nils-herrmann/clean_scirepeval huggingface dataset:
Group Normalization

Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems — BN’s error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN’s usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption. In this paper, we present Group Normalization (GN) as a simple alternative to BN. GN divides the channels into groups and computes within each group the mean and variance for normalization. GN’s computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes. On ResNet-50 trained in ImageNet, GN has 10.6% lower error than its BN counterpart when using a batch size of 2; when using typical batch sizes, GN is comparably good with BN and outperforms other normalization variants. Moreover, GN can be naturally transferred from pre-training to fine-tuning. GN can outperform its BN-based counterparts for object detection and segmentation in COCO, and for video classification in Kinetics, showing that GN can effectively replace the powerful BN in a variety of tasks. GN can be easily implemented by a few lines of code.
Autonomous driving: investigating the feasibility of car-driver handover assistance

Self-driving vehicles are able to drive on their own as long as the requirements of their autonomous systems are met. If the system reaches the boundary of its capabilities, the system has to de-escalate (e.g. emergency braking) or hand over control to the human driver. Accordingly, the design of a functional handover assistant requires that it enable drivers to both take over control and feel comfortable while doing so -- even when they were "out of the loop" with other tasks. We introduce a process to hand over control from a full self-driving system to manual driving, and propose a number of handover implementation strategies. Moreover, we designed and implemented a handover assistant based on users' preferences and conducted a user study with 30 participants, whose distraction was ensured by a realistic distractor task. Our evaluation shows that car-driver handovers prompted by multimodal (auditory and visual) warnings are a promising strategy to compensate for system boundaries of autonomous vehicles. The insights we gained from the take-over behavior of drivers led us to formulate recommendations for more realistic evaluation settings and the design of future handover assistants.
Proximity and precedence in arithmetic.

How does the physical structure of an arithmetic expression affect the computational processes engaged in by reasoners? In handwritten arithmetic expressions containing both multiplications and additions, terms that are multiplied are often placed physically closer together than terms that are added. Three experiments evaluate the role such physical factors play in how reasoners construct solutions to simple compound arithmetic expressions (such as "2 + 3 × 4"). Two kinds of influence are found: First, reasoners incorporate the physical size of the expression into numerical responses, tending to give larger responses to more widely spaced problems. Second, reasoners use spatial information as a cue to hierarchical expression structure: More narrowly spaced subproblems within an expression tend to be solved first and tend to be multiplied. Although spatial relationships besides order are entirely formally irrelevant to expression semantics, reasoners systematically use these relationships to support their success with various formal properties.
Constructing Category-Specific Models for Monocular Object-SLAM

We present a new paradigm for real-time object-oriented SLAM with a monocular camera. Contrary to previous approaches, that rely on object-level models, we construct category-level models from CAD collections which are now widely available. To alleviate the need for huge amounts of labeled data, we develop a rendering pipeline that enables synthesis of large datasets from a limited amount of manually labeled data. Using data thus synthesized, we learn category-level models for object deformations in 3D, as well as discriminative object features in 2D. These category models are instance-independent and aid in the design of object landmark observations that can be incorporated into a generic monocular SLAM framework. Where typical object-SLAM approaches usually solve only for object and camera poses, we also estimate object shape on-the-fty, allowing for a wide range of objects from the category to be present in the scene. Moreover, since our 2D object features are learned discriminatively, the proposed object-SLAM system succeeds in several scenarios where sparse feature-based monocular SLAM fails due to insufficient features or parallax. Also, the proposed category-models help in object instance retrieval, useful for Augmented Reality (AR) applications. We evaluate the proposed framework on multiple challenging real-world scenes and show - to the best of our knowledge - first results of an instance-independent monocular object-SLAM system and the benefits it enjoys over feature-based SLAM methods.
CRISPR therapeutic tools for complex genetic disorders and cancer (Review)

One of the fundamental discoveries in the field of biology is the ability to modulate the genome and to monitor the functional outputs derived from genomic alterations. In order to unravel new therapeutic options, scientists had initially focused on inducing genetic alterations in primary cells, in established cancer cell lines and mouse models using either RNA interference or cDNA overexpression or various programmable nucleases [zinc finger nucleases (ZNF), transcription activator-like effector nucleases (TALEN)]. Even though a huge volume of data was produced, its use was neither cheap nor accurate. Therefore, the clustered regularly interspaced short palindromic repeats (CRISPR) system was evidenced to be the next step in genome engineering tools. CRISPR-associated protein 9 (Cas9)-mediated genetic perturbation is simple, precise and highly efficient, empowering researchers to apply this method to immortalized cancerous cell lines, primary cells derived from mouse and human origins, xenografts, induced pluripotent stem cells, organoid cultures, as well as the generation of genetically engineered animal models. In this review, we assess the development of the CRISPR system and its therapeutic applications to a wide range of complex diseases (particularly distinct tumors), aiming at personalized therapy. Special emphasis is given to organoids and CRISPR screens in the design of innovative therapeutic approaches. Overall, the CRISPR system is regarded as an eminent genome engineering tool in therapeutics. We envision a new era in cancer biology during which the CRISPR-based genome engineering toolbox will serve as the fundamental conduit between the bench and the bedside; nonetheless, certain obstacles need to be addressed, such as the eradication of side-effects, maximization of efficiency, the assurance of delivery and the elimination of immunogenicity.
Content or platform : Why do students complete MOOCs ?

The advent of massive open online courses (MOOCs) poses new learning opportunities for learners as well as challenges for researchers and designers. MOOC students approach MOOCs in a range of fashions, based on their learning goals and preferred approaches, which creates new opportunities for learners but makes it difficult for researchers to figure out what a student’s behavior means, and makes it difficult for designers to develop MOOCs appropriate for all of their learners. Towards better understanding the learners who take MOOCs, we conduct a survey of MOOC learners’ motivations and correlate it to which students complete the course according to the pace set by the instructor/platform (which necessitates having the goal of completing the course, as well as succeeding in that goal). The results showed that course completers tend to be more interested in the course content, whereas non-completers tend to be more interested in MOOCs as a type of learning experience. Contrary to initial hypotheses, however, no substantial differences in mastery-goal orientation or general academic efficacy were observed between completers and non-completers. However, students who complete the course tend to have more self-efficacy for their ability to complete the course, from the beginning.
Deep Image Demosaicking Using a Cascade of Convolutional Residual Denoising Networks

Demosaicking and denoising are among the most crucial steps of modern digital camera pipelines and their joint treatment is a highly ill-posed inverse problem where at-least two-thirds of the information are missing and the rest are corrupted by noise. This poses a great challenge in obtaining meaningful reconstructions and a special care for the efficient treatment of the problem is required. While there are several machine learning approaches that have been recently introduced to deal with joint image demosaicking-denoising, in this work we propose a novel deep learning architecture which is inspired by powerful classical image regularization methods and large-scale convex optimization techniques. Consequently, our derived network is more transparent and has a clear interpretation compared to alternative competitive deep learning approaches. Our extensive experiments demonstrate that our network outperforms any previous approaches on both noisy and noise-free data. This improvement in reconstruction quality is attributed to the principled way we design our network architecture, which also requires fewer trainable parameters than the current state-of-the-art deep network solution. Finally, we show that our network has the ability to generalize well even when it is trained on small datasets, while keeping the overall number of trainable parameters low.
POWER TENSIONS IN HEALTH INFORMATION SYSTEM INTEGRATION IN DEVELOPING COUNTRIES : THE NEED FOR DISTRIBUTED CONTROL

As part of health sector reform, most developing countries are in the process of restructuring the Health Information Systems (HIS) by way of standardizing and integrating various vertical reporting systems. Nevertheless, the pressure resulting from the vertical systems supported by donors renders the integration goal challenging and unachievable. While many studies have argued for the heterogeneity of interests and multiple data needs of the donors as the major cause of the problem, this paper argue for more detailed and critical analysis of the problem. To contribute to this, the paper emphasize the need to understand the main actors involved in the process in terms of the resources and rules available to them; and their implication in the HIS integration venture. By drawing on an empirical case study and Structuration Theory concepts, the paper identified the dialectic power relations between the actors resulting from the asymmetric ownership and control of resources and rules. To alleviate the power tensions, the paper proposed i) the need to build shared meaning of the integration process through communication approaches ii) and the need to distribute the control of the integrated HIS using modularization approach facilitating ‘the tapping on’ of the resources available to each group of actors.
The Visual Display of Quantitative Information

This is a surprisingly entertaining and informative book and should be of interest to all who employ the services of specialists for the presentation of graphics, as well as the producers of such visual aids. It makes a strong case for properly designed and interesting graphics of  a statistical nature,  where  content  and  integrity  rather  than artistic appeal should prevail. The initial chapter,  “Graphical  Excellence,”  establishes  basic ground rules which should be observed in the design and execution of statistical graphics,  to facilitate the  viewer’s  understanding  of complex data sets. The examples provided cover a broad spectrum of subject matter and treatment. Several of these were produced over a century  ago,  and are excellent  examples  in  terms of ease of comprehension of complex statistical data. For the reader not familiar with the history of this topic, the graphics provided include some of the work of C. J. Minard, France, published in the mid-1800’s. These  are quite  impressive,  treating  subjects  involving  rather complex statistics with a quality of design and clarity that is rarely found today. I believe the inclusion of additional good examples of a more contemporary nature might possibly improve the value of this chapter, if a subsequent edition is ever considered. Two chapters are dedicated to the subject of graphical inte,gity, and there are lessons here for everyone, including the viewerhser of data graphics. While, unfortunately, there will always be a market for “Lying Graphics,” the employment of visual and statistical tricks for intentional false impressions, the author’s position is that this practice is detrimental and has inhibited acceptance of data graphics for applications where their full value lies. Another, and more widespread fault, which the author singles out, is the practice of leaving the basic graphic design to those with artistic backgrounds whose priorities are biased in  favor  of  data beautification  rather  than statistical integrity. The remaining chapters deal with the theory, design, and execution of data graphics, and the treatment continues to be interesting, with both good and bad examples provided. Some of the specific issues addressed are the necessity for simplicity and clarity of presentation, the elimination of “chart junk,” maintaining the proper data-to-ink ratio, and the value of the tabular versus graphic presentations when limited data are to be shown. It appears that one of the author’s major goals is to increase the respectability of visual displays of statistical data, thereby broadening their acceptance and utilization for applications which are virtually unlimited. This book provides the rationale and the tools for the effective treatment and display of complex multivariate data, for those responsible for concepts as well as execution of statistical graphics.
Ontologies for Transportation Research : A Survey

Transportation research relies heavily on a variety of data. From sensors to surveys, data supports dayto-day operations as well as long-term planning and decision-making. The challenges that arise due to the volume and variety of data that are found in transportation research can be effectively addressed by ontologies. This opportunity has already been recognized – there are a number of existing transportation ontologies, however the relationship between them is unclear. The goal of this work is to provide an overview of the opportunities for ontologies in transportation research and operation, and to present a survey of existing transportation ontologies to serve two purposes: (1) to provide a resource for the transportation research community to aid in understanding (and potentially selecting between) existing transportation ontologies; and (2) to identify future work for the development of transportation ontologies, by identifying areas that may be lacking.
Depth Estimation from Image Structure

ÐIn the absence of cues for absolute depth measurements as binocular disparity, motion, or defocus, the absolute distance between the observer and a scene cannot be measured. The interpretation of shading, edges, and junctions may provide a 3D model of the scene but it will not provide information about the actual ªscaleº of the space. One possible source of information for absolute depth estimation is the image size of known objects. However, object recognition, under unconstrained conditions, remains difficult and unreliable for current computational approaches. Here, we propose a source of information for absolute depth estimation based on the whole scene structure that does not rely on specific objects. We demonstrate that, by recognizing the properties of the structures present in the image, we can infer the scale of the scene and, therefore, its absolute mean depth. We illustrate the interest in computing the mean depth of the scene with application to scene recognition and object detection.
Modulation recognition with GNU radio, keras, and HackRF

Recognition and classification of the modulation format of received signals in real time is a challenging but necessary task for the intelligence community. Without prior knowledge of the received data, such as power, frequency, and phase, coupled with real-world concerns such as signal degradation and interference, the task of reconstructing the sent information is made even more complex. Our approach for classifying modulation types of live over-the-air signals, a precursor step to demodulation, involves using software defined radio plus two simple and inexpensive transceivers to train a series of neural networks over a set of three data types.
Designing for Flexible Interaction Between Humans and Automation: Delegation Interfaces for Supervisory Control

To develop a method enabling human-like, flexible supervisory control via delegation to automation.



Real-time supervisory relationships with automation are rarely as flexible as human task delegation to other humans. Flexibility in human-adaptable automation can provide important benefits, including improved situation awareness, more accurate automation usage, more balanced mental workload, increased user acceptance, and improved overall performance.



We review problems with static and adaptive (as opposed to "adaptable") automation; contrast these approaches with human-human task delegation, which can mitigate many of the problems; and revise the concept of a "level of automation" as a pattern of task-based roles and authorizations. We argue that delegation requires a shared hierarchical task model between supervisor and subordinates, used to delegate tasks at various levels, and offer instruction on performing them. A prototype implementation called Playbook is described.



On the basis of these analyses, we propose methods for supporting human-machine delegation interactions that parallel human-human delegation in important respects. We develop an architecture for machine-based delegation systems based on the metaphor of a sports team's "playbook." Finally, we describe a prototype implementation of this architecture, with an accompanying user interface and usage scenario, for mission planning for uninhabited air vehicles.



Delegation offers a viable method for flexible, multilevel human-automation interaction to enhance system performance while maintaining user workload at a manageable level.



Most applications of adaptive automation (aviation, air traffic control, robotics, process control, etc.) are potential avenues for the adaptable, delegation approach we advocate. We present an extended example for uninhabited air vehicle mission planning.
A Vision for All-Spin Neural Networks: A Device to System Perspective

Spin-transfer torque (STT) mechanisms in vertical and lateral spin valves and magnetization reversal/domain wall motion with spin-orbit torque (SOT) have opened up new possibilities of efficiently mimicking “neural” and “synaptic” functionalities with much lower area and energy consumption compared to CMOS implementations. In this paper, we review various STT/SOT devices that can provide a compact and area-efficient implementation of artificial neurons and synapses. We provide a device-circuit-system perspective and envision design of an All-Spin neuromorphic processor (with different degrees of bio-fidelity) that can be potentially appealing for ultra-low power cognitive applications.
On-the-fly Operation Batching in Dynamic Computation Graphs

Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offer more flexibility for implementing models that cope with data of varying dimensions and structure, relative to toolkits that operate on statically declared computations (e.g., TensorFlow, CNTK, and Theano). However, existing toolkits—both static and dynamic—require that the developer organize the computations into the batches necessary for exploiting high-performance algorithms and hardware. This batching task is generally difficult, but it becomes a major hurdle as architectures become complex. In this paper, we present an algorithm, and its implementation in the DyNet toolkit, for automatically batching operations. Developers simply write minibatch computations as aggregations of single instance computations, and the batching algorithm seamlessly executes them, on the fly, using computationally efficient batched operations. On a variety of tasks, we obtain throughput similar to that obtained with manual batches, as well as comparable speedups over singleinstance learning on architectures that are impractical to batch manually.2
Mechanisms Employed by Trichoderma Species in the Biological Control of Plant Diseases: The History and Evolution of Current Concepts.

Fungal species belonging to the genus Trichoderma are worldwide in occurrence and easily isolated from soil, decaying wood, and other forms of plant organic matter. They are, for the most part, classified as imperfect fungi, in that they have no known sexual stage. Rapid growth rate in culture and the production of numerous spores (conidia) that are varying shades of green characterize fungi in this genus. The reverse side of colonies is often uncolored, buff, yellow, amber, or yellow-green, and many species produce prodigious quantities of thick-walled spores (chlamydospores) in submerged mycelium (8). The potential of Trichoderma species as biocontrol agents of plant diseases was first recognized in the early 1930s , and in subsequent years, control of many diseases has been added to the list (1,3,5,7,9,11,19, 23,29,34,37,40). This has culminated in the commercial production of several Trichoderma species for the protection and growth enhancement of a number of crops in the United States (24), and in the production of Trichoderma species and mixtures of species in India, Israel, New Zealand, and Sweden (D. R. Fravel, personal communication). One of the most interesting aspects of the science of biological control is the study of the mechanisms employed by biocontrol agents to effect disease control. Past research indicates that the mechanisms are many and varied, even within the genus Trichoderma. In order to make the most effective use of biocontrol agents for the control of plant diseases, we must understand how the agents work and what their limitations are. We can then develop effective means of culturing, storing, applying, and utilizing biocontrol agents so that we harness their best effort for disease control. The selected research papers cited in this article were chosen because they illustrate what has been learned about mechanisms involved in biocontrol with Trichoderma species.
Justifying Shadow IT Usage

Employees and/or functional managers increasingly adopt and use IT systems and services that the IS management of the organization does neither provide nor approve. To effectively counteract such shadow IT in organizations, the understanding of employees’ motivations and drivers is necessary. However, the scant literature on this topic primarily focused on various governance approaches at firm level. With the objective to open the black box of shadow IT usage at the individual unit of analysis, we develop a research model and propose a laboratory experiment to examine users’ justifications for violating implicit and explicit IT usage restrictions based on neutralization theory. To be precise, in this research-in-progress, we posit positive associations between shadow IT usage and human tendencies to downplay such kind of rule-breaking behaviors due to necessity, no injury, and injustice. We expect a lower impact of these neutralization effects in the presence of behavioral IT guidelines that explicitly prohibit users to employ exactly those shadow IT systems.