nils-herrmann/clean_scirepeval huggingface dataset:
Antenna Selection for MIMO Nonorthogonal Multiple Access Systems

This paper considers the antenna selection (AS) problem for a multiple-input multiple-output nonorthogonal multiple access system. In particular, we develop new computationally efficient AS algorithms for two commonly used scenarios: NOMA with fixed power allocation (F-NOMA) and NOMA with cognitive radio-inspired power allocation (CR-NOMA). For the F-NOMA scenario, a new max–max–max AS (A<inline-formula><tex-math notation="LaTeX">$^3$</tex-math> </inline-formula>-AS) scheme is first proposed to maximize the system sum-rate. This is achieved by selecting one antenna at the base station (BS) and corresponding best receive antenna at each user that maximizes the channel gain of the resulting <italic>strong</italic> user. To improve the user fairness, a new max–min–max AS (AIA-AS) scheme is subsequently developed, in which we jointly select one transmit antenna at BS and corresponding best receive antennas at users to maximize the channel gain of the resulting <italic>weak</italic> user. For the CR-NOMA scenario, we propose another new AS algorithm, termed maximum-channel-gain-based antenna selection (MCG-AS), to maximize the achievable rate of the secondary user, under the condition that the primary user's quality-of-service requirement is satisfied. The asymptotic closed-form expressions of the average sum-rate for A<inline-formula> <tex-math notation="LaTeX">$^3$</tex-math></inline-formula>-AS and AIA-AS and that of the average rate of the secondary user for MCG-AS are derived. Numerical results demonstrate that the AIA-AS provides better user fairness, whereas the A<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula>-AS achieves a near-optimal sum-rate in F-NOMA systems. For the CR-NOMA scenario, MCG-AS achieves a near-optimal performance in a wide signal-to-noise-ratio regime. Furthermore, all the proposed AS algorithms yield a significant computational complexity reduction, compared to exhaustive search-based counterparts.
Happiness Economics , Eudaimonia and Positive Psychology : From Happiness Economics to Flourishing Economics

A remarkable current development, happiness economics focuses on the relevance of people’s happiness in economic analyses. As this theory has been criticised for relying on an incomplete notion of happiness, this paper intends to support it with richer philosophical and psychological foundations. Specifically, it suggests that happiness economics should be based on Aristotle’s philosophical eudaimonia concept and on a modified version of ‘positive psychology’ that stresses human beings’ relational nature. First, this analysis describes happiness economics and its shortcomings. Next, it introduces Aristotle’s eudaimonia and takes a look at positive psychology with this lens, elaborating on the need to develop a new approach that goes beyond the economics of happiness: the economics of flourishing. Finally, the paper specifies some possible socio-economic objectives of a eudaimonic economics of happiness.
A Technique for Measuring Data Persistence Using the Ext4 File System Journal

In this paper, we propose a method of measuring data persistence using the Ext4 journal. Digital Forensic tools and techniques are commonly used to extract data from media. A great deal of research has been dedicated to the recovery of deleted data, however, there is a lack of information on quantifying the chance that an investigator will be successful in this endeavor. To that end, we suggest the file system journal be used as a source to gather empirical evidence of data persistence, which can later be used to formulate the probability of recovering deleted data under various conditions. Knowing this probability can help investigators decide where to best invest their resources. We have implemented a proof of concept system that interrogates the Ext4 file system journal and logs relevant data. We then detail how this information can be used to track the reuse of data blocks from the examination of file system metadata structures. This preliminary design contributes a novel method of tracking deleted data persistence that can be used to generate the information necessary to formulate probability models regarding the full and/or partial recovery of deleted data.
Is Tom Cruise Threatened ? Using Netflix Prize Data to Examine the Long Tail of Electronic Commerce

We analyze a large data set from Netflix, the leading online movie rental company, to shed new light on the causes and consequences of the Long Tail effect, which suggests that on the Internet, over time, consumers will increasingly shift away from hit products and toward niche products. We examine the aggregate level demand as well as demand at the individual consumer level and we find that the consumption of both the hit and the niche movies decreased over time when the popularity of the movies is ranked in absolute terms (e.g., the top/bottom 10 titles). However, we also observe that the active product variety has increased dramatically over the study period. To separate out the demand diversification effect from the shift in consumer preferences, we propose to measure the popularity of movies in relative terms by dynamically adjusting for the current product variety (e.g., the top/bottom 1% of titles). Using this alternative definition of popularity, we find that the demand for the hits rises, while the demand for the niches still falls. We conclude that new movie titles appear much faster than consumers discover them. Finally, we find no evidence that niche titles satisfy consumer tastes better than hit titles and that a small number of heavy users are more likely to venture into niches than light users.
Auditory Saliency Using Natural Statistics

In contrast to the wealth of saliency models in the vision literature, there is a relative paucity of models exploring auditory saliency. In this work, we integrate the approaches of (Kayser, Petkov, Lippert, & Logothetis, 2005) and (Zhang, Tong, Marks, Shan, & Cottrell, 2008) and propose a model of auditory saliency. The model combines the statistics of natural soundscapes and the recent past of the input signal to predict the saliency of an auditory stimulus in the frequency domain. To evaluate the model output, a simple behavioral experiment was performed. Results show the auditory saliency maps calculated by the model to be in excellent accord with human judgments of saliency.
The incidence of cervical spondylosis decreases with aging in the elderly, and increases with aging in the young and adult population: a hospital-based clinical analysis

Cervical spondylosis is well accepted as a common degenerative change in the cervical spine. Compelling evidence has shown that the incidence of cervical spondylosis increases with age. However, the relationship between age and the incidence of cervical spondylosis remains obscure. It is essential to note the relationship between age and the incidence of cervical spondylosis through more and more clinical data.



In the case-controlled study reported here, retrospective clinical analysis of 1,276 cases of cervical spondylosis has been conducted. We analyzed the general clinical data, the relationship between age and the incidence of cervical spondylosis, and the relationship between age-related risk factors and the incidence of cervical spondylosis. A chi-square test was used to analyze the associations between different variables. Statistical significance was defined as a P-value of less than 0.05.



The imaging examination demonstrated the most prominent characteristic features of cervical spondylosis: bulge or herniation at C3-C4, C4-C5, and C5-C6. The incidence of cervical spondylosis increased with aging before age 50 years and decreased with aging after age 50 years, especially in the elderly after 60 years old. The occurrence rate of bulge or herniation at C3-C4, C4-C5, C5-C6, and C6-C7 increased with aging before age 50 years and decreased with aging after age 50 years, especially after 60 years. Moreover, the incidence of hyperosteogeny and spinal stenosis increased with aging before age 60 years and decreased with aging after age 60 years, although there was no obvious change in calcification. The age-related risk factors, such as hypertension, hyperlipidemia, diabetes, cerebral infarct, cardiovascular diseases, smoking, and drinking, have no relationship with the incidence of cervical spondylosis.



A decreasing proportion of cervical spondylosis with aging occurs in the elderly, while the proportion of cervical spondylosis increases with aging in the young and the adults. This investigation implicates that aging is not only a contributor to the clinical performance of cervical spondylosis in the elderly, although the incidence of cervical spondylosis is proportional to the progress of age.
A qualitative exploration of individuals' motivators for seeking substance user treatment.

Substance abuse continues to greatly impact the US population. The purpose of this study is to improve understanding of the reasons why individuals seek substance user treatment. Qualitative interviews were conducted with 50 men (n = 25) and women (n = 25) across eight substance user treatment programs in Durham, North Carolina, in 2010. We identify six motivating themes regarding reasons for seeking treatment and explore how these themes vary by the participants' mode of entry into treatment. Findings provide a holistic understanding of how and why individuals come into treatment that can enhance the quality and impact of substance user services.
A fully integrated multi-CPU, GPU and memory controller 32nm processor

This paper describes the 32nm Sandy Bridge processor that integrates up to 4 high performance Intel Architecture (IA) cores, a power/performance optimized graphic processing unit (GPU) and memory and PCIe controllers in the same die. The Sandy Bridge architecture block diagram is shown in Fig. 15.1.1 and the floorplan of a four IA-core version is shown in Fig. 15.1.2. The Sandy Bridge IA core implements an improved branch prediction algorithm, a micro-operation (Uop) cache, a floating point Advanced Vector Extension (AVX), a second load port in the L1 cache and bigger register files in the out-of-order part of the machine; all these architecture improvements boost the IA core performance without increasing the thermal power dissipation envelope or the average power consumption (to preserve battery life in mobile systems). The CPUs and GPU share the same 8MB level-3 cache memory. The data flow is optimized by a high performance on die interconnect fabric (called “ring”) that connects between the CPUs, the GPU, the L3 cache and the system agent (SA) unit that houses a 1600MT/s, dual channel DDR3 memory controller, a 20-lane PCIe gen2 controller, a two parallel pipe display engine, the power management control unit and the testability logic. An on die EPROM is used for configurability and yield optimization.
Multi-level Fusion Based 3D Object Detection from Monocular Images

In this paper, we present an end-to-end multi-level fusion based framework for 3D object detection from a single monocular image. The whole network is composed of two parts: one for 2D region proposal generation and another for simultaneously predictions of objects' 2D locations, orientations, dimensions, and 3D locations. With the help of a stand-alone module to estimate the disparity and compute the 3D point cloud, we introduce the multi-level fusion scheme. First, we encode the disparity information with a front view feature representation and fuse it with the RGB image to enhance the input. Second, features extracted from the original input and the point cloud are combined to boost the object detection. For 3D localization, we introduce an extra stream to predict the location information from point cloud directly and add it to the aforementioned location prediction. The proposed algorithm can directly output both 2D and 3D object detection results in an end-to-end fashion with only a single RGB image as the input. The experimental results on the challenging KITTI benchmark demonstrate that our algorithm significantly outperforms monocular state-of-the-art methods.
Contextual design

C Contextual Design is a state-of-the-art approach to designing products directly from a designer's understanding of how the customer works. Great product ideas come from the marriage of a designer's detailed understanding of a customer's need and his or her in-depth understanding of the possibilities introduced by technology. The best product designs result when the product's designers are involved in collecting and interpreting customer data and appreciate what real people need. Contextual Design gives designers the tools to do just that.
Attachment and borderline personality disorder. A theory and some evidence.

An attachment theory approach to severe personality disorder is described. Evidence is presented that suggests that representations of attachment relationships and attachment behaviors of patients with this diagnosis are commonly disorganized in character. It is argued that the capacity to develop mental representations of mental states in self and other (reflective function) develops in the context of attachment relationships and that disorganization of attachment undermines this process. Such disorganization can be associated with trauma but may also be linked to other biological and psychosocial deficits. Many of the clinical characteristics of patients with borderline personality disorder may be seen as consequences of disordered self-organization and a limited rudimentary capacity to think about behavior in mental state terms. The relevance of this model for the practice of psychotherapy with this group of patients is discussed.
Ant-colony algorithms for permutation flowshop scheduling to minimize makespan/total flowtime of jobs

The problem of scheduling in permutation flowshops is considered with the objective of minimizing the makespan, followed by the consideration of minimization of total flowtime of jobs. Two ant-colony optimization algorithms are proposed and analyzed for solving the permutation flowshop scheduling problem. The first algorithm extends the ideas of the ant-colony algorithm by Stuetzle [], called max–min ant system (MMAS), by incorporating the summation rule suggested by Merkle and Middendorf [ 2000, p. 287] and a newly proposed local search technique. The second ant-colony algorithm is newly developed. The proposed ant-colony algorithms have been applied to 90 benchmark problems taken from Taillard [ (1993) 278]. First, a comparison of the solutions yielded by the MMAS and the two ant-colony algorithms developed in this paper, with the heuristic solutions given by Taillard [ (1993) 278] is undertaken with respect to the minimization of makespan. The comparison shows that the two proposed ant-colony algorithms perform better, on an average, than the MMAS. Subsequently, by considering the objective of minimizing the total flowtime of jobs, a comparison of solutions yielded by the proposed ant-colony algorithms with the best heuristic solutions known for the benchmark problems, as published in an extensive study by Liu and Reeves [ (2001) 439], is carried out. The comparison shows that the proposed ant-colony algorithms are clearly superior to the heuristics analyzed by Liu and Reeves. For 83 out of 90 problems considered, better solutions have been found by the two proposed ant-colony algorithms, as compared to the solutions reported by Liu and Reeves.
Seismic Data Classification Using Machine Learning

Earthquakes around the world have been a cause of major destruction and loss of life and property. An early detection and prediction system using machine learning classification models can prove to be very useful for disaster management teams. The earthquake stations continuously collect data even when there is no event. From this data, we need to distinguish earthquake and non-earthquake. Machine learning techniques can be used to analyze continuous time series data to detect earthquakes effectively. Furthermore, the earthquake data can be used to predict the P-wave and S-wave arrival times.
What do online behavioral advertising privacy disclosures communicate to users?

Online Behavioral Advertising (OBA), the practice of tailoring ads based on an individual's online activities, has led to privacy concerns. In an attempt to mitigate these privacy concerns, the online advertising industry has proposed the use of OBA disclosures: icons, accompanying taglines, and landing pages intended to inform users about OBA and provide opt-out options. We conducted a 1,505-participant online study to investigate Internet users' perceptions of OBA disclosures. The disclosures failed to clearly notify participants about OBA and inform them about their choices. Half of the participants remembered the ads they saw but only 12% correctly remembered the disclosure taglines attached to ads. When shown the disclosures again, the majority mistakenly believed that ads would pop up if they clicked on disclosures, and more participants incorrectly thought that clicking the disclosures would let them purchase advertisements than correctly understood that they could then opt out of OBA. "AdChoices", the most commonly used tagline, was particularly ineffective at communicating notice and choice. A majority of participants mistakenly believed that opting out would stop all online tracking, not just tailored ads. We dicuss challenges in crafting disclosures and provide suggestions for improvement.
Comparison of a 1450-nm diode laser and a 1320-nm Nd:YAG laser in the treatment of atrophic facial scars: a prospective clinical and histologic study.

Atrophic scar revision techniques, although numerous, have been hampered by inadequate clinical responses and prolonged postoperative recovery periods. Nonablative laser treatment has been shown to effect significant dermal collagen remodeling with minimal posttreatment sequelae. Although many studies have been published regarding the effectiveness of these nonablative lasers on rhytides, there are limited data demonstrating their specific effects on atrophic scars.



To evaluate and compare the efficacy and safety of long-pulsed 1320-nm Nd:YAG and 1450-nm diode lasers in the treatment of atrophic facial scarring.



A series of 20 patients (skin phototypes I-V) with mild to moderate atrophic facial acne scars randomly received three successive monthly treatments with a long-pulsed 1320-nm Nd:YAG laser on one facial half and a long-pulsed 1450-nm diode laser on the contralateral facial half. Patients were evaluated using digital photography and three-dimensional in vivo microtopography measurements at each treatment visit and at 1, 3, 6, and 12 months postoperatively. Histologic evaluations of cutaneous biopsies obtained before treatment, immediately after the first treatment, and at 1, 3, 6, and 12 months after the third treatment were performed. Clinical assessment scores were determined at each treatment session and follow-up visit. Patient satisfaction surveys were obtained at the end of the study.



Mild to moderate clinical improvement was observed after the series of three treatments in the majority of patients studied. Patient satisfaction scores and in vivo microtopography measurements paralleled the photographic and histopathologic changes seen. Side effects of treatment were limited to mild transient erythema, edema, and hyperpigmentation. No scarring or adverse textural changes resulted from the use of either laser system.



Nonablative long-pulsed 1320-nm Nd:YAG and 1450-nm diode lasers each offer clinical improvement for patients with atrophic scarring without significant side effects or complications. The 1450-nm diode laser showed greater clinical scar response at the parameters studied. The use of nonablative laser systems is a good treatment alternative for patients with atrophic scarring who are unable or unwilling to endure the prolonged postoperative recovery process associated with ablative laser skin resurfacing procedures.
Automated segmentation of 3D anatomical structures on CT images by using a deep convolutional network based on end-to-end learning approach

We have proposed an end-to-end learning approach that trained a deep convolutional neural network (CNN) for automatic CT image segmentation, which accomplished a voxel-wised multiple classification to directly map each voxel on 3D CT images to an anatomical label automatically. The novelties of our proposed method were (1) transforming the anatomical structures segmentation on 3D CT images into a majority voting of the results of 2D semantic image segmentation on a number of 2D-slices from different image orientations, and (2) using “convolution” and “deconvolution” networks to achieve the conventional “coarse recognition” and “fine extraction” functions which were integrated into a compact all-in-one deep CNN for CT image segmentation. The advantage comparing to previous works was its capability to accomplish real-time image segmentations on 2D slices of arbitrary CT-scan-range (e.g. body, chest, abdomen) and produced correspondingly-sized output. In this paper, we propose an improvement of our proposed approach by adding an organ localization module to limit CT image range for training and testing deep CNNs. A database consisting of 240 3D CT scans and a human annotated ground truth was used for training (228 cases) and testing (the remaining 12 cases). We applied the improved method to segment pancreas and left kidney regions, respectively. The preliminary results showed that the accuracies of the segmentation results were improved significantly (pancreas was 34% and kidney was 8% increased in Jaccard index from our previous results). The effectiveness and usefulness of proposed improvement for CT image segmentations were confirmed.
Ballistocardiography and Seismocardiography: A Review of Recent Advances

In the past decade, there has been a resurgence in the field of unobtrusive cardiomechanical assessment, through advancing methods for measuring and interpreting ballistocardiogram (BCG) and seismocardiogram (SCG) signals. Novel instrumentation solutions have enabled BCG and SCG measurement outside of clinical settings, in the home, in the field, and even in microgravity. Customized signal processing algorithms have led to reduced measurement noise, clinically relevant feature extraction, and signal modeling. Finally, human subjects physiology studies have been conducted using these novel instruments and signal processing tools with promising results. This paper reviews the recent advances in these areas of modern BCG and SCG research.
On self-aggrandizement and anger: a temporal analysis of narcissism and affective reactions to success and failure.

Narcissists are thought to display extreme affective reactions to positive and negative information about the self. Two experiments were conducted in which high- and low-narcissistic individuals, as defined by the Narcissistic Personality Inventory (NPI), completed a series of tasks in which they both succeeded and failed. After each task, participants made attributions for their performance and reported their moods. High-NPI participants responded with greater changes in anxiety, anger, and self-esteem. Low self-complexity was examined, but it neither mediated nor moderated affective responses. High-NPI participants tended to attribute initial success to ability, leading to more extreme anger responses and greater self-esteem reactivity to failure. A temporal sequence model linking self-attribution and emotion to narcissistic rage is discussed.