nils-herrmann/clean_scirepeval huggingface dataset:
SEP-Nets: Small and Effective Pattern Networks

While going deeper has been witnessed to improve the performance of convolutional neural networks (CNN), going smaller for CNN has received increasing attention recently due to its attractiveness for mobile/embedded applications. It remains an active and important topic how to design a small network while retaining the performance of large and deep CNNs (e.g., Inception Nets, ResNets). Albeit there are already intensive studies on compressing the size of CNNs, the considerable drop of performance is still a key concern in many designs. This paper addresses this concern with several new contributions. First, we propose a simple yet powerful method for compressing the size of deep CNNs based on parameter binarization. The striking difference from most previous work on parameter binarization/quantization lies at different treatments of 1× 1 convolutions and k×k convolutions (k > 1), where we only binarize k × k convolutions into binary patterns. The resulting networks are referred to as pattern networks. By doing this, we show that previous deep CNNs such as GoogLeNet and Inception-type Nets can be compressed dramatically with marginal drop in performance. Second, in light of the different functionalities of 1×1 (data projection/transformation) and k × k convolutions (pattern extraction), we propose a new block structure codenamed the pattern residual block that adds transformed feature maps generated by 1×1 convolutions to the pattern feature maps generated by k × k convolutions, based on which we design a small network with ∼ 1 million parameters. Combining with our parameter binarization, we achieve better performance on ImageNet than using similar sized networks including recently released Google MobileNets.
Comparison of the effect of two interdental cleaning devices around implants on the reduction of bleeding: a 30-day randomized clinical trial.

To determine the effectiveness of a water flosser in reducing the bleeding on probing (BOP) index around dental implants as compared to flossing.



Patients with implants were randomly assigned to one of two groups in this examiner-masked, single-center study. The study compared the efficacy of a manual toothbrush paired with either traditional string floss or a water flosser.



The primary outcome was the reduction in the incidence of BOP after 30 days. There were no differences in the percent of bleeding sites between the groups at baseline. At 30 days, 18 of the 22 (81.8%) implants in the water flosser group showed a reduction in BOP compared to 6 of the 18 (33.3%) in the floss group (P=0.0018).



These results demonstrate that the water flosser group had statistically significantly greater bleeding reduction than the string floss group. The authors concluded that water flossing may be a useful adjuvant for implant hygiene maintenance.
Global Sourcing And Procurement Strategy : A Model of Interrelated Decisions

The literature on globalisation reveals wide agreement concerning the relevance of international sourcing strategies as key competitive factors for companies seeking globalisation, considering such strategies to be a purchasing management approach focusing on supplies from vendors in the world market, rather than relying exclusively on domestic offerings (Petersen, Frayer, & Scannel, 2000; Stevens, 1995; Trent & Monczka, 1998). Thus, the notion of “international sourcing” mentioned by these authors describes the level of supply globalisation in companies’ purchasing strategy, as related to supplier source (Giunipero & Pearcy, 2000; Levy, 1995; Trent & Monczka, 2003b).
Pallister-Killian syndrome

Male, 0 FINAL DIAGNOSIS: Pallister-Killian syndrome Symptoms: Decidious tooth • flattened nasal bridge • frontal bossing • grooved palate • low-set ears • mid-facial hypoplasia • nuchal fold thickening • right inquinal testis • shortened upper extremities • undescended left intraabdominal testis • widely spaced nipples



- Clinical Procedure: - Specialty: Pediatrics and Neonatology.



Congenital defects/diseases.



Pallister-Killian syndrome (PKS) is a rare, sporadic, polydysmorphic condition that often has highly distinctive features. The clinical features are highly variable, ranging from mild to severe intellectual disability and birth defects. We here report the first case of PKS diagnosed at our institution in a patient in the second trimester of pregnancy.



A pregnant 43-year-old woman presented for genetic counseling secondary to advanced maternal age and an increased risk for Down syndrome. Ultrasound showed increased fetal nuchal fold thickness, short limbs, polyhydramnios, and a small stomach. The ultrasound evaluation was compromised due to the patient's body habitus. The patient subsequently underwent amniocentesis and the karyotype revealed the presence of an isochromosome in the short arm of chromosome 12 consistent with the diagnosis of Pallister-Killian syndrome. Postnatally, the infant showed frontal bossing, a flattened nasal bridge, mid-facial hypoplasia, low-set ears, a right upper deciduous tooth, grooved palate, nuchal fold thickening, widely spaced nipples, left ulnar polydactyly, simian creases, flexion contractures of the right middle finger, shortened upper extremities, undescended left intraabdominal testis, and right inguinal testis.



The occurrence of PKS is sporadic in nature, but prenatal diagnosis is possible.
ORBIT: Ordering Based Information Transfer Across Space and Time for Global Surface Water Monitoring

Many earth science applications require data at both high spatial and temporal resolution for effective monitoring of various ecosystem resources. Due to practical limitations in sensor design, there is often a trade-off in different resolutions of spatio-temporal datasets and hence a single sensor alone cannot provide the required information. Various data fusion methods have been proposed in the literature that mainly rely on individual timesteps when both datasets are available to learn a mapping between features values at different resolutions using local relationships between pixels. Earth observation data is often plagued with spatially and temporally correlated noise, outliers and missing data due to atmospheric disturbances which pose a challenge in learning the mapping from a local neighborhood at individual timesteps. In this paper, we aim to exploit timeindependent global relationships between pixels for robust transfer of information across different scales. Specifically, we propose a new framework, ORBIT (Ordering Based Information Transfer) that uses relative ordering constraint among pixels to transfer information across both time and scales. The effectiveness of the framework is demonstrated for global surface water monitoring using both synthetic and real-world datasets.
Development of a cost-effective ecg monitor for cardiac arrhythmia detection using heart rate variability

Ischemic Heart Disease (IHD) and stroke are statistically the leading causes of death world-wide. Both diseases deal with various types of cardiac arrhythmias, e.g. premature ventricular contractions (PVCs), ventricular and supra-ventricular tachycardia, atrial fibrillation. For monitoring and detecting such an irregular heart rhythm accurately, we are now developing a very cost-effective ECG monitor, which is implemented in 8-bit MCU with an efficient QRS detector using steep-slope algorithm and arrhythmia detection algorithm using a simple heart rate variability (HRV) parameter. This work shows the results of evaluating the real-time steep-slope algorithm using MIT-BIH Arrhythmia Database. The performance of this algorithm has 99.72% of sensitivity and 99.19% of positive predictivity. We then show the preliminary results of arrhythmia detection using various types of normal and abnormal ECGs from an ECG simulator. The result is, 18 of 20 ECG test signals were correctly detected.
Natural Products From Marine Algae

Biomass derived from marine microalgae and macroalgae is globally recognized as a source of valuable chemical constituents with applications in the agri-horticultural sector (including animal feeds and health and plant stimulants), as human food and food ingredients as well as in the nutraceutical, cosmeceutical, and pharmaceutical industries. Algal biomass supply of suffi cient quality and quantity however remains a concern with increasing environmental pressures confl icting with the growing demand. Recent attempts in supplying consistent, safe and environmentally acceptable biomass through cultivation of (macroand micro-) algal biomass have concentrated on characterizing natural variability in bioactives, and optimizing cultivated materials through strain selection and hybridization, as well as breeding and, more recently, genetic improvements of biomass. Biotechnological tools including metabolomics, transcriptomics, and genomics have recently been extended to algae but, in comparison to microbial or plant biomass, still remain underdeveloped. Current progress in algal biotechnology is driven by an increased demand for new sources of biomass due to several global challenges, new discoveries and technologies available as well as an increased global awareness of the many applications of algae. Algal diversity and complexity provides signifi cant potential provided that shortages in suitable and safe biomass can be met, and consumer demands are matched by commercial investment in product development.
A wearable device for monitoring galvanic skin response to accurately predict changes in blood pressure indexes and cardiovascular dynamics

Predicting acute hypotensive episode (AHE) in patients in emergency rooms and in intensive care units (ICU) is a difficult challenge. As it is well accepted that physiological compensatory adaptations to circulatory shock involve blood flow redistribution and increase in sympathetic stimulation, we recently investigated if galvanic skin response (GSR) or electro-dermal activity (EDA), a measure of sympathetic stimulation, could give information about the impending danger of acute hypotensive episode or circulatory collapse (Subramanya and Mudol, 2012). In this current study, a low-cost wearable device was developed and tested to help progress towards a system for predicting blood pressure (BP) and cardiovascular dynamics. In a pilot study, we examined hypotheses about the relation between GSR values and four BP indexes (systolic BP, diastolic BP, mean arterial pressure and pulse pressure) in apparently healthy human volunteers before and immediately after treadmill exercise. All four BP indexes had significant relationship with GSR, with pulse pressure possibly the strongest predictor of variations in the GSR and vice-versa. This paper opens up opportunities for future investigations to evaluate the utility of continuous monitoring of GSR to forecast imminent cardiovascular collapse, AHE and shock, and could have far-reaching implications for ICU, trauma and critical care management.
Sex-specific content preferences for visual sexual stimuli.

Although experimental studies support that men generally respond more to visual sexual stimuli than do women, there is substantial variability in this effect. One potential source of variability is the type of stimuli used that may not be of equal interest to both men and women whose preferences may be dependent upon the activities and situations depicted. The current study investigated whether men and women had preferences for certain types of stimuli. We measured the subjective evaluations and viewing times of 15 men and 30 women (15 using hormonal contraception) to sexually explicit photos. Heterosexual participants viewed 216 pictures that were controlled for the sexual activity depicted, gaze of the female actor, and the proportion of the image that the genital region occupied. Men and women did not differ in their overall interest in the stimuli, indicated by equal subjective ratings and viewing times, although there were preferences for specific types of pictures. Pictures of the opposite sex receiving oral sex were rated as least sexually attractive by all participants and they looked longer at pictures showing the female actor's body. Women rated pictures in which the female actor was looking indirectly at the camera as more attractive, while men did not discriminate by female gaze. Participants did not look as long at close-ups of genitals, and men and women on oral contraceptives rated genital images as less sexually attractive. Together, these data demonstrate sex-specific preferences for specific types of stimuli even when, across stimuli, overall interest was comparable.
Scalable and Lightweight CTF Infrastructures Using Application Containers

Attack-defence Capture The Flag (CTF) competitions are effective pedagogic platforms to teach secure coding practices due to the interactive and real-world experiences they provide to the contest participants. Two of the key challenges that prevent widespread adoption of such contests are: 1) The game infrastructure is highly resource intensive requiring dedication of significant hardware resources and monitoring by organizers during the contest and 2) the participants find the gameplay to be complicated, requiring performance of multiple tasks that overwhelms inexperienced players. In order to address these, we propose a novel attack-defence CTF game infrastructure which uses application containers. The results of our work showcase effectiveness of these containers and supporting tools in not only reducing the resources organizers need but also simplifying the game infrastructure. The work also demonstrates how the supporting tools can be leveraged to help participants focus more on playing the game i.e. attacking and defending services and less on administrative tasks. The results from this work indicate that our architecture can accommodate over 150 teams with 15 times fewer resources when compared to existing infrastructures of most contests today.
Bayesian Networks in Fault Diagnosis

Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.
Novel Broadband 3dB Directional Coupler Design Method

─ A novel broadband 3-dB directional coupler design method utilizing HFSS and realization are given in this paper. It is realized in stripline, showing great agreement with the simulation and design format. The unique property of this design method is that it unnecessitates both a feedback from the realization for broadbanding and an additional smoothing of transition between coupled sections of the whole directional coupler. There is also no need for either a specialised CAD tool or a computer program. digital frequency discriminator, HFSS, APLAC, broadside coupling
A comparative study between LSTM and ARIMA for sales forecasting in retail

Food waste is a major environmental issue. Expired products are thrown away, implying that too much food is ordered compared to what is sold and that a more accurate prediction model is required within grocery stores. In this study the two prediction models Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) were compared on their prediction accuracy in two scenarios, given sales data for different products, to observe if LSTM is a model that can compete against the ARIMA model in the field of sales forecasting in retail. In the first scenario the models predict sales for one day ahead using given data, while they in the second scenario predict each day for a week ahead. Using the evaluation measures RMSE and MAE together with a t-test the results show that the difference between the LSTM and ARIMA model is not of statistical significance in the scenario of predicting one day ahead. However when predicting seven days ahead, the results show that there is a statistical significance in the difference indicating that the LSTM model has higher accuracy. This study therefore concludes that the LSTM model is promising in the field of sales forecasting in retail and able to compete against the ARIMA model.
Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification

This paper describes our solution for the video recognition task of ActivityNet Kinetics challenge that ranked the 1st place. Most of existing state-of-the-art video recognition approaches are in favor of an end-to-end pipeline. One exception is the framework of DevNet . The merit of DevNet is that they first use the video data to learn a network (i.e. fine-tuning or training from scratch). Instead of directly using the end-to-end classification scores (e.g. softmax scores), they extract the features from the learned network and then fed them into the off-the-shelf machine learning models to conduct video classification. However, the effectiveness of this line work has long-term been ignored and underestimated. In this submission, we extensively use this strategy. Particularly, we investigate four temporal modeling approaches using the learned features: Multi-group Shifting Attention Network, Temporal Xception Network, Multi-stream sequence Model and Fast-Forward Sequence Model. Experiment results on the challenging Kinetics dataset demonstrate that our proposed temporal modeling approaches can significantly improve existing approaches in the large-scale video recognition tasks. Most remarkably, our best single Multi-group Shifting Attention Network can achieve 77.7% in term of top-1 accuracy and 93.2% in term of top-5 accuracy on the validation set.
Calcium in ischemic cell death.

This review article deals with the role of calcium in ischemic cell death. A calcium-related mechanism was proposed more than two decades ago to explain cell necrosis incurred in cardiac ischemia and muscular dystrophy. In fact, an excitotoxic hypothesis was advanced to explain the acetylcholine-related death of muscle end plates. A similar hypothesis was proposed to explain selective neuronal damage in the brain in ischemia, hypoglycemic coma, and status epilepticus.



The original concepts encompass the hypothesis that cell damage in ischemia-reperfusion is due to enhanced activity of phospholipases and proteases, leading to release of free fatty acids and their breakdown products and to degradation of cytoskeletal proteins. It is equally clear that a coupling exists between influx of calcium into cells and their production of reactive oxygen species, such as .O2, H2O2, and .OH. Recent results have underscored the role of calcium in ischemic cell death. A coupling has been demonstrated among glutamate release, calcium influx, and enhanced production of reactive metabolites such as .O2-, .OH, and nitric oxide. It has become equally clear that the combination of .O2- and nitric oxide can yield peroxynitrate, a metabolite with potentially devastating effects. The mitochondria have again come into the focus of interest. This is because certain conditions, notably mitochondrial calcium accumulation and oxidative stress, can trigger the assembly (opening) of a high-conductance pore in the inner mitochondrial membrane. The mitochondrial permeability transition (MPT) pore leads to a collapse of the electrochemical potential for H+, thereby arresting ATP production and triggering production of reactive oxygen species. The occurrence of an MPT in vivo is suggested by the dramatic anti-ischemic effect of cyclosporin A, a virtually specific blocker of the MPT in vitro in transient forebrain ischemia. However, cyclosporin A has limited effect on the cell damage incurred as a result of 2 hours of focal cerebral ischemia, suggesting that factors other than MPT play a role. It is discussed whether this could reflect the operation of phospholipase A2 activity and degradation of the lipid skeleton of the inner mitochondrial membrane.



Calcium is one of the triggers involved in ischemic cell death, whatever the mechanism.
Design of dual-band bandpass filters using cross-shaped resonator and spurline

This paper presents a microstrip dual-band bandpass filter (BPF) based on cross-shaped resonator and spurline. It is shown that spurlines added into input/output ports of a cross-shaped resonator generate an additional notch band. Using even and odd-mode analysis the proposed structure is realized and designed. The proposed bandpass filter has dual passband from 1.9 GHz to 2.4 GHz and 9.5 GHz to 11.5 GHz.
Arnold: An Autonomous Agent to Play FPS Games

Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of these games take place in 2D environments that are fully observable to the agent. In this paper, we present Arnold, a completely autonomous agent to play First-Person Shooter Games using only screen pixel data and demonstrate its effectiveness on Doom, a classical firstperson shooter game. Arnold is trained with deep reinforcement learning using a recent Action-Navigation architecture, which uses separate deep neural networks for exploring the map and fighting enemies. Furthermore, it utilizes a lot of techniques such as augmenting high-level game features, reward shaping and sequential updates for efficient training and effective performance. Arnold outperforms average humans as well as in-built game bots on different variations of the deathmatch. It also obtained the highest kill-to-death ratio in both the tracks of the Visual Doom AI Competition and placed second in terms of the number of frags.
The grammar we teach

Second language teachers are great consumers of grammar. They are mainly interested in pedagogical grammar, but they are generally unaware of the work of theoretical linguists, such as Chomsky and Halliday. Whereas Chomsky himself has never suggested in any way that his work might be of benefit to L2 teaching, Halliday and his many disciples, have. It seems odd that language teachers should choose to ignore the great gurus of grammar. Even if their work is deemed too technical and theoretical for classroom application, it may still shed light on pedagogical grammar and provide a rationale for the way one goes about teaching grammar. In order to make informed decisions about what grammar to teach and how best to teach it, one should take stock of the various schools of grammar that seem to speak in very different voices. In the article, the writer outlines the kinds of grammar that come out of five of these schools, and assesses their usefulness to the L2 teacher.