nils-herrmann/clean_scirepeval huggingface dataset:
A content account of creative analogies in biologically inspired design

The growing movement of biologically inspired design is driven in part by the need for sustainable development and in part by the recognition that nature could be a source of innovation. Biologically inspired design by definition entails cross-domain analogies from biological systems to problems in engineering and other design domains. However, the practice of biologically inspired design at present typically is ad hoc, with little systemization of either biological knowledge for the purposes of engineering design or the processes of transferring knowledge of biological designs to engineering problems. In this paper we present an intricate episode of biologically inspired engineering design that unfolded over an extended period of time. We then analyze our observations in terms of why, what, how, and when questions of analogy. This analysis contributes toward a content theory of creative analogies in the context of biologically inspired design.
Bcl-2 Antiapoptotic Proteins Inhibit Beclin 1-Dependent Autophagy

Apoptosis and autophagy are both tightly regulated biological processes that play a central role in tissue homeostasis, development, and disease. The anti-apoptotic protein, Bcl-2, interacts with the evolutionarily conserved autophagy protein, Beclin 1. However, little is known about the functional significance of this interaction. Here, we show that wild-type Bcl-2 antiapoptotic proteins, but not Beclin 1 binding defective mutants of Bcl-2, inhibit Beclin 1-dependent autophagy in yeast and mammalian cells and that cardiac Bcl-2 transgenic expression inhibits autophagy in mouse heart muscle. Furthermore, Beclin 1 mutants that cannot bind to Bcl-2 induce more autophagy than wild-type Beclin 1 and, unlike wild-type Beclin 1, promote cell death. Thus, Bcl-2 not only functions as an antiapoptotic protein, but also as an antiautophagy protein via its inhibitory interaction with Beclin 1. This antiautophagy function of Bcl-2 may help maintain autophagy at levels that are compatible with cell survival, rather than cell death.
How to Build a CC System

Building a computationally creative system is a challenging undertaking. While such systems are beginning to proliferate, and a good number of them have been reasonably well-documented, it may seem, especially to newcomers to the field, that each system is a bespoke design that bears little chance of revealing any general knowledge about CC system building. This paper seeks to dispel this concern by presenting an abstract CC system description, or, in other words a practical, general approach for constructing CC systems.
Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing

Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources-the transmit precoding matrices of the MUs-and the computational resources-the CPU cycles/second assigned by the cloud to each MU-in order to minimize the overall users' energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.
The emergency response management based on Bayesian decision network

In order to solve the emergency decision management problem with uncertainty, an Emergency Bayesian decision network (EBDN) model is used in this paper. By computing the probability of each node, the EBDN can solve the uncertainty of different response measures. Using Gray system theory to determine the weight of all kinds of emergency losses. And then use genetic algorithm to search the best combination measure by comparing the value of output loss. For illustration, a typhoon example is utilized to show the feasibility of EBDN model. Empirical results show that the EBDN model can combine expert's knowledge and historic data to predict expected effects under different combinations of response measures, and then choose the best one. The proposed EBDN model can combine the decision process into a diagrammatic form, and thus the uncertainty of emergency events in solving emergency dynamic decision making is solved.
Predatory fish sounds can alter crab foraging behaviour and influence bivalve abundance.

The risk of predation can have large effects on ecological communities via changes in prey behaviour, morphology and reproduction. Although prey can use a variety of sensory signals to detect predation risk, relatively little is known regarding the effects of predator acoustic cues on prey foraging behaviour. Here we show that an ecologically important marine crab species can detect sound across a range of frequencies, probably in response to particle acceleration. Further, crabs suppress their resource consumption in the presence of experimental acoustic stimuli from multiple predatory fish species, and the sign and strength of this response is similar to that elicited by water-borne chemical cues. When acoustic and chemical cues were combined, consumption differed from expectations based on independent cue effects, suggesting redundancies among cue types. These results highlight that predator acoustic cues may influence prey behaviour across a range of vertebrate and invertebrate taxa, with the potential for cascading effects on resource abundance.
Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs

Large-scale distributed training of deep neural networks suffer from the generalization gap caused by the increase in the effective mini-batch size. Previous approaches try to solve this problem by varying the learning rate and batch size over epochs and layers, or some ad hoc modification of the batch normalization. We propose an alternative approach using a second-order optimization method that shows similar generalization capability to first-order methods, but converges faster and can handle larger minibatches. To test our method on a benchmark where highly optimized first-order methods are available as references, we train ResNet-50 on ImageNet. We converged to 75% Top-1 validation accuracy in 35 epochs for mini-batch sizes under 16,384, and achieved 75% even with a mini-batch size of 131,072, which took 100 epochs.
Discovering Relations between Noun Categories

Traditional approaches to Relation Extraction from text require manually defining the relations to be extracted. We propose here an approach to automatically discovering relevant relations, given a large text corpus plus an initial ontology defining hundreds of noun categories (e.g., Athlete, Musician, Instrument). Our approach discovers frequently stated relations between pairs of these categories, using a two step process. For each pair of categories (e.g., Musician and Instrument) it first coclusters the text contexts that connect known instances of the two categories, generating a candidate relation for each resulting cluster. It then applies a trained classifier to determine which of these candidate relations is semantically valid. Our experiments apply this to a text corpus containing approximately 200 million web pages and an ontology containing 122 categories from the NELL system [Carlson et al., 2010b], producing a set of 781 proposed candidate relations, approximately half of which are semantically valid. We conclude this is a useful approach to semi-automatic extension of the ontology for large-scale information extraction systems such as NELL.
Cannabidiol (CBD) as an Adjunctive Therapy in Schizophrenia: A Multicenter Randomized Controlled Trial.

Research in both animals and humans indicates that cannabidiol (CBD) has antipsychotic properties. The authors assessed the safety and effectiveness of CBD in patients with schizophrenia.



In an exploratory double-blind parallel-group trial, patients with schizophrenia were randomized in a 1:1 ratio to receive CBD (1000 mg/day; N=43) or placebo (N=45) alongside their existing antipsychotic medication. Participants were assessed before and after treatment using the Positive and Negative Syndrome Scale (PANSS), the Brief Assessment of Cognition in Schizophrenia (BACS), the Global Assessment of Functioning scale (GAF), and the improvement and severity scales of the Clinical Global Impressions Scale (CGI-I and CGI-S).



After 6 weeks of treatment, compared with the placebo group, the CBD group had lower levels of positive psychotic symptoms (PANSS: treatment difference=-1.4, 95% CI=-2.5, -0.2) and were more likely to have been rated as improved (CGI-I: treatment difference=-0.5, 95% CI=-0.8, -0.1) and as not severely unwell (CGI-S: treatment difference=-0.3, 95% CI=-0.5, 0.0) by the treating clinician. Patients who received CBD also showed greater improvements that fell short of statistical significance in cognitive performance (BACS: treatment difference=1.31, 95% CI=-0.10, 2.72) and in overall functioning (GAF: treatment difference=3.0, 95% CI=-0.4, 6.4). CBD was well tolerated, and rates of adverse events were similar between the CBD and placebo groups.



These findings suggest that CBD has beneficial effects in patients with schizophrenia. As CBD's effects do not appear to depend on dopamine receptor antagonism, this agent may represent a new class of treatment for the disorder.
Riskit : Increasing Confidence in Risk Management

Software development is often plagued with unanticipated problems which cause projects to miss deadlines, exceed budgets, or deliver less than satisfactory products. While these problems cannot be eliminated totally, some of them can be controlled better by taking appropriate preventive action. Risk management is an area of project management that deals with these threats before they occur. Organizations may be able to avoid a large number of problems if they use systematic risk management procedures and techniques early in projects.
Bias in Natural Actor-Critic Algorithms

We show that two popular discounted reward natural actor-critics, NAC-LSTD and eNAC, follow biased estimates of the natural policy gradient. We derive the first unbiased discounted reward natural actor-critics using batch and iterative approaches to gradient estimation and prove their convergence to globally optimal policies for discrete problems and locally optimal policies for continuous problems. Finally, we argue that the bias makes the existing algorithms more appropriate for the average reward setting.
Optimal motion planning with the half-car dynamical model for autonomous high-speed driving

We discuss an implementation of the RRT* optimal motion planning algorithm for the half-car dynamical model to enable autonomous high-speed driving. To develop fast solutions of the associated local steering problem, we observe that the motion of a special point (namely, the front center of oscillation) can be modeled as a double integrator augmented with fictitious inputs. We first map the constraints on tire friction forces to constraints on these augmented inputs, which provides instantaneous, state-dependent bounds on the curvature of geometric paths feasibly traversable by the front center of oscillation. Next, we map the vehicle's actual inputs to the augmented inputs. The local steering problem for the half-car dynamical model can then be transformed to a simpler steering problem for the front center of oscillation, which we solve efficiently by first constructing a curvature-bounded geometric path and then imposing a suitable speed profile on this geometric path. Finally, we demonstrate the efficacy of the proposed motion planner via numerical simulation results.
StreetVizor: Visual Exploration of Human-Scale Urban Forms Based on Street Views

Urban forms at human-scale, i.e., urban environments that individuals can sense (e.g., sight, smell, and touch) in their daily lives, can provide unprecedented insights on a variety of applications, such as urban planning and environment auditing. The analysis of urban forms can help planners develop high-quality urban spaces through evidence-based design. However, such analysis is complex because of the involvement of spatial, multi-scale (i.e., city, region, and street), and multivariate (e.g., greenery and sky ratios) natures of urban forms. In addition, current methods either lack quantitative measurements or are limited to a small area. The primary contribution of this work is the design of StreetVizor, an interactive visual analytics system that helps planners leverage their domain knowledge in exploring human-scale urban forms based on street view images. Our system presents two-stage visual exploration: 1) an AOI Explorer for the visual comparison of spatial distributions and quantitative measurements in two areas-of-interest (AOIs) at city- and region-scales; 2) and a Street Explorer with a novel parallel coordinate plot for the exploration of the fine-grained details of the urban forms at the street-scale. We integrate visualization techniques with machine learning models to facilitate the detection of street view patterns. We illustrate the applicability of our approach with case studies on the real-world datasets of four cities, i.e., Hong Kong, Singapore, Greater London and New York City. Interviews with domain experts demonstrate the effectiveness of our system in facilitating various analytical tasks.
Miniscrew-assisted rapid palatal expansion for managing arch perimeter in an adult patient

Etiology of dental crowding may be related to arch constriction in diverse dimensions, and an appropriate manipulation of arch perimeter by intervening in basal bone discrepancies cases, may be a key for crowding relief, especially when incisors movement is limited due to underlying pathology, periodontal issues or restrictions related to soft tissue profile.


 
This case report illustrates a 24-year old woman, with maxillary transverse deficiency, upper and lower arches crowding, Class II, division 1, subdivision right relationship, previous upper incisors traumatic episode and straight profile. A non-surgical and non-extraction treatment approach was feasible due to the miniscrew-assisted rapid palatal expansion technique (MARPE).


 
The MARPE appliance consisted of a conventional Hyrax expander supported by four orthodontic miniscrews. A slow expansion protocol was adopted, with an overall of 40 days of activation and a 3-month retention period. Intrusive traction miniscrew-anchored mechanics were used for correcting the Class II subdivision relationship, managing lower arch perimeter and midline deviation before including the upper central incisors.


 
Post-treatment records show an intermolar width increase of 5 mm, bilateral Class I molar and canine relationships, upper and lower crowding resolution, coincident dental midlines and proper intercuspation.


 
The MARPE is an effective treatment approach for managing arch-perimeter deficiencies related to maxillary transverse discrepancies in adult patients.
INSIGHT: a Semantic Visual Analytics for Programming Discussion Forums

This paper presents INSIGHT, a visual analytics web application, designed to induce & inspire programming language learning from discussion forums. The visual analytics, extracts and displays semantic content from ’Stack Exchange’ in a form of bubble chart. The bubbles represent summarized semantic concepts from the forum posts and outlines the concept specificity of each individual post. The discussion forum content are modeled as concepts based on an innovative Topic Facet Modeling algorithm (a probabilistic topic model that assumes all words in single sentence are generated from one topic facet), and aimed to provide better understanding and solicitation of the increasing large volume of discussion content. We hypothesize that by navigating and interacting (browsing, sorting, searching etc.) with the Facets, will enhance learning. A comprehensive system design rationales and preliminary qualitative study are reported in this paper.
Exploring the Veracity of Online Claims with BackDrop

Using the Web to assess the validity of claims presents many challenges. Whether the data comes from social networks or established media outlets, individual or institutional data publishers, one has to deal with scale and heterogeneity, as well as with incomplete, imprecise and sometimes outright false information. All of these are closely studied issues. Yet in many situations, the claims under scrutiny, and the data itself, have some inherent context-dependency making them impossible to completely disprove, or evaluate through a simple (e.g. scalar) measure. While data models used on the Web typically deal with universal knowledge, we believe the time has come to put context, such as time or provenance, at the forefront and watch knowledge through multiple lenses. We present BackDrop, an application that enables annotating knowledge and ontologies found online to explore how the veracity of claims varies with context. BackDrop comes in the form of a Web interface, in which users can interactively populate and annotate knowledge bases, and explore under which circumstances certain claims are more or less credible.
Literal and Metaphorical Senses in Compositional Distributional Semantic Models

Metaphorical expressions are pervasive in natural language and pose a substantial challenge for computational semantics. The inherent compositionality of metaphor makes it an important test case for compositional distributional semantic models (CDSMs). This paper is the first to investigate whether metaphorical composition warrants a distinct treatment in the CDSM framework. We propose a method to learn metaphors as linear transformations in a vector space and find that, across a variety of semantic domains, explicitly modeling metaphor improves the resulting semantic representations. We then use these representations in a metaphor identification task, achieving a high performance of 0.82 in terms of F-score.
A Fast Algorithm for Computing the Euler Number of an Image and its VLSI Implementation

Digital images are convenient media for describ ing and storing spatial temporal spectral and physical components of information contained in a variety of domains e g aerial satellite images in remote sensing medical images in telemedicine ngerprints in forensics museum collections in art history and registration of trademarks and logos Euler number is a fundamental topological feature of an image The e ciency of computation of topo logical features of an image is critical for many digi tal imaging applications including image matching database retrieval and computer vision that re quire real time response In this paper a novel algorithm for computing the Euler number of a bi nary image based on divide and conquer paradigm is proposed which outperforms signi cantly the conventional techniques used in image processing tools The algorithm can be easily parallelized for computing the Euler number of an N N image in O N time with O N processors Using a simple architecture the proposed method can be imple mented as a special purpose VLSI chip Index Terms Digital imaging Euler number VLSI parallel processing binary image
Combined treatment with statins and aminobisphosphonates extends longevity in a mouse model of human premature aging

Several human progerias, including Hutchinson-Gilford progeria syndrome (HGPS), are caused by the accumulation at the nuclear envelope of farnesylated forms of truncated prelamin A, a protein that is also altered during normal aging. Previous studies in cells from individuals with HGPS have shown that farnesyltransferase inhibitors (FTIs) improve nuclear abnormalities associated with prelamin A accumulation, suggesting that these compounds could represent a therapeutic approach for this devastating progeroid syndrome. We show herein that both prelamin A and its truncated form progerin/LAΔ50 undergo alternative prenylation by geranylgeranyltransferase in the setting of farnesyltransferase inhibition, which could explain the low efficiency of FTIs in ameliorating the phenotypes of progeroid mouse models. We also show that a combination of statins and aminobisphosphonates efficiently inhibits both farnesylation and geranylgeranylation of progerin and prelamin A and markedly improves the aging-like phenotypes of mice deficient in the metalloproteinase Zmpste24, including growth retardation, loss of weight, lipodystrophy, hair loss and bone defects. Likewise, the longevity of these mice is substantially extended. These findings open a new therapeutic approach for human progeroid syndromes associated with nuclear-envelope abnormalities.
Database Scalability, Elasticity, and Autonomy in the Cloud - (Extended Abstract)

Cloud computing has emerged as an extremely successful paradigm for deploying web applications. Scalability, elasticity, pay-per-use pricing, and economies of scale from large scale operations are the major reasons for the successful and widespread adoption of cloud infrastructures. Since a majority of cloud applications are data driven, database management systems (DBMSs) powering these applications form a critical component in the cloud software stack. In this article, we present an overview of our work on instilling these above mentioned “cloud features” in a database system designed to support a variety of applications deployed in the cloud: designing scalable database management architectures using the concepts of data fission and data fusion, enabling lightweight elasticity using low cost live database migration, and designing intelligent and autonomic controllers for system management without human intervention.
CHANGE DETECTION IN CONTEXT

Although change detection constitutes an important and pervasive process in our everyday lives, phenomena such as change blindness show that we are quite limited in our ability to notice even large changes in visual scenes. Change detection is greatly dependant on attention deployment which can be influenced by the contextual setting in which a target item is presented. In this study we wanted to further address the influence of semantic relatedness of the changing item and the surrounding stimuli on the efficiency of change detection. The obtained results indicate that the contextual setting in which a changing element is presented influences the detection of changes. Change detection is generally more efficient in situations when changes are related to items incongruent with the surrounding context when compared to situations where an object belonging to the same category as surrounding items changes into another object from the same category.