION]. We first show that the negative effects of the heterogeneity of cache sizes and partitioning on coded caching performance neutralize each other, which is the theoretical foundation of our partitioning schemes. We then prove the submodularity of the coded caching traffic volume function, based on which we develop a partition algorithm for minimizing the traffic volume. We show that the approximation ratio of proposed algorithm is better than the state-of-art result. Moreover, we develop a rounding algorithm to obtain the final partition based on Lovász extension, which resolves the issue of unbalanced partition. We implement our schemes in a testbed and validate our analysis and design with experiment results.
CD146 Deletion in the Nervous System Impairs Appetite, Locomotor Activity and Spatial Learning in Mice

Cell adhesion molecules (CAMs) are crucial effectors for the development and maintenance of the nervous system. Mutations in human CAM genes are linked to brain disorders and psychological diseases, and CAM knockout mice always exhibit similar behavioral abnormalities. CD146 is a CAM of the immunoglobulin superfamily that interacts with Neurite Outgrowth Factor and involved in neurite extension in vitro. However, little is known about its in vivo function in the nervous system. In this study, we used a murine CD146 nervous system knockout (CD146ns-ko) model. We found that the brains of some CD146ns-ko mice were malformed with small olfactory bulbs. CD146ns-ko mice exhibited lower body weights and smaller food intake when compared with wild type littermates. Importantly, behavior tests revealed that CD146ns-ko mice exhibited significant decreased locomotor activity and impaired capacity for spatial learning and memory. Our results demonstrate that CD146 is important for mammalian nervous system development and proper behavior patterns.
The Status of Honey Bee Health in Italy: Results from the Nationwide Bee Monitoring Network

In Italy a nation-wide monitoring network was established in 2009 in response to significant honey bee colony mortality reported during 2008. The network comprised of approximately 100 apiaries located across Italy. Colonies were sampled four times per year, in order to assess the health status and to collect samples for pathogen, chemical and pollen analyses. The prevalence of Nosema ceranae ranged, on average, from 47–69% in 2009 and from 30–60% in 2010, with strong seasonal variation. Virus prevalence was higher in 2010 than in 2009. The most widespread viruses were BQCV, DWV and SBV. The most frequent pesticides in all hive contents were organophosphates and pyrethroids such as coumaphos and tau-fluvalinate. Beeswax was the most frequently contaminated hive product, with 40% of samples positive and 13% having multiple residues, while 27% of bee-bread and 12% of honey bee samples were contaminated. Colony losses in 2009/10 were on average 19%, with no major differences between regions of Italy. In 2009, the presence of DWV in autumn was positively correlated with colony losses. Similarly, hive mortality was higher in BQCV infected colonies in the first and second visits of the year. In 2010, colony losses were significantly related to the presence of pesticides in honey bees during the second sampling period. Honey bee exposure to poisons in spring could have a negative impact at the colony level, contributing to increase colony mortality during the beekeeping season. In both 2009 and 2010, colony mortality rates were positively related to the percentage of agricultural land surrounding apiaries, supporting the importance of land use for honey bee health.
The Design and Development of Shop-Level Information Integration System

The shop-level information integrated system is the glue of system integration between enterprise upper management systems and the underlying process control systems, the basic of executing enterprises' agile manufacture strategy. The field management and shop information integration are given more and more attention by enterprises, the methods and technologies of traditional shop management can't meet current needs of enterprise. This shop-level information integration system adopted OPC protocol based on XML and SOAP and the multi-field bus integration technology of enterprise based on EDDL, it can effectively solve cross-platform, information integration and the communication problem of different equipments, as well as the problem of the network communication of multi-field bus environment and the remote supervision of the state of the equipment. At the same time, an interface management platform was designed and developed by adopting SOA architecture concept; it can achieve data integration and sharing between shop management system and enterprise upper management systems.
Channel Modeling and Reliability Enhancement Design Techniques for STT-MRAM

Spin transfer torque magnetic random access memory (STT-MRAM) has emerged as a potential candidate for the next-generation universal memory technology. However, many challenges still exist that block its commercialization and application. One of the main challenges is the reliability concerns, especially as technology scales down to nanometer nodes. For example, the intrinsic stochastic switching characteristics and asymmetry of the STT driven magnetic tunnel junction (MTJ), as well as the process, voltage and temperature (PVT) variations of the manufacturing process. Generally, there exists a technical gap between the device-level designers and system-level circuit or system designers for the STT-MRAM optimizations. To our knowledge, no efficient evaluation tools have been proposed to provide an effective link between them until now. In this paper, we aim to firstly give quantitative analyses of the reliability issues of STT-MRAM, taking into consideration the physical properties and manufacturing process. Secondly, based on these analysis results, a channel model from informative perspective would then be proposed. This model provides an effective simulation tool to evaluate the reliability performance of STT-MRAM. Finally, reliability enhancement design considerations and strategies are presented for the STT-MRAM circuit and system designers.
A Case Based Reasoning Framework for Service Selection and Adaptation in Mobile Networks

Service selection and adaptation is of paramount importance in contemporary mobile networks. Many diverse parameters should be taken into account (e.g., user context, terminal and network capabilities) for the selection of the appropriate service or the required service adaptations. In this paper we propose a framework for service selection and adaptation. A case based reasoning system (CBRS) is used to select the most appropriate service. Services are modelled using formal semantics. The CBRS retrieves the most appropriate service by comparing previous cases with the current service request. This comparison is performed using similarity metrics. We elaborate on the different aspects of the discussed architecture and provide indicative examples to illustrate the versatility of the proposed scheme.
Determining Functional Attributes of Objects in Images

I have used a data-driven approach to detect functional attributes of objects in images. Using ImageNet and Amazon Mechanical Turk, I have created a dataset of more than 7000 images with functional attribute labels. I have used these labels to train functional classifiers, and I compared the precision and recall of these classifiers to the precision and recall of the Amazon Mechanical Turk workers who provided the labels.
UNCOVERING OVERLAPPING STRUCTURES VIA STOCHASTIC COMPETITIVE LEARNING

Resumo: Competitive learning is an important approach in Machine Learning. In this paper, we present a method for determining overlapping structures or vertices in the network using a stochastic competitive model, where several particles walk in the network and compete with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The proposed measure for detecting overlapping structures is built from the rich information that is inherently embedded within the model description. Therefore, no extra processing is necessary to detect the overlapping structures in the data. Computer simulations reveal that this overlapping index works well in real-world data sets.
Neural Circuit Mechanisms Underlying Dopamine Reward Prediction Errors

Dopamine neurons are thought to facilitate learning by signaling reward prediction errors (RPEs), the discrepancy between actual and expected reward. However, how RPEs are calculated remains unknown. In Chapter 1, I tested the hypothesis that RPE signals in dopamine neurons are inherited entirely from the lateral habenula, by examining how lesions of the habenular complex affect the response of optogenetically-identified dopamine neurons in mice. I found that despite large lesions of habenula, dopamine neurons maintained features of RPE coding pertaining to phasic dopamine responses. Interesting, a specific aspect of RPE signaling— the inhibitory responses caused by reward omission—was greatly diminished in habenula lesion animals. These results suggested that the RPE signals in dopamine neurons were not simply relayed from habenula and that multiple mechanisms underlie RPE signaling. In Chapter 2, I systematically examined how RPE is computed at a neural circuit level, by combining rabies virus-based monosynaptic retrograde tracing with optogenetic cell identification during electrophysiological recording. We characterized the firing patterns of 205 neurons presynaptic to dopamine neurons (“input neurons”) from 7 major VTA input areas in behaving mice. We found that relatively few input neurons signaled purely ‘actual’ reward or ‘expected’ reward. Instead, many input neurons across brain areas signaled combinations of these types of information. We also found that some input neurons signaled already-computed RPEs. These results demonstrate that the information required for dopamine neurons to compute
6DOF entropy minimization SLAM

In this paper, we propose and validate an entropy minimization algorithm for solving the SLAM problem in the 6DOF case with semi-sparse (stereo) data. The proposed SLAM solution relies on both an efficient and robust strategy for egomotion estimation and an effective global rectification strategy. Our global rectification method is scalable because it relies on dynamically compressing actions, in order to reduce the number of variables to optimize, and thus on integrating/fusing observations. We have implemented a wearable stereo device that runs the SLAM algorithm in real time and we have tested such implementation both in indoor and outdoor scenarios. Our experiments show that action compression is a critical element for yielding acceptable and efficient solutions to the global optimization problem in the 6DOF case
Ranking Preserving Hashing for Fast Similarity Search

Hashing method becomes popular for large scale similarity search due to its storage and computational efficiency. Many machine learning techniques, ranging from unsupervised to supervised, have been proposed to design compact hashing codes. Most of the existing hashing methods generate binary codes to efficiently find similar data examples to a query. However, the ranking accuracy among the retrieved data examples is not modeled. But in many real world applications, ranking measure is important for evaluating the quality of hashing codes. In this paper, we propose a novel Ranking Preserving Hashing (RPH) approach that directly optimizes a popular ranking measure, Normalized Discounted Cumulative Gain (NDCG), to obtain effective hashing codes with high ranking accuracy. The main difficulty in the direct optimization of NDCG measure is that it depends on the ranking order of data examples, which forms a non-convex nonsmooth optimization problem. We address this challenge by optimizing the expectation of NDCG measure calculated based on a linear hashing function. A gradient descent method is designed to achieve the goal. An extensive set of experiments on two large scale datasets demonstrate the superior ranking performance of the proposed approach over several state-of-the-art hashing methods.
Imposing Hard Constraints on Deformable Models through Optimization in Orthogonal Subspaces

An approach is presented for imposing generic hard constraints on deformable models at a low computational cost, while preserving the good convergence properties of snake-like models. We believe this capability to be essential not only for the accurate modeling of individual objects that obey known geometric and semantic constraints but also for the consistent modeling of sets of objects. Many of the approaches to this problem that have appeared in the vision literature rely on adding penalty terms to the objective functions. They rapidly become intractable when the number of constraints increases. Applied mathematicians have developed powerful constrainted optimization algorithms that, in theory, can address this problem. However, these algorithms typically do not take advantage of the specific properties of snakes. We have therefore designed a new algorithm that is closely related to Lagrangian methods but is tailored to accommodate the particular brand of deformable models used in the image understanding community. We demonstrate the validity of our approach first in two dimensions using synthetic images and then in three dimensions using real aerial images to simultaneously model terrain, roads, and ridgelines under consistency constraints.
Texture instance similarity via dense correspondences

This paper concerns the task of evaluating the similarity of textures instances: Rather than discriminating between different texture classes, our goal is to identify when two images display the same texture instance. To address this problem, we propose an approach inspired by alignment based recognition theories. We offer a pixel-based method, employing a robust, dense correspondence estimation engine, applied to an efficient, novel representation, to match the pixels of two texture photos. We describe means for quantifying the quality of these matches, considering in particular the quality of the flow established between the two images. These quality measures are effectively combined into similarity scores by using standard linear SVM classifiers. By relying on a general, alignment based approach our method can be applied to different problem domains (different texture classes) with little modification. We demonstrate this by reporting state-of-the-art results on benchmarks for fingerprint recognition and two new benchmarks for texture-based animal identification.
Optimal gaits for compass-like biped robot

With free walk velocity; the individual will naturally enlist the speed that will provide maximum energy efficiency. Inspired by this idea, we exploit a passivity based control strategy to study, the preferred walking gait of a compass-like biped robot. In fact, for any ground slope, we use a dynamic optimization of the mechanical cost which is proportional to the mechanical work in the system joints, to calculate the forward velocity that minimizes the energy expenditures.
Anonymous Processors with Synchronous Shared Memory

We investigate anonymous processors computing in a synchronous manner and communicating via read-write shared memory. This system is known as a parallel random access machine (PRAM). It is parameterized by a number of processors~$n$ and a number of shared memory cells. We consider the problem of assigning unique integer names from the interval $[1,n]$ to all $n$ processors of a PRAM. We develop algorithms for each of the eight specific cases determined by which of the following independent properties hold: (1) concurrently attempting to write distinct values into the same memory cell either is allowed or not, (2) the number of shared variables either is unlimited or it is a constant independent of $n$, and (3) the number of processors~$n$ either is known or it is unknown. Our algorithms terminate almost surely, they are Las Vegas when $n$ is known, they are Monte Carlo when $n$ is unknown, and they always use the $O(n\log n)$ expected number of random bits. We show lower bounds on time, depending on whether the amounts of shared memory are constant or unlimited. In view of these lower bounds, all the Las Vegas algorithms we develop are asymptotically optimal with respect to their expected time, as determined by the available shared memory. Our Monte Carlo algorithms are correct with probabilities that are $1-n^{-\Omega(1)}$, which is best possible when terminating almost surely and using $O(n\log n)$ random bits.
The communication and streaming complexity of computing the longest common and increasing subsequences

We consider the communication complexity of finding the longest increasing subsequence (LIS) of a string shared between two parties. We prove tight bounds for the space complexity of randomized one-pass streaming algorithms for this problem. Our bounds are parameterized in terms of the LIS of the inputs. This resolves an open question in [19]. We also give the first bounds for approximating the LIS and its length. Next, we consider the communication complexity of finding the longest common subsequece (LCS) of two strings held by different parties, as well as the problem of approximating its length. We improve the existing lower bounds for these problems, even in the most difficult case when both parties have a permutation of N symbols. Our results yield tight space bounds for multipass deterministic streaming algorithms. For randomized mutlipass algorithms, our bounds are tight up to a logarithmic factor.
A continuous query processing approach for window semantics in data stream system

For the data stream characteristics of continuous, unbounded and real-time, continuous queries over data streams are generally based on windows. Windows placed on data streams are maintained by operators themselves for a query in most DSMSs. However, some operators can't maintain the windows properly, and a lot of redundancy and inconsistency may be incurred because the tuples are heavily copied and operators interfere with each other. In this paper, we thoroughly discuss the query processing mechanism based on windows and analyze the window semantics in a query sentence, then propose a query processing approach MullayerQuery that extract stream window and operator windows from the query window. A strategy and some algorithms are given to keep the consistency among windows in a query. The experiments show that MullayerQuery runs effectively and efficiently.
Distinct cortical locations for integration of audiovisual speech and the McGurk effect

Audiovisual (AV) speech integration is often studied using the McGurk effect, where the combination of specific incongruent auditory and visual speech cues produces the perception of a third illusory speech percept. Recently, several studies have implicated the posterior superior temporal sulcus (pSTS) in the McGurk effect; however, the exact roles of the pSTS and other brain areas in “correcting” differing AV sensory inputs remain unclear. Using functional magnetic resonance imaging (fMRI) in ten participants, we aimed to isolate brain areas specifically involved in processing congruent AV speech and the McGurk effect. Speech stimuli were composed of sounds and/or videos of consonant–vowel tokens resulting in four stimulus classes: congruent AV speech (AVCong), incongruent AV speech resulting in the McGurk effect (AVMcGurk), acoustic-only speech (AO), and visual-only speech (VO). In group- and single-subject analyses, left pSTS exhibited significantly greater fMRI signal for congruent AV speech (i.e., AVCong trials) than for both AO and VO trials. Right superior temporal gyrus, medial prefrontal cortex, and cerebellum were also identified. For McGurk speech (i.e., AVMcGurk trials), two clusters in the left posterior superior temporal gyrus (pSTG), just posterior to Heschl’s gyrus or on its border, exhibited greater fMRI signal than both AO and VO trials. We propose that while some brain areas, such as left pSTS, may be more critical for the integration of AV speech, other areas, such as left pSTG, may generate the “corrected” or merged percept arising from conflicting auditory and visual cues (i.e., as in the McGurk effect). These findings are consistent with the concept that posterior superior temporal areas represent part of a “dorsal auditory stream,” which is involved in multisensory integration, sensorimotor control, and optimal state estimation (Rauschecker and Scott, 2009).