nils-herrmann/clean_scirepeval huggingface dataset:
Multipotent Differentiation of Human Dental Pulp Stem Cells: a Literature Review

The advent of regenerative medicine has brought us the opportunity to regenerate, modify and restore human organs function. Stem cells, a key resource in regenerative medicine, are defined as clonogenic, self-renewing, progenitor cells that can generate into one or more specialized cell types. Stem cells have been classified into three main groups: embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs) and adult/postnatal stem cells (ASCs). The present review focused the attention on ASCs, which have been identified in many perioral tissues such as dental pulp, periodontal ligament, follicle, gingival, alveolar bone and papilla. Human dental pulp stem cells (hDPSCs) are ectodermal-derived stem cells, originating from migrating neural crest cells and possess mesenchymal stem cell properties. During last decade, hDPSCs have received extensive attention in the field of tissue engineering and regenerative medicine due to their accessibility and ability to differentiate in several cell phenotypes. In this review, we have carefully described the potential of hDPSCs to differentiate into odontoblasts, osteocytes/osteoblasts, adipocytes, chondrocytes and neural cells.
Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto

Population is steadily increasing worldwide, resulting in intractable traffic congestion in dense urban areas. Adaptive traffic signal control (ATSC) has shown strong potential to effectively alleviate urban traffic congestion by adjusting signal timing plans in real time in response to traffic fluctuations to achieve desirable objectives (e.g., minimize delay). Efficient and robust ATSC can be designed using a multiagent reinforcement learning (MARL) approach in which each controller (agent) is responsible for the control of traffic lights around a single traffic junction. Applying MARL approaches to the ATSC problem is associated with a few challenges as agents typically react to changes in the environment at the individual level, but the overall behavior of all agents may not be optimal. This paper presents the development and evaluation of a novel system of multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC). MARLIN-ATSC offers two possible modes: 1) independent mode, where each intersection controller works independently of other agents; and 2) integrated mode, where each controller coordinates signal control actions with neighboring intersections. MARLIN-ATSC is tested on a large-scale simulated network of 59 intersections in the lower downtown core of the City of Toronto, ON, Canada, for the morning rush hour. The results show unprecedented reduction in the average intersection delay ranging from 27% in mode 1 to 39% in mode 2 at the network level and travel-time savings of 15% in mode 1 and 26% in mode 2, along the busiest routes in Downtown Toronto.
Databases of Expressive Speech

This paper discusses the construction of speech databases for research into speech information processing and describes a problem illustrated by the case of emotional speech synthesis. It introduces a project for the processing of expressive speech, and describes the data collection techniques and the subsequent analysis of supra-linguistic, and emotional features signalled in the speech. It presents annotation guidelines for distinguishing speaking-style differences, and argues that the focus of analysis for expressive speech processing applications should be on the speaker relationships (defined herein), rather than on emotions.
Development and implementation using Arduino and Raspberry Pi based Ignition control system

This paper proposes an ignition system for real time detection of driver’s face recognition, finger print authentication as well as alcohol intoxication and subsequently alerting them. The main aim of this proposed system is to reduce the number of accidents due to driver’s drowsiness and alcohol intake to increase the transportation safety as well as protect the vehicle from theft. This proposed system contains 8-megapixels digital USB camera, Raspberry-pi loaded. Face detection is the important part of this project will be done using Open CV.  .
Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making

Tactical driving decision making is crucial for autonomous driving systems and has attracted considerable interest in recent years. In this paper, we propose several practical components that can speed up deep reinforcement learning algorithms towards tactical decision making tasks: 1) nonuniform action skipping as a more stable alternative to action-repetition frame skipping, 2) a counterbased penalty for lanes on which ego vehicle has less right-of-road, and 3) heuristic inference-time action masking for apparently undesirable actions. We evaluate the proposed components in a realistic driving simulator and compare them with several baselines. Results show that the proposed scheme provides superior performance in terms of safety, efficiency, and comfort.
From Citation Network to Study Map: A Novel Model to Reorganize Academic Literatures

As the number of academic papers and new technologies soars, it has been increasingly difficult for researchers, especially beginners, to enter a new research field. Researchers often need to study a promising paper in depth to keep up with the forefront of technology. Traditional Query-Oriented study method is time-consuming and even tedious. For a given paper, existent academic search engines like Google Scholar tend to recommend relevant papers, failing to reveal the knowledge structure. The state-of-the-art MapOriented study methods such as AMiner and AceMap can structure scholar information, but they’re too coarse-grained to dig into the underlying principles of a specific paper. To address this problem, we propose a Study-Map Oriented method and a novel model called RIDP (Reference Injection based Double-Damping PageRank) to help researchers study a given paper more efficiently and thoroughly. RIDP integrates newly designed Reference Injection based Topic Analysis method and Double-Damping PageRank algorithm to mine a Study Map out of massive academic papers in order to guide researchers to dig into the underlying principles of a specific paper. Experiment results on real datasets and pilot user studies indicate that our method can help researchers acquire knowledge more efficiently, and grasp knowledge structure systematically.
A Review of Experiences with Reliable Multicast

By understanding how real users have employed reliable multicast in real distributed systems, we can develop insight concerning the degree to which this technology has matched expectations. This paper reviews a number of applications with that goal in mind. Our findings point to tradeoffs between the form of reliability used by a system and its scalability and performance. We also find that to reach a broad user community (and a commercially interesting market) the technology must be better integrated with component and object-oriented systems architectures. Looking closely at these architectures, however, we identify some assumptions about failure handling which make reliable multicast difficult to exploit. Indeed, the major failures of reliable multicast are associated wit failures. The broader opportunity appears to involve relatively visible embeddings of these tools int h attempts to position it within object oriented systems in ways that focus on transparent recovery from server o object-oriented architectures enabling knowledgeable users to make tradeoffs. Fault-tolerance through transparent server replication may be better viewed as an unachievable holy grail.
Frequency Modulation based microstepping of stepper motor for real time position tracking applications

Microstepping drive for stepper motor is a well-known technique to improve stepper motor performance. Normally microstepping is carried at fixed pulse width planned. But this method is suitable only when motors are driven at fixed pre-defined speeds for non-real time applications. The problem discussed in this paper considers an application where motors need to track a given reference position profile in real time whose speed is varying at every interval of time. In such scenario fixed speed microstep drive does not meet the high pointing requirements and leads to poor system performance. For such applications, an innovative Frequency Modulation (FM) based microstep drive algorithm is developed which meets high degree of motor pointing even at higher output angular rates. As per this scheme, pulse width and number of steps motor supposed to move are derived from the reference position itself in a given amount of time and motor is actuated with corresponding number of steps in real time. FM based microstep drive is implemented with PWM based chopper current controller in an optimized way without compromising on the motor torque margins. The experimental results obtained are very encouraging. The paper presents the design, analysis, simulation and experimental results of FM based microstep drive.
High-density mammalian cell cultures in stirred-tank bioreactor without external pH control.

Maintaining desired pH is a necessity for optimal cell growth and protein production. It is typically achieved through a two-sided pH control loop on the bioreactor controller. Here we investigated cell culture processes with minimum or no pH control and demonstrated that high-density mammalian cell cultures could be maintained for long-term protein production without pH control. The intrinsic interactions between pCO2, lactate, and pH were leveraged to maintain culture pH. Fed-batch cultures at the same lower pH limit of 6.75 but different upper pH limits (7.05, 7.30, 7.45, 7.65) were evaluated in the 3L bioreactors and comparable results were obtained. Neither CO2 sparging nor base addition was required to control pH in the pH range of 6.75-7.65. The impact of sparger configurations (drilled hole sparger vs. frit sparger) and scales (3L vs. 200L) on CO2 accumulation and culture pH was also demonstrated. The same principle was applied in two perfusion cultures with steady state cell densities at 42.5±3.3 or 68.3±6.0×10(6)cells/mL with low cell specific perfusion rates (15±2 to 23±3pL/cell/day), achieving up to 1.9±0.1g/L/day bioreactor productivity. Culture pH level in the 3L perfusion bioreactors was steadily maintained by controlling the residual lactate and pCO2 levels without the requirement of external pH control for up to 40days with consistent productivity and product quality. Furthermore, culture pH could be potentially modulated via adjusting residual glucose levels and CO2 stripping capability in perfusion cultures. To the best of our knowledge, this is the first time a systematic study was performed to evaluate the long-term cell cultivation and protein production in stirred-tank bioreactors without external pH control.
Disturbance-Observer-Based Position Tracking Controller in the Presence of Biased Sinusoidal Disturbance for Electrohydraulic Actuators

A nonlinear position tracking controller with a disturbance observer (DOB) is proposed to track the desired position in the presence of the disturbance for electrohydraulic actuators (EHAs). The DOB is designed in the form of a second-order high-pass filter in order to estimate the disturbance. The nonlinear controller is designed for position tracking as a near input-output linearizing inner-loop load pressure controller and a backstepping outer-loop position controller. Variable structure control is implemented in order to compensate for the error in disturbance estimation. The desired load pressure is designed to generate the pressure using the differential flatness property of the EHA's mechanical subsystem. The disturbance within the bandwidth of the DOB can be cancelled by the proposed method. The performance of the proposed method is validated via simulations and experiments.
Distributional Lesk: Effective Knowledge-Based Word Sense Disambiguation

We propose a simple, yet effective, Word Sense Disambiguation method that uses a combination of a lexical knowledge-base and embeddings. Similar to the classic Lesk algorithm, it exploits the idea that overlap between the context of a word and the definition of its senses provides information on its meaning. Instead of counting the number of words that overlap, we use embeddings to compute the similarity between the gloss of a sense and the context. Evaluation on both Dutch and English datasets shows that our method outperforms other Lesk methods and improves upon a state-of-theart knowledge-based system. Additional experiments confirm the effect of the use of glosses and indicate that our approach works well in different domains.
Biospeckle image stack process based on artificial neural networks

This paper proposes the identification of regions of interest in biospeckle patterns using unsupervised neural networks of the type Self-Organizing Maps. Segmented images are obtained from the acquisition and processing of laser speckle sequences. The dynamic speckle is a phenomenon that occurs when a beam of coherent light illuminates a sample in which there is some type of activity, not visible, which results in a variable pattern over time. In this particular case the method is applied to the evaluation of bacterial chemotaxis. Image stacks provided by a set of experiments are processed to extract features of the intensity dynamics. A Self-Organizing Map is trained and its cells are colored according to a criterion of similarity. During the recall stage the features of patterns belonging to a new biospeckle sample impact on the map, generating a new image using the color of the map cells impacted by the sample patterns. It is considered that this method has shown better performance to identify regions of interest than those that use a single descriptor. To test the method a chemotaxis assay experiment was performed, where regions were differentiated according to the bacterial motility within the sample.
Handling bitcoin conflicts through a glimpse of structure

Double spending and blockchain forks are two main issues that the Bitcoin crypto-system is confronted with. The former refers to an adversary's ability to use the very same coin more than once while the latter reflects the occurrence of transient inconsistencies in the history of the blockchain distributed data structure. We present a new approach to tackle these issues: it consists in adding some local synchronization constraints on Bitcoin's validation operations, and in making these constraints independent from the native blockchain protocol. Synchronization constraints are handled by nodes which are randomly and dynamically chosen in the Bitcoin system. We show that with such an approach, content of the blockchain is consistent with all validated transactions and blocks which guarantees the absence of both double-spending attacks and blockchain forks.
A Discriminative Model for Query Spelling Correction with Latent Structural SVM

Discriminative training in query spelling correction is difficult due to the complex internal structures of the data. Recent work on query spelling correction suggests a two stage approach a noisy channel model that is used to retrieve a number of candidate corrections, followed by discriminatively trained ranker applied to these candidates. The ranker, however, suffers from the fact the low recall of the first, suboptimal, search stage. This paper proposes to directly optimize the search stage with a discriminative model based on latent structural SVM. In this model, we treat query spelling correction as a multiclass classification problem with structured input and output. The latent structural information is used to model the alignment of words in the spelling correction process. Experiment results show that as a standalone speller, our model outperforms all the baseline systems. It also attains a higher recall compared with the noisy channel model, and can therefore serve as a better filtering stage when combined with a ranker.
Malayalam OCR: N-gram approach using SVM classifier

Optical Character Recognition which could be defined as the process of isolating textual scripts from a scanned document, is not in its 100% efficiency when it comes to a complex Dravidian language, Malayalam. Here, we present a different approach of combining n-gram segmentation along with geometric feature extraction methodology to train a Support Vector Machine in order to obtain a recognizing accuracy better than the existing methods. N-gram isolation has not been implemented so far for the curvy language Malayalam and thus such an approach gives a competence of 98% which uses Otsu Algorithm as its base. Highly efficient segmentation process gives better accuracy in feature extraction which is being fed as the input of SVM. The proposed tactic gives an adept output of 95.6% efficacy in recognizing Malayalam printed scripts and word snippets.
A Microstructural Approach to Cytoskeletal Mechanics based on Tensegrity

Mechanical properties of living cells are commonly described in terms of the laws of continuum mechanics. The purpose of this report is to consider the implications of an alternative approach that emphasizes the discrete nature of stress bearing elements in the cell and is based on the known structural properties of the cytoskeleton. We have noted previously that tensegrity architecture seems to capture essential qualitative features of cytoskeletal shape distortion in adherent cells (Ingber, 1993a; Wang et al., 1993). Here we extend those qualitative notions into a formal microstructural analysis. On the basis of that analysis we attempt to identify unifying principles that might underlie the shape stability of the cytoskeleton. For simplicity, we focus on a tensegrity structure containing six rigid struts interconnected by 24 linearly elastic cables. Cables carry initial tension (‘‘prestress’’) counterbalanced by compression of struts. Two cases of interconnectedness between cables and struts are considered: one where they are connected by pin-joints, and the other where the cables run through frictionless loops at the junctions. At the molecular level, the pinned structure may represent the case in which different cytoskeletal filaments are cross-linked whereas the looped structure represents the case where they are free to slip past one another. The system is then subjected to uniaxial stretching. Using the principal of virtual work, stretching force vs. extension and structural stiffness vs. stretching force relationships are calculated for different prestresses. The stiffness is found to increase with increasing prestress and, at a given prestress, to increase approximately linearly with increasing stretching force. This behavior is consistent with observations in living endothelial cells exposed to shear stresses (Wang & Ingber, 1994). At a given prestress, the pinned structure is found to be stiffer than the looped one, a result consistent with data on mechanical behavior of isolated, cross-linked and uncross-linked actin networks (Wachsstock et al., 1993). On the basis of our analysis we concluded that architecture and the prestress of the cytoskeleton might be key features that underlie a cell’s ability to regulate its shape. 7
Multidimensional approach to the differential diagnosis of central auditory processing disorders in children.

Central auditory processing disorder (CAPD) may be viewed as a multidimensional entity with far-reaching communicative, educational, and psychosocial implications for which differential diagnosis not only is possible but also is essential to an understanding of its impact and to the development of efficacious, deficit-specific management plans. This paper begins with a description of some behavioral central auditory assessment tools in current clinical use. Four case studies illustrate the utility of these tools in clarifying the nature of auditory difficulties. Appropriate treatment options that flow logically from the diagnoses are given in each case. The heterogeneity of the population presenting with auditory processing problems, not unexpected based on this model, is made clear, as is the clinical utility of central auditory tests in the transdisciplinary assessment and management of children's language and learning difficulties.
Empirical Comparison of Prediction Methods for Electricity Consumption Forecasting

Recent years have seen an increasing interest in providing accurate prediction models for electrical energy consumption. In Smart Grids, energy consumption optimization is critical to enhance power grid reliability, and avoid supply-demand mismatches. Utilities rely on real-time power consumption data from individual customers in their service area to forecast the future demand and initiate energy curtailment programs. Currently however, little is known about the differences in consumption characteristics of various customer types, and their impact on the prediction method’s accuracy. While many studies have concentrated on aggregate loads, showing that accurate consumption prediction at the building level can be achieved, there is a lack of results regarding individual customers consumption prediction. In this study, we perform an empirical quantitative evaluation of various prediction methods of kWh energy consumption of two distinct customer types: 1) small, highly variable individual customers, and 2) aggregated, more stable consumption at the building level. We show that prediction accuracy heavily depends on customer type. Contrary to previous studies, we consider the consumption data granularity to be very small (i.e., 15-min interval), and focus on very short term predictions (next few hours). As Smart Grids move closer to dynamic curtailment programs, which enables demand response (DR) events not only on weekdays, but also during weekends, existing DR strategies prove to be inadequate. Here, we relax the constraint of workdays, and include weekends, where ISO models consistently under perform. Nonetheless, we show that simple ISO baselines, and short-term Time Series, which only depend on recent historical data, achieve superior prediction accuracy. This result suggests that large amounts of historical training data are not required, rather they should be avoided.
Optimal training design for MIMO OFDM systems in mobile wireless channels

This paper describes a least squares (LS) channel estimation scheme for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems based on pilot tones. We first compute the mean square error (MSE) of the LS channel estimate. We then derive optimal pilot sequences and optimal placement of the pilot tones with respect to this MSE. It is shown that the optimal pilot sequences are equipowered, equispaced, and phase shift orthogonal. To reduce the training overhead, an LS channel estimation scheme over multiple OFDM symbols is also discussed. Moreover, to enhance channel estimation, a recursive LS (RLS) algorithm is proposed, for which we derive the optimal forgetting or tracking factor. This factor is found to be a function of both the noise variance and the channel Doppler spread. Through simulations, it is shown that the optimal pilot sequences derived in this paper outperform both the orthogonal and random pilot sequences. It is also shown that a considerable gain in signal-to-noise ratio (SNR) can be obtained by using the RLS algorithm, especially in slowly time-varying channels.