nils-herrmann/clean_scirepeval huggingface dataset:
Deep learning and the information bottleneck principle

Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. We first show that any DNN can be quantified by the mutual information between the layers and the input and output variables. Using this representation we can calculate the optimal information theoretic limits of the DNN and obtain finite sample generalization bounds. The advantage of getting closer to the theoretical limit is quantifiable both by the generalization bound and by the network's simplicity. We argue that both the optimal architecture, number of layers and features/connections at each layer, are related to the bifurcation points of the information bottleneck tradeoff, namely, relevant compression of the input layer with respect to the output layer. The hierarchical representations at the layered network naturally correspond to the structural phase transitions along the information curve. We believe that this new insight can lead to new optimality bounds and deep learning algorithms.
Deep LDA-Pruned Nets for Efficient Facial Gender Classification

Many real-time tasks, such as human-computer interaction, require fast and efficient facial gender classification. Although deep CNN nets have been very effective for a multitude of classification tasks, their high space and time demands make them impractical for personal computers and mobile devices without a powerful GPU. In this paper, we develop a 16-layer, yet lightweight, neural network which boosts efficiency while maintaining high accuracy. Our net is pruned from the VGG-16 model starting from the last convolutional (conv) layer where we find neuron activations are highly uncorrelated given the gender. Through Fisher's Linear Discriminant Analysis (LDA), we show that this high decorrelation makes it safe to discard directly last conv layer neurons with high within-class variance and low between-class variance. Combined with either Support Vector Machines (SVM) or Bayesian classification, the reduced CNNs are capable of achieving comparable (or even higher) accuracies on the LFW and CelebA datasets than the original net with fully connected layers. On LFW, only four Conv5_3 neurons are able to maintain a comparably high recognition accuracy, which results in a reduction of total network size by a factor of 70X with a 11 fold speedup. Comparisons with a state-of-the-art pruning method (as well as two smaller nets) in terms of accuracy loss and convolutional layers pruning rate are also provided.
Fundamental Tradeoff in Knowledge Representation and Reasoning ( Revised Versionl )

A fundamental computational limit on automated reasoning and its effect on Knowledge Representation is examined. Basically, the problem is that it can be more difficult to reason correctly ;Nith one representationallanguage than with another and, moreover, that this difficulty increases dramatically as the expressive power of the language increases. This leads to a tradeoff between the expressiveness of a representational language and its computational tractability. Here we show that this tradeoff can be seen to underlie the differences among a number of existing representational formalisms, in addition to motivating many of the current research issues in Knowledge Representation.
A Practical Evaluation of Surge Arrester Placement for Transmission Line Lightning Protection

The use of metal-oxide varistor surge arresters (MOVs) in lightning protection of overhead transmission lines to improve reliability is of great interest to electric utilities. However due to economic reasons, it is not possible to completely equip an overhead transmission line with surge arresters at each transmission structure. In this paper, an evaluation of lightning protection design on a 115 kV transmission line using surge arresters, utilizing a model based on field data, is presented. The model developed is used for computer simulation using the Alternative Transients Program. Various design procedures aimed at maximizing the reliability of service on the transmission line using a minimal number of surge arresters are analyzed. Different designs considered for transmission line lightning protection using MOV arresters include: the use of a different number of surge arresters per tower, distance between towers with surge arresters and the dependence of these configurations on tower footing resistance. The lightning protection designs are analyzed using `lightning flashover charts,' proposed in this paper. Also, an analytical model of two 115 kV transmission lines in Southwest U.S. has been developed and different surge arrester location strategies used on these transmission lines have been analyzed. Practical experiences and effectiveness of various lightning protection designs used on these transmission lines are discussed.
CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices

Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performance while maintaining accuracy. For DNNs, the model size is an important factor affecting performance, scalability and energy efficiency. Weight pruning achieves good compression ratios but suffers from three drawbacks: 1) the irregular network structure after pruning, which affects performance and throughput; 2) the increased training complexity; and 3) the lack of rigirous guarantee of compression ratio and inference accuracy.
 To overcome these limitations, this paper proposes CirCNN, a principled approach to represent weights and process neural networks using block-circulant matrices. CirCNN utilizes the Fast Fourier Transform (FFT)-based fast multiplication, simultaneously reducing the computational complexity (both in inference and training) from O(n2) to O(n log n) and the storage complexity from O(n2) to O(n), with negligible accuracy loss. Compared to other approaches, CirCNN is distinct due to its mathematical rigor: the DNNs based on CirCNN can converge to the same "effectiveness" as DNNs without compression. We propose the CirCNN architecture, a universal DNN inference engine that can be implemented in various hardware/software platforms with configurable network architecture (e.g., layer type, size, scales, etc.). In CirCNN architecture: 1) Due to the recursive property, FFT can be used as the key computing kernel, which ensures universal and small-footprint implementations. 2) The compressed but regular network structure avoids the pitfalls of the network pruning and facilitates high performance and throughput with highly pipelined and parallel design. To demonstrate the performance and energy efficiency, we test CirCNN in FPGA, ASIC and embedded processors. Our results show that CirCNN architecture achieves very high energy efficiency and performance with a small hardware footprint. Based on the FPGA implementation and ASIC synthesis results, CirCNN achieves 6 - 102X energy efficiency improvements compared with the best state-of-the-art results.
Image Processing Based Detection of Counterfeit Indian Bank Notes

Currency counterfeiting is a significant offense which has a profound impact on the assets and capital of the citizens thus having an adverse effect on the nation's finances. The schemes currently existing to combat the falsification of banknotes are complex, hardware-based and inaccessible to the common people. In this paper, a unique authentication system is proposed which is compact, mobile and devoid of any hardware components. Certain security features, such as security thread and latent image, embedded on the note are utilized to help ascertain its legitimacy. The methodology involves the extraction and encoding of these security features. Given the prominence of the security thread in certain image planes, a clustering algorithm, k-means is applied for classification. The latent image, segmented via template matching was encoded using HOG descriptor and classified with an SVM model. The result is illustrated with the aid of performance parameters and overall accuracy.
Closed-Loop and Activity-Guided Optogenetic Control

Advances in optical manipulation and observation of neural activity have set the stage for widespread implementation of closed-loop and activity-guided optical control of neural circuit dynamics. Closing the loop optogenetically (i.e., basing optogenetic stimulation on simultaneously observed dynamics in a principled way) is a powerful strategy for causal investigation of neural circuitry. In particular, observing and feeding back the effects of circuit interventions on physiologically relevant timescales is valuable for directly testing whether inferred models of dynamics, connectivity, and causation are accurate in vivo. Here we highlight technical and theoretical foundations as well as recent advances and opportunities in this area, and we review in detail the known caveats and limitations of optogenetic experimentation in the context of addressing these challenges with closed-loop optogenetic control in behaving animals.
Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets

Detecting extreme events in large datasets is a major challenge in climate science research. Current algorithms for extreme event detection are build upon human expertise in defining events based on subjective thresholds of relevant physical variables. Often, multiple competing methods produce vastly different results on the same dataset. Accurate characterization of extreme events in climate simulations and observational data archives is critical for understanding the trends and potential impacts of such events in a climate change content. This study presents an application of Deep Learning techniques as alternative methodology for climate extreme events detection. Deep neural networks are able to learn high-level representations of a broad class of patterns from labeled data. In this work, we developed deep Convolutional Neural Network (CNN) classification system and demonstrated the usefulness of Deep Learning technique for tackling climate pattern detection problems. Coupled with Bayesian based hyper-parameter optimization scheme, our deep CNN system achieves 89%-99% of accuracy in detecting extreme events (Tropical Cyclones, Atmospheric Rivers and Weather Fronts).
Interpreting "altmetrics": viewing acts on social media through the lens of citation and social theories

1.  More than 30 years after Blaise Cronin's seminal paper (Cronin, 1981; see reprint in this book) the metrics community is once again in need of a new theory, this time one for so-called " altmetrics ". Altmetrics, short for alternative (to citation) metrics —and as such a misnomer— refers to a new group of metrics based (largely) on social media events relating to scholarly communication. The term originated on 29 September 2010 in a tweet by Jason Priem in which he uttered his preference for the word altmetrics in the context of various metrics provided for PLOS journal articles: " I like the term #articlelevelmetrics, but it fails to imply *diversity* of measures. Lately, I'm liking #altmetrics. " (Priem, 2010). Although Priem is responsible for coining the term, the idea of measuring broader scientific impact through the web and had been discussed by Cronin and others (e. see also Thelwall's chapter in this book) in the context of webometrics years before: Scholars may be cited formally, or merely mentioned en passant in listservs and others electronic discussion fora, or they may find that they have been included in reading lists or electronic syllabi. Polymorphous mentioning is likely to become a defining feature of Web-based scholarly communication. (Cronin et al., 1998) There will soon be a critical mass of web-based digital objects and usage statistics on which to Priem —co-author of the altmetrics manifesto (Priem, Taraborelli, Groth, & Neylon, 2010) and co-founder of ImpactStory 1 , an online tool aggregating various metrics on the researcher level— and colleagues argued that metrics based on 'traces' of use and production of scholarly output on social media platforms could help to improve scholarly communication and research evaluation. The term altmetrics was introduced out of the need to differentiate these new metrics from traditional citation-based indicators, which the altmetrics movement is seeking to replace or use as an alternative. The altmetrics manifesto and other work by Priem and colleagues appeal to the scientific community and research managers to " value 1 https://impactstory.org/ 2 all research products " (Piwowar, 2013), not just journal articles, and to measure impact in a broader sense by looking at more than just citations. The manifesto lists various sources of new metrics that would complement and replace traditional forms of publication, peer review, and citation analysis (Priem et al., 2010). Priem (2014) claimed that with scholarship moving online, former …
Positive psychology progress: empirical validation of interventions.

Positive psychology has flourished in the last 5 years. The authors review recent developments in the field, including books, meetings, courses, and conferences. They also discuss the newly created classification of character strengths and virtues, a positive complement to the various editions of the Diagnostic and Statistical Manual of Mental Disorders (e. g., American Psychiatric Association, 1994), and present some cross-cultural findings that suggest a surprising ubiquity of strengths and virtues. Finally, the authors focus on psychological interventions that increase individual happiness. In a 6-group, random-assignment, placebo-controlled Internet study, the authors tested 5 purported happiness interventions and 1 plausible control exercise. They found that 3 of the interventions lastingly increased happiness and decreased depressive symptoms. Positive interventions can supplement traditional interventions that relieve suffering and may someday be the practical legacy of positive psychology.
Development and Validation of a Reliable and Robust Method for the Analysis of Cannabinoids and Terpenes in Cannabis.

The requirements for an acceptable cannabis assay have changed dramatically over the years resulting in a large number of laboratories using a diverse array of analytical methodologies that have not been properly validated. Due to the lack of sufficiently validated methods, we conducted a single- laboratory validation study for the determination of cannabinoids and terpenes in a variety of commonly occurring cultivars. The procedure involves high- throughput homogenization to prepare sample extract, which is then profiled for cannabinoids and terpenes by HPLC-diode array detector and GC-flame ionization detector, respectively. Spike recovery studies for terpenes in the range of 0.03-1.5% were carried out with analytical standards, while recovery studies for Δ9-tetrahydrocannabinolic acid, cannabidiolic acid, Δ9-tetrahydrocannabivarinic acid, and cannabigerolic acid and their neutral counterparts in the range of 0.3-35% were carried out using cannabis extracts. In general, accuracy at all levels was within 5%, and RSDs were less than 3%. The interday and intraday repeatabilities of the procedure were evaluated with five different cultivars of varying chemotype, again resulting in acceptable RSDs. As an example of the application of this assay, it was used to illustrate the variability seen in cannabis coming from very advanced indoor cultivation operations.
Histogram of Oriented Uniform Patterns for robust place recognition and categorization

This paper presents a novel context-based scene recognition method that enables mobile robots to recognize previously observed topological places in known environments or categorize previously unseen places in new environments. We achieve this by introducing the Histogram of Oriented Uniform Patterns (HOUP), which provides strong discriminative power for place recognition, while offering a significant level of generalization for place categorization. HOUP descriptors are used for image representation within a subdivision framework, where the size and location of sub-regions are determined using an informative feature selection method based on kernel alignment. Further improvement is achieved by developing a similarity measure that accounts for perceptual aliasing to eliminate the effect of indistinctive but visually similar regions that are frequently present in outdoor and indoor scenes. An extensive set of experiments reveals the excellent performance of our method on challenging categorization and recognition tasks. Specifically, our proposed method outperforms the current state of the art on two place categorization datasets with 15 and 5 place categories, and two topological place recognition datasets, with 5 and 27 places.
Byte Code Engineering

The term “Java” is used to denote two different concepts: the language itself and the related execution environment, the Java Virt ual Machine (JVM), which executesbyte codeinstructions. Several research projects deal with byte code-generating compilers or the implementation of new fea tur s via byte code transformations. Examples are code optimization, the impl e entation of parameterized types for Java, or the adaptation of run-time behav ior through load-time transformations. Many programmers are doing this by implem enting their own specialized byte code manipulation tools, which are, howev er, restricted in the range of their reusability. Therefore, we have developed a g eneral purpose framework for the static analysis and dynamic creation or transfo rmation of byte code. In this paper we present its main features and possible appli cation areas.
The design and implementation of a notional machine for teaching introductory programming

Comprehension of programming and programs is known to be a difficult task for many beginning students, with many computing courses showing significant drop out and failure rates. In this paper, we present a new notional machine design and implementation to help with understanding of programming and its dynamics for beginning learners. The notional machine offers an abstraction of the physical machine designed for comprehension and learning purposes. We introduce the notional machine and describe an implementation in
Constructivist Grounded Theory ?

I refer to and use as scholarly inspiration CHARMAZ’s excellent article on constructivist grounded theory as a tool of getting to the fundamental issues on why grounded theory is not constructivist. I show that constructivist data, if it exists at all, is a very very small part of the data that grounded theory uses. Constructivist Grounded Theory (GT) is a misnomer. GT can use any data; it remains to be figured out what it is. In my book “The Grounded Theory Perspective” (GLASER 2001) I wrote a chapter (11) that dealt with “all is data.” I said: “All is data” is a well known Glaser dictum. What does it mean? It means exactly what is going on in the research scene is the data, whatever the source, whether interview, observations, documents, in whatever combination. It is not only what is being told, how it is being told and the conditions of its being told, but also all the data surrounding what is being told. It means what is going on must be figured out exactly what it is is to be used for, that is conceptualization, not for accurate description. Data is always as good as far as it goes, and there is always more data to keep correcting the categories with more relevant properties. (p.145) “All is Data” is a GT statement, NOT applicable to Qualitative Data Analysis (QDA) and its worrisome accuracy abiding concern. Data is discovered for conceptualization to be what it is – theory. The data is what it is and the researcher collects, codes and analyzes exactly what he has whether baseline data, properline data or objective data or misinterpreted data. It is what the researcher is receiving, as a pattern, and as a human being (which is inescapable). It just depends on the research. Remember again, the product will be transcending abstraction, NOT accurate description. The product, a GT, will be an abstraction from time, place and ∗ Address all communications to: Barney G. Glaser, POB 400, Mill Valley, Ca 94942, USA;  First published in: FQS – Forum Qualitative Sozialforschung / Forum: Qualitative Social Research [On-line Journal], 6(2), 3(3). <http://www.qualitative-research.net/fqs-texte/302/3-02glaser-e.htm>. Reprint with the friendly permission of the author and FQS. 1 See GLASER (1998a, p.9)
Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade. Additionally, sensor data from machines is noisy and oen suers from missing values in many practical seings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. EmbedRUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. e embeddings for normal and degraded machines tend to be dierent, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall paern in the time series while ltering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have signicant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported [24] state-of-the-art on several metrics.
Measuring Team Knowledge

Multioperator tasks often require complex cognitive processing at the team level. Many team cognitive processes, such as situation assessment and coordination, are thought to rely on team knowledge. Team knowledge is multifaceted and comprises relatively generic knowledge in the form of team mental models and more specific team situation models. In this methodological review paper, we review recent efforts to measure team knowledge in the context of mapping specific methods onto features of targeted team knowledge. Team knowledge features include type, homogeneity versus heterogeneity, and rate of knowledge change. Measurement features include knowledge elicitation method, team metric, and aggregation method. When available, we highlight analytical conclusions or empirical data that support a connection between team knowledge and measurement method. In addition, we present empirical results concerning the relation between team knowledge and performance for each measurement method and identify research and methodological needs. Addressing issues surrounding the measurement of team knowledge is a prerequisite to understanding team cognition and its relation to team performance and to designing training programs or devices to facilitate team cognition.