nils-herrmann/clean_scirepeval huggingface dataset:
Residual Parameter Transfer for Deep Domain Adaptation

The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or none. Most current approaches have focused on learning feature representations that are invariant to the changes that occur when going from one domain to the other, which means using the same network parameters in both domains. While some recent algorithms explicitly model the changes by adapting the network parameters, they either severely restrict the possible domain changes, or significantly increase the number of model parameters. By contrast, we introduce a network architecture that includes auxiliary residual networks, which we train to predict the parameters in the domain with little annotated data from those in the other one. This architecture enables us to flexibly preserve the similarities between domains where they exist and model the differences when necessary. We demonstrate that our approach yields higher accuracy than state-of-the-art methods without undue complexity.
Competitive Two-Level Adaptive Scheduling Using Resource Augmentation

As multi-core processors proliferate, it has become more important than ever to ensure efficient execution of parallel jobs on multiprocessor systems. In this paper, we study the problem of scheduling parallel jobs with arbitrary release time on multiprocessors while minimizing the jobs’ mean response time. We focus on non-clairvoyant scheduling schemes that adaptively reallocate processors based on periodic feedbacks from the individual jobs. Since it is known that no deterministic non-clairvoyant algorithm is competitive for this problem,we focus on resource augmentation analysis, and show that two adaptive algorithms, Agdeq and Abgdeq, achieve competitive performance using O(1) times faster processors than the adversary. These results are obtained through a general framework for analyzing the mean response time of any two-level adaptive scheduler. Our simulation results verify the effectiveness of Agdeq and Abgdeq by evaluating their performances over a wide range of workloads consisting of synthetic parallel jobs with different parallelism characteristics.
A Visual Representation of Clusters Characteristics Using Edge Bundling for Parallel Coordinates

This paper proposes an edge bundling approach applied on parallel coordinates to improve the visualization of cluster information directly from the overview. Lines belonging to a cluster are bundled into a single curve between axes, where the horizontal and vertical positioning of the bundling intersection (known as bundling control points) to encode pertinent information about the cluster in a given dimension, such as variance, standard deviation, mean, median, and so on. The hypothesis is that adding this information to the overview improves the visualization overview at the same that it does not prejudice the understanding in other aspects. We have performed tests with participants to compare our approach with classic parallel coordinates and other consolidated bundling technique. The results showed most of the initially proposed hypotheses to be confirmed at the end of the study, as the tasks were performed successfully in the majority of tasks maintaining a low response time in average, as well as having more aesthetic pleasing according to participants' opinion.
Accelerated Mini-batch Randomized Block Coordinate Descent Method

We consider regularized empirical risk minimization problems. In particular, we minimize the sum of a smooth empirical risk function and a nonsmooth regularization function. When the regularization function is block separable, we can solve the minimization problems in a randomized block coordinate descent (RBCD) manner. Existing RBCD methods usually decrease the objective value by exploiting the partial gradient of a randomly selected block of coordinates in each iteration. Thus they need all data to be accessible so that the partial gradient of the block gradient can be exactly obtained. However, such a "batch" setting may be computationally expensive in practice. In this paper, we propose a mini-batch randomized block coordinate descent (MRBCD) method, which estimates the partial gradient of the selected block based on a mini-batch of randomly sampled data in each iteration. We further accelerate the MRBCD method by exploiting the semi-stochastic optimization scheme, which effectively reduces the variance of the partial gradient estimators. Theoretically, we show that for strongly convex functions, the MRBCD method attains lower overall iteration complexity than existing RBCD methods. As an application, we further trim the MRBCD method to solve the regularized sparse learning problems. Our numerical experiments shows that the MRBCD method naturally exploits the sparsity structure and achieves better computational performance than existing methods.
Deep Investigation of Cross-Language Plagiarism Detection Methods

This paper is a deep investigation of cross-language plagiarism detection methods on a new recently introduced open dataset, which contains parallel and comparable collections of documents with multiple characteristics (different genres, languages and sizes of texts). We investigate cross-language plagiarism detection methods for 6 language pairs on 2 granularities of text units in order to draw robust conclusions on the best methods while deeply analyzing correlations across document styles and languages.
Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data

Very High Spatial Resolution (VHSR) large-scale SAR image databases are still an unresolved issue in the Remote Sensing field. In this work, we propose such a dataset and use it to explore patch-based classification in urban and periurban areas, considering 7 distinct semantic classes. In this context, we investigate the accuracy of large CNN classification models and pre-trained networks for SAR imaging systems. Furthermore, we propose a Generative Adversarial Network (GAN) for SAR image generation and test, whether the synthetic data can actually improve classification accuracy.
Efficient dense reconstruction using geometry and image consistency constraints

We introduce a method for creating very dense reconstructions of datasets, particularly turn-table varieties. The method takes in initial reconstructions (of any origin) and makes them denser by interpolating depth values in two-dimensional image space within a superpixel region and then optimizing the interpolated value via image consistency analysis across neighboring images in the dataset. One of the core assumptions in this method is that depth values per pixel will vary gradually along a gradient for a given object. As such, turntable datasets, such as the dinosaur dataset, are particularly easy for our method. Our method modernizes some existing techniques and parallelizes them on a GPU, which produces results faster than other densification methods.
Short-Term Traffic Forecasting Using Self-Adjusting k-Nearest Neighbours

Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbours (kNN) method is widely used for short-term traffic forecasting. However, the self-adjustment of kNN parameters has been a problem due to dynamic traffic characteristics. This paper proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training for the parameters. A real-world dataset with more than one year traffic records is used to conduct experiments. The results show that DP-kNN can perform better than manually adjusted kNN and other benchmarking methods in terms of accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement.
Dynamic causal modelling for EEG and MEG

Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnetic resonance imaging (fMRI) to quantify effective connectivity between brain areas. Recently, this framework has been extended and established in the magneto/encephalography (M/EEG) domain. DCM for M/EEG entails the inversion a full spatiotemporal model of evoked responses, over multiple conditions. This model rests on a biophysical and neurobiological generative model for electrophysiological data. A generative model is a prescription of how data are generated. The inversion of a DCM provides conditional densities on the model parameters and, indeed on the model itself. These densities enable one to answer key questions about the underlying system. A DCM comprises two parts; one part describes the dynamics within and among neuronal sources, and the second describes how source dynamics generate data in the sensors, using the lead-field. The parameters of this spatiotemporal model are estimated using a single (iterative) Bayesian procedure. In this paper, we will motivate and describe the current DCM framework. Two examples show how the approach can be applied to M/EEG experiments.
Characteristics of Land Use / Cover and Macroscopic Ecological Changes in the Headwaters of the Yangtze River and of the Yellow River over the Past 30 Years

Based on land use and land cover (LULC) datasets in the late 1970s, the early 1990s, 2004 and 2012, we analyzed characteristics of LULC change in the headwaters of the Yangtze River and Yellow River over the past 30 years contrastively, using the transition matrix and LULC change index. The results showed that, in 2012, the LULC in the headwaters of the Yellow River were different compared to those of the headwaters of the Yangtze River, with more grassland and wetand marshland. In the past 30 years, the grassland and wetand marshland increasing at the expense of sand, gobi, and bare land and desert were the main LULC change types in the headwaters of the Yangtze River, with the macro-ecological situation experiencing a process of degeneration, slight melioration, and continuous melioration, in that order. In the headwaters of the Yellow River, severe reduction of grassland coverage, shrinkage of wetand marshland and the consequential expansion of sand, gobi and bare land were noticed. The macro-ecological situation experienced a process of degeneration, obvious degeneration, and slight melioration, in that order, and the overall change in magnitude was more dramatic than that in the headwaters of the Yangtze River. These different LULC change courses were jointly driven by climate change, grassland-grazing pressure, and the implementation of ecological construction projects.
The Frequency of Multifactorial Syndromes in Geriatrics of Tuzla Canton Population

There are four main multifactorial syndromes in geriatrics the so-called "4N", which specifically occur at elderly patients. Listed syndromes often occur related, and they can be the cause and the result of many other syndromes at geriatric patients.



determine the difference in the assessment of the level of immobility, instability, dependence, urinary incontinence ("4N") in elderly groups.



The research included total 200 elderly respondents experimental group made of elderly persons (>65 years) living alone. Control group included elderly persons living in a family environment. Universal geriatric questionnaire was made for this research. For fast orientation the redone questionnaire was used for our conditions: examination in clinics for usual elderly problems "Short list for examination". For the assessment of the mental abilities reduction at elderly we used "Short portable mental status questionnaire" (SPMSQ).



In total sample the research included 200 elderly respondents, 45% in experimental group and 55% in control group. The average age (±SD) was 75,4±6,2 years in the experimental group, while in the control group the average age was 74,9±5,6 years. We notice nearly equal distribution of falling risk according to groups (50%, 47%). In total sample there were 62% mobile, 22,5% limited mobility, and 4% immobile. Dependence frequency was more represented at examination group respondents (p=0,002). Dependence chances (OR) were 2,05 times larger (95 %CI=1,12-3,75) in examination group than in control group respondents. Frequency of urinary incontinence problem is significantly represented at all our respondents (42,2% v.s. 35%).



Permanent gerontology and geriatrics training is needed both family medicine doctors and other experts in the field of elderly health protection and preventive health measures, pharmacotherapy, palliative care, especially about four main geriatrics syndromes at elderly.
Designing of self tuning PID controller for AR drone quadrotor

In this paper, a gradient decent based methodology is employed to tune the Proportional-Integral-Derivative (PID) controller parameters for AR Drone quadrotor. The three PID controller parameters, proportional gain (Kp), integral gain (Ki) and derivative gain (Kd) are tuned online while flying. The proposed technique has been demonstrated through two test cases. One is the way-point navigation and other is the leader-follower formation control. The experimental result as well as simulations result have shown for both the cases.
Sleep bruxism: Current knowledge and contemporary management

Bruxism is defined as the repetitive jaw muscle activity characterized by the clenching or grinding of teeth. It can be categorized into awake and sleep bruxism (SB). Frequent SB occurs in about 13% of adults. The exact etiology of SB is still unknown and probably multifactorial in nature. Current literature suggests that SB is regulated centrally (pathophysiological and psychosocial factors) and not peripherally (morphological factors). Cited consequences of SB include temporomandibular disorders, headaches, tooth wear/fracture, implant, and other restoration failure. Chairside recognition of SB involves the use of subjective reports, clinical examinations, and trial oral splints. Definitive diagnosis of SB can only be achieved using electrophysiological tools. Pharmacological, psychological, and dental strategies had been employed to manage SB. There is at present, no effective treatment that "cures" or "stops" SB permanently. Management is usually directed toward tooth/restoration protection, reduction of bruxism activity, and pain relief.
A 120pW 50Hz notch filter for low-frequency physiological acquisition

A CMOS OTA-C notch filter for 50Hz interference was presented in this paper. The OTAs were working in weak inversion region in order to achieve ultra low transconductance and power consumptions. The circuits were designed using SMIC mixed-signal 0.18nm 1P6M process. The post-annotated simulation indicated that an attenuation of 47.2dB for power line interference and a 120pW consumption. The design achieved a dynamic range of 75.8dB and a THD of 0.1%, whilst the input signal was a 1 Hz 20mVpp sine wave.
Text Classification using String Kernels

We introduce a novel kernel for comparing two text documents. The kernel is an inner product in the feature space consisting of all subsequences of length k. A subsequence is any ordered sequence of k characters occurring in the text though not necessarily contiguously. The subsequences are weighted by an exponentially decaying factor of their full length in the text, hence emphasising those occurrences which are close to contiguous. A direct computation of this feature vector would involve a prohibitive amount of computation even for modest values of k, since the dimension of the feature space grows exponentially with k. The paper describes how despite this fact the inner product can be efficiently evaluated by a dynamic programming technique. A preliminary experimental comparison of the performance of the kernel compared with a standard word feature space kernel [4] is made showing encouraging results.
ShuffleDog: Characterizing and Adapting User-Perceived Latency of Android Apps

Numerous complains have been made by Android users who severely suffer from the sluggish response when interacting with their devices. However, very few studies have been conducted to understand the user-perceived latency  or mitigate the UI-lagging problem. In this paper, we conduct the first systematic measurement study to quantify the user-perceived latency using typical interaction-intensive Android apps in running with and without background workloads. We reveal the insufficiency of Android system in ensuring the performance of foreground apps and therefore design a new system to address the insufficiency accordingly. We develop a lightweight tracker to accurately identify all delay-critical threads that contribute to the slow response of user interactions. We then build a resource manager that can efficiently schedule various system resources including CPU, I/O, and GPU, for optimizing the performance of these threads. We implement the proposed system on commercial smartphones and conduct comprehensive experiments to evaluate our implementation. Evaluation results show that our system is able to significantly reduce the user-perceived latency of foreground apps in running with aggressive background workloads, up to 10x, while incurring negligible system overhead of less than 3.1 percent CPU and 7 MB memory.
Context-Aware Trajectory Prediction

Human motion and behaviour in crowded spaces is influenced by several factors, such as the dynamics of other moving agents in the scene, as well as the static elements that might be perceived as points of attraction or obstacles. In this work, we present a new model for human trajectory prediction which is able to take advantage of both human-human and human-space interactions. The future trajectory of humans, are generated by observing their past positions and interactions with the surroundings. To this end, we propose a “context-aware” recurrent neural network LSTM model, which can learn and predict human motion in crowded spaces such as a sidewalk, a museum or a shopping mall. We evaluate our model on a public pedestrian datasets, and we contribute a new challenging dataset that collects videos of humans that navigate in a (real) crowded space such as a big museum. Results show that our approach can predict human trajectories better when compared to previous state-of-the-art forecasting models.
Lithium-Ion Battery Systems

The production of lithium-ion (Li-ion) batteries has been continually increasing since their first introduction into the market in 1991 because of their excellent performance, which is related to their high specific energy, energy density, specific power, efficiency, and long life. Li-ion batteries were first used for consumer electronics products such as mobile phones, camcorders, and laptop computers, followed by automotive applications that emerged during the last decade and are still expanding, and finally industrial applications including energy storage. There are four promising cell chemistries considered for energy storage applications: 1) LiMn2O4/graphite cell chemistry uses low-cost materials that are naturally abundant; 2) LiNi1-X-Y2CoXAlYO2/graphite cell chemistry has high specific energy and long life; 3) LiFePO4/graphite (or carbon) cell chemistry has good safety characteristics; and 4) Li4Ti5O12 is used as the negative electrode material in Li-ion batteries with long life and good safety features. However, each of the cell chemistries has some disadvantages, and the development of these technologies is still in progress. Therefore, it is too early to predict which cell chemistry will be the main candidate for energy storage applications, and we have to remain vigilant with respect to trends in technological progress and also consider changes in economic and social conditions before this can be determined.
Simulation Research on Sensorless Speed Control System of Permanent Magnet Synchronous Motor Based on SMO

The vector control system of permanent magnet synchronous motor based on sliding mode observer (SMO) is studied in this paper. On the basis of analyzing the traditional sliding mode observer, an improved sliding mode observer is proposed. Firstly, by using the hyperbolic tangent function instead of the traditional symbol function, the chattering of the system is suppressed. Secondly, a low pass filter which has the variable cutoff frequency along with the rotor speed is designed to reduce the phase delay. Then, by using Kalman filter, it could make back EMF information more smoothly. Finally, in order to obtain accurate position and velocity information, the method of phase-locked loop (PLL) is proposed to estimate the position and speed of the rotor. The simulation results show that the new algorithm can not only improve the accuracy of the position and speed estimation of the rotor but reduce the chattering of the system.
3D printing for the design and fabrication of polymer-based gradient scaffolds.

To accurately mimic the native tissue environment, tissue engineered scaffolds often need to have a highly controlled and varied display of three-dimensional (3D) architecture and geometrical cues. Additive manufacturing in tissue engineering has made possible the development of complex scaffolds that mimic the native tissue architectures. As such, architectural details that were previously unattainable or irreproducible can now be incorporated in an ordered and organized approach, further advancing the structural and chemical cues delivered to cells interacting with the scaffold. This control over the environment has given engineers the ability to unlock cellular machinery that is highly dependent upon the intricate heterogeneous environment of native tissue. Recent research into the incorporation of physical and chemical gradients within scaffolds indicates that integrating these features improves the function of a tissue engineered construct. This review covers recent advances on techniques to incorporate gradients into polymer scaffolds through additive manufacturing and evaluate the success of these techniques. As covered here, to best replicate different tissue types, one must be cognizant of the vastly different types of manufacturing techniques available to create these gradient scaffolds. We review the various types of additive manufacturing techniques that can be leveraged to fabricate scaffolds with heterogeneous properties and discuss methods to successfully characterize them.



Additive manufacturing techniques have given tissue engineers the ability to precisely recapitulate the native architecture present within tissue. In addition, these techniques can be leveraged to create scaffolds with both physical and chemical gradients. This work offers insight into several techniques that can be used to generate graded scaffolds, depending on the desired gradient. Furthermore, it outlines methods to determine if the designed gradient was achieved. This review will help to condense the abundance of information that has been published on the creation and characterization of gradient scaffolds and to provide a single review discussing both methods for manufacturing gradient scaffolds and evaluating the establishment of a gradient.