nils-herrmann/clean_scirepeval huggingface dataset:
Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coefficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters.
Chapter 3-Spacing and Testing Effects : A Deeply Critical , Lengthy , and At Times Discursive Review of the Literature

What appears to be a simple pattern of results—distributed-study opportunities usually produce bettermemory thanmassed-study opportunities—turns out to be quite complicated.Many ‘‘impostor’’ effects such as rehearsal borrowing, strategy changes during study, recency effects, and item skipping complicate the interpretation of spacing experiments. We suggest some best practices for future experiments that diverge from the typical spacing experiments in the literature. Next, we outline themajor theories that have been advanced to account for spacing studies while highlighting the critical experimental evidence that a theory of spacingmust explain. We then propose a tentative verbal theory based on the SAM/REMmodel that utilizes contextual variability and study-phase retrieval to explain the major findings, as well as predict some novel results. Next, we outline the major phenomena supporting testing as superior to restudy on long-term retention tests, and review theories of the testing phenomenon, along with some possible boundary conditions. Finally, we suggest some ways that spacing and testing can be integrated into the classroom, and ask to what extent educators already capitalize on these phenomena. Along the way, we present several new experiments that shed light on various facets of the spacing and testing effects.
Unsupervised Machine Translation Using Monolingual Corpora Only

Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data. We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data. We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French datasets, without using even a single parallel sentence at training time.
Revisiting Distillation and Incremental Classifier Learning

One of the key differences between the learning mechanism of humans and Artificial Neural Networks (ANNs) is the ability of humans to learn one task at a time. ANNs, on the other hand, can only learn multiple tasks simultaneously. Any attempts at learning new tasks incrementally cause them to completely forget about previous tasks. This lack of ability to learn incrementally, called Catastrophic Forgetting, is considered a major hurdle in building a true AI system. In this paper, our goal is to isolate the truly effective existing ideas for incremental learning from those that only work under certain conditions. To this end, we first thoroughly analyze the current state of the art (iCaRL) method for incremental learning and demonstrate that the good performance of the system is not because of the reasons presented in the existing literature. We conclude that the success of iCaRL is primarily due to knowledge distillation and recognize a key limitation of knowledge distillation, i.e, it often leads to bias in classifiers. Finally, we propose a dynamic threshold moving algorithm that is able to successfully remove this bias. We demonstrate the effectiveness of our algorithm on CIFAR100 and MNIST datasets showing near-optimal results. Our implementation is available at : https://github.com/Khurramjaved96/ incremental-learning.
Computation Offloading for Service Workflow in Mobile Cloud Computing

The development of cloud computing and virtualization techniques enables mobile devices to overcome the severity of scarce resource constrained by allowing them to offload computation and migrate several computation parts of an application to powerful cloud servers. A mobile device should judiciously determine whether to offload computation as well as what portion of an application should be offloaded to the cloud. This paper considers a mobile computation offloading problem where multiple mobile services in workflows can be invoked to fulfill their complex requirements and makes decision on whether the services of a workflow should be offloaded. Due to the mobility of portable devices, unstable connectivity of mobile networks can impact the offloading decision. To address this issue, we propose a novel offloading system to design robust offloading decisions for mobile services. Our approach considers the dependency relations among component services and aims to optimize execution time and energy consumption of executing mobile services. To this end, we also introduce a mobility model and a trade-off fault-tolerance mechanism for the offloading system. A genetic algorithm (GA) based offloading method is then designed and implemented after carefully modifying parts of a generic GA to match our special needs for the stated problem. Experimental results are promising and show nearoptimal solutions for all of our studied cases with almost linear algorithmic complexity with respect to the problem size.
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity

Regularization of Deep Neural Networks (DNNs) for the sake of improving their generalization capability is important and challenging. The development in this line benefits theoretical foundation of DNNs and promotes their usability in different areas of artificial intelligence. In this paper, we investigate the role of Rademacher complexity in improving generalization of DNNs and propose a novel regularizer rooted in Local Rademacher Complexity (LRC). While Rademacher complexity is well known as a distribution-free complexity measure of function class that help boost generalization of statistical learning methods, extensive study shows that LRC, its counterpart focusing on a restricted function class, leads to sharper convergence rates and potential better generalization given finite training sample. Our LRC based regularizer is developed by estimating the complexity of the function class centered at the minimizer of the empirical loss of DNNs. Experiments on various types of network architecture demonstrate the effectiveness of LRC regularization in improving generalization. Moreover, our method features the state-of-the-art result on the CIFAR-10 dataset with network architecture found by neural architecture search.
Assessing Computational Thinking in CS Unplugged Activities

Computer Science (CS) Unplugged activities have been deployed in many informal settings to present computing concepts in an engaging manner. To justify use in the classroom, however, it is critical for activities to have a strong educational component. For the past three years, we have been developing and refining a CS Unplugged curriculum for use in middle school classrooms. In this paper, we describe an assessment that maps questions from a comprehensive project to computational thinking (CT) skills and Bloom's Taxonomy. We present results from two different deployments and discuss limitations and implications of our approach.
Overview of the FIRE 2016 Microblog track: Information Extraction from Microblogs Posted during Disasters

The FIRE 2016 Microblog track focused on retrieval of microblogs (tweets posted on Twitter) during disaster events. A collection of about 50,000 microblogs posted during a recent disaster event was made available to the participants, along with a set of seven practical information needs during a disaster situation. The task was to retrieve microblogs relevant to these needs. 10 teams participated in the task, submitting a total of 15 runs. The task resulted in comparison among performances of various microblog retrieval strategies over a benchmark collection, and brought out the challenges in microblog retrieval.
Changes in mental state associated with prison environments: a systematic review.

To develop an understanding of the stability of mental health during imprisonment through review of existing research evidence relating physical prison environment to mental state changes in prisoners.



A systematic literature search was conducted looking at changes in mental state and how this related to various aspects of imprisonment and the prison environment.



Fifteen longitudinal studies were found, and from these, three broad themes were delineated: being imprisoned and aspects of the prison regime; stage of imprisonment and duration of sentence; and social density. Reception into prison results in higher levels of psychiatric symptoms that seem to improve over time; otherwise, duration of imprisonment appears to have no significant impact on mental health. Regardless of social density, larger prisons are associated with poorer mental state, as are extremes of social density.



There are large gaps in the literature relating prison environments to changes in mental state; in particular, high-quality longitudinal studies are needed. Existing research suggests that although entry to prison may be associated with deterioration in mental state, it tends to improve with time. Furthermore, overcrowding, ever more likely as prison populations rise, is likely to place a particular burden on mental health services.
How do neurons operate on sparse distributed representations? A mathematical theory of sparsity, neurons and active dendrites

We propose a formal mathematical model for sparse representations in neocortex based on a neuron model and associated operations. The design of our model neuron is inspired by recent experimental findings on active dendritic processing and NMDA spikes in pyramidal neurons. We derive a number of scaling laws that characterize the accuracy of such neurons in detecting activation patterns in a neuronal population under adverse conditions. We introduce the union property which shows that synapses for multiple patterns can be randomly mixed together within a segment and still lead to highly accurate recognition. We describe simulation results that provide overall insight into sparse representations as well as two primary results. First we show that pattern recognition by a neuron can be extremely accurate and robust with high dimensional sparse inputs even when using a tiny number of synapses to recognize large patterns. Second, equations representing recognition accuracy of a dendrite predict optimal NMDA spiking thresholds under a generous set of assumptions. The prediction tightly matches NMDA spiking thresholds measured in the literature. Our model neuron matches many of the known properties of pyramidal neurons. As such the theory provides a unified and practical mathematical framework for understanding the benefits and limits of sparse representations in cortical networks.
The Effect of Movie Subtitling on Incidental Vocabulary Learning among EFL Learners

This study investigated the effect of subtitling modality on incidental vocabulary learning among Iranian EFL learners. To this end, 90 freshmen students studying English Translation at BA level in Abadan Azad University were selected after taking a proficiency test to ensure their homogeneity. Participants were randomly assigned to three experimental groups, namely: Bimodal group (A), Standard group (B) and Reversed group(C). They watched eight video clips selected from three animated movies with different modes of subtitles: A) Bimodal subtitles, B) Standard subtitles and C) Reversed subtitles. Research instrumentation included a pre-test and a post-test following an experimental design. Participants took a pre-test containing new words selected from the clips. After eight treatment sessions, the post-test was administered. Data were analyzed descriptively and inferentially. To arrive at any difference between the three different modes of subtitles, the researcher conducted one-way ANOVA. The results obtained from the tests showed that participants in reversed subtitling group performed significantly different and learned more new vocabulary items. Standard subtitling was the second type of subtitling which revealed to be more effective than bimodal subtitling.
Translating Building Information Modeling to Building Energy Modeling Using Model View Definition

This paper presents a new approach to translate between Building Information Modeling (BIM) and Building Energy Modeling (BEM) that uses Modelica, an object-oriented declarative, equation-based simulation environment. The approach (BIM2BEM) has been developed using a data modeling method to enable seamless model translations of building geometry, materials, and topology. Using data modeling, we created a Model View Definition (MVD) consisting of a process model and a class diagram. The process model demonstrates object-mapping between BIM and Modelica-based BEM (ModelicaBEM) and facilitates the definition of required information during model translations. The class diagram represents the information and object relationships to produce a class package intermediate between the BIM and BEM. The implementation of the intermediate class package enables system interface (Revit2Modelica) development for automatic BIM data translation into ModelicaBEM. In order to demonstrate and validate our approach, simulation result comparisons have been conducted via three test cases using (1) the BIM-based Modelica models generated from Revit2Modelica and (2) BEM models manually created using LBNL Modelica Buildings library. Our implementation shows that BIM2BEM (1) enables BIM models to be translated into ModelicaBEM models, (2) enables system interface development based on the MVD for thermal simulation, and (3) facilitates the reuse of original BIM data into building energy simulation without an import/export process.
Big Data Analytics for Smart Manufacturing : Case Studies in Semiconductor Manufacturing

Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predictive maintenance. For most of these capabilities: (1) data quality is the most important big data factor in delivering high quality solutions; and (2) incorporating subject matter expertise in analytics is often required for realizing effective on-line manufacturing solutions. In the future, an improved big data environment incorporating smart manufacturing concepts such as digital twin will further enable analytics; however, it is anticipated that the need for incorporating subject matter expertise in solution design will remain.
On decoding of low-density parity-check codes over the binary erasure channel

This paper investigates decoding of low-density parity-check (LDPC) codes over the binary erasure channel (BEC). We study the iterative and maximum-likelihood (ML) decoding of LDPC codes on this channel. We derive bounds on the ML decoding of LDPC codes on the BEC. We then present an improved decoding algorithm. The proposed algorithm has almost the same complexity as the standard iterative decoding. However, it has better performance. Simulations show that we can decrease the error rate by several orders of magnitude using the proposed algorithm. We also provide some graph-theoretic properties of different decoding algorithms of LDPC codes over the BEC which we think are useful to better understand the LDPC decoding methods, in particular, for finite-length codes.
An integrated 6 MV linear accelerator model from electron gun to dose in a water tank

The details of a full simulation of an inline side-coupled 6 MV linear accelerator linac from the electron gun to the target are presented. Commissioning of the above simulation was performed by using the derived electron phase space at the target as an input into Monte Carlo studies of dose distributions within a water tank and matching the simulation results to measurement data. This work is motivated by linac-MR studies, where a validated full linac simulation is first required in order to perform future studies on linac performance in the presence of an external magnetic field.  An electron gun was initially designed and optimized with a 2D finite difference program using Child’s law. The electron gun simulation served as an input to a 6 MV linac waveguide simulation, which consisted of a 3D finite element radio-frequency field solution within the waveguide and electron trajectories determined from particle dynamics modeling. The electron gun design was constrained to match the cathode potential and electron gun current of a Varian 600C, while the linac waveguide was optimized to match the measured target current. Commissioning of the full simulation was performed by matching the simulated Monte Carlo dose distributions in a water tank to measured distributions.  The full linac simulation matched all the electrical measurements taken from a Varian 600C and the commissioning process lead to excellent agreements in the dose profile measurements. Greater than 99% of all points met a 1%/1mm acceptance criterion for all field sizes analyzed, with the exception of the largest 40 40 cm2 field for which 98% of all points met the 1%/1mm acceptance criterion and the depth dose curves matched measurement to within 1% deeper than 1.5 cm depth. The optimized energy and spatial intensity distributions, as given by the commissioning process, were determined to be non-Gaussian in form for the inline side-coupled 6 MV linac simulated.  An integrated simulation of an inline side-coupled 6 MV linac has been completed and benchmarked matching all electrical and dosimetric measurements to high accuracy. The results showed non-Gaussian spatial intensity and energy distributions for the linac modeled.
Peace Journalism : Between advocacy journalism and constructive conflict coverage

The professional norms of good journalism include in particular the following: truthfulness, objectivity, neutrality and detachment. For Public Relations these norms are at best irrelevant. The only thing that matters is success. And this success is measured in terms ofachieving specific communication aims which are "externally defined by a client, host organization or particular groups ofstakeholders" (Hanitzsch, 2007, p. 2). Typical aims are, e.g., to convince the public of the attractiveness of a product, of the justice of one's own political goals or also of the wrongfulness of a political opponent.
Role of metal contacts in designing high-performance monolayer n-type WSe2 field effect transistors.

This work presents a systematic study toward the design and first demonstration of high-performance n-type monolayer tungsten diselenide (WSe2) field effect transistors (FET) by selecting the contact metal based on understanding the physics of contact between metal and monolayer WSe2. Device measurements supported by ab initio density functional theory (DFT) calculations indicate that the d-orbitals of the contact metal play a key role in forming low resistance ohmic contacts with monolayer WSe2. On the basis of this understanding, indium (In) leads to small ohmic contact resistance with WSe2 and consequently, back-gated In-WSe2 FETs attained a record ON-current of 210 μA/μm, which is the highest value achieved in any monolayer transition-metal dichalcogenide- (TMD) based FET to date. An electron mobility of 142 cm(2)/V·s (with an ON/OFF current ratio exceeding 10(6)) is also achieved with In-WSe2 FETs at room temperature. This is the highest electron mobility reported for any back gated monolayer TMD material till date. The performance of n-type monolayer WSe2 FET was further improved by Al2O3 deposition on top of WSe2 to suppress the Coulomb scattering. Under the high-κ dielectric environment, electron mobility of Ag-WSe2 FET reached ~202 cm(2)/V·s with an ON/OFF ratio of over 10(6) and a high ON-current of 205 μA/μm. In tandem with a recent report of p-type monolayer WSe2 FET ( Fang , H . et al.  2012 , 12 , ( 7 ), 3788 - 3792 ), this demonstration of a high-performance n-type monolayer WSe2 FET corroborates the superb potential of WSe2 for complementary digital logic applications.
On becoming ready to pursue a goal you don't know you have: effects of nonconscious goals on evaluative readiness.

Findings showed that the nonconscious activation of a goal in memory led to increased positive implicit attitudes toward stimuli that could facilitate the goal. This evaluative readiness to pursue the nonconscious goal emerged even when participants were consciously unaware of the goal-relevant stimuli. The effect emerged the most strongly for those with some skill at the goal and for those for whom the goal was most currently important. The effect of implicit goal activation on implicit attitudes emerged in both an immediate condition as well as a delay condition, suggesting that a goal rather than a nonmotivational construct was activated. Participants' implicit attitudes toward a nonconscious goal also predicted their goal-relevant behavior. These findings suggest that people can become evaluatively ready to pursue a goal whenever it has been activated--a readiness that apparently does not require conscious awareness or deliberation about either the goal or the goal-relevant stimuli. Theoretical implications of this type of implicit goal readiness are discussed.