nils-herrmann/clean_scirepeval huggingface dataset:
Cross-Lingual Sentiment Analysis Without (Good) Translation

Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine translation systems. Here we show that it is possible to use a single linear transformation, with as few as 2000 word pairs, to capture fine-grained sentiment relationships between words in a cross-lingual setting. We apply these cross-lingual sentiment models to a diverse set of tasks to demonstrate their functionality in a non-English context. By effectively leveraging English sentiment knowledge without the need for accurate translation, we can analyze and extract features from other languages with scarce data at a very low cost, thus making sentiment and related analyses for many languages inexpensive.
Evolution of artistic image variants through feature based diversity optimisation

Measures aimed to improve the diversity of images and image features in evolutionary art help to direct search toward more novel and creative parts of the artistic search domain. To date such measures have not focused on selecting from all individuals based on their contribution to diversity of feature metrics. In recent work on TSP problem instance classification, selection based on a direct measure of each individual's contribution to diversity was successfully used to generate hard and easy TSP instances. In this work we use this search framework to evolve diverse variants of a source image in one and two feature dimensions. The resulting images show the spectrum of effects from transforming images to score across the range of each feature. The results also reveal interesting correlations between feature values in two dimensions.
Boron Nitride Nanotube : Synthesis and Applications

Scientists have predicted that carbon’s immediate neighbors on the periodic chart, boron and nitrogen, may also form perfect nanotubes, since the advent of carbon nanotubes (CNTs) in 1991. First proposed then synthesized by researchers at UC Berkeley in the mid 1990’s, the boron nitride nanotube (BNNT) has proven very difficult to make until now. Herein we provide an update on a catalyst-free method for synthesizing highly crystalline, small diameter BNNTs with a high aspect ratio using a high power laser under a high pressure and high temperature environment first discovered jointly by NASA/NIA JSA. Progress in purification methods, dispersion studies, BNNT mat and composite formation, and modeling and diagnostics will also be presented. The white BNNTs offer extraordinary properties including neutron radiation shielding, piezoelectricity, thermal oxidative stability (> 800 ̊C in air), mechanical strength, and toughness. The characteristics of the novel BNNTs and BNNT polymer composites and their potential applications are discussed.
Demonstrating the Effect of Software Feedback on a Distributed Real-Time MPEG Video Audio Player

Modern workstation and network technology has made software-only solutions feasible for real-time playback of compressed continuous video and audio across the Internet. The Internet environment is characterised by widespread resource sharing, dynamic workload, great diversity in host processing speed and network bandwidth, no end-to-end resource reservation facility, and lack of a common clock. To meet the strict timing requirements of distributed multimedia presentation in the face of such characteristics requires new approaches to client/server synchronization, Quality-of-Service (QoS) control and system adaptiveness.
Vertical jump performance in Italian male and female national team soccer players.

The aim of this study was to examine the validity of vertical jump (VJ) performance variables in elite-standard male and female Italian soccer players. One hundred eighteen national team soccer players (n = 56 men and n = 62 women) were tested for countermovement (CMJ) and squatting jump (SJ) heights. The stretch-shortening cycle efficiency (SSCE) was assessed as percentage of CMJ gain over SJ ([INCREMENT]CMJ-SJ), difference (CMJ-SJ), and ratio (CMJ:SJ). Results showed significant sex difference in SJ and CMJ. Differences in SSCE were mainly in the absolute variables between sexes. Cutoff values for CMJ and SJ using sex as construct were 34.4 and 32.9 cm, respectively. No competitive level differences in VJ performance were detected in the male players. Female national team players showed VJ performance higher than the under 17 counterpart. The results of this study showed that VJ performance could not discriminate between competitive levels in male national team-selected soccer players. However, the use of CMJ and SJ normative data may help strength and conditioning coaches in prescribing lower limb explosive strength training in elite soccer players. In this, variations in VJ performance in the range of approximately 1 cm may be regarded as of interest in tracking noncasual variation in elite-standard soccer players.
A 94 GHz 3D Image Radar Engine With 4TX/4RX Beamforming Scan Technique in 65 nm CMOS Technology

This paper presents a fully-integrated 3D image radar engine utilizing beamforming for electrical scanning and precise ranging technique for distance measurement. Four transmitters and four receivers form a sensor frontend with phase shifters and power combiners adjusting the beam direction. A built-in 31.3 GHz clock source and a frequency tripler provide both RF carrier and counting clocks for the distance measurement. Flip-chip technique with low-temperature co-fired ceramic (LTCC) antenna design creates a miniature module as small as 6.5 × 4.4 × 0.8 cm3. Designed and fabricated in 65 nm CMOS technology, the transceiver array chip dissipates 960 mW from a 1.2-V supply and occupies chip area of 3.6 × 2.1 mm 2. This prototype achieves ±28° scanning range, 2-m maximum distance, and 1 mm depth resolution.
A Universal Voltage Level Converter for Multi-VDD Based Low-Power Nano-CMOS Systems-on-Chips ( SoCs )

Level Converters are key components of multi-voltage based systems-on-chips. Recently, a great deal of research has been focused on power dissipation reduction using various types of level converters in multi-voltage systems. These level converters include either level up conversion or level down conversion. In this paper we propose a unique level converter called universal level converter (ULC). This level converter is capable of four types of level converting functions, such as up conversion, down conversion, passing and blocking. The universal level converter is simulated in CADENCE using 90nm PTM technology model files. Three types of analysis such as power, parametric and load analysis are performed on the proposed level converter. The power analysis results prove that the proposed level converter has an average power reduction of approximately 87.2% compared to other existing level converters at different technology nodes. The parametric analysis and load analysis show that the proposed level converter provides a stable output for input voltages as low as 0.6V with a varying load from 1fF-200fF. The universal level converter works at dual voltages of 1.2V and 1.02V (85% of Vddh) with VTH value for NMOS as 0.339V and for PMOS as -0.339V. The ULC has an average power consumption of 27.1μW at a load
Acupuncture for frozen shoulder.

This randomised controlled trial was undertaken to evaluate the effectiveness of acupuncture as a treatment for frozen shoulder. Thirty-five patients with a diagnosis of frozen shoulder were randomly allocated to an exercise group or an exercise plus acupuncture group and treated for a period of 6 weeks. Functional mobility, power, and pain were assessed by a blinded assessor using the Constant Shoulder Assessment, at baseline, 6 weeks and 20 weeks. Analysis was based on the intention-to-treat principle. Compared with the exercise group, the exercise plus acupuncture group experienced significantly greater improvement with treatment. Improvements in scores by 39.8% (standard deviation, 27.1) and 76.4% (55.0) were seen for the exercise and the exercise plus acupuncture groups, respectively at 6 weeks (P=0.048), and were sustained at the 20-week re-assessment (40.3% [26.7] and 77.2% [54.0], respectively; P=0.025). We conclude that the combination of acupuncture with shoulder exercise may offer effective treatment for frozen shoulder.
Semi-Recurrent Cnn-Based Vae-Gan for Sequential Data Generation

A semi-recurrent hybrid VAE-GAN model for generating sequential data is introduced. In order to consider the spatial correlation of the data in each frame of the generated sequence, CNNs are utilized in the encoder, generator, and discriminator. The subsequent frames are sampled from the latent distributions obtained by encoding the previous frames. As a result, the dependencies between the frames are maintained. Two testing frameworks for synthesizing a sequence with any number of frames are also proposed. The promising experimental results on piano music generation indicates the potential of the proposed framework in modelling other sequential data such as video.
Treatment of war wounds: a historical review.

The treatment of war wounds is an ancient art, constantly refined to reflect improvements in weapons technology, transportation, antiseptic practices, and surgical techniques. Throughout most of the history of warfare, more soldiers died from disease than combat wounds, and misconceptions regarding the best timing and mode of treatment for injuries often resulted in more harm than good. Since the 19th century, mortality from war wounds steadily decreased as surgeons on all sides of conflicts developed systems for rapidly moving the wounded from the battlefield to frontline hospitals where surgical care is delivered. We review the most important trends in US and Western military trauma management over two centuries, including the shift from primary to delayed closure in wound management, refinement of amputation techniques, advances in evacuation philosophy and technology, the development of antiseptic practices, and the use of antibiotics. We also discuss how the lessons of history are reflected in contemporary US practices in Iraq and Afghanistan.
Devices, systems, and methods for automated monitoring enabling precision agriculture

Addressing the challenges of feeding the burgeoning world population with limited resources requires innovation in sustainable, efficient farming. The practice of precision agriculture offers many benefits towards addressing these challenges, such as improved yield and efficient use of such resources as water, fertilizer and pesticides. We describe the design and development of a light-weight, multi-spectral 3D imaging device that can be used for automated monitoring in precision agriculture. The sensor suite consists of a laser range scanner, multi-spectral cameras, a thermal imaging camera, and navigational sensors. We present techniques to extract four key data products - plant morphology, canopy volume, leaf area index, and fruit counts - using the sensor suite. We demonstrate its use with two systems: multi-rotor micro aerial vehicles and on a human-carried, shoulder-mounted harness. We show results of field experiments conducted in collaboration with growers and agronomists in vineyards, apple orchards and orange groves.
BioReact : Visualization of Systems Biology Network

It is often difficult to visualize large networks effectively. In BioReact, we filter large systems biology network data by querying to select partial network as the input for visualization. Each query is parameterized by a node name, the direction of graph search, and the scope of the search. We present two layouts of the same network to clearly show network topology: a force-directed layout expands neighbouring nodes to maiximize spatial separation between nodes and links, and a downward edge layout to preserve a sense of unidirectional flow. Navigation of the network such as locating a particular node/link and linked highlighting between multiple views optimize user experience.
Distribution of Cotesia rubecula (Hymenoptera: Braconidae) and Its Displacement of Cotesia glomerata in Eastern North America

A survey was conducted from May to Oct of 2011 of the parasitoid community of the imported cabbageworm, Pieris rapae (Lepidoptera: Pieridae), in cole crops in part of the eastern United States and southeastern Canada. The findings of our survey indicate that Cotesia rubecula (Hymenoptera: Braconidae) now occurs as far west as North Dakota and has become the dominant parasitoid of P. rapae in the northeastern and north central United States and adjacent parts of southeastern Canada, where it has displaced the previously common parasitoid Cotesia glomerata (Hymenoptera: Braconidae). Cotesia glomerata remains the dominant parasitoid in the mid-Atlantic states, from Virginia to North Carolina and westward to southern Illinois, below latitude N 38° 48’. This pattern suggests that the released populations of C. rubecula presently have a lower latitudinal limit south of which they are not adapted.
Projections of cancer prevalence in the United Kingdom, 2010–2040

There are currently two million cancer survivors in the United Kingdom, and in recent years this number has grown by 3% per annum. The aim of this paper is to provide long-term projections of cancer prevalence in the United Kingdom.National cancer registry data for England were used to estimate cancer prevalence in the United Kingdom in 2009. Using a model of prevalence as a function of incidence, survival and population demographics, projections were made to 2040. Different scenarios of future incidence and survival, and their effects on cancer prevalence, were also considered. Colorectal, lung, prostate, female breast and all cancers combined (excluding non-melanoma skin cancer) were analysed separately.Assuming that existing trends in incidence and survival continue, the number of cancer survivors in the United Kingdom is projected to increase by approximately one million per decade from 2010 to 2040. Particularly large increases are anticipated in the oldest age groups, and in the number of long-term survivors. By 2040, almost a quarter of people aged at least 65 will be cancer survivors.Increasing cancer survival and the growing/ageing population of the United Kingdom mean that the population of survivors is likely to grow substantially in the coming decades, as are the related demands upon the health service. Plans must, therefore, be laid to ensure that the varied needs of cancer survivors can be met in the future.
Non-parametric Jensen-Shannon Divergence

Quantifying the difference between two distributions is a common problem in many machine learning and data mining tasks. What is also common in many tasks is that we only have empirical data. That is, we do not know the true distributions nor their form, and hence, before we can measure their divergence we first need to assume a distribution or perform estimation. For exploratory purposes this is unsatisfactory, as we want to explore the data, not our expectations. In this paper we study how to non-parametrically measure the divergence between two distributions. More in particular, we formalise the well-known JensenShannon divergence using cumulative distribution functions. This allows us to calculate divergences directly and efficiently from data without the need for estimation. Moreover, empirical evaluation shows that our method performs very well in detecting differences between distributions, outperforming the state of the art in both statistical power and efficiency for a wide range of tasks.
[Clinical randomized study of bee-sting therapy for rheumatoid arthritis].

To observe the clinical effect of bee-sting (venom) therapy in the treatment of rheumatoid arthritis (RA).



One hundred RA patients were randomly divided into medication (control) group and bee-venom group, with 50 cases in each. Patients of control group were treated with oral administration of Methotrexate (MTX, 7.5 mg/w), Sulfasalazine (0.5 g,t. i.d.), Meloxicam (Mobic,7. 5 mg, b. i. d.); and those of bee-venom group treated with Bee-sting of Ashi-points and the above-mentioned Western medicines. Ashi-points were selected according to the position of RA and used as the main acupoints, supplemented with other acupoints according to syndrome differentiation. The treatment was given once every other day and all the treatments lasted for 3 months.



Compared with pre-treatment, scores of joint swelling degree, joint activity, pain, and pressing pain, joint-swelling number, grasp force, 15 m-walking duration, morning stiff duration in bee-venom group and medication group were improved significantly (P<0.05, 0.01). Comparison between two groups showed that after the therapy, scores of joint swelling, pain and pressing pain, joint-swelling number and morning stiff duration, and the doses of the administered MTX and Mobic in bee-venom group were all significantly lower than those in medication group (P<0.05, 0.01); whereas the grasp force in been-venom group was markedly higher than that in medication group (P<0.05). In addition, the relapse rate of bee-venom group was obviously lower than that of medication group (P<0.05; 12% vs 32%).



Combined application of bee-venom therapy and medication is superior to simple use of medication in relieving RA, and when bee-sting therapy used, the commonly-taken doses of western medicines may be reduced, and the relapse rate gets lower.
Intelligent Knowledge Tracing: More Like a Real Learning Process of a Student

Knowledge tracing (KT) refers to a machine learning technique to assess a student’s level of understanding (so-called knowledge state) of a certain concept based on the student performance on problem solving. KT accepts a series of question-answer pairs as an input and iteratively updates the knowledge state of the student, eventually returning the probability of the student solving an unseen question. From the viewpoint of neuroeducation (the field of applying neuroscience, cognitive science, and psychology to education), however, KT leaves much room for improvement in terms of explaining the complex process of human learning. In this paper, we identify three problems of KT (namely non adaptive knowledge growth, neglected latent information, and unintended negative influence) and propose a memorynetwork-based technique named intelligent knowledge tracing (IKT) to address them, thus approaching one step closer to understanding the complex mechanism underlying human learning. In addition, we propose a new performance metric called correct update count (CUC) that can measure the degree of unintended negative influence, thus quantifying how closely a student model resembles the human learning process. The proposed CUC metric can complement the area under the curve (AUC) metric, allowing us to evaluate competing models more effectively. According to our experiments using a widely used public benchmark, IKT significantly (over two times) outperformed the existing KT approaches in terms of CUC, while preserving the correctness behavior measured in AUC.
An algebra of product families

Experience from recent years has shown that it is often advantageous not to build a single product but rather a family of similar products that share at least one common functionality while having well-identified variabilities. Such product families are built from elementary features that reach from hardware parts to software artefacts such as requirements, architectural elements or patterns, components, middleware, or code. We use the well established mathematical structure of idempotent semirings as the basis for a product family algebra that allows a formal treatment of the above notions. A particular application of the algebra concerns the multi-view reconciliation problem that arises when complex systems are modelled. We use algebraic integration constraints linking features in one view to features in the same or a different view and show in several examples the suitability of this approach for a wide class of integration constraint formulations. Our approach is illustrated with a Haskell prototype implementation of one particular model of product family algebra.
Depth Creates No Bad Local Minima

In deep learning, depth, as well as nonlinearity, create non-convex loss surfaces. Then, does depth alone create bad local minima? In this paper, we prove that without nonlinearity, depth alone does not create bad local minima, although it induces non-convex loss surface. Using this insight, we greatly simplify a recently proposed proof to show that all of the local minima of feedforward deep linear neural networks are global minima. Our theoretical results generalize previous results with fewer assumptions, and this analysis provides a method to show similar results beyond square loss in deep linear models.
Surface Elevation Change and Susceptibility of Different Mangrove Zones to Sea-Level Rise on Pacific High Islands of Micronesia

Mangroves on Pacific high islands offer a number of important ecosystem services to both natural ecological communities and human societies. High islands are subjected to constant erosion over geologic time, which establishes an important source of terrigeneous sediment for nearby marine communities. Many of these sediments are deposited in mangrove forests and offer mangroves a potentially important means for adjusting surface elevation with rising sea level. In this study, we investigated sedimentation and elevation dynamics of mangrove forests in three hydrogeomorphic settings on the islands of Kosrae and Pohnpei, Federated States of Micronesia (FSM). Surface accretion rates ranged from 2.9 to 20.8 mm y−1, and are high for naturally occurring mangroves. Although mangrove forests in Micronesian high islands appear to have a strong capacity to offset elevation losses by way of sedimentation, elevation change over 6½ years ranged from −3.2 to 4.1 mm y−1, depending on the location. Mangrove surface elevation change also varied by hydrogeomorphic setting and river, and suggested differential, and not uniformly bleak, susceptibilities among Pacific high island mangroves to sea-level rise. Fringe, riverine, and interior settings registered elevation changes of −1.30, 0.46, and 1.56 mm y−1, respectively, with the greatest elevation deficit (−3.2 mm y−1) from a fringe zone on Pohnpei and the highest rate of elevation gain (4.1 mm y−1) from an interior zone on Kosrae. Relative to sea-level rise estimates for FSM (0.8–1.8 mm y−1) and assuming a consistent linear trend in these estimates, soil elevations in mangroves on Kosrae and Pohnpei are experiencing between an annual deficit of 4.95 mm and an annual surplus of 3.28 mm. Although natural disturbances are important in mediating elevation gain in some situations, constant allochthonous sediment deposition probably matters most on these Pacific high islands, and is especially helpful in certain hydrogeomorphic zones. Fringe mangrove forests are most susceptible to sea-level rise, such that protection of these outer zones from anthropogenic disturbances (for example, harvesting) may slow the rate at which these zones convert to open water.
A Practical Soft Type System for Scheme

Soft typing is a generalization of static type checking that accommodates both dynamic typing and static typing in one framework. A soft type checker infers types for identifiers and inserts explicit run-time checks to transform untypable programs into typable form. Soft Scheme is a practical soft type system for R4RS Scheme. The type checker uses a representation for types that is expressive, easy to interpret, and supports efficient type inference. Soft Scheme supports all of R4RS Scheme, including uncurried procedures of fixed and variable arity, assignment, and continuations.