nils-herrmann/clean_scirepeval huggingface dataset:
A Unified Framework for the Scheduling of Guaranteed Targeted Display Advertising Under Reach and Frequency Requirements

Motivated by recent trends in online advertising and advancements made by online publishers, we consider a new form of contract which allows advertisers to specify the number of unique individuals that should see their ad (reach), and the minimum number of times each individual should be exposed (frequency). We develop an optimization framework that aims for minimal under-delivery and proper spread of each campaign over its targeted demographics. As well, we introduce a pattern-based delivery mechanism which allows us to integrate a variety of interesting features into a website’s ad allocation optimization problem which have not been possible before. For example, our approach allows publishers to implement any desired pacing of ads over time at the user level or control the number of competing brands seen by each individual. We develop a two-phase algorithm that employs column generation in a hierarchical scheme with three parallelizable components. Numerical tests with real industry data show that our algorithm produces high-quality solutions and has promising run-time and scalability. Several extensions of the model are presented, e.g., to account for multiple ad positions on the webpage, or randomness in the website visitors’ arrival process.
The impact of marijuana policies on youth: clinical, research, and legal update.

This technical report updates the 2004 American Academy of Pediatrics technical report on the legalization of marijuana. Current epidemiology of marijuana use is presented, as are definitions and biology of marijuana compounds, side effects of marijuana use, and effects of use on adolescent brain development. Issues concerning medical marijuana specifically are also addressed. Concerning legalization of marijuana, 4 different approaches in the United States are discussed: legalization of marijuana solely for medical purposes, decriminalization of recreational use of marijuana, legalization of recreational use of marijuana, and criminal prosecution of recreational (and medical) use of marijuana. These approaches are compared, and the latest available data are presented to aid in forming public policy. The effects on youth of criminal penalties for marijuana use and possession are also addressed, as are the effects or potential effects of the other 3 policy approaches on adolescent marijuana use. Recommendations are included in the accompanying policy statement.
Tuning the ITU-R P.1546 model in a MIMO scenario and urban environment

A possible scenario for the evolution of Television Broadcast is the adoption of 8 K resolution video broadcasting. To achieve the required bit-rates MIMO technologies are an actual candidate. In this scenario, this paper collected electric field levels from a MIMO experimental system for TV broadcasting to tune the parameters of the ITU-R P.1546 propagation model, which has been employed to model VHF and UHF broadcast channels. The parameters are tuned for each polarization alone and for both together. This is done considering multiple reception points and also a larger capturing time interval for a fixed reception site. Significant improvements on the match between the actual and measured link budget are provided by the optimized parameters.
LIDAR based perception solution for autonomous vehicles

In this work, a solution for clustering and tracking obstacles in the area covered by a LIDAR sensor is presented. It is based on a combination of simple artificial intelligence techniques and it is conceived as an initial version of a detection and tracking system for objects of any shape that an autonomous vehicle might find in its surroundings. The proposed solution divides the problem into three consecutive phases: 1) segmentation, 2) fragmentation detection and clustering and 3) tracking. The work done has been tested with real world LIDAR scan samples taken from an instrumented vehicle.
Verifiable Reinforcement Learning via Policy Extraction

While deep reinforcement learning has successfully solved many challenging control tasks, its real-world applicability has been limited by the inability to ensure the safety of learned policies. We propose an approach to verifiable reinforcement learning by training decision tree policies, which can represent complex policies (since they are nonparametric), yet can be efficiently verified using existing techniques (since they are highly structured). The challenge is that decision tree policies are difficult to train. We propose VIPER, an algorithm that combines ideas from model compression and imitation learning to learn decision tree policies guided by a DNN policy (called the oracle) and its Q-function, and show that it substantially outperforms two baselines. We use VIPER to (i) learn a provably robust decision tree policy for a variant of Atari Pong with a symbolic state space, (ii) learn a decision tree policy for a toy game based on Pong that provably never loses, and (iii) learn a provably stable decision tree policy for cart-pole. In each case, the decision tree policy achieves performance equal to that of the original DNN policy.
Heterogeneity of Dyggve-Melchior-Clausen dwarfism

Sibs with apparent Dyggve-Melchior-Clausen (DMC) dwarfism and normal intelligence are described. Three other familial and 3 sporadic cases with DMC dwarfism and normal intelligence are known. Twelve familial and 9 sporadic cases are known with the usual combination of DMC dwarfism and severe mental retardation. Since the two conditions appear to breed true they seem to be genetically different. We propose to name the former “Smith-McCort dwarfism” to clearly distinguish it from the DMC syndrome in which mental retardation is a constituent part. Both conditions are inherited as autosomal recessive traits. Spinal cord compression due to atlantoaxial instability is a serious and preventable complication of both disorders.
Efficient Iris Spoof Detection via Boosted Local Binary Patterns

Recently, spoof detection has become an important and challenging topic in iris recognition. Based on the textural differences between the counterfeit iris images and the live iris images, we propose an efficient method to tackle this problem. Firstly, the normalized iris image is divided into sub-regions according to the properties of iris textures. Local binary patterns (LBP) are then adopted for texture representation of each sub-region. Finally, Adaboost learning is performed to select the most discriminative LBP features for spoof detection. In particular, a kernel density estimation scheme is proposed to complement the insufficiency of counterfeit iris images during Adaboost training. The comparison experiments indicate that the proposed method outperforms state-of-the-art methods in both accuracy and speed.
VM Economics for Java Cloud Computing: An Adaptive and Resource-Aware Java Runtime with Quality-of-Execution

Resource management in Cloud Computing has been dominated by system-level virtual machines to enable the management of resources using a coarse grained approach, largely in a manner independent from the applications running on these infrastructures. However, in such environments, although different types of applications can be running, the resources are delivered equally to each one, missing the opportunity to manage the available resources in a more efficient and application driven way. So, as more applications target managed runtimes, high level virtualization is a relevant abstraction layer that has not been properly explored to enhance resource usage, control, and effectiveness. We propose a VM economics model to manage cloud infrastructures, governed by a quality-of-execution (QoE) metric and implemented by an extended virtual machine. The Adaptive and Resource-Aware Java Virtual Machine (ARA-JVM) is a cluster-enabled virtual execution environment with the ability to monitor base mechanisms (e.g. thread cheduling, garbage collection, memory or network consumptions) to assess application's performance and reconfigure these mechanisms in runtime according to previously defined resource allocation policies. Reconfiguration is driven by incremental gains in quality-of-execution (QoE), used by the VM economics model to balance relative resource savings and perceived performance degradation. Our work in progress, aims to allow cloud providers to exchange resource slices among virtual machines, continually addressing where those resources are required, while being able to determine where the reduction will be more economically effective, i.e., will contribute in lesser extent to performance degradation.
Special Issue on Design and Development: Product Development Decisions: A Review of the Literature

This paper is a review of research in product development, which we define as the transformation of a market opportunity into a product available for sale. Our review is broad, encompassing work in the academic fields of marketing, operations management, and engineering design. The value of this breadth is in conveying the shape of the entire research landscape. We focus on product development projects within a single firm. We also devote our attention to the development of physical goods, although much of the work we describe applies to products of all kinds. We look inside the “black box” of product development at the fundamental decisions that are made by intention or default. In doing so, we adopt the perspective of product development as a deliberate business process involving hundreds of decisions, many of which can be usefully supported by knowledge and tools. We contrast this approach to prior reviews of the literature, which tend to examine the importance of environmental and contextual variables, such as market growth rate, the competitive environment, or the level of top-management support.
Segmental Darier's disease postpartum and following tubal ligation.

Segmental Darier's disease is a rare clinical variant of autosomal dominant Darier's disease (keratosis follicularis) exhibiting eruptions in a unilateral arrangement following the lines of Blaschko. It occurs in approximately 10% of patients with Darier's disease. We report two cases of type 1 segmental Darier's disease that appeared a few months after childbirth; in one case, recurrence of the disease occurred after tubal ligation.
Laryngeal tuberculosis in the United States of America: a forgotten disease.

Laryngeal tuberculosis (TB) was a common manifestation of TB in the early twentieth century, but now represents only 1% of all cases. Most modern case series of laryngeal TB originate outside the USA. We report a case of laryngeal TB from our institution and review other US cases published between 1970 and 2012. One hundred twenty-seven cases were identified. The mean patient age was 49 y and 28% were female. The mean duration of symptoms was 19 weeks. Dysphonia and weight loss were the most common manifestations, seen in 96% and 47% of cases, respectively. These symptoms were usually attributed to malignancy initially. Most cases involved the vocal cords. Eighty-six percent of cases had underlying pulmonary involvement. Mortality was 3%. In the USA, laryngeal TB is rarely suspected and often confused with malignancy. This infection should be considered in patients with unexplained dysphonia and weight loss.
Hyperbilirubinemia in the newborn.

After completing this article, readers should be able to: 1. List the risk factors for severe hyperbilirubinemia. 2. Distinguish between physiologic jaundice and pathologic jaundice of the newborn. 3. Recognize the clinical manifestations of acute bilirubin encephalopathy and the permanent clinical sequelae of . Describe the evaluation of hyperbilirubinemia from birth through 3 months of age. 5. Manage neonatal hyperbilirubinemia, including referral to the neonatal intensive care unit for exchange transfusion.
Comparison of tree and graph encodings as function of problem complexity

In this paper, we analyze two general-purpose encoding types, trees and graphs systematically, focusing on trends over increasingly complex problems. Tree and graph encodings are similar in application but offer distinct advantages and disadvantages in genetic programming. We describe two implementations and discuss their evolvability. We then compare performance using symbolic regression on hundreds of random nonlinear target functions of both 1-dimensional and 8-dimensional cases. Results show the graph encoding has less bias for bloating solutions but is slower to converge and deleterious crossovers are more frequent. The graph encoding however is found to have computational benefits, suggesting it to be an advantageous trade-off between regression performance and computational effort.
Understanding personality of portrait by social embedding visual features

Image understanding achieves unprecedented performance in content recognition and emotion rating recently. However, previous research mainly focused on visual features. In this paper, inspired by human cognitive activities, we discuss how to measure portrait distance in respective of high level semantic: personality, by employing features from both visual contents and behavior contents. Firstly, a new image distance metric, named Social and Visual Portrait Distance, is designed by jointly considering visual features and human social media behavior features. In portrait images, visual features are defined globally and locally utilizing theoretical and empirical concepts from psychology theory. While in social media, behavior features are designed with considerations of demographic factors, identity claims and behavioral residue. And the new distance is estimated referred to feature reliability. Secondly, we modify the proposed distance calculation formula in order to apply it on only visual features but preserving both visual and social relations by a new Social Embedding Portrait Distance Learning method. In this manner, we could measure the social embedding visual distance of common portrait images in absence of social media information, such as web portrait, or daily photos. Comprehensive experiments are employed to investigate the effectiveness of the new portrait distance and the metric learning method in representing personality distance compared with several baselines. Moreover, the learned distance matrix reveals a reasonable explanation of social preferred visual features with contribute partitions.
Dynamic Coordination in Fleet Management Systems: Toward Smart Cyber Fleets

Fleet management systems are commonly used to coordinate mobility and delivery services in a broad variety of domains. However, their traditional top-down control architecture becomes a bottleneck in open and dynamic environments, where scalability, proactiveness, and autonomy are becoming key factors for their success. Here, the authors present an abstract event-based architecture for fleet management systems that supports tailoring dynamic control regimes for coordinating fleet vehicles, and illustrate it for the case of medical emergency management. Then, they go one step ahead in the transition toward automatic or driverless fleets, by conceiving fleet management systems in terms of cyber-physical systems, and putting forward the notion of cyber fleets.
Syntactic N-grams as machine learning features for natural language processing

In this paper we introduce and discuss a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner how we construct them, i.e., what elements are considered neighbors. In case of sn-grams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking words as they appear in a text, i.e., sn-grams are constructed by following paths in syntactic trees. In this manner, sn-grams allow bringing syntactic knowledge into machine learning methods; still, previous parsing is necessary for their construction. Sn-grams can be applied in any natural language processing (NLP) task where traditional n-grams are used. We describe how sn-grams were applied to authorship attribution. We used as baseline traditional n-grams of words, part of speech (POS) tags and characters; three classifiers were applied: support vector machines (SVM), naive Bayes (NB), and tree classifier J48. Sn-grams give better results with SVM classifier.
A practical approach for the differential diagnosis of chronic leg pain in the athlete.

Chronic lower leg pain results from various conditions, most commonly, medial tibial stress syndrome, stress fracture, chronic exertional compartment syndrome, nerve entrapment, and popliteal artery entrapment syndrome. Symptoms associated with these conditions often overlap, making a definitive diagnosis difficult. As a result, an algorithmic approach was created to aid in the evaluation of patients with complaints of lower leg pain and to assist in defining a diagnosis by providing recommended diagnostic studies for each condition. A comprehensive physical examination is imperative to confirm a diagnosis and should begin with an inquiry regarding the location and onset of the patient's pain and tenderness. Confirmation of the diagnosis requires performing the appropriate diagnostic studies, including radiographs, bone scans, magnetic resonance imaging, magnetic resonance angiography, compartmental pressure measurements, and arteriograms. Although most conditions causing lower leg pain are treated successfully with nonsurgical management, some syndromes, such as popliteal artery entrapment syndrome, may require surgical intervention. Regardless of the form of treatment, return to activity must be gradual and individualized for each patient to prevent future athletic injury.
Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics

This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision theory, procedural personas are implemented using a variation of Monte Carlo Tree Search (MCTS) where the node selection criteria are developed using evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using these personas we demonstrate how generative player models can be applied to a varied corpus of game levels and demonstrate how different play styles can be enacted in each level. In short, we use artificially intelligent personas to construct synthetic playtesters. The proposed approach could be used as a tool for automatic play testing when human feedback is not readily available or when quick visualization of potential interactions is necessary. Possible applications include interactive tools during game development or procedural content generation systems where many evaluations must be conducted within a short time span.
Single or Multiple Frames Content Delivery for Next-Generation Networks?

This paper addresses the four enabling technologies, namely multi-user sparse code multiple access (SCMA), content caching, energy harvesting, and physical layer security for proposing an energy and spectral efficient resource allocation algorithm for the access and backhaul links in heterogeneous cellular networks. Although each of the above mentioned issues could be a topic of research, in a real situation, we would face a complicated scenario where they should be considered jointly, and hence, our target is to consider these technologies jointly in a unified framework. Moreover, we propose two novel content delivery scenarios: 1) single frame content delivery (SFCD), and 2) multiple frames content delivery (MFCD), where the time duration of serving user requests is divided into several frames. In the first scenario, the requested content by each user is served over one frame. However, in the second scenario, the requested content by each user can be delivered over several frames. We formulate the resource allocation for the proposed scenarios as optimization problems where our main aim is to maximize the energy efficiency of access links subject to the transmit power and rate constraints of access and backhaul links, caching and energy harvesting constraints, and SCMA codebook allocation limitations. Due to the practical limitations, we assume that the channel state information values between eavesdroppers and base stations are uncertain and design the network for the worst case scenario. Since the corresponding optimization problems are mixed integer non-linear and nonconvex programming, NP-hard, and intractable, we propose an iterative algorithm based on the well-known alternate and successive convex approximation methods. In addition, the proposed algorithms are studied from the computational complexity, convergence, and performance perspectives. Moreover, the proposed caching scheme outperforms the existing traditional caching schemes like random caching and most popular caching. We also study the effect of joint and disjoint considerations of enabling technologies for the performance of nextgeneration networks. We also show that the proposed caching strategy, MFCD and joint solutions have 43%, 9.4% and %51.3 performance gain compared to no cahcing, SFCD and disjoint solutions, respectively.
Reliability and accuracy of cone-beam computed tomography dental measurements.

Dental measurements are an integral part of the orthodontic records necessary for proper diagnosis and treatment planning. In this study, we investigated the reliability and accuracy of dental measurements made on cone-beam computed tomography (CBCT) reconstructions.



Thirty human skulls were scanned with dental CBCT, and 3-dimensional reconstructions of the dentitions were generated. Ten measurements (overbite, overjet, maxillary and mandibular intermolar and intercanine widths, arch length available, and arch length required) were made directly on the dentitions of the skulls with a high-precision digital caliper and on the digital reconstructions with commercially available software. Reliability and accuracy were assessed by using intraclass correlation and paired Student t tests. A P value of < or = 0.05 was used to assign statistical significance.



Both the CBCT and the caliper measurements were highly reliable (r >0.90). The CBCT measurements tended to slightly underestimate the anatomic truth. This was statistically significant only for compounded measurements.



Dental measurements from CBCT volumes can be used for quantitative analysis. With the CBCT images, we found a small systematic error, which became statistically significant only when combining several measurements. An adjustment for this error allows for improved accuracy.
On-line personalization of a touch screen based keyboard

The user expectations for usability and personalization along with decreasing size of handheld devices challenge traditional keypad layout design. We have developed a method for on-line adaptation of a touch pad keyboard layout. The method starts from an original layout and monitors the usage of the keyboard by recording and analyzing the keystrokes. An on-line learning algorithm subtly moves the keys according to the spatial distribution of keystrokes. In consequence, the keyboard matches better to the users physical extensions and grasp of the device, and makes the physical trajectories during typing more comfortable. We present two implementations that apply different vector quantization algorithms to produce an adaptive keyboard with visual on-line feedback. Both qualitative and quantitative results show that the changes in the keyboard are consistent, and related to the user's handedness and hand extensions. The testees found the on-line personalization positive. The method can either be applied for on-line personalization of keyboards or for ergonomics research