nils-herrmann/clean_scirepeval huggingface dataset:
Suicide risk in mood disorders.

The aim of this review is to highlight the traditional and newly recognized suicide risk factors in patients with mood disorders.



Current research findings clearly suggest that suicidal behaviour in patients with mood disorder is a 'state-dependent' phenomenon. Recently, there is, however, a growing body of evidence that besides the well accepted clinically explorable suicide risk factors in mood disorders (e.g., severe depression, prior suicide attempt, comorbid anxiety, substance use, personality disorders and so on), mixed state of depression could also be an important precursor of suicidal behaviour. This might be particularly true in unrecognized cases of bipolar depressives, when antidepressant monotherapy (unprotected by mood stabilizers or atypical antipsychotics) can worsen the clinical picture and rarely induce an aggressive or self-destructive behaviour.



In the majority of patients with mood disorders, suicidal behaviour is predictable and preventable, with a good chance. A careful and systematic exploration of suicide risk factors in patients with mood disorder helps clinicians to identify patients at high suicide risk. A successful, acute and long-term treatment of these patients substantially reduces the suicidal behaviour even in this high-risk population.
Attentional biases for emotional faces in young children of mothers with chronic or recurrent depression.

Attentional biases for negative stimuli have been observed in school-age and adolescent children of depressed mothers and may reflect a vulnerability to depression. The direction of these biases and whether they can be identified in early childhood remains unclear. The current study examined attentional biases in 5-7-year-old children of depressed and non-depressed mothers. Following a mood induction, children participated in a dot-probe task assessing biases for sad and happy faces. There was a significant interaction of group and sex: daughters of depressed mothers attended selectively to sad faces, while children of controls and sons of depressed mothers did not exhibit biases. No effects were found for happy stimuli. These findings suggest that attentional biases are discernible in early childhood and may be vulnerability markers for depression. The results also raise the possibility that sex differences in cognitive biases are evident before the emergence of sex differences in the prevalence of depression.
Effects of pomegranate juice consumption on oxidative stress in patients with type 2 diabetes: a single-blind, randomized clinical trial.

Increased free radicals production due to hyperglycemia produces oxidative stress in patients with diabetes. Pomegranate juice (PJ) has antioxidant properties. This study was conducted to determine the effects of PJ consumption in oxidative stress in type 2 diabetic patients. This study was a randomized clinical trial performed on 60, 40-65 years old diabetic patients. The patients were randomly allocated either to PJ consumption group or control. Patients in PJ group consumed 200 ml of PJ daily for six weeks. Sex distribution and the mean age were not different between two groups. After six weeks intervention, oxidized LDL and anti-oxidized LDL antibodies decreased and total serum antioxidant capacity and arylesterase activity of paraoxonase increased significantly in the PJ-treated group compared to the control group. Our data have shown that six weeks supplementation of PJ could have favorable effects on oxidative stress in patients with type 2 diabetes (T2D).
Knowledge Graph Identification

Large-scale information processing systems are able to extract massive collections of interrelated facts, but unfortunately transforming these candidate facts into useful knowledge is a formidable challenge. In this paper, we show how uncertain extractions about entities and their relations can be transformed into a knowledge graph. The extractions form an extraction graph and we refer to the task of removing noise, inferring missing information, and determining which candidate facts should be included into a knowledge graph as knowledge graph identification. In order to perform this task, we must reason jointly about candidate facts and their associated extraction confidences, identify coreferent entities, and incorporate ontological constraints. Our proposed approach uses probabilistic soft logic (PSL), a recently introduced probabilistic modeling framework which easily scales to millions of facts. We demonstrate the power of our method on a synthetic Linked Data corpus derived from the MusicBrainz music community and a real-world set of extractions from the NELL project containing over 1M extractions and 70K ontological relations. We show that compared to existing methods, our approach is able to achieve improved AUC and F1 with significantly lower running time.
Beyond TCAMs: An SRAM-Based Parallel Multi-Pipeline Architecture for Terabit IP Lookup

Continuous growth in network link rates poses a strong demand on high speed IP lookup engines. While Ternary Content Addressable Memory (TCAM) based solutions serve most of today's high-end routers, they do not scale well for the next-generation. On the other hand, pipelined SRAM- based algorithmic solutions become attractive. Intuitively multiple pipelines can be utilized in parallel to have a multiplicative effect on the throughput. However, several challenges must be addressed for such solutions to realize high throughput. First, the memory distribution across different stages of each pipeline as well as across different pipelines must be balanced. Second, the traffic on various pipelines should be balanced. In this paper, we propose a parallel SRAM-based multi- pipeline architecture for terabit IP lookup. To balance the memory requirement over the stages, a two-level mapping scheme is presented. By trie partitioning and subtrie-to-pipeline mapping, we ensure that each pipeline contains approximately equal number of trie nodes. Then, within each pipeline, a fine-grained node-to-stage mapping is used to achieve evenly distributed memory across the stages. To balance the traffic on different pipelines, both pipelined prefix caching and dynamic subtrie-to-pipeline remapping are employed. Simulation using real-life data shows that the proposed architecture with 8 pipelines can store a core routing table with over 200 K unique routing prefixes using 3.5 MB of memory. It achieves a throughput of up to 3.2 billion packets per second, i.e. 1 Tbps for minimum size (40 bytes) packets.
Efficient pattern matching over event streams

Pattern matching over event streams is increasingly being employed in many areas including financial services, RFIDbased inventory management, click stream analysis, and electronic health systems. While regular expression matching is well studied, pattern matching over streams presents two new challenges: Languages for pattern matching over streams are significantly richer than languages for regular expression matching. Furthermore, efficient evaluation of these pattern queries over streams requires new algorithms and optimizations: the conventional wisdom for stream query processing (i.e., using selection-join-aggregation) is inadequate.
 In this paper, we present a formal evaluation model that offers precise semantics for this new class of queries and a query evaluation framework permitting optimizations in a principled way. We further analyze the runtime complexity of query evaluation using this model and develop a suite of techniques that improve runtime efficiency by exploiting sharing in storage and processing. Our experimental results provide insights into the various factors on runtime performance and demonstrate the significant performance gains of our sharing techniques.
Predicting function from sequence in venom peptide families

Toxins from animal venoms are small peptides that recognize specific molecular targets in the brains of prey or predators. Next generation sequencing has uncovered thousands of diverse toxin sequences, but the functions of these peptides are poorly understood. Here we demonstrate that the use of machine learning techniques on sequence-derived features enables high accuracy in the task of predicting a toxin’s functionality using only its amino acid sequence. Comparison of the performance of several learning algorithms in this prediction task demonstrates that both physiochemical properties of the amino acid residues in a sequence as well as noncontiguous sequence motifs can be used independently to model the sequence dependence of venom function. We rationalize the observed model performance using unsupervised learning and make broad predictions about the distribution of toxin functions in the venome. Bioinformatics, machine learning, protein function prediction, venomics.
Birdbrains could teach basal ganglia research a new song

Recent advances in anatomical, physiological and histochemical characterization of avian basal ganglia neurons and circuitry have revealed remarkable similarities to mammalian basal ganglia. A modern revision of the avian anatomical nomenclature has now provided a common language for studying the function of the cortical-basal-ganglia-cortical loop, enabling neuroscientists to take advantage of the specialization of basal ganglia areas in various avian species. For instance, songbirds, which learn their vocal motor behavior using sensory feedback, have specialized a portion of their cortical-basal ganglia circuitry for song learning and production. This discrete circuit dedicated to a specific sensorimotor task could be especially tractable for elucidating the interwoven sensory, motor and reward signals carried by basal ganglia, and the function of these signals in task learning and execution.
Credit card fraud detection using Naïve Bayes model based and KNN classifier

Machine Learning is the technology, in which algorithms which are capable of learning from previous cases and past experiences are designed. It is implemented using various algorithms which reiterate over the same data repeatedly to analyze the pattern of data. The techniques of data mining are no far behind and are widely used to extract data from large databases to discover some patterns making decisions. This paper presents the Naïve Bayes improved K-Nearest Neighbor method (NBKNN) for Fraud Detection of Credit Card. Experimental results illustrate that both classifiers work differently for the same dataset. The purpose is to enhance the accuracy and enhance the flexibility of the algorithm.
Fundamental Concepts in Programming Languages

Christopher Strachey’s paper on Fundamental Concepts in Programming Languages was written in the autumn of 1967, based on his lectures given t an International School in Computer Programming, held in Copenhagen. He intended that his paper be published in the proceedings of the School, but the proceedings never materialized, and it remained an unpublished pre-print for more than thirty years. Its a highly influential paper which has been widely circulated among the community of computer scientists. The paper begins with a philosphical remarks on semantic issues we have in design of programming languages. He emphasis that a formal jargon is necessary as core concepts are so vague. He gives us clear and analytical description of many of his insights into programming language design and formal semantics, covering many topics. He then proceeds to propose a semantics, initially refered to as ’mathematical semantics’ or simply ’Strachey Semantics’, which was later developed to be known as ’Denotational Semantics’. After the untimely death of Strachey, this paper has been exploited in various textbooks and papers by numerous authors and they have in turn become an established basis for our understanding of issues. Thus this paper has indeed a high degree of historical interest and relevance. The paper can be recommended to anyone for its simplistic approach backed by clearly formulated concepts. However I would recommend you proceed with caution. To highlight one, most of the illustrations are in a very lesser known Language called CPL, a language developed by Strachey himself and colleagues. It was supposed to be a successor of Algol, but was never fully made. But fortunately CPL is understandable. He provides an extremely broad survey of core issues in programming language design that provided much of the terminology we use today, including definitions of the kinds of polymorphism and the kinds of expressions we see in programming languages. The reader should not be disconcerted by Strachey’s operational interpretation of λ-expressions: the paper was written a full two years before Scott provided a model for the λ-calculus and established the domain theory that forms the mathematical foundations of denotational semantics . Strachey writes also (at the very end of the paper, when comparing his proposal for a semantic method to previous proposals) In conclusion, Strachey’s paper is still well worth reading carefully, some 48 years after he wrote it. Enjoy it!
On the rate of channel polarization

A bound is given on the rate of channel polarization. As a corollary, an earlier bound on the probability of error for polar coding is improved. Specifically, it is shown that, for any binary-input discrete memoryless channel W with symmetric capacity I(W) and any rate R ≪ I(W), the polar-coding block-error probability under successive cancellation decoding satisfies P<inf>e</inf>(N, R) ≤ 2<sup>−Nβ</sup> for any β ≪ 1/2 when the block-length N is large enough.
Understanding packet delivery performance in dense wireless sensor networks

Wireless sensor networks promise fine-grain monitoring in a wide variety of environments. Many of these environments (e.g., indoor environments or habitats) can be harsh for wireless communication. From a networking perspective, the most basic aspect of wireless communication is the packet delivery performance: the spatio-temporal characteristics of packet loss, and its environmental dependence. These factors will deeply impact the performance of data acquisition from these networks.In this paper, we report on a systematic medium-scale (up to sixty nodes) measurement of packet delivery in three different environments: an indoor office building, a habitat with moderate foliage, and an open parking lot. Our findings have interesting implications for the design and evaluation of routing and medium-access protocols for sensor networks.
Topology Control of Multihop Wireless Networks Using Transmit Power Adjustment

We consider the problem of adjusting the transmit powers of nodes in a multihop wireless network (also called an ad hoc network) to create a desired topology. We formulate it as a constrained optimization problem with two constraints connectivity and biconnectivity, and one optimization objective maximum power used. We present two centralized algorithms for use in static networks, and prove their optimality. For mobile networks, we present two distributed heuristics that adaptively adjust node transmit powers in response to topological changes and attempt to maintain a connected topology using minimum power. We analyze the throughput, delay, and power consumption of our algorithms using a prototype software implementation, an emulation of a power-controllable radio, and a detailed channel model. Our results show that the performance of multihop wireless networks in practice can be substantially increased with topology control.
A 0.15 V Input Energy Harvesting Charge Pump With Dynamic Body Biasing and Adaptive Dead-Time for Efficiency Improvement

A charge pump using 0.13- μm CMOS process for low-voltage energy harvesting is presented. A low-power adaptive dead-time (AD) circuit is used which automatically optimizes the dead-time according to the input voltage. A negative charge pump is also utilized for high efficiency at low input voltages (VIN). The AD circuit improves efficiency by 17% at VIN of 0.2 V compared to the fixed dead time circuit as well as enables the charge pump to work at VIN down to 0.15 V. Dynamic body bias (DBB) and switch-conductance enhancement techniques are applied to a unit stage of the three-stage charge pump. The reverse current flowing through the cross-coupled NMOS switches is prevented and the current transfer is also maximized. Together with the AD circuit and the DBB technique, the maximum output current was improved by 240% as compared to the conventional charge pump design using only the forward body bias.
Good Background Colors for Readers: A Study of People with and without Dyslexia

The use of colors to enhance the reading of people with dyslexia have been broadly discussed and is often recommended, but evidence of the effectiveness of this approach is lacking. This paper presents a user study with 341 participants (89 with dyslexia) that measures the effect of using background colors on screen readability. Readability was measured via reading time and distance travelled by the mouse. Comprehension was used as a control variable. The results show that using certain background colors have a significant impact on people with and without dyslexia. Warm background colors, Peach, Orange and Yellow, significantly improved reading performance over cool background colors, Blue, Blue Grey and Green. These results provide evidence to the practice of using colored backgrounds to improve readability; people with and without dyslexia benefit, but people with dyslexia may especially benefit from the practice given the difficulty they have in reading in general.
OmniTouch: wearable multitouch interaction everywhere

OmniTouch is a wearable depth-sensing and projection system that enables interactive multitouch applications on everyday surfaces. Beyond the shoulder-worn system, there is no instrumentation of the user or environment. Foremost, the system allows the wearer to use their hands, arms and legs as graphical, interactive surfaces. Users can also transiently appropriate surfaces from the environment to expand the interactive area (e.g., books, walls, tables). On such surfaces - without any calibration - OmniTouch provides capabilities similar to that of a mouse or touchscreen: X and Y location in 2D interfaces and whether fingers are "clicked" or hovering, enabling a wide variety of interactions. Reliable operation on the hands, for example, requires buttons to be 2.3cm in diameter. Thus, it is now conceivable that anything one can do on today's mobile devices, they could do in the palm of their hand.
Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped

Robotics controllers often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. Simulation can aid in optimizing these controllers if parameters learned in simulation transfer to hardware. Unfortunately, this is often not the case in legged locomotion, necessitating learning directly on hardware. This motivates using data-efficient learning techniques like Bayesian Optimization (BO) to minimize collecting expensive data samples. BO is a black-box data-efficient optimization scheme, though its performance typically degrades in higher dimensions. We aim to overcome this problem by incorporating domain knowledge, with a focus on bipedal locomotion. In our previous work, we proposed a feature transformation that projected a 16-dimensional locomotion controller to a 1-dimensional space using knowledge of human walking. When optimizing a human-inspired neuromuscular controller in simulation, this feature transformation enhanced sample efficiency of BO over traditional BO with a Squared Exponential kernel. In this paper, we present a generalized feature transform applicable to non-humanoid robot morphologies and evaluate it on the ATRIAS bipedal robot, in both simulation and hardware. We present three different walking controllers and two are evaluated on the real robot. Our results show that this feature transform captures important aspects of walking and accelerates learning on hardware and simulation, as compared to traditional BO.
Efficient computation of adaptive threshold surfaces for image binarization

The problem of binarization of gray level images acquired under nonuniform illumination is reconsidered. Yanowitz and Bruckstein proposed to use for image binarization an adaptive threshold surface, determined by interpolation o f the image gray levels at points where the image gradient is high. The rationale is that high image gradient indicates probable object edges, and there the image values are between the object and the background gray levels. The threshold surface was determined by successive overrelaxation as the solution of the Laplace equation. This work proposes a different method to determine an adaptive threshold surface. In this new method, inspired by multires olution approximation, the threshold surface is construct ed with considerably lower computational complexity and is smooth, yielding faster image binarizations and better visual performance.
Binary Optimized Hashing

This paper studies the problem of learning to hash, which is essentially a mixed integer optimization problem, containing both the binary hash code output and the (continuous) parameters forming the hash functions. Different from existing relaxation methods in hashing, which have no theoretical guarantees for the error bound of the relaxations, we propose binary optimized hashing (BOH), in which we prove that if the loss function is Lipschitz continuous, the binary optimization problem can be relaxed to a bound-constrained continuous optimization problem. Then we introduce a surrogate objective function, which only depends on unbinarized hash functions and does not need the slack variables transforming unbinarized hash functions to discrete functions, to approximate the relaxed objective function. We show that the approximation error is bounded and the bound is small when the problem is optimized. We apply the proposed approach to learn hash codes from either handcraft feature inputs or raw image inputs. Extensive experiments are carried out on three benchmarks, demonstrating that our approach outperforms state-of-the-arts with a significant margin on search accuracies.
The scientific research potential of virtual worlds.

Online virtual worlds, electronic environments where people can work and interact in a somewhat realistic manner, have great potential as sites for research in the social, behavioral, and economic sciences, as well as in human-centered computer science. This article uses Second Life and World of Warcraft as two very different examples of current virtual worlds that foreshadow future developments, introducing a number of research methodologies that scientists are now exploring, including formal experimentation, observational ethnography, and quantitative analysis of economic markets or social networks.
A FLIGHT CONTROL SYSTEM FOR AERIAL ROBOTS : ALGORITHMS AND EXPERIMENTS

This paper presents a flight control system designed as on-board intelligence for rotorcraft-based unmanned aerial vehicles (RUAVs). This hierarchical flight control system, endowed with autonomy resembling sense-reason-act processes of intelligent agents in nature, gradually refines given abstract mission commands into real-time control signals for each vehicle. A tracking control layer is designed on the identified vehicle dynamics and integrated with a trajectory generator for logistical action planning. The proposed structure has been implemented on radio-controlled helicopters and validated in a variety of experiments. Results from way-point navigation, a probabilistic pursuit-evasion game and vision-based tracking of a moving target show the promising potential of intelligent flying robots.