Miracle-dz/UniFAR-FTUs huggingface dataset:
ArchExplorer.org: joint compiler/hardware exploration for fair comparison of architectures

While reproducing experimental results of research articles is standard practice in mature domains of science, such as physics or biology, it has not yet become mainstream in computer architecture. However, recent research shows that the lack of a fair and broad comparison of research ideas can be significantly detrimental to the progress, and thus the productivity, of research. At the same time, the complexity of architecture simulators and the fact that simulators are not systematically disseminated with novel ideas are largely responsible for this situation. While this methodology has a fundamental impact on research, it is by essence a practical issue. In this article, we present and put to task an atypical approach which aims at overcoming this practical methodology issue, and which takes the form of an open and continuous exploration through a server-side web infrastructure. First, rather than requiring from a researcher to engage in the daunting task of seeking, installing and running the simulators of many alternative mechanisms, we propose that researchers upload their simulator to the infrastructure, where the corresponding mechanism is automatically compared against all known ideas so far. Second, the comparison takes the form of a broad compiler/hardware exploration, so that a new mechanism is deemed superior only if it can outperform a tuned baseline and all known tuned mechanisms, for a given area and/or power budget. These two principles considerably facilitate a fair and quantitative comparison of research ideas. The web infrastructure is now publicly open, and we put the overall approach to task with a set of data cache mechanisms. We explain how the tools and methodological issues of contributed simulators can be overcome, and we show that this broad exploration can challenge some earlier assessments about data cache research.
Research on Localization and Mapping for Lunar Rover Based on RBPF-SLAM

The capability of autonomous navigation is very important for lunar rover exploring in an unknown environment. In this paper, a method of simultaneous localization and mapping based on Rao-Blackwellized particle filters (RBPF-SLAM) is adopted to improve the precision of inertial positioning for the rover and to build a 2D grid map for the environment. Lunar rover’s motion model is built by combining Strapdown Inertial Navigation System (SINS) with a kind of odometry model, and the observation model of LiDAR is built using a likelihood field (LF) approach. Then the traditional RBPF-SLAM algorithm is improved¿First, the most recent observation and the global map built before are considered in the proposal distribution; second, a grid-based incremental mapping method is presented. The results of simulation experiment show that the precision of localization by SINS is improved significantly using this method of RBPF-SLAM and an accurate and consistent 2D grid map is built successfully.
Towards effective subspace clustering with an evolutionary algorithm

We propose a new evolutionary algorithm for subspace clustering in very large and high-dimensional databases. The design includes task-specific coding and genetic operators, along with a nonrandom initialization procedure. Experimental results show that the algorithm scales almost linearly with the size and dimensionality of the database as well as the dimensionality of the hidden clusters. Our algorithm is able to discover clusters of different densities embedded in both low and high dimensional subspaces of the original space. Finally, the discovered knowledge is presented in the form of nonoverlapping clustering rules where only those features relevant to the clustering are reported. These two properties contributes to the relatively high comprehensibility of the clustering output.
Optimal path generation for excavator with neural networks based soil models

In order to automate the excavating process, the path of the excavator bucket tip should be optimally generated. The following four factors must be considered when the bucket path is determined: bucket volume (soil capacity in a bucket), reachability (backhoe structure limitation), time efficiency, and soil property. Among them, the soil property is hardly quantified due to the complexity of its mechanical behavior. This paper deals with a neural network model to identify the soil property. Human operator usually determines soil type by sensing its hardness given a specific path and then plans a safe and workable path. The neural network model proposed in this paper outputs the soil type with the force and trajectory inputs. The feasibility of the proposed system is proved through the experiments with a robot equipped with a force sensor.
Contention-Free Fair Queuing for High-Speed Storage with RAID-0 Architecture

Controlling the latency and throughput is a crucial part of designing an I/O scheduler that can provide guaranteed quality-of-service of a shared storage resource to multiple competing applications. Previously proposed I/O schedulers have mainly focused on ensuring proportional bandwidth allocation and a user-defined latency. However, the algorithms are not optimized for high-speed storage with restricted resources such as solid-state drives (SSD), because the storage performance can widely vary depending on the I/O patterns or the delays associated with different I/O operations as well as the I/O size. To solve these problems, we propose contention-free fair queuing (CFFQ) that accurately predicts the processing time of I/O requests considering the internal constraints of the storage, such as static striping and limited concurrency. Moreover, CFFQ compensates for the loss time of unselected I/O requests to ensure the bounded worst-case latency. In experiments, we have evaluated the guaranteed QoS of CFFQ versus the previous I/O scheduler (SFQ(D)) for diverse workloads. The experiments show that the throughput of CFFQ versus SFQ(D) is increased by up to 65% according to the workload while guaranteeing fairness and the worst-case latency.
Intrusive Thoughts Elicited by Direct Electrical Stimulation during Stereo-Electroencephalography

Cortical direct electrical stimulation (DES) is a method of brain mapping used during invasive presurgical evaluation of patients with intractable epilepsy. Intellectual auras like intrusive thoughts, also known as forced thinking (FT), have been reported during frontal seizures. However, there are few reports on FT obtained during DES in frontal cortex. We report three cases in which we obtained intrusive thoughts while stimulating the dorsolateral prefrontal cortex and the white matter in the prefrontal region. In order to highlight the effective connectivity that might explain this clinical response, we have analyzed cortico-cortical potentials evoked by single pulse electrical stimulation.
A fully implicit parallel EOS compositional simulator for large scale reservoir simulation.

This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Abstract A fully implicit parallel equation-of-state (EOS) compositional simulator for large-scale reservoir simulation is presented. This simulator is developed under the framework named IPARS (Integrated Parallel Accurate Reservoir Simulator) and is constructed using a Newton-type formulation. The Peng-Robinson EOS is used for the hydrocarbon phase behavior calculations. The linear solvers from the PETSc package (Portable Extensible Toolkit for Scientific Computation) are used for the solution of the underlying linear equations. The framework provides input/output, table lookups, Fortran array memory allocation, domain decomposition, and message passing between processors for updating physical properties in mass-balance equations in overlapping regions. PETSc handles communications between processors needed for the linear solver. Many test runs were performed with up to four million gridblocks for a dry-gas injection process on an IBM SP machine and half a million gridblocks on a cluster of 16 PCs. Results indicate that the scalability of the simulator is very good. The linear solver takes around half of the total computational time for homogeneous reservoirs. For layered heterogeneous reservoirs, the linear solver took a larger fraction of the total computational time as the permeability contrast increased. The time for the communication between processors for updating the flow equations is insignificant. The PC cluster is roughly a factor of two slower than the SP for parallel runs, which is very encouraging. This factor is strongly related to the hardware configuration of the computers, which is detailed in the paper.
Automatic detection of meniscal area in the knee MR images

Nowadays computer-aided medical systems has become widespread. These systems assist the scientists in the medical field with diagnosis and treatment. In the same vein, in this study detection of medial meniscus from MR images of the knee is performed automatically. Knee MR images used in this study were obtained from Osteoarthritis initiative. 75% of MR images were used for training, while the remainder was used for the test. Attributes to be used in the training and test process were obtained by the Histogram of Oriented Gradients (HOG) method. The regression approach used in the training process and found correlation and mean square error value for patch in different sizes. The maximum correlation value detected is about 91%. The objective of the study will be to accelerate the current system for minimizing the time for treatment in the later stages and to provide a functional decision support system.
Active Learning of Custering with Side Information Using $\eps$-Smooth Relative Regret Approximations

Clustering is considered a non-supervised learning setting, in which the goal is to partition a collection of data points into disjoint clusters. Often a bound $k$ on the number of clusters is given or assumed by the practitioner. Many versions of this problem have been defined, most notably $k$-means and $k$-median. An underlying problem with the unsupervised nature of clustering it that of determining a similarity function. One approach for alleviating this difficulty is known as clustering with side information, alternatively, semi-supervised clustering. Here, the practitioner incorporates side information in the form of "must be clustered" or "must be separated" labels for data point pairs. Each such piece of information comes at a "query cost" (often involving human response solicitation). The collection of labels is then incorporated in the usual clustering algorithm as either strict or as soft constraints, possibly adding a pairwise constraint penalty function to the chosen clustering objective. Our work is mostly related to clustering with side information. We ask how to choose the pairs of data points. Our analysis gives rise to a method provably better than simply choosing them uniformly at random. Roughly speaking, we show that the distribution must be biased so as more weight is placed on pairs incident to elements in smaller clusters in some optimal solution. Of course we do not know the optimal solution, hence we don't know the bias. Using the recently introduced method of $\eps$-smooth relative regret approximations of Ailon, Begleiter and Ezra, we can show an iterative process that improves both the clustering and the bias in tandem. The process provably converges to the optimal solution faster (in terms of query cost) than an algorithm selecting pairs uniformly.
Abstract Linguistically Motivated Syntax for Machine Translation Introduction. Machine Translation (mt) Systems Attempt to Translate Texts from One

When automatically translating between languages with very dierent word order patterns, knowledge of sentence structure rules, or syntax, is crucial. Syntax provides abstract rules for how sentences are formed, and can guide translation systems when they relate two languages whose structures diverge. Incorporating syntactic information into automatic translation, however, is problematic. This is because syntactic theories and translation software systems often disagree about how to segment sentences into word groups. The most widely used approach to translation, called phrase-based statistical machine translation (or PSMT), learns its translation rules directly from human translations without syntactic annotations. PSMT systems often parcel out the translation of a sentence using word groups that can be any unbroken sequence of words, whether or not a syntactic theory would consider these groups to be syntactic phrases. This incompatibility hampers the use of theoretical syntactic insights that might help translation systems better account for the movement of word groups during translation. The proposed work addresses this conict by using a exible syntactic formalism known as Combinatory Categorial Grammar (or CCG) to translate from German to English. CCG allows the same syntactic phrase to be formed in multiple ways, similar to how a mathematical equation can be solved by cancelling out variables in dierent orders. This exibility makes it possible to assign syntactic labels to PSMT translation word groups that a traditional syntactic theory could not, and thereby allows translations to be assembled directly with CCG's syntactic rules. The expected result is that a CCG-augmented translation system will produce translations that are more natural and grammatically correct, and therefore more useable. This result will also address a long-standing and timely question in the automatic translation research community viz., whether syntactic theory can be used to improve data-driven automatic translation. We hope to answer this question in the armative for German-English translation, and, in so doing, provide insights for other dicult language pairs, such as Chinese-English and Arabic-French.
Pumping Water to Compete in Electricity Markets

The pump storage technique allows to use cheap thermal electricity at periods of low demand to restore water resources that can be used to generate electricity at periods of peak demand. When the thermal plant and the hydro plant are managed by the same operator, the two plants are used in an efficient way to substitute low cost fuel to high cost fuel. When there are two independent managers, competition requires careful analysis. The paper first analyses the optimal dispatch and the profit-maximizing dispatch of the thermal and hydro-generation units when water can be pumped up into reservoirs. We identify the demand and cost conditions where the pumping device must be used. We then switch to the case where the two plants are operated by separate owners. We analyse the Nash equilibrium of the game where the hydro unit is the client of the thermal unit at off-peak period, and compete against it at peak period under three alternative legal arrangements and/or technical constraints: i) the thermal producer cannot separate the energy sold to the hydroproducer from the energy sold to final consumers; ii) the thermal producer is obliged to sell at market price the energy demanded by the hydroproducer and iii) the hydroproducer is obliged to buy at market price the quantity of energy assigned by the thermal producer.
MRI‐based technique for determining nonuniform deformations throughout the volume of articular cartilage explants

Articular cartilage is critical to the normal function of diarthrodial joints. Despite the importance of the tissue and the prevalence of cartilage degeneration (e.g., osteoarthritis), the technology required to noninvasively describe nonuniform deformations throughout the volume of the tissue has not been available until recently. The objectives of the work reported in this paper were to 1) describe a noninvasive technique (termed the cartilage deformation by tag registration (CDTR) technique) to determine nonuniform deformations in articular cartilage explants with the use of specialized MRI tagging and image processing methods, 2) evaluate the strain error of the CDTR technique using a custom MRI‐compatible phantom material, and 3) demonstrate the applicability of the CDTR technique to articular cartilage by determining 3D strain fields throughout the volume of a bovine articular cartilage explant. A custom MRI pulse sequence was designed to tag and image articular cartilage explants at 7 Tesla in undeformed and deformed states during the application of multiple load cycles. The custom pulse sequence incorporated the “delays alternating with nutations for tailored excitation” (DANTE) pulse sequence to apply tags. This was followed by a “fast spin echo” (FSE) pulse sequence to create images of the tags. The error analysis using the phantom material indicated that deformations can be determined with an error, defined as the strain precision, better than 0.83% strain. When this technique was applied to a single articular cartilage explant loaded in unconfined compression, hetereogeneous deformations throughout the volume of the tissue were evident. This technique potentially can be applied to determine normal cartilage deformations, analyze degenerated cartilage, and evaluate cartilage surgical repair and treatment methodologies. In addition, this technique may be applied to other soft tissues that can be appropriately imaged by MR. Magn Reson Med 53:321–328, 2005. © 2005 Wiley‐Liss, Inc.
Reliable wireless error correction technique for secure image transmission

Robust and secure transmission strategy for high-quality image through wireless networks is considered a great challenge. However, majority of encrypted image transmission schemes do not consider well the effect of bit errors occurring during transmission and this issue is considered a problem that should be handled by an efficient coding scheme. In this paper, a modified wireless image transmission scheme that combines chaotic encryption and turbo coding technique into one processing step is proposed. In the proposed scheme, selective encryption algorithm based on two-dimensional chaotic map is utilized for data security. Furthermore, error correction technique based on turbo coding is employed as channel coding for data communication in order to solve the problem of limited bandwidth and throughput. Simulation results show that the proposed scheme achieves high degree of robustness against channel impairments and improve image quality with acceptable data rates.
Comprehensive Extrinsic Calibration of a Camera and a 2D Laser Scanner for a Ground Vehicle

Cameras and laser scanners are two important kinds of perceptive sensors and both become more and more commonly used for intelligent ground vehicles; the calibration of these sensors is a fundamental task. A new method is proposed to perform COMPREHENSIVE extrinsic calibration of a SINGLE camera-2D laser scanner pair, i.e. the process of revealing ALL the spatial relationships among the camera coordinates system, the laser scanner coordinates system, the ground coordinates system, and the vehicle coordinates system. The proposed method is mainly based on the convenient and widely used chessboard calibration practice and can be conveniently implemented. The proposed method has been tested on both synthetic data and real data based experiments, which validate the effectiveness of the proposed method.
Achieving Probabilistic Anonymity in a Linear and Hybrid Randomization Model

The randomization methods that are applied for privacy-preserving data mining are commonly subject to reconstruction, linkage, and semantic-related attacks. Some existing works employed random noise addition to realize probabilistic anonymity, aiming only at linkage attacks. Random noise addition is vulnerable to reconstruction attacks, and is unable to achieve semantic closeness, particularly on high-dimensional data, to prevent semantic-related attacks. For linkage attacks, the main security vulnerability of their proposed probabilistic anonymity lies in the assumption that the attacker had a priori knowledge of the quasi-identifiers of all individuals. When only some individuals leak their quasi-identifiers, the proposed model will become incapable, because the attacker can deploy a different linkage attack that has not been studied before. This type of attack is much easier to deploy and is thus very harmful. In this paper, we propose new frameworks of probabilistic (1, k)and (k, k)-anonymity to defend against all these linkage attacks, and realize the frameworks on a hybrid randomization model. The model is also secure against reconstruction attacks. We further achieve statistical semantic closeness of high-dimensional data to prevent semantic-related attacks on the model. The frameworks also allow us to re-design the traditional K-nearest neighbor algorithm to leverage the introduced data uncertainty and improve the mining results. This paper demonstrates the promising applications in large-scale and high-dimensional data mining in clouds, by providing high efficiency and security to protect data privacy, guaranteeing high data utility for mining purposes, on-time processing, and non-interactive data publishing.
PPARG is central to the initiation and propagation of human angiomyolipoma, suggesting its potential as a therapeutic target

Angiomyolipoma (AML), the most common benign renal tumor, can result in severe morbidity from hemorrhage and renal failure. While mTORC1 activation is involved in its growth, mTORC1 inhibitors fail to eradicate AML, highlighting the need for new therapies. Moreover, the identity of the AML cell of origin is obscure. AML research, however, is hampered by the lack of in vivo models. Here, we establish a human AML‐xenograft (Xn) model in mice, recapitulating AML at the histological and molecular levels. Microarray analysis demonstrated tumor growth in vivo to involve robust PPARγ‐pathway activation. Similarly, immunostaining revealed strong PPARγ expression in human AML specimens. Accordingly, we demonstrate that while PPARγ agonism accelerates AML growth, PPARγ antagonism is inhibitory, strongly suppressing AML proliferation and tumor‐initiating capacity, via a TGFB‐mediated inhibition of PDGFB and CTGF. Finally, we show striking similarity between AML cell lines and mesenchymal stem cells (MSCs) in terms of antigen and gene expression and differentiation potential. Altogether, we establish the first in vivo human AML model, which provides evidence that AML may originate in a PPARγ‐activated renal MSC lineage that is skewed toward adipocytes and smooth muscle and away from osteoblasts, and uncover PPARγ as a regulator of AML growth, which could serve as an attractive therapeutic target.