Miracle-dz/UniFAR-FTUs huggingface dataset:
Acceleration of quantum algorithms using three-qubit gates

Quantum-circuit optimization is essential for any practical realization of quantum computation, in order to beat decoherence. We present a scheme for implementing the final stage in the compilation of quantum circuits, i.e. for finding the actual physical realizations of the individual modules in the quantum-gate library. We find that numerical optimization can be efficiently utilized in order to generate the appropriate control-parameter sequences which produce the desired three-qubit modules within the Josephson charge-qubit model. Our work suggests ways in which one can in fact considerably reduce the number of gates required to implement a given quantum circuit, hence diminishing idle time and significantly accelerating the execution of quantum algorithms.
Fast grasp planning by using cord geometry to find grasping points

In this paper, we propose a novel idea to address the problem of fast computation of enveloping grasp configurations for a multi-fingered hand with 3D polygonal models represented as polygon soups. The proposed method performs a low-level shape matching by wrapping multiple cords around an object in order to quickly isolate promising grasping spots. From these spots, hand palm posture can be computed followed by a standard close-until-contact procedure to find the contact points. Along with the contacts information, the finger kinematics is then used to filter the unstable grasps. Through multiple simulated examples with a twelve degrees-of-freedom anthropomorphic hand, we demonstrate that our method can compute good grasps for objects with complex geometries in a short amount of time. Best of all, this is achieved without complex model preprocessing like segmentation by parts and medial axis extraction.
Efficient GPU-Based Graph Cuts for Stereo Matching

Although graph cuts (GC) is popularly used in many computer vision problems, slow execution time due to its high complexity hinders wide usage. Manycore solution using Graphics Processing Unit (GPU) may solve this problem. However, conventional GC implementation does not fully exploit GPU's computing power. To address this issue, a new GC algorithm which is suitable for GPU environment is presented in this paper. First, we present a novel graph construction method that accelerates the convergence speed of GC. Next, a repetitive block-based push and relabel method is used to increase the data transfer efficiency. Finally, we propose a low-overhead global relabeling algorithm to increase the GPU occupancy ratio. The experiments on Middlebury stereo dataset shows that 5.2X speedup can be achieved over the baseline implementation, with identical GPU platform and parameters.
Weakly Supervised Semantic Labelling and Instance Segmentation

Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose to recursively train a convnet such that outputs are improved after each iteration. We explore which aspects affect the recursive training, and which is the most suitable box-guided segmentation to use as initialisation. Our results improve significantly over previously reported ones, even when using rectangles as rough initialisation. Overall, our weak supervision approach reaches ~95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.
A Phenomenological Analysis of the Posthumanized Future Workplace

Increasingly, organizations are becoming “technologically posthumanized” through the integration of social robots, AI, virtual reality, and ubiquitous computing into the workplace. Here a phenomenological approach is used to anticipate architectural transformations of the workplace resulting from posthumanization’s challenge to traditional anthropocentric paradigms of the workplace as a space that exists at “human” scale, possesses a trifold boundary, and serves as a spatiotemporal filter.
CMRULES: An Efficient Algorithm for Mining Sequential Rules Common to Several Sequences

We propose CMRULES, an algorithm for mining sequential rules common to many sequences in sequence databases –not for mining rules appearing frequently in sequences. For this reason, the algorithm does not use a sliding-window approach. Instead, it first finds association rules to prune the search space for items that occur jointly in many sequences. Then it eliminates association rules that do not meet minimum confidence and support thresholds according to the time ordering. We evaluated the performance of CMRULES in three different ways. First, we provide an analysis of its time complexity. Second, we compared its performance on a public dataset with a variation of an algorithm from the literature. Results show that CMRULES is more efficient for low support thresholds, and has a better scalability. Lastly, we report a real application of the algorithm in a complex system.
A Space-Mapping Framework for Engineering Optimization&#8212;Theory and Implementation

This paper presents a comprehensive approach to engineering design optimization exploiting space mapping (SM). The algorithms employ input SM and a new generalization of implicit SM to minimize the misalignment between the coarse and fine models of the optimized object over a region of interest. Output SM ensures the matching of responses and first-order derivatives between the mapped coarse model and the fine model at the current iteration point in the optimization process. We provide theoretical results that show the importance of the explicit use of sensitivity information to the convergence properties of our family of algorithms. Our algorithm is demonstrated on the optimization of a microstrip bandpass filter, a bandpass filter with double-coupled resonators, and a seven-section impedance transformer. We describe the novel user-oriented software package SMF that implements the new family of SM optimization algorithms
Jittering Broadcast Transmissions in MANETs: Quantification and Implementation Strategies

Delaying the transmission time of messages for a short random period, a.k. a. jittering, is a known approach for preventing concurrent transmissions, and consequently collisions, in multiple access networks (e.g., RFC 5148). It is particularly useful for increasing the reliability of unacknowledged broadcast messages in the 802.11 protocol. Yet, transmission jittering comes with a price. It increases the average end-to-end latency and could potentially decrease the throughput of the network. This paper investigates the relation between the maximal jitter duration and the probability of successful transmissions. Specifically, we propose and compare three implementation strategies: inside the MAC protocol, in the IP layer, and a cross-layer implementation. The comparison establishes that our practical crosslayer implementation minimizes the suggested protocol changes to 802.11 MAC protocol, while incurring the smallest latency, thus being the most attractive one. In fact, under IEEE 802.11e extension, our implementation does not require any MAC change. Our results show that by carefully designing the system, a very small jitter value of hundreds of microseconds suffices to obtain a high transmission success probability, a network utilization factor of above 70%, and subsequently to ensure high flooding reliability. This is in contrast with prior works, which have suggested to use two orders of magnitude larger jitter (i.e., tens of milliseconds). All our results are backed up by extensive simulations.
An adaptive file-system-oriented FTL mechanism for flash-memory storage systems

As flash memory becomes popular over various platforms, there is a strong demand regarding the performance degradation problem, due to the special characteristics of flash memory. This research proposes the design of a file-system-oriented flash translation layer, in which a filter mechanism is designed to separate the access requests of file-system metadata and file contents for better performance. A recovery scheme is then proposed for maintaining the integrity of a file system. The proposed flash translation layer is implemented as a Linux device driver and evaluated with respect to ext2 and ext3 file systems. Experiments were also done over NTFS by a series of realistic traces. The experimental results show significant performance improvement over ext2, ext3, and NTFS file systems with limited system overheads.
Intelligent Sensor Validation and Sensor Fusion for Reliability and Safety Enhancement in Vehicle Control UCB-ITS-PRR-95-40 California PATH Research Report

In this report we present an evaluation of methods for validation and fusion of sensor readings obtained from multiple sensors, to be used in tracking automated vehicles and avoidance of obstacle in its path. The validation and fusion is performed in two modules which are part of a larger five-module hierarchical supervisory control architecture. This supervisory control architecture operates at two levels of the Automated Vehicle Control Systems (AVCS): the regulation and the coordination level. Supervisory control activities at the regulation layer deal with validation and fusion of the sensor data, as well as fault diagnosis of the actuators, sensors, and the vehicle itself. Supervisory control activities at the coordination layer deal with detecting potential hazards, recommending the feasibility of potential maneuvers and making recommendations to avert accidents in emergency situations. In this grant we formulated the need for an hierarchical approach and then focussed in depth on the two modules sensor validation and sensor fusion. Tracking models were introduced for the various operating states of the automated vehicle, namely vehicle following, maneuvering, i.e. split, merge, lane change, emergencies, and for the lead vehicle in a platoon. The Probabilistic Data Association Filter (based on Kalman filtering) is proposed for the formation of real time validation gates and for fusing the validated readings. A topology for an influence diagram which captures the interaction of the various vehicle components and the sensing equipment, as well as the algorithmic sensor validation algorithms were developed. Furthermore, experiments for characterization of the optical triangulation longitudinal sensor developed by Qualimatrix Corp. were carried out. The other two longitudinal sensors, namely the radar and the sonar sensor, were tested as well. These tests were conducted under both dynamic and static test conditions as well as under vibrations.
Grafting: A Technique to Modify Ion Accumulation in Horticultural Crops

Grafting is a centuries-old technique used in plants to obtain economic benefits. Grafting increases nutrient uptake and utilization efficiency in a number of plant species, including fruits, vegetables, and ornamentals. Selected rootstocks of the same species or close relatives are utilized in grafting. Rootstocks absorb more water and ions than self-rooted plants and transport these water and ions to the aboveground scion. Ion uptake is regulated by a complex communication mechanism between the scion and rootstock. Sugars, hormones, and miRNAs function as long-distance signaling molecules and regulate ion uptake and ion homeostasis by affecting the activity of ion transporters. This review summarizes available information on the effect of rootstock on nutrient uptake and utilization and the mechanisms involved. Information on specific nutrient-efficient rootstocks for different crops of commercial importance is also provided. Several other important approaches, such as interstocking (during double grafting), inarching, use of plant-growth-promoting rhizobacteria, use of arbuscular mycorrhizal fungi, use of plant growth substances (e.g., auxin and melatonin), and use of genetically engineered rootstocks and scions (transgrafting), are highlighted; these approaches can be combined with grafting to enhance nutrient uptake and utilization in commercially important plant species. Whether the rootstock and scion affect each other's soil microbiota and their effect on the nutrient absorption of rootstocks remain largely unknown. Similarly, the physiological and molecular bases of grafting, crease formation, and incompatibility are not fully identified and require investigation. Grafting in horticultural crops can help reveal the basic biology of grafting, the reasons for incompatibility, sensing, and signaling of nutrients, ion uptake and transport, and the mechanism of heavy metal accumulation and restriction in rootstocks. Ion transporter and miRNA-regulated nutrient studies have focused on model and non-grafted plants, and information on grafted plants is limited. Such information will improve the development of nutrient-efficient rootstocks.
Journal of Graph Algorithms and Applications Planar Graphs as Vpg-graphs

A graph is B k-VPG when it has an intersection representation by paths in a rectangular grid with at most k bends (turns). It is known that all planar graphs are B3-VPG and this was conjectured to be tight. We disprove this conjecture by showing that all planar graphs are B2-VPG. We also show that the 4-connected planar graphs constitute a subclass of the intersection graphs of Z-shapes (i.e., a special case of B2-VPG). Additionally, we demonstrate that a B2-VPG representation of a planar graph can be constructed in O(n 3/2) time. We further show that the triangle-free planar graphs are contact graphs of: L-shapes, Γ-shapes, vertical segments, and horizontal segments (i.e., a special case of contact B1-VPG). From this proof we obtain a new proof that bipartite planar graphs are a subclass of 2-DIR.
Preprocessed and postprocessed quantization index modulation methods for digital watermarking

Quantization index modulation (QIM) methods, a class of digital watermarking and information embedding methods, achieve very efficient trade-offs among the amount of embedded information (rate), the amount of embedding-induced distortion to the host signal, and the robustness to intentional and unintentional attacks. For example, we show that against independent additive Gaussian attacks, which are good models for at least some types of uniformed and unintentional attacks, QIM methods exist that achieve the best possible rate-distortion-robustness trade-offs (i.e., capacity) asymptotically at high rates and achieve performance within a few dB of capacity at all finite rates. Furthermore, low- complexity realizations of QIM methods, such as so-called dither modulation, have also been shown to achieve favorable rate-distortion-robustness trade-offs. We further develop preprocessing and postprocessing techniques that enable QIM to fully achieve capacity, not only against Gaussian attacks but also against other types of attacks as well. One practical postprocessing technique we develop we refer to as distortion compensation. Distortion compensation has the property that when suitably optimized it is sufficient for use in conjunction with QIM to achieve capacity against Gaussian attacks and against square-error distortion-constrained attacks. More generally, we present the results of a comparative information theoretic analysis of the fundamental performance limits of QIM, distortion-compensated QIM, and other watermarking methods and demonstrate practically achievable gains with experimental results.
Exploring Self-Training and Co-training for Hindi Dependency Parsing Using Partial Parses

In this paper, we explore the effect of self-training and co-training on Hindi dependency parsing using partial parses. We use Partial parser and apply self-training using a large unannotated corpus. For co-training, we use Malt and MST parser along with Partial Parser. We explore different criteria for choosing partial parses to be used for bootstrapping. Through these experiments, we compare the impact of self-training and co-training on Hindi dependency parsing.
An adaptive LQG TCP congestion controller for the Internet

This paper addresses the problem of congestion control for transmission control protocol (TCP) traffic in the Internet. The method proposed builds on the ideas of TCP Vegas, a true feedback control approach to congestion management of TCP traffic. The new method is based on an adaptive linear quadratic Gaussian (LQG) formulation which uses an extended least squares system identification algorithm combined with optimal LQG control. Simulation experiments indicate that the new technique inherits good equilibrium properties from TCP Vegas, but has much superior transient responses which, the paper argues, is important for good dynamic congestion control. Keywords— TCP, congestion control, LQG, adaptive control.
Tissue- and Age-Dependent Differences in the Complexation of Cadmium and Zinc in the Cadmium/Zinc Hyperaccumulator Thlaspi caerulescens (Ganges Ecotype) Revealed by X-Ray Absorption Spectroscopy1[w]

Extended x-ray absorption fine structure measurements were performed on frozen hydrated samples of the cadmium (Cd)/zinc (Zn) hyperaccumulator Thlaspi caerulescens (Ganges ecotype) after 6 months of Zn2+ treatment with and without addition of Cd2+. Ligands depended on the metal and the function and age of the plant tissue. In mature and senescent leaves, oxygen ligands dominated. This result combined with earlier knowledge about metal compartmentation indicates that the plants prefer to detoxify hyperaccumulated metals by pumping them into vacuoles rather than to synthesize metal specific ligands. In young and mature tissues (leaves, petioles, and stems), a higher percentage of Cd was bound by sulfur (S) ligands (e.g. phytochelatins) than in senescent tissues. This may indicate that young tissues require strong ligands for metal detoxification in addition to the detoxification by sequestration in the epidermal vacuoles. Alternatively, it may reflect the known smaller proportion of epidermal metal sequestration in younger tissues, combined with a constant and high proportion of S ligands in the mesophyll. In stems, a higher proportion of Cd was coordinated by S ligands and of Zn by histidine, compared with leaves of the same age. This may suggest that metals are transported as stable complexes or that the vacuolar oxygen coordination of the metals is, like in leaves, mainly found in the epidermis. The epidermis constitutes a larger percentage of the total volume in leaves than in stems and petioles. Zn-S interaction was never observed, confirming earlier results that S ligands are not involved in Zn resistance of hyperaccumulator plants.
Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials

Kernel methods are flexible and easy to interpret and have been successfully used in genomic-enabled prediction of various plant species. Kernel methods used in genomic prediction comprise the linear genomic best linear unbiased predictor (GBLUP or GB) kernel, and the Gaussian kernel (GK). In general, these kernels have been used with two statistical models: single-environment and genomic × environment (GE) models. Recently near infrared spectroscopy (NIR) has been used as an inexpensive and non-destructive high-throughput phenotyping method for predicting unobserved line performance in plant breeding trials. In this study, we used a non-linear arc-cosine kernel (AK) that emulates deep learning artificial neural networks. We compared AK prediction accuracy with the prediction accuracy of GB and GK kernel methods in four genomic data sets, one of which also includes pedigree and NIR information. Results show that for all four data sets, AK and GK kernels achieved higher prediction accuracy than the linear GB kernel for the single-environment and GE multi-environment models. In addition, AK achieved similar or slightly higher prediction accuracy than the GK kernel. For all data sets, the GE model achieved higher prediction accuracy than the single-environment model. For the data set that includes pedigree, markers and NIR, results show that the NIR wavelength alone achieved lower prediction accuracy than the genomic information alone; however, the pedigree plus NIR information achieved only slightly lower prediction accuracy than the marker plus the NIR high-throughput data.
Code design for MIMO downlink with imperfect CSIT

In this letter, we implement a simplified version of the Cover van der Meulen Hajek Pursley (CMHP) coding originally characterized by Wajcer, Wiesel, and Shamai. The vector Gaussian broadcast channel with imperfect channel state information at the transmitter (CSIT) is considered where the transmitter only knows the channel mean and variance. Our focus is on the implementation and performance analysis of CMHP under the imperfect CSIT model using practical codes. Turbo codes described in IEEE 802.20 draft specification and quadrature amplitude modulation are used to implement CMHP. In order to find the optimal power allocation and beamforming vectors which maximize the sum rate with practical codes, we introduce the SINR penalty factor. The SNRs that achieve various target spectral efficiency are presented and analyzed.
Towards automatic program partitioning

There is a trend towards using accelerators to increase performance and energy efficiency of general-purpose processors. Adoption of accelerators, however, depends on the availability of tools to facilitate programming these devices. In this paper, we present techniques for automatically partitioning programs for execution on accelerators. We call the off-loaded code regions sub-algorithms, which are parts of the program that are loosely connected to the remainder of the program. We present three heuristics for automatically identifying sub-algorithms based on control flow and data flow properties. Analysis of SPECint and MiBench benchmarks shows that on average 12 sub-algorithms are identified (up to 54), covering the full execution time for 27 out of 30 benchmarks. We show that these sub-algorithms are suitable for off-loading them to accelerators by manually implementing sub-algorithms for 2 SPECint benchmarks on the Cell processor.
Helicobacter pylori overcomes natural immunity in repeated infections

Repeated experimental reinfection of two subjects indicates that Helicobacter pylori infection does not promote an immune response protective against future reinfection. Our results highlight the importance of preventing reinfection after eradication, through public health initiatives, and possibly treatment of family members. They indicate difficulties for vaccine development, especially therapeutic vaccines.
Spatial, Phylogenetic, Environmental and Biological Components of Variation in Extinction Risk: A Case Study Using Banksia

Comparative analyses of extinction risk routinely apply methods that account for phylogenetic non-independence, but few analyses of extinction risk have addressed the possibility of spatial non-independence. We explored patterns of extinction risk in Banksia, a plant genus largely endemic to Australia’s southwest biodiversity hotspot, using methods to partition the variance in two response variables (threat status and range size) into phylogenetic, spatial, and independent components. We then estimated the effects of a number of biological and external predictors on extinction risk independently of phylogeny and space. The models explained up to 34.2% of the variation in range size and up to 9.7% of the variation in threat status, nearly all of which was accounted for by the predictors, not by phylogeny or space. In the case of Banksia, therefore, high extinction risk can be clearly linked with biological syndromes (such as a brief flowering period) or geographic indicators of human impact (such as extensive habitat loss), but cannot be predicted from phylogenetic relatedness or geographic proximity.