nils-herrmann/clean_scirepeval huggingface dataset:
Review of Machine Learning Algorithms in Differential Expression Analysis

In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases. In this paper we (1) illustrate the importance of machine learning in the analysis of large scale sequencing data, (2) present an illustrative standardized workflow of the analysis process, (3) perform a Differential Expression (DE) analysis of a publicly available RNA sequencing (RNASeq) data set to demonstrate the capabilities of various algorithms at each step of the workflow, and (4) show a machine learning solution in improving the computing time, storage requirements, and minimize utilization of computer memory in analyses of RNA-Seq datasets. The source code of the analysis pipeline and associated scripts are presented in the paper appendix to allow replication of experiments.
Data mining for censored time-to-event data: a Bayesian network model for predicting cardiovascular risk from electronic health record data

Models for predicting the risk of cardiovascular (CV) events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such cohorts restrict the predictive power and generalizability of these risk models to other populations. Electronic health data (EHD) from large health care systems provide access to data on large, heterogeneous, and contemporaneous patient populations. The unique features and challenges of EHD, including missing risk factor information, non-linear relationships between risk factors and CV event outcomes, and differing effects from different patient subgroups, demand novel machine learning approaches to risk model development. In this paper, we present a machine learning approach based on Bayesian networks trained on EHD to predict the probability of having a CV event within 5 years. In such data, event status may be unknown for some individuals, as the event time is right-censored due to disenrollment and incomplete follow-up. Since many traditional data mining methods are not well-suited for such data, we describe how to modify both modeling and assessment techniques to account for censored observation times. We show that our approach can lead to better predictive performance than the Cox proportional hazards model (i.e., a regression-based approach commonly used for censored, time-to-event data) or a Bayesian network with ad hoc approaches to right-censoring. Our techniques are motivated by and illustrated on data from a large US Midwestern health care system.
A simple procedure for pruning back-propagation trained neural networks

The sensitivity of the global error (cost) function to the inclusion/exclusion of each synapse in the artificial neural network is estimated. Introduced are shadow arrays which keep track of the incremental changes to the synaptic weights during a single pass of back-propagating learning. The synapses are then ordered by decreasing sensitivity numbers so that the network can be efficiently pruned by discarding the last items of the sorted list. Unlike previous approaches, this simple procedure does not require a modification of the cost function, does not interfere with the learning process, and demands a negligible computational overhead.
Genetic Evidence That Carbohydrate-Stimulated Insulin Secretion Leads to Obesity.

A fundamental precept of the carbohydrate-insulin model of obesity is that insulin secretion drives weight gain. However, fasting hyperinsulinemia can also be driven by obesity-induced insulin resistance. We used genetic variation to isolate and estimate the potentially causal effect of insulin secretion on body weight.



Genetic instruments of variation of insulin secretion [assessed as insulin concentration 30 min after oral glucose (insulin-30)] were used to estimate the causal relationship between increased insulin secretion and body mass index (BMI), using bidirectional Mendelian randomization analysis of genome-wide association studies. Data sources included summary results from the largest published metaanalyses of predominantly European ancestry for insulin secretion (n = 26037) and BMI (n = 322154), as well as individual-level data from the UK Biobank (n = 138541). Data from the Cardiology and Metabolic Patient Cohort study at Massachusetts General Hospital (n = 1675) were used to validate genetic associations with insulin secretion and to test the observational association of insulin secretion and BMI.



Higher genetically determined insulin-30 was strongly associated with higher BMI (β = 0.098, P = 2.2 × 10-21), consistent with a causal role in obesity. Similar positive associations were noted in sensitivity analyses using other genetic variants as instrumental variables. By contrast, higher genetically determined BMI was not associated with insulin-30.



Mendelian randomization analyses provide evidence for a causal relationship of glucose-stimulated insulin secretion on body weight, consistent with the carbohydrate-insulin model of obesity.
Few-shot Learning Using a Small-Sized Dataset of High-Resolution FUNDUS Images for Glaucoma Diagnosis

Deep learning has recently attracted a lot of attention, mainly thanks to substantial gains in terms of effectiveness. However, there is still room for significant improvement, especially when dealing with use cases that come with a limited availability of data, as is often the case in the area of medical image analysis. In this paper, we introduce a novel approach for early diagnosis of glaucoma in high-resolution FUNDUS images, only requiring a small number of training samples. In particular, we developed a predictive model based on a matching neural network architecture, integrating a high-resolution deep convolutional network that allows preserving the high-fidelity nature of the medical images. Our experimental results show that our predictive model is able to obtain higher levels of effectiveness than vanilla deep convolutional neural networks.
3-D micromachined hemispherical shell resonators with integrated capacitive transducers

We present a self-aligned fabrication method developed for three-dimensional (3-D) microscale hemispherical shell resonators with integrated capacitive transducers and a center post for electrical access to the shell. The self-aligned process preserves the axisymmetry for robust, balanced resonators that can potentially reach very high-Q due to suppressed anchor loss. High-Q operation of a thin polycrystalline silicon shell resonator is verified by exciting devices capacitively into a breathing resonance mode, with measured Q of 8,000 at 412 kHz in vacuum. This process can be further optimized to batch-fabricate micro-hemispherical resonator gyroscopes for portable inertial navigation.
Un-Crafting: De-Constructive Engagements with Interactive Artifacts

Crafting interactive artifacts is typically associated with synthesizing, making, constructing, putting things together. In this paper, we discuss how these activities can be contrasted with de-synthesizing activities that revolve around decomposition, dissection, and taking things apart. Based on framings that emphasize how related practices are valuable in engineering processes, we aim to unlock the potential of de-constructive engagements with interactive technologies as a material driven (design) practice. We propose un-crafting as a framework of four modes of taking interactive artifacts apart (i.e., un-crafting for material exposition, material inspiration, material inquiry, and material exploration) aiming to pinpoint de-constructive episodes inherent to design processes, as well as to encourage the refinement of respective techniques and methods.
Nobody ’ s watching ? Subtle cues affect generosity in an anonymous economic game

Models indicate that opportunities for reputation formation can play an important role in sustaining cooperation and prosocial behavior. Results from experimental economic games support this conclusion, as manipulating reputational opportunities affects prosocial behavior. Noting that some prosocial behavior remains even in anonymous noniterated games, some investigators argue that humans possess a propensity for prosociality independent of reputation management. However, decision-making processes often employ both explicit propositional knowledge and intuitive or affective judgments elicited by tacit cues. Manipulating game parameters alters explicit information employed in overt strategizing but leaves intact cues that may affect intuitive judgments relevant to reputation formation. To explore how subtle cues of observability impact prosocial behavior, we conducted five dictator games, manipulating both auditory cues of the presence of others (via the use of sound-deadening earmuffs) and visual cues (via the presentation of stylized eyespots). Although earmuffs appeared to reduce generosity, this effect was not significant. However, as predicted, eyespots substantially increased generosity, despite no differences in actual anonymity; when using a computer displaying eyespots, almost twice as many participants gave money to their partners compared with the
Timing Attacks on Implementations of Diffie-Hellman, RSA, DSS, and Other Systems

By carefully measuring the amount of time required to perform private key operations, attackers may be able to nd xed Di eHellman exponents, factor RSA keys, and break other cryptosystems. Against a vulnerable system, the attack is computationally inexpensive and often requires only known ciphertext. Actual systems are potentially at risk, including cryptographic tokens, network-based cryptosystems, and other applications where attackers can make reasonably accurate timing measurements. Techniques for preventing the attack for RSA and Di e-Hellman are presented. Some cryptosystems will need to be revised to protect against the attack, and new protocols and algorithms may need to incorporate measures to prevent timing attacks.
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality

Recently, it has been shown that in super-resolution, there exists a tradeoff relationship between the quantitative and perceptual quality of super-resolved images, which correspond to the similarity to the ground-truth images and the naturalness, respectively. In this paper, we propose a novel super-resolution method that can improve the perceptual quality of the upscaled images while preserving the conventional quantitative performance. The proposed method employs a deep network for multi-pass upscaling in company with a discriminator network and two quantitative score predictor networks. Experimental results demonstrate that the proposed method achieves a good balance of the quantitative and perceptual quality, showing more satisfactory results than existing methods.
A review to detect leukemia cancer in medical images

Health informatics has been qualified as prominent province in the headway of information technology. Ascribable to such a sophisticated evolution in the health care informatics, it is viable at the present period of time to diagnose several ailments in a short span of time. In relation to complaints, there is one disease dub leukemia which can be recognised by manipulating different techniques of information technology. Leukemia customarily occurs when a big portion of nonstandard White Blood Cells produced in the body by bone-marrow. Hematologist makes usage of microscopic study of human blood-cells which leads towards the requirement of several different methods that consist of microscopic-images, segmentation process, grouping as well as classification that can allow proper identification of numerous distinct patients that are having leukemia disease. The image data-set of microscopic ridges would be inspected visually by using some hematologists as well as this process is quite time consuming along with exhausting. The well-timed and fast discovery of leukemia considerably aids in providingaptcure to the sick-patient. The necessity for computerization of detection of this disease generally rises perpetually since modern techniques that include proper manual-investigation of the tissues of the blood as primary step in the direction of disease diagnosis. This procedure is relatively time-consuming, along with their proper accurateness depend upon the proficiency of operator's. So, prevention of leukemia is quite important. This paper has surveyed several methods utilized by prior authors such as ANN (Artificial Neural Network), image processing, LDA (Linear Dependent Analysis), SOM (Self Organizing Map) etc.
Edge and Curve Detection for Visual Scene Analysis

Simple sets of parallel operations are described which can be used to detect texture edges, "spots," and "streaks" in digitized pictures. It is shown that, by comparing the outputs of the operations corresponding to (e.g.,) edges of different sizes, one can construct a composite output in which edges between differently textured regions are detected, and isolated objects are also detected, but the objects composing the textures are ignored. Relationships between this class of picture processing operations and the Gestalt psychologists' laws of pictorial pattern organization are also discussed.
Towards a model of safety culture

Organisational culture is a concept often used to describe shared corporate values that aect and in ̄uence members' attitudes and behaviours. Safety culture is a sub-facet of organisational culture, which is thought to aect members' attitudes and behaviour in relation to an organisation's ongoing health and safety performance. However, the myriad of de®nitions of `organisational culture' and `safety culture' that abound in both the management and safety literature suggests that the concept of business-speci®c cultures is not clear-cut. Placing such `culture' constructs into a goal-setting paradigm appears to provide greater clarity than has hitherto been the case. Moreover, as yet there is no universally accepted model with which to formulate testable hypotheses that take into account antecedents, behaviour(s) and consequence(s). A reciprocal model of safety culture drawn from Social Cognitive Theory (Bandura, 1986. Social Foundation of Thought and Action: A Social Cognitive Theory. Prentice Hall, Englewood Clis, NJ.) is oered so as to provide both a theoretical and practical framework with which to measure and analyse safety culture. Implications for future research to establish the model's utility and validity are addressed.
Vehicle detection and tracking in wide field-of-view aerial video

This paper presents a joint probabilistic relation graph approach to simultaneously detect and track a large number of vehicles in low frame rate aerial videos. Due to low frame rate, low spatial resolution and sheer number of moving objects, detection and tracking in wide area video poses unique challenges. In this paper, we explore vehicle behavior model from road structure and generate a set of constraints to regulate both object based vertex matching and pairwise edge matching schemes. The proposed relation graph approach then unifies these two matching schemes into a single cost minimization framework to produce a quadratic optimized association result. The experiments on hours of real videos demonstrate the graph matching framework with vehicle behavior model effectively improves tracking performance in large scale dense traffic scenarios.
Coupled stepped-impedance resonator based dual-band adaptable bandpass filter with tunable lower band central frequency and bandwidth

We report a microstrip coupled stepped-impedance resonators (CSIRs) based concurrent dual-band reconfigurable band-pass filter (BPF) with tunable lower band central frequency and bandwidth through a single DC bias control voltage. Semiconductor varactor diodes have been used as the tuning element for getting continuously tunable frequency and bandwidth response. Tuning has been introduced in lower band central frequency and lower frequency bandwidth by properly incorporating varactor diodes in the CSIR structure. Varactor diodes incorporated in series configuration with the resonator in the CSIR structure provides adaptability in lower frequency band while upper band is fixed. Design approach and analysis have been provided along with the measurement results for the validation of the proposed concept.
Semantics-Aware Machine Learning for Function Recognition in Binary Code

Function recognition in program binaries serves as the foundation for many binary instrumentation and analysis tasks. However, as binaries are usually stripped before distribution, function information is indeed absent in most binaries. By far, identifying functions in stripped binaries remains a challenge. Recent research work proposes to recognize functions in binary code through machine learning techniques. The recognition model, including typical function entry point patterns, is automatically constructed through learning. However, we observed that as previous work only leverages syntax-level features to train the model, binary obfuscation techniques can undermine the pre-learned models in real-world usage scenarios. In this paper, we propose FID, a semantics-based method to recognize functions in stripped binaries. We leverage symbolic execution to generate semantic information and learn the function recognition model through well-performing machine learning techniques.FID extracts semantic information from binary code and, therefore, is effectively adapted to different compilers and optimizations. Moreover, we also demonstrate that FID has high recognition accuracy on binaries transformed by widely-used obfuscation techniques. We evaluate FID with over four thousand test cases. Our evaluation shows that FID is comparable with previous work on normal binaries and it notably outperforms existing tools on obfuscated code.
Cross-Pair Text Representations for Answer Sentence Selection

High-level semantics tasks, e.g., paraphrasing, textual entailment or question answering, involve modeling of text pairs. Before the emergence of neural networks, this has been mostly performed using intra-pair features, which incorporate similarity scores or rewrite rules computed between the members within the same pair. In this paper, we compute scalar products between vectors representing similarity between members of different pairs, in place of simply using a single vector for each pair. This allows us to obtain a representation specific to any pair of pairs, which delivers the state of the art in answer sentence selection. Most importantly, our approach can outperform much more complex algorithms based on neural networks.
A simple classifier for multiple criteria ABC analysis

To have an efficient control of a huge amount of inventory items, traditional approach is to classify the inventory into different groups. Different inventory control policies can then applied to different groups. The well-known ABC classification is simple-to-understand and easy-to-use. However, ABC analysis is based on only single measurement such as annual dollar usage. It has been recognized that other criteria are also important in inventory classification. In the paper, we propose a simple model for multiple criteria inventory classification. The model converts all criteria measures of an inventory item into a scalar score. The classification based on the calculated scores using ABC principle is then applied. With proper transformation, we can obtain the scores of inventory items without a linear optimizer. The model can be widely applied to inventory managers with minimal backgrounds in optimization.
Visual feature integration and the temporal correlation hypothesis.

The mammalian visual system is endowed with a nearly infinite capacity for the recognition of patterns and objects. To have acquired this capability the visual system must have solved what is a fundamentally combinatorial problem. Any given image consists of a collection of features, consisting of local contrast borders of luminance and wavelength, distributed across the visual field. For one to detect and recognize an object within a scene, the features comprising the object must be identified and segregated from those comprising other objects. This problem is inherently difficult to solve because of the combinatorial nature of visual images. To appreciate this point, consider a simple local feature such as a small vertically oriented line segment placed within a fixed location of the visual field. When combined with other line segments, this feature can form a nearly infinite number of geometrical objects. Any one of these objects may coexist with an equally large number of other
A Comparative Evaluation of Implicit Authentication Schemes

Implicit authentication (IA) schemes use behavioural biometrics to continuously and transparently authenticate mobile device users. Several IA schemes have been proposed by researchers which employ different behavioural features and provide reasonable detection accuracy. While these schemes work in principle, it is difficult to comprehend from these individual efforts which schemes work best (in terms of detection accuracy, detection delay and processing complexity) under different operating conditions (in terms of attack scenarios and availability of training and classification data). Furthermore, it is critical to evaluate these schemes on unbiased, real-world datasets to determine their efficacy in realistic operating conditions. In this paper, we evaluate six diverse IA schemes on four independently collected datasets from over 300 participants. We first evaluate these schemes in terms of: accuracy; training time and delay on real-world datasets; detection delay; processing and memory complexity for feature extraction, training and classification operations; vulnerability to mimicry attacks; and deployment issues on mobile platforms. We also leverage our real-world device usage traces to determine the proportion of time these schemes are able to afford protection to device owners. Based on our evaluations, we identify: 1) promising IA schemes with high detection accuracy, low performance overhead, and near real-time detection delays, 2) common pitfalls in contemporary IA evaluation methodology, and 3) open challenges for IA research. Finally, we provide an open source implementation of the IA schemes evaluated in this work that can be used for performance benchmarking by future IA research.
Automotive radar target characterization from 22 to 29 GHz and 76 to 81 GHz

The radar signatures of automotive targets were measured from 22 to 29 GHz and 76 to 81 GHz inside an anechoic chamber using a vector network analyzer. Radar cross section maps of a sedan, truck, van, pedestrian, motorcycle, and bicycle as a function of effective radar sensor bandwidth, center frequency, and polarization are presented.