nils-herrmann/clean_scirepeval huggingface dataset:
A new strawberry harvesting robot for elevated-trough culture

In order to improve robotic harvesting and reduce production cost, a harvesting robot system for strawberry on the elevated-trough culture was designed. It was supposed to serve for sightseeing agriculture and technological education. Based on the sonar-camera sensor, an autonomous navigation system of the harvesting robot was built to move along the trough lines independently. The mature fruits were recognized according to the H (Hue) and S (Saturation) color feature and the picking-point were located by the binocular-vision unit. A nondestructive end-effector, used to suck the fruit, hold and cut the fruit-stem, was designed to prevent pericarp damage and disease infection. A joint-type industrial manipulator with six degrees-of-freedom (DOF) was utilized to carry the end-effector. The key points and time steps for the collision-free and rapid motion of manipulator were planned. Experimental results showed that all the 100 mature strawberry targets were recognized automatically in the harvesting test. The success harvesting rate was 86%, and the success harvesting operation cost 31.3 seconds on average, including a single harvest operation of 10 seconds. The average error for fruit location was less than 4.6 mm.
Optogenetic investigation of neural circuits underlying brain disease in animal models

Optogenetic tools have provided a new way to establish causal relationships between brain activity and behaviour in health and disease. Although no animal model captures human disease precisely, behaviours that recapitulate disease symptoms may be elicited and modulated by optogenetic methods, including behaviours that are relevant to anxiety, fear, depression, addiction, autism and parkinsonism. The rapid proliferation of optogenetic reagents together with the swift advancement of strategies for implementation has created new opportunities for causal and precise dissection of the circuits underlying brain diseases in animal models.
Space and Place The Perspective of Experience

1 Introduction "S pace" and "place" are familiar words denoting common "Sexperiences. We live in space. There is no space for an-< • / other building on the lot. The Great Plains look spacious. Place is security, space is freedom: we are attached to the one and long for the other. There is no place like home. What is home? It is the old homestead, the old neighborhood, home-town, or motherland. Geographers study places. Planners would like to evoke "a sense of place." These are unexceptional ways of speaking. Space and place are basic components of the lived world; we take them for granted. When we think about them, however, they may assume unexpected meanings and raise questions we have not thought to ask. What is space? Let an episode in the life of the theologian Paul Tillich focus the question so that it bears on the meaning of space in experience. Tillich was born and brought up in a small town in eastern Germany before the turn of the century. The town was medieval in character. Surrounded by a wall and administered from a medieval town hall, it gave the impression of a small, protected, and self-contained world. To an imaginative child it felt narrow and restrictive. Every year, however young Tillich was able to escape with his family to the Baltic Sea. The flight to the limitless horizon and unrestricted space 3 4 Introduction of the seashore was a great event. Much later Tillich chose a place on the Atlantic Ocean for his days of retirement, a decision that undoubtedly owed much to those early experiences. As a boy Tillich was also able to escape from the narrowness of small-town life by making trips to Berlin. Visits to the big city curiously reminded him of the sea. Berlin, too, gave Tillich a feeling of openness, infinity, unrestricted space. 1 Experiences of this kind make us ponder anew the meaning of a word like "space" or "spaciousness" that we think we know well. What is a place? What gives a place its identity, its aura? These questions occurred to the physicists Niels Bohr and Werner Heisenberg when they visited Kronberg Castle in Denmark. Bohr said to Heisenberg: Isn't it strange how this castle changes as soon as one imagines that Hamlet lived here? As scientists we believe that a castle consists only of stones, and admire the way the …
Respiratory Rate is a Valid and Reliable Marker for the Anaerobic Threshold: Implications for Measuring Change in Fitness.

The anaerobic threshold (AT) has been defined as the theoretical highest exercise level that can be maintained for prolonged periods. It is of practical importance to the competitive endurance athlete to measure progress and plan training programs. The primary objective of this study was to assess the reliability and validity of breakpoint in the respiratory rate (RR) during incremental exercise as a marker for the AT. Secondary objectives were 1) to assess the reliability of the ventilatory threshold (VE) and ventilatory equivalent (VE/VO2) breakpoint, and 2) to assess differences in these 3 methods for their potential to measure change in fitness, as measured by standard error of measurement (SEM), coefficient of variability (CV), and correlation coefficient (R). Fifteen competitive male cyclists (5 category II, 6 category III, 1 category IV, 3 category V United States Cycling Federation) completed 2 maximal oxygen consumption tests within one week on an electronically braked cycle ergometer. A repeated measures Analysis of Variance using 2x3 design (test and methods) resulted in no significant differences (F = 0.02, p = 0.978), indicating that 1)all 3 methods are reproducible, and 2) RR, when compared to VE and VE/VO2, is a valid method of assessing the anaerobic threshold. The lowest SEM, lowest CV and highest R were obtained with the VE method (SEM = 19.4 watts, CV = 6.7%, R = 0.872), compared to VE/VO2 (SEM = 21.5 watts, CV = 7.4%, R=.811) and RR (SEM = 35.3 watts, CV = 12.2%, R = 0.800). From the results of this study, it is concluded that the RR method is a valid and reliable method for detecting AT. However, due to the relatively high SEM and CV, and low R, when compared to VE and VE/VO2, its insensitivity to small changes seen in highly fit athletes would preclude its use in measuring changes in AT. It appears that either VE or VE/VO2 would be appropriate for measuring AT changes in highly fit athletes. Key PointsRespiratory rate is a valid and reliable marker of the anaerobic threshold.Due to a relatively high standard error of measurement and coefficient of variability for the respiratory rate method, use of ventilation (VE) and ventilatory equivalent for oxygen (VE/VO2 is preferred when assessing changes in anaerobic threshold.When assessing changes in maximal aerobic capacity, maximal watts has a lower standard error of measurement and coefficient of variability and is preferred over changes in maximal oxygen consumption.
Device-to-device communications achieve efficient load balancing in LTE-advanced networks

In LTE-Advanced networks, besides the overall coverage provided by traditional macrocells, various classes of low-power nodes (e.g., pico eNBs, femto eNBs, and relays) can be distributed throughout the macrocells as a more targeted underlay to further enhance the area's spectral efficiency, alleviate traffic hot zones, and thus improve the end-user experience. Considering the limited backhaul connections within lowpower nodes and the imbalanced traffic distribution among different cells, it is highly possible that some cells are severely congested while adjacent cells are very lightly loaded. Therefore, it is of critical importance to achieve efficient load balancing among multi-tier cells in LTE-Advanced networks. However, available techniques such as smart cell and biasing, although able to alleviate congestion or distribute traffic to some extent, cannot respond or adapt flexibly to the real-time traffic distributions among multi-tier cells. Toward this end, we propose in this article a device-to-device communication-based load balancing algorithm, which utilizes D2D communications as bridges to flexibly offload traffic among different tier cells and achieve efficient load balancing according to their real-time traffic distributions. Besides identifying the research issues that deserve further study, we also present numerical results to show the performance gains that can be achieved by the proposed algorithm.
Bob: a free signal processing and machine learning toolbox for researchers

Bob is a free signal processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, Switzerland. The toolbox is designed to meet the needs of researchers by reducing development time and efficiently processing data. Firstly, Bob provides a researcher-friendly Python environment for rapid development. Secondly, efficient processing of large amounts of multimedia data is provided by fast C++ implementations of identified bottlenecks. The Python environment is integrated seamlessly with the C++ library, which ensures the library is easy to use and extensible. Thirdly, Bob supports reproducible research through its integrated experimental protocols for several databases. Finally, a strong emphasis is placed on code clarity, documentation, and thorough unit testing. Bob is thus an attractive resource for researchers due to this unique combination of ease of use, efficiency, extensibility and transparency.
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing

Alex Smola· GMD First Rudower Shausee 5 12489 Berlin asm@big.att.com The Support Vector (SV) method was recently proposed for estimating regressions, constructing multidimensional splines, and solving linear operator equations [Vapnik, 1995]. In this presentation we report results of applying the SV method to these problems.
Wave optics theory and 3-D deconvolution for the light field microscope.

Light field microscopy is a new technique for high-speed volumetric imaging of weakly scattering or fluorescent specimens. It employs an array of microlenses to trade off spatial resolution against angular resolution, thereby allowing a 4-D light field to be captured using a single photographic exposure without the need for scanning. The recorded light field can then be used to computationally reconstruct a full volume. In this paper, we present an optical model for light field microscopy based on wave optics, instead of previously reported ray optics models. We also present a 3-D deconvolution method for light field microscopy that is able to reconstruct volumes at higher spatial resolution, and with better optical sectioning, than previously reported. To accomplish this, we take advantage of the dense spatio-angular sampling provided by a microlens array at axial positions away from the native object plane. This dense sampling permits us to decode aliasing present in the light field to reconstruct high-frequency information. We formulate our method as an inverse problem for reconstructing the 3-D volume, which we solve using a GPU-accelerated iterative algorithm. Theoretical limits on the depth-dependent lateral resolution of the reconstructed volumes are derived. We show that these limits are in good agreement with experimental results on a standard USAF 1951 resolution target. Finally, we present 3-D reconstructions of pollen grains that demonstrate the improvements in fidelity made possible by our method.
Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach

Understanding and forecasting mobile traffic of large scale cellular networks is extremely valuable for service providers to control and manage the explosive mobile data, such as network planning, load balancing, and data pricing mechanisms. This paper targets at extracting and modeling traffic patterns of 9,000 cellular towers deployed in a metropolitan city. To achieve this goal, we design, implement, and evaluate a time series analysis approach that is able to decompose large scale mobile traffic into regularity and randomness components. Then, we use time series prediction to forecast the traffic patterns based on the regularity components. Our study verifies the effectiveness of our utilized time series decomposition method, and shows the geographical distribution of the regularity and randomness component. Moreover, we reveal that high predictability of the regularity component can be achieved, and demonstrate that the prediction of randomness component of mobile traffic data is impossible.
Stasis: Flexible Transactional Storage

An increasing range of applications requires robust support for atomic, durable and concurrent transactions. Databases provide the default solution, but force applications to interact via SQL and to forfeit control over data layout and access mechanisms. We argue there is a gap between DBMSs and file systems that limits designers of data-oriented applications.
 Stasis is a storage framework that incorporates ideas from traditional write-ahead logging algorithms and file systems. It provides applications with flexible control over data structures, data layout, robustness, and performance. Stasis enables the development of unforeseen variants on transactional storage by generalizing write-ahead logging algorithms. Our partial implementation of these ideas already provides specialized (and cleaner) semantics to applications.
 We evaluate the performance of a traditional transactional storage system based on Stasis, and show that it performs favorably relative to existing systems. We present examples that make use of custom access methods, modified buffer manager semantics, direct log file manipulation, and LSN-free pages. These examples facilitate sophisticated performance optimizations such as zero-copy I/O. These extensions are composable, easy to implement and significantly improve performance.
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation

Data augmentation is a key element in training high-dimensional models. In this approach, one synthesizes new observations by applying pre-specified transformations to the original training data; e.g. new images are formed by rotating old ones. Current augmentation schemes, however, rely on manual specification of the applied transformations, making data augmentation an implicit form of feature engineering. With an eye towards true end-to-end learning, we suggest learning the applied transformations on a per-class basis. Particularly, we align image pairs within each class under the assumption that the spatial transformation between images belongs to a large class of diffeomorphisms. We then learn a class-specific probabilistic generative models of the transformations in a Riemannian submanifold of the Lie group of diffeomorphisms. We demonstrate significant performance improvements in training deep neural nets over manually-specified augmentation schemes. Our code and augmented datasets are available online. Appearing in Proceedings of the 19 International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. JMLR: W&CP volume 41.
The location of trait emotional intelligence in personality factor space.

The construct of trait emotional intelligence (trait EI or trait emotional self-efficacy) provides a comprehensive operationalization of emotion-related self-perceptions and dispositions. In the first part of the present study (N=274, 92 males), we performed two joint factor analyses to determine the location of trait EI in Eysenckian and Big Five factor space. The results showed that trait EI is a compound personality construct located at the lower levels of the two taxonomies. In the second part of the study, we performed six two-step hierarchical regressions to investigate the incremental validity of trait EI in predicting, over and above the Giant Three and Big Five personality dimensions, six distinct criteria (life satisfaction, rumination, two adaptive and two maladaptive coping styles). Trait EI incrementally predicted four criteria over the Giant Three and five criteria over the Big Five. The discussion addresses common questions about the operationalization of emotional intelligence as a personality trait.
The smart floor: a mechanism for natural user identification and tracking

We have created a system for identifying people based on their footstep force profiles and have tested its accuracy against a large pool of footstep data. This floor system may be used to identify users transparently in their everyday living and working environments. We have created user footstep models based on footstep profile features and have been able to achieve a recognition rate of 93%. We have also shown that the effect of footwear is negligible on recognition accuracy.
“Knock! Knock! Who is it?” probabilistic person identification in TV-series

We describe a probabilistic method for identifying characters in TV series or movies. We aim at labeling every character appearance, and not only those where a face can be detected. Consequently, our basic unit of appearance is a person track (as opposed to a face track). We model each TV series episode as a Markov Random Field, integrating face recognition, clothing appearance, speaker recognition and contextual constraints in a probabilistic manner. The identification task is then formulated as an energy minimization problem. In order to identify tracks without faces, we learn clothing models by adapting available face recognition results. Within a scene, as indicated by prior analysis of the temporal structure of the TV series, clothing features are combined by agglomerative clustering. We evaluate our approach on the first 6 episodes of The Big Bang Theory and achieve an absolute improvement of 20% for person identification and 12% for face recognition.
Spec2Fab: a reducer-tuner model for translating specifications to 3D prints

Multi-material 3D printing allows objects to be composed of complex, heterogenous arrangements of materials. It is often more natural to define a functional goal than to define the material composition of an object. Translating these functional requirements to fabri-cable 3D prints is still an open research problem. Recently, several specific instances of this problem have been explored (e.g., appearance or elastic deformation), but they exist as isolated, monolithic algorithms. In this paper, we propose an abstraction mechanism that simplifies the design, development, implementation, and reuse of these algorithms. Our solution relies on two new data structures: a reducer tree that efficiently parameterizes the space of material assignments and a tuner network that describes the optimization process used to compute material arrangement. We provide an application programming interface for specifying the desired object and for defining parameters for the reducer tree and tuner network. We illustrate the utility of our framework by implementing several fabrication algorithms as well as demonstrating the manufactured results.
Reflections on the Efficient Market Hypothesis : 30 Years Later

In recent years financial economists have increasingly questioned the efficient market hypothesis. But surely if market prices were often irrational and if market returns were as predictable as some critics have claimed, then professionally managed investment funds should easily be able to outdistance a passive index fund. This paper shows that professional investment managers, both in The U.S. and abroad, do not outperform their index benchmarks and provides evidence that by and large market prices do seem to reflect all available information.
Submodular Functions, Matroids, and Certain Polyhedra

I The viewpoint of the subject of matroids, and related areas of lattice theory, has always been, in one way or another, abstraction of algebraic dependence or, equivalently, abstraction of the incidence relations in geometric representations of algebra. Often one of the main derived facts is that all bases have the same cardinality.  From the viewpoint of mathematical programming, the equal cardinality of all bases has special meaning — namely, that every basis is an optimum-cardinality basis. We are thus prompted to study this simple property in the context of linear programming. It turns out to be useful to regard " pure matroid theory " , which is only incidentally related to the aspects of algebra which it abstracts, as the study of certain classes of convex polyhedra. (1) A matroid M = (E, F) can be defined as a finite set E and a nonempty family F of so-called independent subsets of E such that (a) Every subset of an independent set is independent, and (b) For every A ⊆ E, every maximal independent subset of A, i.e., every basis of A, has the same cardinality, called the rank, r(A), of A (with respect to M). (This definition is not standard. It is prompted by the present interest).
Consensus Protocols for Networks of Dynamic Agents

In this paper, we introduce linear and nonlinear consensus protocols for networks of dynamic agents that allow the agents to agree in a distributed and cooperative fashion. We consider the cases of networks with communication time-delays and channels that have filtering effects. We find a tight upper bound on the maximum fixed time-delay that can be tolerated in the network. It turns out that the connectivity of the network is the key in reaching a consensus. The case of agreement with bounded inputs is considered by analyzing the convergence of a class of nonlinear protocols. A Lyapunov function is introduced that quantifies the total disagreement among the nodes of a network. Simulation results are provided for agreement in networks with communication time-delays and constrained inputs.
CRITICAL SUCCESS FACTORS (CSFS) OF ENTERPRISE RESOURCE PLANNING (ERP) SYSTEM IMPLEMENTATION IN HIGHER EDUCATION INSTITUTIONS (HEIS): CONCEPTS AND LITERATURE REVIEW

Nowadays, Information Technology (IT) plays an important role in efficiency and effectiveness of the organizational performance. As an IT application, Enterprise Resource Planning (ERP) systems is considered one of the most important IT applications because it enables the organizations to connect and interact with its administrative units in order to manage data and organize internal procedures. Many institutions use ERP systems, most notably Higher Education Institutions (HEIs). However, many projects fail or exceed scheduling and budget constraints; the rate of failure in HEIs sector is higher than in other sectors. With HEIs’ recent movement to implement ERP systems and the lack of research studies examining successful implementation in HEIs, this paper provides a critical literature review with a special focus on Saudi Arabia. Further, it defines Critical Success Factors (CSFs) contributing to the success of ERP implementation in HEIs. This paper is part of a larger research effort aiming to provide guidelines and useful findings that help HEIs to manage the challenges for ERP systems and define CSFs that will help practitioners to implement them in the Saudi context.
Graph-Based Wrong IsA Relation Detection in a Large-Scale Lexical Taxonomy

Knowledge base(KB) plays an important role in artificial intelligence. Much effort has been taken to both manually and automatically construct web-scale knowledge bases. Comparing with manually constructed KBs, automatically constructed KB is broader but with more noises. In this paper, we study the problem of improving the quality for automatically constructed web-scale knowledge bases, in particular, lexical taxonomies of isA relationships. We find that these taxonomies usually contain cycles, which are often introduced by incorrect isA relations. Inspired by this observation, we introduce two kinds of models to detect incorrect isA relations from cycles. The first one eliminates cycles by extracting directed acyclic graphs, and the other one eliminates cycles by grouping nodes into different levels. We implement our models on Probase, a state-of-the-art, automatically constructed, web-scale taxonomy. After processing tens of millions of relations, our models eliminate 74 thousand wrong relations with 91% accuracy.