Input Length Research Articles

Improving Clock Prediction Algorithm for BDS-2/3 Satellites Based on LS-SVM Method

The satellite clock prediction is crucial to support real-time global satellite precise positioning services. Currently, the clock prediction for the Chinese BeiDou navigation satellite system (BDS) is still challenging to satisfy the precise positioning applications. Based on the exploration of existing prediction models, an improved model combing the spectrum analysis model (SAM) and the least-squares support-vector machine (LS-SVM) is proposed especially for BDS-2/3 satellites. Considering satellite-specific characteristics, the parameters of the LS-SVM method are optimized satellite by satellite, including input length, regularization and kernel parameters. The improved model is evaluated by comparing the predicted clocks of existing methods and the improved model. The bias of the predicted clock offsets are within ±1.0 ns for most medium Earth orbits (MEOs) over three hours employing the improved model, which is better than that of the existing methods and can be applied for several real-time precise positioning applications. The predicted clock offsets are further evaluated by applying clock corrections to precise point positioning (PPP) in both static and kinematic modes for 10 international GNSS service (IGS) Multi-GNSS Experiment (MGEX) stations, including five stations in the Asia-Pacific region. According to the practical engineering experience, 2 dm and 5 dm are defined for static and kinematic PPP, respectively, as a convergence threshold. Then, in the static PPP, the improved model is demonstrated to be effective, and positioning accuracies of some stations obtain more than 15% improvements on average for each direction, which enables them to get sub-decimeter positioning, especially in the Asia-Pacific region. In the kinematic PPP, the improved model performs much better than the others in terms of both the convergence time and the positioning accuracy. The convergence time can be shortened from 1.0 h to below 0.5 h, while the positioning accuracies are enhanced by 16.3%, 10.8%, and 18.9% on average in east, north, and up direction, respectively.

Sentence Similarity Techniques for Short vs Variable Length Text using Word Embeddings

In goal-oriented conversational agents like Chatbots, finding the similarity between user input and representative text result is a big challenge. Generally, the conversational agent developers tend to provide a minimal number of utterances per intent, which makes the classification task difficult. The problem becomes more complex when the length of the representative text per action is short and the length of the user input is long. We propose a methodology that derives Sentence Similarity score based on N-gram and Sliding Window and uses the FastText Word Embeddings technique which outperforms the current state-of-the-art Sentence Similarity results. We are also publishing a dataset on the shopping domain, to build conversational agents. And the extensive experiments done on the dataset fetched better results in accuracy, precision and recall by 6%, 2% and 80% respectively. It also evinces that our solution generalizes well on the low corpus and requires no training.

Empirical Study on the Relationship between Total Factor Financial Input and Coastline Length in China's Coastal Regions

Qin, Z.; Jin, J.-Y.; Zhao, R.; Chuan, M., and He, L.-S., 2019. Empirical study on the relationship between total factor financial input and coastline length in China's coastal regions. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 897–902. Coconut Creek (Florida), ISSN 0749-0208.In terms of economic development, financial development is a priority. In particular, Finance is the driving force of blue marine economic development. In this paper, the continental coastline length and financial total factor input index of 10 coastal provinces/municipalities in China were selected. Factor correlation analysis and regression model establishment were performed in order to demonstrate the relationship between coastline length and financial total factor input. Through an empirical study of the national coastal areas, the Yangtze River Delta Economic Circle, the Bohai Rim Economic Circle and the Pearl River Delta Economic Circle, it was found that the length of the coastline is positively correlated to financial factor inputs. Therefore, it is necessary to develop and utilize coastline resources rationally in order to attract financial support from financial institutions, security markets and insurance markets to coastal areas. This will aid in the promotion of the economic contribution of regional marine industries, and the enhancement of the economic development capacity of coastal cities.

Type-two Iteration with Bounded Query Revision

Motivated by recent results of Kapron and Steinberg (LICS 2018) we introduce new forms of iteration on length in the setting of applied lambda-calculi for higher-type poly-time computability. In particular, in a type-two setting, we consider functionals which capture iteration on input length which bound interaction with the type-one input parameter, by restricting to a constant either the number of times the function parameter may return a value of increasing size, or the number of times the function parameter may be applied to an argument of increasing size. We prove that for any constant bound, the iterators obtained are equivalent, with respect to lambda-definability over type-one poly-time functions, to the recursor of Cook and Urquhart which captures Cobham's notion of limited recursion on notation in this setting.

Performance evaluation of sorption compressor based J-T refrigerator using R134a and mixture refrigerants as working fluids

The idea of adsorption based refrigerator was conceived in the year 1961 by Vickers. The present work reports development of a sorption compressor based J-T refrigerator with R134a alone as a working fluid and a three component mixture of R134a, Ethane and Methane. Commercially available activated carbon, obtained from coconut shell, is used as an adsorbent. The reported compressor is a four cell structure and uses isenthalpic expansion of R134a for cooling purpose. A lowest temperature of −8°C is obtained giving 5 W of refrigeration effect for R134a. The work is further extended to study the effect of various parameters like adsorption-desorption cycle time, heater power input and capillary tube length on the low temperature obtained from the refrigerator. Experiments are then carried out using a three component mixed refrigerant, a low temperature of −54°C at no load is achieved. Also a continuous trouble-free operation is observed.

Prediction of Patient-specific Acute Hypotensive Episodes in ICU Using Deep Models.

Forecasting acute hypotensive episodes (AHE) in intensive care patients has been of recent interest to researchers in the healthcare domain. Advance warning of an impending AHE may give care providers additional information to help mitigate the negative clinical impact of a serious event such as an AHE or prompt a search for an evolving disease process. However, the currently accepted definition of AHE is restrictive does not account for inter-patient variability. In this paper, we propose a novel definition of an AHE based on patient-specific features of blood pressure recordings. Next, we utilize a deep learning-based method to predict the onset of an AHE from multiple physiological readings for different definitions of the prediction task including variable input and gap lengths. Using a cohort of 538 patients, our model was able to successfully predict the onset of an AHE with an accuracy and AUC score of 0.80 and 0.87 respectively. Compared to a baseline logistic regression model, our model outperforms the baseline in most of the definitions of the prediction task.

Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network

Automatic arrhythmia detection from Electrocardiogram (ECG) plays an important role in early prevention and diagnosis of cardiovascular diseases. Convolutional neural network (CNN) is a simpler, more noise-immune solution than traditional methods in multi-class arrhythmia classification. However, suffering from lack of consideration for temporal feature of ECG signal, CNN couldn’t accept varied-length ECG signal and had limited performance in detecting paroxysmal arrhythmias. To address these issues, we proposed attention-based time-incremental convolutional neural network (ATI-CNN), a deep neural network model achieving both spatial and temporal fusion of information from ECG signals by integrating CNN, recurrent cells and attention module. Comparing to CNN model, this model features flexible input length, halved parameter amount as well as more than 90% computation reduction in real-time processing. The experiment result shows that, ATI-CNN reached an overall classification accuracy of 81.2%. In comparison with a classical 16-layer CNN named VGGNet, ATI-CNN achieved accuracy increases of 7.7% in average and up to 26.8% in detecting paroxysmal arrhythmias. Combining all these excellent features, ATI-CNN offered an exemplification for all kinds of varied-length signal processing problems.

On Derandomized Composition of Boolean Functions

The (block-)composition of two Boolean functions $$f : \{0, 1\}^{m} \rightarrow \{0, 1\}, g : \{0, 1\}^{n} \rightarrow \{0, 1\}$$ is the function $$f \diamond g$$ that takes as inputs m strings $$x_{1}, \ldots , x_{m} \in \{0, 1\}^{n}$$ and computes $$(f \diamond g)(x_{1}, \ldots , x_{m}) = f (g(x_{1}), \ldots , g(x_{m})).$$ This operation has been used several times in the past for amplifying different hardness measures of f and g. This comes at a cost: the function $$f \diamond g$$ has input length $$m \cdot n$$ rather than m or n, which is a bottleneck for some applications. In this paper, we propose to decrease this cost by “derandomizing” the composition: instead of feeding into $$f \diamond g$$ independent inputs $$x_{1}, \ldots , x_{m},$$ we generate $$x_{1}, \ldots , x_{m}$$ using a shorter seed. We show that this idea can be realized in the particular setting of the composition of functions and universal relations (Gavinsky et al. in SIAM J Comput 46(1):114–131, 2017; Karchmer et al. in Computat Complex 5(3/4):191–204, 1995b). To this end, we provide two different techniques for achieving such a derandomization: a technique based on averaging samplers and a technique based on Reed–Solomon codes.

Efficient solution to the millionaires' problem based on asymmetric commutative encryption scheme

AbstractSecure multiparty computation is an important scheme in cryptography and can be applied in various real‐life problems. The first secure multiparty computation problem is the millionaires' problem, and its protocol is an important building block. Because of the less efficiency of public key encryption scheme, most existing solutions based on public key cryptography to this problem are inefficient. Thus, a solution based on the symmetric encryption scheme has been proposed. In this paper, we formally analyse the vulnerability of this solution, and propose a new scheme based on the decisional Diffie‐Hellman assumption. Our solution also uses 0‐encoding and 1‐encoding generated by our modified encoding method to reduce the computation cost. We implement the solution based on symmetric encryption scheme and our protocol. Extensive experiments are conducted to evaluate the efficiency of our solution, and the experimental results show that our solution can be much more efficient and be approximately 8000 times faster than the solution based on symmetric encryption scheme for a 32‐bit input and short‐term security. Moreover, our solution is also more efficient than the state‐of‐the‐art solution without precomputation and can also compare well with the state‐of‐the‐art protocol while the bit length of private inputs is large enough.

An EPTAS for Scheduling on Unrelated Machines of Few Different Types

In the classical problem of scheduling on unrelated parallel machines, a set of jobs has to be assigned to a set of machines. The jobs have a processing time depending on the machine and the goal is to minimize the makespan, that is, the maximum machine load. It is well known that this problem is NP-hard and does not allow polynomial time approximation algorithms with approximation guarantees smaller than 1.5 unless P = NP. We consider the case that there are only a constant number K of machine types. Two machines have the same type if all jobs have the same processing time for them. This variant of the problem is strongly NP-hard already for $$K=1$$ . We present an efficient polynomial time approximation scheme (EPTAS) for the problem, that is, for any $$\varepsilon > 0$$ an assignment with makespan of length at most $$(1+\varepsilon )$$ times the optimum can be found in polynomial time in the input length and the exponent is independent of $$1/\varepsilon $$ . In particular, we achieve a running time of $$2^{{\mathcal {O}}(K\log (K) \nicefrac {1}{\varepsilon }\log ^4 \nicefrac {1}{\varepsilon })}+\mathrm {poly}(|I|)$$ , where |I| denotes the input length. Furthermore, we study three other problem variants and present an EPTAS for each of them: The Santa Claus problem, where the minimum machine load has to be maximized; the case of scheduling on unrelated parallel machines with a constant number of uniform types, where machines of the same type behave like uniformly related machines; and the multidimensional vector scheduling variant of the problem where both the dimension and the number of machine types are constant. For the Santa Claus problem we achieve the same running time. The results are achieved, using mixed integer linear programming and rounding techniques.

Approximation Schemes for Machine Scheduling with Resource (In-)dependent Processing Times

We consider two related scheduling problems: single resource-constrained scheduling on identical parallel machines and a generalization with resource-dependent processing times. In both problems, jobs require a certain amount of an additional resource and have to be scheduled on machines minimizing the makespan, while at every point in time a given resource capacity is not exceeded. In the first variant of the problem, the processing times and resource amounts are fixed, while in the second the former depends on the latter. Both problems contain bin packing with cardinality constraint as a special case, and, therefore, these problems are strongly NP-complete even for a constant number of machines larger than three, which can be proven by a reduction from 3-Partition. Furthermore, if the number of machines is part of the input, then we cannot hope for an approximation algorithm with absolute approximation ratio smaller than 3/2. We present asymptotic fully polynomial time approximation schemes (AFPTAS) for the problems: For any ε > 0, a schedule of length at most (1+ε) times the optimum plus an additive term of O ( p max log (1/ε)/ε) is provided, and the running time is polynomially bounded in 1/ε and the input length. Up to now, only approximation algorithms with absolute approximation ratios were known. Furthermore, the AFPTAS for resource-constrained scheduling on identical parallel machines directly improves the additive term of the best AFPTAS for bin packing with cardinality constraint so far.

Linear-time limited automata

The time complexity of 1-limited automata is investigated from a descriptional complexity view point. Though the model recognizes regular languages only, it may use quadratic time in the input length. We show that, with a polynomial increase in size and preserving determinism, each 1-limited automaton can be transformed into a linear-time equivalent one. We also obtain polynomial transformations into related models, including weight-reducing Hennie machines (i.e., one-tape Turing machines syntactically forced to operate in linear-time), and we show exponential gaps for the converse transformations in the deterministic case.

Sequence Segmentation Using Semi-Markov Conditional Random Fields

Many applications in natural language, speech processing and data integration require model-based segmentation of sequences. Semi-Markov conditional random fields (semi-CRFs) are a generalization of CRFs and provide a full conditional distribution over all possible segmentation of a sequence. Semi-CRFs are particularly suitable for tasks that entail segment-level features such as match with existing dictionary of segments. Empirical results on real-life NER tasks show that they yield higher accuracy than CRFs, but the straightforward foreword–backward inference algorithm requires 3–10 times the computation cost of CRFs. This running time can be reduced significantly by exploiting overlapping features across segments. We present a succinct representation of overlapping features and an efficient training algorithm that can sum over all possible input segmentation in time that is sub-quadratic in the input length, even while imposing no bound on the maximum segment length. Consequently, the running time becomes comparable to CRFs even with the addition of useful entity-level features on large input segments.

Real-time dynamic strain sensing in optical fibers using artificial neural networks.

We propose to use artificial neural networks (ANNs) for raw measurement data interpolation and signal shift computation and to demonstrate advantages for wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) and dynamic strain distribution measurement along optical fibers. The ANNs are trained with synthetic data to predict signal shifts from wavelength scans. Domain adaptation to measurement data is achieved, and standard correlation algorithms are outperformed. First and foremost, the ANN reduces the data analysis time by more than two orders of magnitude, making it possible for the first time to predict strain in real-time applications using the WS-COTDR approach. Further, strain noise and linearity of the sensor response are improved, resulting in more accurate measurements. ANNs also perform better for low signal-to-noise measurement data, for a reduced length of correlation input (i.e., extended distance range), and for coarser sampling settings (i.e., extended strain scanning range). The general applicability is demonstrated for distributed measurement of ground movement along a dark fiber in a telecom cable. The presented ANN-based techniques can be employed to improve the performance of a wide range of correlation or interpolation problems in fiber sensing data analysis and beyond.

Designing, Modelling and Analysis of The Edge Geometry of Linear Lathe Tool Bit

In this publication, with the help of our modelling software we have designed and modelled the edge geometry of the linear lathe tool in the model space. Our aim has been to develop a program which calculates the appropriate edge geometry with the help of some input length and angle dimensions. This is modelled in a wire frame model in a kind a file format that can be easily opened in the latest CAD softwares – so if some later modifications are needed, that can be done easily on the model. With the help of Finite Element Simulation based on given loads, boundary conditions, technological parameters and material quality, an optimal edge geometry can be determined on the cutting tool for reaching the best cutting ratio.

New approach to low-area, low-latency memory-based systolic architecture for FIR filters

A new approach to memory based systolic architecture for higher order FIR filter has been proposed. The memory- based multiplier used in this systolic FIR filter design is different from the earlier used memory-based multipliers. This design is applicable for low-area, low latency and high throughput FIR filters. Both 1-D and 2-D systolic computing structures for higher order FIR filter are derived and implemented in Xillinx Virtex-7 XC7vx330tffg1157 FPGA using VHDL. Various performance metrics like number of slices, latency of the filter and maximum frequency of operation of the filter are compared for different filter orders at various input sample length. The parameters are compared for both 1-D and 2-D systolic structure. For a 128-order filter with an input length of 32-bit, the proposed 2-D structure at eighth-level decomposition occupies 88.9% less area as compared to 1-D structure. The latency of the filter is reduced from 127.5ns to 3.5ns for a 128-order filter using the proposed decomposed structure.

A comparative study of analytical thermal models to predict the orthogonal cutting temperature of AISI 1045 steel

Elevated temperature in the machining process is detrimental to the cutting tool due to a thermal softening effect. The increased material diffusion deteriorates the quality of the machined part. Experimental techniques and finite element method-based numerical models in temperature investigation are limited by the restricted accessibility and high computational cost respectively. Physic-based analytical models are developed to overcome those issues. This study investigated three analytical models, namely a modified chip formation model, Komanduri-Hou two heat sources model, and Ning-Liang material flow stress model, in the prediction of machining temperatures in orthogonal cutting. The evaluation and comparison between three models aim to promote the use of the analytical models in real applications, in which real-time prediction is highly appreciated. Temperatures in machining AISI 1045 steel were predicted under various cutting conditions. Acceptable agreements were observed between predictions and documented values in the literature. In the modified chip formation model, machining temperatures and forces were solved iteratively with complex mathematical equations, which reduced computational efficiency, and thus prevented a real-time temperature prediction. The heat partition factors were empirically determined, which resulted in unoptimized prediction accuracy. In Komanduri-Hou model, the input lengths of two shear zones and shear angle cannot be easily obtained from experiments due to the restricted accessibility. With the benefits of high prediction accuracy, high computational efficiency, and low experimental complexity of model inputs, Ning-Liang model was favored in the real-time prediction of machining temperatures.

A Two-Level Recurrent Neural Network Language Model Based on the Continuous Bag-of-Words Model for Sentence Classification

In this paper, a new two-level recurrent neural network language model (RNNLM) based on the continuous bag-of-words (CBOW) model for application to sentence classification is presented. The vector representations of words learned by a neural network language model have been shown to carry semantic sentiment and are useful in various natural language processing tasks. A disadvantage of CBOW is that it only considers the fixed length of a context because its basic structure is a neural network with a fixed length of input. In contrast, the RNNLM does not have a size limit for a context but only considers the previous context’s words. Therefore, the advantage of RNNLM is complementary to the disadvantage of CBOW. Herein, our proposed model encodes many linguistic patterns and improves upon sentiment analysis and question classification benchmarks compared to previously reported methods.

An indicator for organic matter dynamics in temperate agricultural soils

The heterogeneity of soil organic matter (SOM) and the small changes in soil organic carbon (SOC) compared to large total SOC stocks hinder a robust estimation of SOC turnover, in particular for more stable SOC. We developed a simple fractionation protocol for agricultural topsoils and tested it extensively on a range of soils in southern Belgium, including farmed soils, soils from long-term field trials, and paired sites after recent conversion to conservation farming. Our simple fractionation involves shaking the soil, wet sieving over 20 μm and analysing the SOC concentration in the soil as well as in the fine fraction (<20 μm). Eight biological indicators measured in an earlier study across the same monitoring network for the 0–10 cm topsoil were analysed in a conditional inference forest model in order to investigate the factors influencing the SOC fractions. Soil microbial biomass N explained the largest proportion of variation in both fractions. The fine fraction was also associated with factors explaining the regional trend in SOC distribution such as farmyard manure input, precipitation, land use and flow length. The variation in SOC content between treatments both in long-term trials and in farmers’ fields converted to conservation management was mainly attributed to changes within the coarse fraction. Thus, this fraction proves to be sensitive to management changes, although care should be taken to sample deep enough to represent the former plough layer inherited from the conventional tillage practice. Furthermore, the ratio between the coarse and the fine fraction showed a linear relationship (r² = 0.66) with the relative changes in SOC concentration over the last ten years. These fractions derived from a simple analytical approach are thus useful as an indicator for changes in SOC concentration. In analogy to biological indicators such as the soil microbial biomass C, the relationship between the fractions and relative changes in SOC concentration are likely to depend on climate conditions. Our methodology provides an indicator for use in routine analysis of agricultural topsoils, which is capable of predicting the effects of management practices on SOC concentrations in the short to mid-term (5–10 years).

Length Independent Writer Identification Based on the Fusion of Deep and Hand-Crafted Descriptors

Writer's identification from a handwritten text is one of the most challenging machines learning problems because of the variable handwritten sources, various languages, the similarity between writer's pattern, context variation, and implicit characteristics of handwriting styles. In this paper, a combination of the deep and hand-crafted descriptor is utilized to learn patterns from the handwritten images. First, to do so, the local patches are extracted from the handwritten images. Then, these patches are simultaneously fed to deep and hand-crafted descriptors to generate the local descriptions. The extracted local features are then assembled to make the whole description matrix. Finally, by applying the vector of locally aggregated descriptors (VLAD) encoding on the description matrix, a 1-D feature vector is extracted to represent the writer's pattern. It is worthwhile to mention that the generated description does not rely on any language model or context information. Thus, the proposed approach is language and content independent. In addition, the proposed method does not have any restriction on the input length, hence, the writer's sample can be a passage, paragraph, line, sentence, or even a word. The obtained results on three public benchmark datasets of IAM, CVL, and Khatt indicate that the proposed method has a high-accuracy rate in writing identification task. Furthermore, the performance of the proposed method on CVL dataset using both German and English samples demonstrates that the proposed approach has a high capability in learning a writer's pattern from both languages at the same time.