Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Keyframe Selection and Switching for ICP-based Camera Pose Estimation

Simultaneous localization and mapping algorithms are important for high-quality registration used in augmented reality applications. Keyframe based SLAM can effectively reduce local drift by aligning a frame to the corresponding keyframe; however, it still suffers from losing trace for frames far from the keyframe. This work presents a fast keyframe selection and switching algorithm to replace unsuitable keyframes with qualified backup frames. The overhead of using backup process is greatly reduced by only inspecting the inlier information produced at the first iteration of iterative closest point (ICP) algorithm. Moreover, several useful criteria considering spatial and/or temporal relationships are also presented to evaluate the quality of keyframes and backup frames. Experimental results show that about 11.37% of relative pose error and 16.79% of the I CP iterations can be reduced by applying the proposed schemes as compared to the traditional keyframe-based approach. The reduction in computational time is achieved by speeding up the convergence of ICP, which is an additional benefit from applying the proposed schemes.
A Utilization Aware Robust Channel Access Mechanism for Wireless NoCs

Wireless Network-on-Chip (WNoC) has been proposed to overcome long-distance communication bottlenecks of wired NoCs. Token passing mechanism has generally been adapted to allocate the wireless channel among Wireless Interfaces (WIs). In this work, we propose a comparator based controller to provide a flexible and efficient channel allocation scheme. It utilizes a comparator attached to the antenna, along with modifications to header flit to perform channel allocation along with power gating WIs to save energy. Evaluation of proposed scheme on CPU/GPU system shows 53% reduction in token passes and 9% energy saving as compared to timer based approach.
Verification of Medication Dispensing Using the Attentive Computer Vision Approach

This paper is dedicated to the development of an assistive computer vision-based system for medication verification before dispensing it to patients, e.g., in long-term healthcare facilities. The algorithmic basis of the system is the attentive vision approach to robust and fast object detection in images. It consists of time-efficient image analysis by a multi-scale visual attention operator to detect feature-point areas located inside the pill regions. The medication identification algorithm is based on the matching of pill descriptor vectors with the reference ones, which are obtained by the attentive machine-learning algorithm during the training phase. A new set of rotation-invariant image descriptors is proposed, which fully discriminates between different medication pills.
FPGA Implementation of a Memory-Efficient Hough Parameter Space for the Detection of Lines

The Line Hough Transform (LHT) is a robust and accurate line detection algorithm, useful for applications such as lane detection in Advanced Driver Assistance Systems. For real-time implementation, the LHT is demanding in terms of computation and memory, and hence Field Programmable Gate Arrays (FPGAs) are often deployed. However, many small FPGAs are incapable of implementing the LHT due to the large memory requirement of the Hough Parameter Space (HPS). This paper presents a memory-efficient architecture of the LHT named the Angular Regions — Line Hough Transform (AR-LHT). We present a suitable FPGA implementation of the AR-LHT and provide a performance and resource analysis after targeting a Xilinx xc7z010-1 device. Results demonstrate that, for an image of 1024×1024 pixels, approximately 48% less memory is used than the Standard LHT. The FPGA architecture is capable of processing a single image in 9.03ms.
Ray-space360: An extension of ray-space for omnidirectional free viewpoint

Conventional 3DoF (Degree of Freedom) systems provide only rotational view transformation. 6DoF VR systems offer a more realistic VR by supporting view transformation for translational movement. This paper proposes ray-space 360 which supports both rotational and translational view transformation simultaneously. Ray-space360 is an extension of ray-space [1–3] to an omnidirectional view in order to support a new camera structure in a square form for ray-space360 configuration. Ray-space360 also provides a way to use the intersection to connect independent ray-spaces. Thanks to its low complexity, the proposed system can provide real-time service with a low performance processor such as a mobile processor. This paper presents the result of 4DoF ray-space360 which provides view transformation of yaw, pitch, x, and z axes. The ray-space360 can be extended to 6DoF simply by stacking cameras.
Consensus Tracking of Multi-Agent Systems With Directed Switching Topology: A Multiple Lyapunov Functions Method

This paper addresses the consensus tracking problem of multi-agent systems (MASs) with directed switching topologies based on the multiple Lyapunov functions (MLFs) approach. The special feature of Laplacian matrices for topology candidates is explored to construct a new class of MLFs for the tracking error systems of leader-following MASs with directed switching topologies. Then the average dwell time (ADT) method is utilized to establish a sufficient condition that guarantees consensus tracking in the closed-loop MASs. The efficiency of the derived theoretical results and the merits of the proposed MLFs are demonstrated by computer simulations.
Inherent stochasticity precludes hysteresis in gene regulatory networks

Cell fate determination, the process through which cells commit to differentiated states, has been shown to be mediated by gene regulatory motifs with mutually exclusive expression states. The classical picture for deterministic cell decision making includes bistability and hysteresis. Despite numerous experimental works supporting evidence of hysteresis in gene regulatory networks, such phenomenon may not be compatible with the stochasticity underlying gene regulation dynamics. Here we show how under sufficiently slow dynamics, the dependency of the transient solutions on the initial state of the cells can be mistakenly interpreted as hysteresis and, to quantify this phenomenon, we provide an estimate of the convergence rate to the equilibrium. We also introduce the equation of a natural landscape capturing the evolution of the system that, unlike traditional cell fate potential landscapes, is compatible with the notion of coexistence at the microscopic level.
Connect 4: A Novel Paradigm to Elicit Positive and Negative Insight and Search Problem Solving

Researchers have typically defined insight as a sudden new idea or understanding accompanied by an emotional feeling of Aha. Recently, examples of negative insight in everyday creative problem solving have been identified. These are seen as sudden and sickening moments of realization experienced as an Uh-oh rather than Aha. However, such experiences have yet to be explored from an experimental perspective. One barrier to doing so is that methods to elicit insight in the laboratory are constrained to positive insight. This study therefore aimed to develop a novel methodology that elicits both positive and negative insight solving, and additionally provides the contrasting experiences of analytic search solving in the same controlled conditions. The game of Connect 4 was identified as having the potential to produce these experiences, with each move representing a solving episode (where best to place the counter). Eighty participants played six games of Connect 4 against a computer and reported each move as being a product of positive search, positive insight, negative search or negative insight. Phenomenological ratings were then collected to provide validation of the experiences elicited. The results demonstrated that playing Connect 4 saw reporting of insight and search experiences that were both positive and negative, with the majority of participants using all four solving types. Phenomenological ratings suggest that these reported experiences were comparable to those elicited by existing laboratory methods focused on positive insight. This establishes the potential for Connect 4 to be used in future problem solving research as a reliable elicitation tool of insight and search experiences for both positive and negative solving. Furthermore, Connect 4 may be seen to offer more true to life solving experiences than other paradigms where a series of problems are solved working toward an overall superordinate goal rather than the presentation of stand-alone and un-related problems. Future work will need to look to develop versions of Connect 4 with greater control in order to fully utilize this methodology for creative problem solving research in experimental psychology and neuroscience contexts.
Stability of, and Associations Between, Parent and Child Locus of Control Expectancies

The purpose of the present study was to assess the stability of locus of control (LOC) scores over time using data gathered from tests constructed to be consistent with Rotter’s definition of LOC. We compared LOC scores of parents (measured prior to the birth of the index child and at 6 and 18 years later) and their offspring (at ages 8 and 16) to explore how stable adult and child LOC was over time and to see how parental LOC was associated with the LOC of the child aged 8 and again at 16. Locus of control was measured using modified versions of adult (ANSIE, Nowicki and Duke, 1974) and child (CNSIE, Nowicki and Strickland, 1973) LOC scales, administered to participants in the Avon Longitudinal Study of Parents and Children in the United Kingdom. We predicted that: (1) adult scores would be more stable than children’s and (2) parents’ and children’s LOC scores would be related to one another. Analyses of the data found that individual’s LOC scores were significantly associated over time, with adult scores (r ∼ 0.50) more highly correlated than children’s (r ∼ 0.20). Correlations suggest more stability for adults than children, but also indicate the occurrence of substantial change across time. Although statistically significant, correlations between family members were small at both childhood and adolescent time points. Additional analyses suggested that mother and father LOC scores were more highly correlated with opposite rather than with same sex children, but again though significant the coefficients were small. We also analyzed the binary outcomes of externality to assess parental contributions to externality in the 8 and 16-year-old children and found correlations were significant, but small. Possible explanations are offered for why the associations between parent and child LOC were not higher. We concluded that researchers need to focus more on clarifying how children’s LOC is acquired.
Technology Mapping for Circuits with Simple Cells

This paper presents two main contributions toward efficient VLSI circuits mapped with simple cells: (1) a complete synthesis flow to provide good-quality circuits mapped only with simple cells; and (2) an area-oriented, level-aware buffering algorithm based on inverter trees to fix cell fanout violations. We show that efficient implementations in terms of inverter count, transistor count, area, power and delay can be generated from circuits with a reduced number of both simple cells and inverters, combined with XOR/XNOR-based optimizations. The proposed buffering algorithm can handle all unfeasible fanout occurrences, while (i) optimizing the number of added inverters; and (ii) assigning cells to the inverter tree based on their level criticality. When comparing with academic and commercial approaches, we are able to simultaneously reduce the average number of inverters, transistors, area, power dissipation and delay up to 48%, 4%, 8%, 14%, and 16%, respectively.
A 974GOPS/W Multi-level Parallel Architecture for Binary Weight Network Acceleration

Deep neural networks dominate in the machine learning field. However, deploying deep neural networks on mobile devices requires aggressive compression of models due to huge amounts of parameters. An extreme case is to restrict weights to binary values {+1/−1} without much loss of accuracy. This promising method not only reduces hardware overhead of memory and computation, but also improves the performance of network inference. In this work, a flexible architecture for binary weight network acceleration is proposed. The architecture fully exploits the inherent multi-level parallelism of neural networks, resulting in utilization of processing elements over 80% for different layers. In addition, we present efficient data placement and transmission methods in coordination with multi-level parallel processing. The accelerator is implemented using SMIC 40nm technology. It operates at 1.2V and achieves up to 974GOPS/W power efficiency.
Faster approximation algorithms for computing shortest cycles on weighted graphs

Given an $n$-vertex $m$-edge graph $G$ with non negative edge-weights, the girth of $G$ is the weight of a shortest cycle in $G$. For any graph $G$ with polynomially bounded integer weights, we present a deterministic algorithm that computes, in $\tilde{\cal O}(n^{5/3}+m)$-time, a cycle of weight at most twice the girth of $G$. Our approach combines some new insights on the previous approximation algorithms for this problem (Lingas and Lundell, IPL'09; Roditty and Tov, TALG'13) with Hitting Set based methods that are used for approximate distance oracles and date back from (Thorup and Zwick, JACM'05). Then, we turn our algorithm into a deterministic $(2+\varepsilon)$-approximation for graphs with arbitrary non negative edge-weights, at the price of a slightly worse running-time in $\tilde{\cal O}(n^{5/3}\log^{{\cal O}(1)}{(1/\varepsilon)}+m)$. Finally, if we insist in removing the dependency in the number $m$ of edges, we can transform our algorithms into an $\tilde{\cal O}(n^{5/3})$-time randomized $4$-approximation for the graphs with non negative edge-weights -- assuming the adjacency lists are sorted. Combined with the aforementioned Hitting Set based methods, this algorithm can be derandomized, thereby yielding an $\tilde{\cal O}(n^{5/3})$-time deterministic $4$-approximation for the graphs with polynomially bounded integer weights, and an $\tilde{\cal O}(n^{5/3}\log^{{\cal O}(1)}{(1/\varepsilon)})$-time deterministic $(4+\varepsilon)$-approximation for the graphs with non negative edge-weights. To the best of our knowledge, these are the first known subquadratic-time approximation algorithms for computing the girth of weighted graphs.
Low-Quiescent Current Class-AB CMOS LDO Voltage Regulator

A low-quiescent current output-capacitorless class-AB CMOS low-dropout voltage regulator (LDO) capable to source/sink current to/from the load is presented, which is suitable for hybrid or linear-assisted structures utilized in envelope elimination and restoration (EER) applications. The proposed LDO regulator is designed and characterized in 0.18 μm CMOS process to provide a 1 V stable output voltage with 200 mV dropout without any off-chip output capacitor and can deliver a current range of 160 mA between −80 mA and +80 mA to the load, while consumes only 1.8 μA quiescent current.
An Approximate Memory Architecture for a Reduction of Refresh Power Consumption in Deep Learning Applications

A DRAM device requires periodic refresh operations to preserve data integrity, which incurs significant power consumption. This paper proposes a new memory architecture to reduce the power consumption by refresh operations by slowing down the refresh rate. Slow refresh may cause a loss of data stored in a DRAM cell, which affects the correctness of the computation using the lost data. The proposed memory architecture attempts to avoid the problem caused by lost data by taking advantage of the error-tolerant property of deep learning applications that are tolerant to presence of a small amount of errors. For data storage in deep learning applications, the approximate DRAM architecture stores the data in a transposed manner so that data are sorted according to their significance. DRAM organization is modified to support the control of the refresh period according to the significance of stored data. Simulation results with GoogLeNet and VGG-16 show that the power consumption is reduced by 69.68% with a negligible drop of the classification accuracy for both GoogLeNet and VGG-16.
Interpreting Black Box Predictions using Fisher Kernels

Research in both machine learning and psychology suggests that salient examples can help humans to interpret learning models. To this end, we take a novel look at black box interpretation of test predictions in terms of training examples. Our goal is to ask `which training examples are most responsible for a given set of predictions'? To answer this question, we make use of Fisher kernels as the defining feature embedding of each data point, combined with Sequential Bayesian Quadrature (SBQ) for efficient selection of examples. In contrast to prior work, our method is able to seamlessly handle any sized subset of test predictions in a principled way. We theoretically analyze our approach, providing novel convergence bounds for SBQ over discrete candidate atoms. Our approach recovers the application of influence functions for interpretability as a special case yielding novel insights from this connection. We also present applications of the proposed approach to three use cases: cleaning training data, fixing mislabeled examples and data summarization.
Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach

We study the problem of black-box optimization of a noisy function in the presence of low-cost approximations or fidelities, which is motivated by problems like hyper-parameter tuning. In hyper-parameter tuning evaluating the black-box function at a point involves training a learning algorithm on a large data-set at a particular hyper-parameter and evaluating the validation error. Even a single such evaluation can be prohibitively expensive. Therefore, it is beneficial to use low-cost approximations, like training the learning algorithm on a sub-sampled version of the whole data-set. These low-cost approximations/fidelities can however provide a biased and noisy estimate of the function value. In this work, we combine structured state-space exploration through hierarchical partitioning with querying these partitions at multiple fidelities, and develop a multi-fidelity bandit based tree-search algorithm for noisy black-box optimization. We derive simple regret guarantees for our algorithm and validate its performance on real and synthetic datasets.
Clinical significance of subtypes of Gleason pattern 4 prostate cancer

Major updates in prostate cancer grading have been adopted in recent times. These include redefinitions of Gleason pattern (GP) 4 architectural variants and reporting of the grade group (GG) system, which divides prostate cancer into five groups that better stratify patients. Still, the GG system uses the GPs 3, 4 or 5 to define each GG. Patients belonging to GG 2, 3 and 4 have increasing amounts of GP 4 in the composition of their tumors. GP4 is a heterogeneous group of morphologic variants that include poorly formed glands, glomeruloid structures, cribriform glands, and fused glands. Recently published studies suggest that the morphologic subtypes of GP 4 have different clinical significance. While the diagnostic reproducibility of poorly formed glands and fused glands is low, glomeruloid structures and cribriform glands are easier to be distinguished from other morphologies. A growing body of evidence suggests that the presence of cribriform glands is associated with an aggressive clinical course compared with other architectural subtypes. The latest 2014 guidelines issued by the International Society of Urologic Pathology recommend that the percentage of GP 4 be reported on needle biopsies and radical prostatectomy (RP) specimens. The data reviewed here invites consideration for the need to report the subtype of GP 4, especially the presence or absence of cribriform glands.
A 7-GHz Fast-Lock 2-Step TDC-based All-Digital DLL for Post-DDR4 SDRAMs

A new high-speed, low-power, and fast-lock all-digital delay-locked loop (DLL) for high-speed next-generation memory interface is presented. The proposed all-digital DLL utilizes a new 2-step time-to-digital converter (TDC) scheme which results in a fast-locking time of only 6 clock cycles, regardless of the long replica clock buffer (RCB) delay located in the feedback path of the DLL. The proposed DLL is designed in a 65 nm CMOS process, and it achieves a wide operating frequency range of 1.65–7.0 GHz without any harmonic lock problems. The proposed DLL dissipates 7.1 mW of power from a 1.0 V supply, achieves an effective peak-to-peak jitter of 4.55 ps at 7 GHz, and occupies an area of 0.02 mm2.
Robust adaptive filtering via convex combination of l0-RLS adaptive filters

In this paper, we first derive closed form expressions for the steady state mean and mean square deviations (MSD) of an l0-RLS adaptive filter. The latter is seen to depend on the sparsity promoting parameter (which is denoted by ‘β’), and the dependence is seen to be a function of the signal to noise ratio (SNR). Since the optimum value of β, for which the steady state MSD is minimized, is a function of the SNR, it is difficult to choose a suitable β when the SNR is time-varying, or, can not be known a priori. To circumvent this problem, this paper presents a new convex combination based l0-RLS adaptive filter for identifying a sparse system under variable SNR. The proposed scheme combines M number of differently parameterized l0-RLS adaptive filters, each having a parameter value (β) suitable for a specific SNR. The combining coefficients are updated so that the best filter in terms of optimum or near optimum β is chosen. Simulation results establish excellent robustness of the proposed method against variable SNR.
Evaluation of Support Vector Machine Kernels for Detecting Network Anomalies

Border Gateway Protocol (BGP) is used to exchange routing information across the Internet. BGP anomalies severely affect network performance and, hence, algorithms for anomaly detection are important for improving BGP convergence. Efficient and effective anomaly detection mechanisms rely on employing machine learning techniques. Support Vector Machine (SVM) is a widely used machine learning algorithm. In this paper, we evaluate performance of SVM with linear, quadratic, and cubic kernels. The SVM kernels are compared based on accuracy and the F-Score when detecting BGP anomalies in Internet traffic traces. The performance heavily depends on the selected features and their combinations.
Fault prediction and modelling in transport networks

Engineered transport networks occur in many domains: road networks, power grids, the internet and utility distribution. Such networks present a generic problem — how does one model and predict failure of components within them? In this paper we will use the example of a metropolitan water distribution network to model and predict failure based on topology, topological network distance, and spatial-temporal geographical distance. We show that certain topologies are either more or less likely to fail than chance, and that co-occurrence of failure within certain geographical distances can be reliably modelled and predicted in certain specific circumstances.