nils-herrmann/clean_scirepeval huggingface dataset:
Drone forensic framework: Sensor and data identification and verification

Ease of availability and affordability of unmanned aerial vehicles (UAV) have led to an increase in its popularity amongst the public. The proliferation of UAVs has also augmented several security issues. These devices are used for illegal activities such as drug smuggling and privacy invasion. The purpose of this research paper is to analyze the basic architecture of a drone, and to propose a generic drone forensic model that would improve the digital investigation process. This paper also provides recommendations on how one should perform forensics on the various components of a drone such as camera and Wi-Fi.
The Mental Health Needs of Unaccompanied Immigrant Children : Lawyers ’ Role as a Conduit to Services

Unaccompanied immigrant children are a highly vulnerable population, but research into their mental health and psychosocial context remains limited. This study elicited lawyers’ perceptions of the mental health needs of unaccompanied children in U.S. deportation proceedings and their mental health referral practices with this population. A convenience sample of 26 lawyers who work with unaccompanied children completed a semi-structured, online survey. Lawyers surveyed frequently had mental health concerns about their unaccompanied child clients, used clinical and lay terminology to describe symptoms, referred for both expert testimony and treatment purposes, frequently encountered barriers to accessing appropriate services, and expressed interest in mental health training. The results of this study suggest a complex intersection between the legal and mental health needs of unaccompanied children, and the need for further research and improved service provision in support of their wellbeing.
A high Q, large tuning range, tunable capacitor for RF applications

Using a new, double-sided adhesive process, an analog tunable capacitor has been designed and fabricated with an extremely large tuning range and a high Q. New design components such as two-sided metal deposition, low resistivity silicon, thicker device layers, and double beam suspensions have improved RF performance drastically. In the 200-400 MHz range that this device is intended for, Q values are in excess of 100. In addition, an 8.4 to 1 tuning ratio has been achieved with continuous tuning over a 1.4 to 11.9 pF range. To further improve dynamic performance, devices were operated in a high viscosity gas environment and near critical damping was achieved.
The use of antioxidants with first-line chemotherapy in two cases of ovarian cancer.

Because of poor overall survival in advanced ovarian malignancies, patients often turn to alternative therapies despite controversy surrounding their use. Currently, the majority of cancer patients combine some form of complementary and alternative medicine with conventional therapies. Of these therapies, antioxidants, added to chemotherapy, are a frequent choice.



For this preliminary report, two patients with advanced epithelial ovarian cancer were studied. One patient had Stage IIIC papillary serous adenocarcinoma, and the other had Stage IIIC mixed papillary serous and seromucinous adenocarcinoma. Both patients were optimally cytoreduced prior to first-line carboplatinum/paclitaxel chemotherapy. Patient 2 had a delay in initiation of chemotherapy secondary to co-morbid conditions and had evidence for progression of disease prior to institution of therapy. Patient 1 began oral high-dose antioxidant therapy during her first month of therapy. This consisted of oral vitamin C, vitamin E, beta-carotene, coenzyme Q-10 and a multivitamin/mineral complex. In addition to the oral antioxidant therapy, patient 1 added parenteral ascorbic acid at a total dose of 60 grams given twice weekly at the end of her chemotherapy and prior to consolidation paclitaxel chemotherapy. Patient 2 added oral antioxidants just prior to beginning chemotherapy, including vitamin C, beta-carotene, vitamin E, coenzyme Q-10 and a multivitamin/mineral complex. Patient 2 received six cycles of paclitaxel/carboplatinum chemotherapy and refused consolidation chemotherapy despite radiographic evidence of persistent disease. Instead, she elected to add intravenous ascorbic acid at 60 grams twice weekly. Both patients gave written consent for the use of their records in this report.



Patient 1 had normalization of her CA-125 after the first cycle of chemotherapy and has remained normal, almost 3(1/2) years after diagnosis. CT scans of the abdomen and pelvis remain without evidence of recurrence. Patient 2 had normalization of her CA-125 after the first cycle of chemotherapy. After her first round of chemotherapy, the patient was noted to have residual disease in the pelvis. She declined further chemotherapy and added intravenous ascorbic acid. There is no evidence for recurrent disease by physical examination, and her CA-125 has remained normal three years after diagnosis.



Antioxidants, when added adjunctively, to first-line chemotherapy, may improve the efficacy of chemotherapy and may prove to be safe. A review of four common antioxidants follows. Because of the positive results found in these two patients, a randomized controlled trial is now underway at the University of Kansas Medical Center evaluating safety and efficacy of antioxidants when added to chemotherapy in newly diagnosed ovarian cancer.
Cellular Effects of HER3-Specific Affibody Molecules

Recent studies have led to the recognition of the epidermal growth factor receptor HER3 as a key player in cancer, and consequently this receptor has gained increased interest as a target for cancer therapy. We have previously generated several Affibody molecules with subnanomolar affinity for the HER3 receptor. Here, we investigate the effects of two of these HER3-specific Affibody molecules, Z05416 and Z05417, on different HER3-overexpressing cancer cell lines. Using flow cytometry and confocal microscopy, the Affibody molecules were shown to bind to HER3 on three different cell lines. Furthermore, the receptor binding of the natural ligand heregulin (HRG) was blocked by addition of Affibody molecules. In addition, both molecules suppressed HRG-induced HER3 and HER2 phosphorylation in MCF-7 cells, as well as HER3 phosphorylation in constantly HER2-activated SKBR-3 cells. Importantly, Western blot analysis also revealed that HRG-induced downstream signalling through the Ras-MAPK pathway as well as the PI3K-Akt pathway was blocked by the Affibody molecules. Finally, in an in vitro proliferation assay, the two Affibody molecules demonstrated complete inhibition of HRG-induced cancer cell growth. Taken together, our findings demonstrate that Z05416 and Z05417 exert an anti-proliferative effect on two breast cancer cell lines by inhibiting HRG-induced phosphorylation of HER3, suggesting that the Affibody molecules are promising candidates for future HER3-targeted cancer therapy.
Identifying influential spreaders in complex networks

Maksim Kitsak, 2 Lazaros K. Gallos, Shlomo Havlin, Fredrik Liljeros, Lev Muchnik, H. Eugene Stanley, and Hernán A. Makse Center for Polymer Studies and Physics Department, Boston University, Boston, Massachusetts 02215, USA Cooperative Association for Internet Data Analysis (CAIDA), University of California-San Diego, La Jolla, California 92093, USA Levich Institute and Physics Department, City College of New York, New York, New York 10031, USA Minerva Center and Department of Physics, Bar-Ilan University, Ramat Gan, Israel Department of Sociology, Stockholm University, S-10691, Stockholm, Sweden Information Operations and Management Sciences Department, Stern School of Business, New York University, New York, New York 10012, USA (Dated: October 5, 2011)
Streaming in a Connected World

Today, a majority of data is fundamentally distributed in nature. Data for almost any task is collected over a broad area, and streams in at a much greater rate than ever before. In particular, advances in sensor technology and miniaturization have led to the concept of the sensor network: a (typically wireless) collection of sensing devices collecting detailed data about their surroundings. A fundamental question arises: how to query and monitor this rich new source of data? Similar scenarios emerge within the context of monitoring more traditional, wired networks, and in other emerging models such as P2P networks and grid-based computing. The prevailing paradigm in database systems has been understanding management of centralized data: how to organize, index, access, and query data that is held centrally on a single machine or a small number of closely linked machines. In these distributed scenarios, the axiom is overturned: now, data typically streams into remote sites at high rates. Here, it is not feasible to collect the data in one place: the volume of data collection is too high, and the capacity for data communication relatively low. For example, in battery-powered wireless sensor networks, the main drain on battery life is communication, which is orders of magnitude more expensive than computation or sensing. This establishes a fundamental concept for distributed stream monitoring: if we can perform more computational work within the network to reduce the communication needed, then we can significantly improve the value of our network, by increasing its useful life and extending the range of computation possible over the network. We consider two broad classes of approaches to such in-network query processing, by analogy to query types in traditional DBMSs. In the one shot model, a query is issued by a user at some site, and must be answered based on the current state of data in the network. We identify several possible approaches to this problem. For simple queries, partial computation of the result over a tree can reduce the data transferred significantly. For “holistic” queries, such as medians, count distinct and so on, clever composable summaries give a compact way to accurately approximate query answers. Lastly, careful modeling of correlations between measurements and other trends in the data can further reduce the number of sensors probed. In the continuous model, a query is placed by a user which re-
Undangle: early detection of dangling pointers in use-after-free and double-free vulnerabilities

Use-after-free vulnerabilities are rapidly growing in popularity, especially for exploiting web browsers. Use-after-free (and double-free) vulnerabilities are caused by a program operating on a dangling pointer. In this work we propose early detection, a novel runtime approach for finding and diagnosing use-after-free and double-free vulnerabilities. While previous work focuses on the creation of the vulnerability (i.e., the use of a dangling pointer), early detection shifts the focus to the creation of the dangling pointer(s) at the root of the vulnerability. Early detection increases the effectiveness of testing by identifying unsafe dangling pointers in executions where they are created but not used. It also accelerates vulnerability analysis and minimizes the risk of incomplete fixes, by automatically collecting information about all dangling pointers involved in the vulnerability. We implement our early detection technique in a tool called Undangle. We evaluate Undangle for vulnerability analysis on 8 real-world vulnerabilities. The analysis uncovers that two separate vulnerabilities in Firefox had a common root cause and that their patches did not completely fix the underlying bug. We also evaluate Undangle for testing on the Firefox web browser identifying a potential vulnerability.
Modeling of Multi-Input Arrayed Waveguide Grating and Its Application to Design of Flat-Passband Response Using Cascaded Mach–Zehnder Interferometers

We present a theoretical model of a multi-input arrayed waveguide grating (AWG) based on Fourier optics and apply the model to the design of a flattened passband response. This modeling makes it possible to systematically analyze spectral performance and to clarify the physical mechanisms of the multi-input AWG. The model suggested that the width of an input/output mode-field function and the number of waveguides in the array are important factors to flatten the response. We also developed a model for a novel AWG employing cascaded Mach-Zehnder interferometers connected to the AWG input ports and numerically analyzed its optical performance to achieve low-loss, low-crosstalk, and flat-passband response. We demonstrated the usability of this model through investigations of filter performance. We also compared the filter spectrum given by this model with that given by simulation using the beam propagation method
An Aging-Resistant RO-PUF for Reliable Key Generation

Physical unclonable functions (PUFs) have emerged as a promising security primitive for low-cost authentication and cryptographic key generation. However, PUF stability with respect to temporal variations still limits its utility and widespread acceptance. Previous techniques in the literature have focused on improving PUF robustness against voltage and temperature variations, but the issues associated with aging have been largely neglected. In this paper, we address aging in the popular ring oscillator (RO)-PUF. We propose a new aging-resistant design that reduces sensitivity to negative-bias temperature instability and hot-carrier injection stresses. Simulation results demonstrate that our aging-resistant RO-PUF (ARO-PUF) can produce unique, random, and more reliable keys. On an average, only 3.8% bits of an ARO-PUF flip over a ten-year operational period because of aging, compared with a 12.8% bit flip for a conventional RO-PUF. The proposed ARO-PUF allows us to eliminate the need for error correction by adding extra ROs. The result shows that an ARO-PUF saves ~32x area overhead compared with a conventional RO-PUF with required error correction schemes for a reliable key.
Cell motility in cancer invasion and metastasis: insights from simple model organisms

Metastasis remains the greatest challenge in the clinical management of cancer. Cell motility is a fundamental and ancient cellular behaviour that contributes to metastasis and is conserved in simple organisms. In this Review, we evaluate insights relevant to human cancer that are derived from the study of cell motility in non-mammalian model organisms. Dictyostelium discoideum, Caenorhabditis elegans, Drosophila melanogaster and Danio rerio permit direct observation of cells moving in complex native environments and lend themselves to large-scale genetic and pharmacological screening. We highlight insights derived from each of these organisms, including the detailed signalling network that governs chemotaxis towards chemokines; a novel mechanism of basement membrane invasion; the positive role of E-cadherin in collective direction-sensing; the identification and optimization of kinase inhibitors for metastatic thyroid cancer on the basis of work in flies; and the value of zebrafish for live imaging, especially of vascular remodelling and interactions between tumour cells and host tissues. While the motility of tumour cells and certain host cells promotes metastatic spread, the motility of tumour-reactive T cells likely increases their antitumour effects. Therefore, it is important to elucidate the mechanisms underlying all types of cell motility, with the ultimate goal of identifying combination therapies that will increase the motility of beneficial cells and block the spread of harmful cells.
Frontal to profile face verification in the wild

We have collected a new face data set that will facilitate research in the problem of frontal to profile face verification `in the wild'. The aim of this data set is to isolate the factor of pose variation in terms of extreme poses like profile, where many features are occluded, along with other `in the wild' variations. We call this data set the Celebrities in Frontal-Profile (CFP) data set. We find that human performance on Frontal-Profile verification in this data set is only slightly worse (94.57% accuracy) than that on Frontal-Frontal verification (96.24% accuracy). However we evaluated many state-of-the-art algorithms, including Fisher Vector, Sub-SML and a Deep learning algorithm. We observe that all of them degrade more than 10% from Frontal-Frontal to Frontal-Profile verification. The Deep learning implementation, which performs comparable to humans on Frontal-Frontal, performs significantly worse (84.91% accuracy) on Frontal-Profile. This suggests that there is a gap between human performance and automatic face recognition methods for large pose variation in unconstrained images.
Conceptual problem solving in high school physics

Jennifer L. Docktor, Natalie E. Strand, José P. Mestre, and Brian H. Ross Department of Physics, University of Wisconsin–La Crosse, La Crosse, Wisconsin 54601, USA Department of Physics, University of Illinois, Urbana, Illinois 61801, USA Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, Illinois 61801, USA Department of Educational Psychology, University of Illinois, Champaign, Illinois 61820, USA Department of Psychology, University of Illinois, Champaign, Illinois 61820, USA (Received 30 April 2015;
A study of the factors affecting the metabolic clearance of quinine in malaria

To assess the factors that contribute to impaired quinine clearance in acute falciparum malaria.  Sixteen adult Thai patients with severe or moderately severe falciparum malaria were studied, and 12 were re-studied during convalescence.  The clearance of quinine, dihydroquinine (an impurity comprising up to 10% of commercial quinine formulations), antipyrine (a measure of hepatic mixed-function oxidase activity), indocyanine green (ICG) (a measure of liver blood flow), and iothalamate (a measure of glomerular filtration rate) were measured simultaneously, and the relationship of these values to the␣biotransformation of quinine to the active metabolite 3-hydroxyquinine was assessed.  During acute malaria infection, the systemic clearance of quinine, antipyrine and ICG and the biotransformation of quinine to 3-hydroxyquinine were all reduced significantly when compared with values during convalescence. Iothalamate clearance was not affected significantly and did not correlate with the clearance of any of the other compounds. The clearance of total and free quinine correlated significantly with antipyrine clearance (r s = 0.70, P = 0.005 and r s = 0.67, P = 0.013, respectively), but not with ICG clearance (r s = 0.39 and 0.43 respectively, P > 0.15). In a multiple regression model, antipyrine clearance and plasma protein binding accounted for 71% of the variance in total quinine clearance in acute malaria. The pharmacokinetic properties of dihydroquinine were generally similar to those of quinine, although dihydroquinine clearance was less affected by acute malaria. The mean ratio of quinine to 3-hydroxyquinine area under the plasma concentration-time curve (AUC) values in acute malaria was 12.03 compared with 6.92 during convalescence P=0.01. The mean plasma protein binding of 3-hydroxyquinine was 46%, which was significantly lower than that of quinine (90.5%) or dihydroquinine (90.5%).  The reduction in quinine clearance in acute malaria results predominantly from a disease-induced dysfunction in hepatic mixed-function oxidase activity (principally CYP 3A) which impairs the conversion of quinine to its major metabolite, 3-hydroxyquinine. The metabolite contributes approximately 5% of the antimalarial activity of the parent compound in malaria, but up to 10% during convalescence.
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease

The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed.
F-Band Bidirectional Amplifier Using 75-nm InP HEMTs

We have developed an F-band (90 to 140 GHz) bidirectional amplifier MMIC using a 75-nm InP HEMT technology for short-range millimeter-wave multi-gigabit communication systems. Inherent symmetric common-gate transistors and parallel circuits consisting of an inductor and a switch realizes a bidirectional operation with a wide bandwidth of over 50 GHz. Small signal gains of 12-15 dB and 9-12 dB were achieved in forward and reverse directions, respectively. Fractional bandwidths of the developed bidirectional amplifier were 39% for the forward direction and 32% for the reverse direction, which were almost double as large as those of conventional bidirectional amplifiers. The power consumption of the bidirectional amplifier was 15 mW under a 2.4-V supply. The chip measures 0.70 × 0.65 mm. The simulated NF is lower than 5 dB, and Psat is larger than 5 dBm. The use of this bidirectional amplifier provides miniaturization of the multi-gigabit communication systems and eliminates signal switching loss.
State of the Art in Research on Microgrids: A Review

The significant benefits associated with microgrids have led to vast efforts to expand their penetration in electric power systems. Although their deployment is rapidly growing, there are still many challenges to efficiently design, control, and operate microgrids when connected to the grid, and also when in islanded mode, where extensive research activities are underway to tackle these issues. It is necessary to have an across-the-board view of the microgrid integration in power systems. This paper presents a review of issues concerning microgrids and provides an account of research in areas related to microgrids, including distributed generation, microgrid value propositions, applications of power electronics, economic issues, microgrid operation and control, microgrid clusters, and protection and communications issues.
Predicting Which Topics You Will Join in the Future on Social Media

Every day, social media users send millions of microblogs on every imaginable topics. If we could predict which topics a user will join in the future, it would be easy to determine what topics will become popular and what kinds of users a topic may attract. It also can be of great interest for many applications. In this study, we investigate the problem of predicting whether a user will join a topic based on his posting history. We introduce a novel deep convolutional neural network with external neural memory and attention mechanism to perform this problem. User's posting history and topics were modeled with an external neural memory architecture. The convolutional neural network based matching methods were used to construct the relations between users and topics. Final decisions were made based on these matching results. To train and evaluate the proposed method, we collected a large-scale dataset from Twitter. The experimental results demonstrated that the proposed method could perform significantly better than other methods. Comparing to the state-of-the-art deep neural networks, our approach achieves a relative improvement of 18.2\% in F1-score and 28.9\% in MAP@10.