nils-herrmann/clean_scirepeval huggingface dataset:
Experience sampling during fMRI reveals default network and executive system contributions to mind wandering.

Although mind wandering occupies a large proportion of our waking life, its neural basis and relation to ongoing behavior remain controversial. We report an fMRI study that used experience sampling to provide an online measure of mind wandering during a concurrent task. Analyses focused on the interval of time immediately preceding experience sampling probes demonstrate activation of default network regions during mind wandering, a finding consistent with theoretical accounts of default network functions. Activation in medial prefrontal default network regions was observed both in association with subjective self-reports of mind wandering and an independent behavioral measure (performance errors on the concurrent task). In addition to default network activation, mind wandering was associated with executive network recruitment, a finding predicted by behavioral theories of off-task thought and its relation to executive resources. Finally, neural recruitment in both default and executive network regions was strongest when subjects were unaware of their own mind wandering, suggesting that mind wandering is most pronounced when it lacks meta-awareness. The observed parallel recruitment of executive and default network regions--two brain systems that so far have been assumed to work in opposition--suggests that mind wandering may evoke a unique mental state that may allow otherwise opposing networks to work in cooperation. The ability of this study to reveal a number of crucial aspects of the neural recruitment associated with mind wandering underscores the value of combining subjective self-reports with online measures of brain function for advancing our understanding of the neurophenomenology of subjective experience.
Reducing Controversy by Connecting Opposing Views

Society is often polarized by controversial issues that split the population into groups with opposing views. When such issues emerge on social media, we often observe the creation of `echo chambers', i.e., situations where like-minded people reinforce each other's opinion, but do not get exposed to the views of the opposing side. In this paper we study algorithmic techniques for bridging these chambers, and thus reduce controversy. Specifically, we represent the discussion on a controversial issue with an endorsement graph, and cast our problem as an edge-recommendation problem on this graph. The goal of the recommendation is to reduce the controversy score of the graph, which is measured by a recently-developed metric based on random walks. At the same time, we take into account the acceptance probability of the recommended edge, which represents how likely the edge is to materialize in the endorsement graph.
 We propose a simple model based on a recently-developed user-level controversy score, that is competitive with state-of-the-art link-prediction algorithms. Our goal then becomes finding the edges that produce the largest reduction in the controversy score, in expectation. To solve this problem, we propose an efficient algorithm that considers only a fraction of all the possible combinations of edges. Experimental results show that our algorithm is more efficient than a simple greedy heuristic, while producing comparable score reduction. Finally, a comparison with other state-of-the-art edge-addition algorithms shows that this problem is fundamentally different from what has been studied in the literature.
Back to the Future: Towards a Theory of Timed Regular Languages

Timed automata are nite-state machines constrained by timing requirements so that they accept timed words | words in which every symbol is labeled with a real-valued time. These automata were designed to lead to a theory of nite-state real-time properties with applications to the automatic veri cation of real-time systems. However, both deterministic and nondeterministic versions su er from drawbacks: several key problems, such as language inclusion, are undecidable for nondeterministic timed automata, whereas deterministic timed automata lack considerable expressive power when compared to decidable real-time logics. This is why we introduce two-way timed automata | timed automata that can move back and forth while reading a timed word. Two-wayness in its unrestricted form leads, like nondeterminism, to the undecidability of language inclusion. However, if we restrict the number of times an input symbol may be revisited, then two-wayness is both harmless and desirable. We show that the resulting class of bounded two-way deterministic timed automata is closed under all boolean operations, has decidable (PSPACE-complete) emptiness and inclusion problems, and subsumes all decidable real-time logics we know. We obtain a strict hierarchy of real-time properties: deterministic timed automata can accept more languages as the bound on the number of times an input symbol may be revisited is increased. This hierarchy is also enforced by the number of alternations between past and future operators in temporal logic. The combination of our results leads to a decision procedure for a real-time logic with past operators.
Low-Power Low-Noise CTIA Readout Integrated Circuit Design for Thermal Imaging Applications

This paper targets the design of a high dynamic range lowpower, low-noise pixel readout integrated circuit (ROIC) that handles the infrared (IR) detector’s output signal of the uncooled thermal IR camera. Throughout the paper, both the optics and the IR detector modules of the IR camera are modeled using the analogue hardware description language (AHDL) to enable extracting the proper input signal required for the ROIC design. A capacitive trans-impedance amplifier (CTIA) is selected for design as a column level ROIC. The core of the CTIA is designed for minimum power consumption by operation in the sub-threshold region. In addition, a design of correlated double sampling (CDS) technique is applied to the CTIA to minimize the noise and the offset levels. The presented CTIA design achieves a power consumption of 5.2μW and root mean square (RMS) output noise of 6.9μV. All the circuits were implemented in 0.13μm CMOS process technology. The design rule check (DRC), layout versus schematic (LVS), parasitic extraction (PE), Process-voltage-temperature (PVT) analysis and post-layout simulation are performed for all designed circuits. The postlayout simulation results illustrate enhancement of the power consumption and noise performance compared to other published ROIC designs. Finally, a new widening dynamic range (WDR) technique is applied to the CTIA with the CDS circuit designs to increase the dynamic range (DR).
Many-Objective Evolutionary Algorithms: A Survey

Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.
MER : a Minimal Named-Entity Recognition Tagger and Annotation Server

Named-Entity Recognition (NER) aims at identifying the fragments of a given text that mention a given entity of interest. This manuscript presents our Minimal named-Entity Recognizer (MER), designed with flexibility, autonomy and efficiency in mind. To annotate a given text, MER only requires a lexicon (text file) with the list of terms representing the entities of interest; and a GNU Bash shell grep and awk tools. MER was deployed in a cloud infrastructure using multiple Virtual Machines to work as an annotation server and participate in the Technical Interoperability and Performance of annotation Servers (TIPS) task of BioCreative V.5. Preliminary results show that our solution processed each document (text retrieval and annotation) in less than 3 seconds on average without using any type of cache. MER is publicly available in a GitHub repository (https://github.com/lasigeBioTM/MER) and through a RESTful Web service (http://labs.fc.ul.pt/mer/).
Research and Design of PV MPPT Based on STM32

Aiming at the problem of maximum power point tracking (MPPT) of PV power generation, combined with the purpose of temperature adjustment of rural PV heating system, the MPPT method based on the model of adjustable load resistance matching is proposed. The output characteristics of PV cells and the principle of load matching are analyzed, and the MPPT of PV generation is realized by perturbation and observation (P&O) method. Regarding the heating equipment as the resistive load, a group of resistors with a wide range and small step length which can be controlled by the relay are designed to test. Finally, taking STM32 as the core processor, adjusting the step size and tracking the change of PV output voltage, the controller can track the change of the external environment accurately, that is, it can track the maximum power point of a PV power system effectively.
3D LIDAR-camera intrinsic and extrinsic calibration: Identifiability and analytical least-squares-based initialization

In this paper we address the problem of estimating the intrinsic parameters of a 3D LIDAR while at the same time computing its extrinsic calibration with respect to a rigidly connected camera. Existing approaches to solve this nonlinear estimation problem are based on iterative minimization of nonlinear cost functions. In such cases, the accuracy of the resulting solution hinges on the availability of a precise initial estimate, which is often not available. In order to address this issue, we divide the problem into two least-squares sub-problems, and analytically solve each one to determine a precise initial estimate for the unknown parameters. We further increase the accuracy of these initial estimates by iteratively minimizing a batch nonlinear least-squares cost function. In addition, we provide the minimal identifiability conditions, under which it is possible to accurately estimate the unknown parameters. Experimental results consisting of photorealistic 3D reconstruction of indoor and outdoor scenes, as well as standard metrics of the calibration errors, are used to assess the validity of our approach.
Internet marketing: a content analysis of the research

The amount of research related to Internet marketing has grown rapidly since the dawn of the Internet Age. A review of the literature base will help identify the topics that have been explored as well as identify topics for further research. This research project collects, synthesizes, and analyses both the research strategies (i.e., methodologies) and content (e.g., topics, focus, categories) of the current literature, and then discusses an agenda for future research efforts. We analyzed 411 articles published over the past eighteen years (1994-present) in thirty top Information Systems (IS) journals and 22 articles in the top 5 Marketing journals. The results indicate an increasing level of activity during the 18-year period, a biased distribution of Internet marketing articles focused on exploratory methodologies, and several research strategies that were either underrepresented or absent from the pool of Internet marketing research. We also identified several subject areas that need further exploration. The compilation of the methodologies used and Internet marketing topics being studied can serve to motivate researchers to strengthen current research and explore new areas of this research.
A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes

Many school districts have developed successful intervention programs to help students graduate high school on time. However, identifying and prioritizing students who need those interventions the most remains challenging. This paper describes a machine learning framework to identify such students, discusses features that are useful for this task, applies several classification algorithms, and evaluates them using metrics important to school administrators. To help test this framework and make it practically useful, we partnered with two U.S. school districts with a combined enrollment of approximately 200,000 students. We together designed several evaluation metrics to assess the goodness of machine learning algorithms from an educator's perspective. This paper focuses on students at risk of not finishing high school on time, but our framework lays a strong foundation for future work on other adverse academic outcomes.
Micro-crowdfunding: achieving a sustainable society through economic and social incentives in micro-level crowdfunding

This paper introduces a new approach, named micro-crowdfunding, for motivating people to participate in achieving a sustainable society. Increasing people's awareness of how they participate in maintaining the sustainability of common resources, such as public sinks, toilets, shelves, and office areas, is central to achieving a sustainable society. Micro-crowdfunding, as proposed in the paper, is a new type of community-based crowdsourcing architecture that is based on the crowdfunding concept and uses the local currency idea as a tool for encouraging people who live in urban environments to increase their awareness of how important it is to sustain small, common resources through their minimum efforts. Because our approach is lightweight and uses a mobile phone, people can participate in micro-crowdfunding activities with little effort anytime and anywhere.
 We present the basic concept of micro-crowdfunding and a prototype system. We also describe our experimental results, which show how economic and social factors are effective in facilitating micro-crowdfunding. Our results show that micro-crowdfunding increases the awareness about social sustainability, and we believe that micro-crowdfunding makes it possible to motivate people for achieving a sustainable society.
Analysis and Simulation of a Rocker-bogie Exploration Rover

Rovers will continue to play an important role in planetary exploration. Plans include the use of the rocker-bogie rover configuration. Here, models of the mechanics of this configuration are presented. Methods for solving the inverse kinematics of the system and quasi-static force analysis are described. Also described is a simulation based on the models of the rover’s performance. Experimental results confirm the validity of the models.
Bidirectional recurrent neural networks

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported.
A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations

Recognizing analogies, synonyms, antonyms, and associations appear to be four distinct tasks, requiring distinct NLP algorithms. In the past, the four tasks have been treated independently, using a wide variety of algorithms. These four semantic classes, however, are a tiny sample of the full range of semantic phenomena, and we cannot afford to create ad hoc algorithms for each semantic phenomenon; we need to seek a unified approach. We propose to subsume a broad range of phenomena under analogies. To limit the scope of this paper, we restrict our attention to the subsumption of synonyms, antonyms, and associations. We introduce a supervised corpus-based machine learning algorithm for classifying analogous word pairs, and we show that it can solve multiple-choice SAT analogy questions, TOEFL synonym questions, ESL synonym-antonym questions, and similar-associated-both questions from cognitive psychology.
Formal Barriers to Longest-Chain Proof-of-Stake Protocols

The security of most existing cryptocurrencies is based on a concept called Proof-of-Work, in which users must solve a computationally hard cryptopuzzle to authorize transactions (“one unit of computation, one vote”). This leads to enormous expenditure on hardware and electricity in order to collect the rewards associated with transaction authorization. Proof-of-Stake is an alternative concept that instead selects users to authorize transactions proportional to their wealth (“one coin, one vote”). Some aspects of the two paradigms are the same. For instance, obtaining voting power in Proof-of-Stake has a monetary cost just as in Proof-of-Work: a coin cannot be freely duplicated any more easily than a unit of computation. However some aspects are fundamentally different. In particular, exactly because Proof-of-Stake is wasteless, there is no inherent resource cost to deviating (commonly referred to as the “Nothing-at-Stake” problem). In contrast to prior work, we focus on incentive-driven deviations (any participant will deviate if doing so yields higher revenue) instead of adversarial corruption (an adversary may take over a significant fraction of the network, but the remaining players follow the protocol). The main results of this paper are several formal barriers to designing incentive-compatible proof-of-stake cryptocurrencies (that don’t apply to proof-of-work). ∗Computer Science Department, UC Berkeley. Email:jonahbc@eecs.berkeley.edu †Computer Science Department, Princeton University. Email:arvindn@cs.princeton.edu ‡Computer Science Department, Carnegie Mellon University. Email:cpsomas@cs.cmu.edu §Computer Science Department, Princeton University. Email:smweinberg@princeton.edu. Supported by NSF CCF-1717899. Work done in part while the author was a Research Fellow at the Simons Institute for the Theory of Computing. 0 ar X iv :1 80 9. 06 52 8v 1 [ cs .G T ] 1 8 Se p 20 18
Conversations Gone Awry: Detecting Early Signs of Conversational Failure

One of the main challenges online social systems face is the prevalence of antisocial behavior, such as harassment and personal attacks. In this work, we introduce the task of predicting from the very start of a conversation whether it will get out of hand. As opposed to detecting undesirable behavior after the fact, this task aims to enable early, actionable prediction at a time when the conversation might still be salvaged. To this end, we develop a framework for capturing pragmatic devices—such as politeness strategies and rhetorical prompts—used to start a conversation, and analyze their relation to its future trajectory. Applying this framework in a controlled setting, we demonstrate the feasibility of detecting early warning signs of antisocial behavior in online discussions.
Caudal septoplasty for treatment of septal deviation: aesthetic and functional correction of the nasal base.

To describe our technique in the treatment of significant caudal septal deviation; to evaluate the effectiveness of our technique of caudal septoplasty in the treatment of caudal septal deviations.



Retrospective review of cases taken from a database of more than 2000 patients who underwent rhinoplasty performed by 1 surgeon in a private facial plastic surgery practice.



Medical charts were reviewed to determine the rate of preoperative nasal obstruction in 59 (95%) of 62 patients as well as nasal obstruction postoperatively 11 (17%) of 62 (P < .001). Photographs were reviewed to determine the severity of caudal deviation and the postoperative result. By photographic evaluation, we found that all but 3 patients had significant improvement in their postoperative appearance. Twenty-six patients had no evidence of residual asymmetry. The rate of revision was 5 (8%) of 62 patients.



The caudal septoplasty technique is effective, relatively easy to perform, and shows long-term reliability in correcting caudal septal deviation. In properly selected patients, the technique is effective in improving cosmesis and nasal airflow.
Memory Augmented Neural Networks with Wormhole Connections

Recent empirical results on long-term dependency tasks have shown that neural networks augmented with an external memory can learn the long-term dependency tasks more easily and achieve better generalization than vanilla recurrent neural networks (RNN). We suggest that memory augmented neural networks can reduce the effects of vanishing gradients by creating shortcut (or wormhole) connections. Based on this observation, we propose a novel memory augmented neural network model called TARDIS (Temporal Automatic Relation Discovery in Sequences). The controller of TARDIS can store a selective set of embeddings of its own previous hidden states into an external memory and revisit them as and when needed. For TARDIS, memory acts as a storage for wormhole connections to the past to propagate the gradients more effectively and it helps to learn the temporal dependencies. The memory structure of TARDIS has similarities to both Neural Turing Machines (NTM) and Dynamic Neural Turing Machines (D-NTM), but both read and write operations of TARDIS are simpler and more efficient. We use discrete addressing for read/write operations which helps to substantially to reduce the vanishing gradient problem with very long sequences. Read and write operations in TARDIS are tied with a heuristic once the memory becomes full, and this makes the learning problem simpler when compared to NTM or D-NTM type of architectures. We provide a detailed analysis on the gradient propagation in general for MANNs. We evaluate our models on different long-term dependency tasks and report competitive results in all of them.