nils-herrmann/clean_scirepeval huggingface dataset:
Modular Proximal Optimization for Multidimensional Total-Variation Regularization

We study TV regularization, a widely used technique for eliciting structured sparsity. In particular, we propose efficient algorithms for computing prox-operators for `p-norm TV. The most important among these is `1-norm TV, for whose prox-operator we present a new geometric analysis which unveils a hitherto unknown connection to taut-string methods. This connection turns out to be remarkably useful as it shows how our geometry guided implementation results in efficient weighted and unweighted 1D-TV solvers, surpassing state-of-the-art methods. Our 1D-TV solvers provide the backbone for building more complex (two or higher-dimensional) TV solvers within a modular proximal optimization approach. We review the literature for an array of methods exploiting this strategy, and illustrate the benefits of our modular design through extensive suite of experiments on (i) image denoising, (ii) image deconvolution, (iii) four variants of fused-lasso, and (iv) video denoising. To underscore our claims and permit easy reproducibility, we provide all the reviewed and our new TV solvers in an easy to use multi-threaded C++, Matlab and Python library.
Efficient Graph Similarity Search Over Large Graph Databases

Since many graph data are often noisy and incomplete in real applications, it has become increasingly important to retrieve graphs g in the graph database D that approximately match the query graph q, rather than exact graph matching. In this paper, we study the problem of graph similarity search, which retrieves graphs that are similar to a given query graph under the constraint of graph edit distance. We propose a systematic method for edit-distance based similarity search problem. Specifically, we derive two lower bounds, i.e., partition-based and branch-based bounds, from different perspectives. More importantly, a hybrid lower bound incorporating both ideas of the two lower bounds is proposed, which is theoretically proved to have higher (at least not lower) pruning power than using the two lower bounds together. We also present a uniform index structure, namely u-tree, to facilitate effective pruning and efficient query processing. Extensive experiments confirm that our proposed approach outperforms the existing approaches significantly, in terms of both the pruning power and query response time.
On First-Order Meta-Learning Algorithms

This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously unseen task sampled from this distribution. We analyze a family of algorithms for learning a parameter initialization that can be fine-tuned quickly on a new task, using only firstorder derivatives for the meta-learning updates. This family includes and generalizes first-order MAML, an approximation to MAML obtained by ignoring second-order derivatives. It also includes Reptile, a new algorithm that we introduce here, which works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task. We expand on the results from Finn et al. showing that first-order meta-learning algorithms perform well on some well-established benchmarks for few-shot classification, and we provide theoretical analysis aimed at understanding why these algorithms work.
A practical VEP-based brain-computer interface

This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to >90% of people with a high ITR in living environments.
Vehicle Detection and Compass Applications using AMR Magnetic Sensors

The earliest magnetic field detectors allowed navigation over trackless oceans by sensing the earth’s magnetic poles. Magnetic field sensing has vastly expanded as industry has adapted a variety of magnetic sensors to detect the presence, strength, or direction of magnetic fields not only from the earth, but also from permanent magnets, magnetized soft magnets, vehicle disturbances, brain wave activity, and fields generated from electric currents. Magnetic sensors can measure these properties without physical contact and have become the eyes of many industrial and navigation control systems. This paper will describe the current state of magnetic sensing within the earth’s field range and how these sensors are applied. Several applications will be presented for magnetic sensing in systems with emphasis on vehicle detection and navigation based on magnetic fields.
A 3.5-GHz PLL for fast low-IF/zero-IF LO switching in an 802.11 transceiver

A PLL technique is introduced that enables fast and accurate frequency switching, independent of the loop bandwidth. It uses separate tuning paths, each driving a separate VCO tune port. Different frequencies are produced by letting the VCO make different weighted combinations of the stable tuning voltages. The PLL converges to the stable tuning voltages by switching it a few times between the desired frequencies and tuning paths. Once the stabilized tuning voltages are found, one can switch between frequencies as fast as one can switch between K/sub VCO/s. The technique is applied to a 3.5-GHz integer-N PLL to enable fast jumping of the local oscillator (LO) frequency when an 802.11 transceiver is switched between a low and a zero intermediate frequency (LIF/ZIF). It uses dual phase/frequency detectors (PFD), charge pumps (CPs), and on-chip loop filters to control two separate low-leakage VCO tune ports. Each PFD/tune port combination can be (de)activated separately, without disturbing the loop filters' charge. The 50-kHz bandwidth PLL achieves a measured 7-MHz jump with /spl plusmn/20 kHz accuracy within 6 /spl mu/s. The measured phase noise is -123 dBc/Hz at 1-MHz offset.
Integrating technology in the classroom: a visual conceptualization of teachers' knowledge, goals and beliefs

In this paper, we devise a diagrammatic conceptualization to describe and represent the complex interplay of a teacher’s knowledge (K), goals (G) and beliefs (B) in leveraging technology effectively in the classroom. The degree of coherency between the KGB region and the affordances of the technology serves as an indicator of the teachers’ developmental progression through the initiation, implementation and maturation phases of using technology in the classroom. In our study, two teachers with differing knowledge, goals and beliefs are studied as they integrated GroupScribbles technology in their classroom lessons over a period of 1 year. Our findings reveal that the transition between the teacher’s developmental states (as indicated by coherency diagrams) is nonlinear, and thus the importance of ensuring high coherency right at the initiation stage. Support for the teacher from other teachers and researchers remains an important factor in developing the teacher’s competency to leverage the technology successfully. The stability of the KGB region further ensures smooth progression of the teacher’s effective integration of technology in the classroom.
Linear Algorithms for Online Multitask Classification

We design and analyze interacting online algorithms for multitask classification that perform better than independent learners whenever the tasks are related in a certain sense. We formalize task relatedness in different ways, and derive formal guarantees on the performance advantage provided by interaction. Our online analysis gives new stimulating insights into previously known co-regularization techniques, such as the multitask kernels and the margin correlation analysis for multiview learning. In the last part we apply our approach to spectral co-regularization: we introduce a natural matrix extension of the quasiadditive algorithm for classification and prove bounds depending on certain unitarily invariant norms of the matrix of task coefficients.
The medial patellofemoral ligament: location of femoral attachment and length change patterns resulting from anatomic and nonanatomic attachments.

Incompetence of the medial patellofemoral ligament (MPFL) is an integral factor in patellofemoral instability. Reconstruction of this structure is gaining increasing popularity. However, the natural behavior of the ligament is still not fully understood, and crucially, the correct landmark for femoral attachment of the MPFL at surgery is poorly defined.



To determine the length change pattern of the native MPFL, investigate the effect of nonanatomic femoral and differing patellar attachment sites on length changes, and recommend a reproducible femoral attachment site for undertaking anatomic MPFL reconstruction.



Descriptive laboratory study.



Eight cadaveric knees were dissected of skin and subcutaneous fat and mounted in a kinematics rig with the quadriceps tensioned. The MPFL length change patterns were measured for combinations of patellar and femoral attachments using a suture and displacement transducer. Three attachments were along the superomedial border of the patella, and 5 femoral attachments were at the MPFL center and 5 mm proximal, distal, anterior, and posterior to this point. Reproducibility of attachment sites was validated radiographically.



The femoral attachment point, taking the anterior-posterior medial femoral condyle diameter to be 100%, was identified 40% from the posterior, 50% from the distal, and 60% from the anterior border of the medial femoral condyle. This point was most isometric, with a mean maximal length change to the central patellar attachment of 2.1 mm from 0° to 110° of knee flexion. The proximal femoral attachment resulted in up to 6.4 mm mean lengthening and the distal attachment up to 9.1 mm mean shortening through 0° to 110° of knee flexion, resulting in a significantly nonisometric graft (P < .05).



We report the anatomic femoral and patellar MPFL graft attachments, with confirmation of the reproducibility of their location and resulting kinematic behavior. Nonanatomic attachments caused significant loss of isometry.



The importance of an anatomically positioned MPFL reconstruction is highlighted, and an identifiable radiographic point for femoral tunnel position is suggested for use intraoperatively.
A general class of zero-or-one inflated beta regression models

This paper proposes a general class of regression models for continuous proportions when the data contain zeros or ones. The proposed class of models assumes that the response variable has a mixed continuous–discrete distribution with probability mass at zero or one. The beta distribution is used to describe the continuous component of the model, since its density has a wide range of different shapes depending on the values of the two parameters that index the distribution. We use a suitable parameterization of the beta law in terms of its mean and a precision parameter. The parameters of the mixture distribution are modeled as functions of regression parameters. We provide inference, diagnostic, and model selection tools for this class of models. A practical application that employs real data is presented.
Intelligent fractional-order PID (FOPID) heart rate controller for cardiac pacemaker

Efficient and robust control of cardiac pacemaker is essential for providing life-saving control action to regulate Heart Rate (HR) in a dynamic environment. Several controller designs involving proportional-integral-derivative (PID) and fuzzy logic controllers (FLC) have been reported but each have their limitations to face the dynamic challenge of regulating HR. Fractional-order control (FOC) systems provide controllers that are described by fractional-order differential equations that offers fine tuning of the control parameters to provide robust and efficient performance. In this work a robust fractional-order PID (FOPID) controller is designed based on Ziegler-Nichols tuning method. The stable FOPID controller outperformed PID controllers with different tuning methods and also the FLC in terms of rise time, settling time and % overshoot. The FOPID controller also demonstrated feasibility for rate-adaptive pacing. However, the FOPID controller designed in this work is not optimal and is limited by the tuning procedure. More efficient design using optimization techniques such as particle swarm intelligence or genetic algorithm tuning can offer optimal control of the cardiac pacemaker.
An On-Chip CMOS Relaxation Oscillator With Voltage Averaging Feedback

An on-chip CMOS relaxation oscillator with voltage averaging feedback using a reference proportional to supply voltage is presented. A voltage-averaging feedback (VAF) concept is proposed to overcome conventional relaxation oscillator problems such as sensitivity to comparator delay, aging, and flicker noise of current sources. A test-chip with typical frequency of 14.0 MHz was fabricated in a 0.18 μm standard CMOS process and measured frequency variations of ±0.16 % for supply changes from 1.7 to 1.9 V and ±0.19% for temperature changes from -40 to 125°C. The prototype draws 25 μA from a 1.8 V supply, occupies 0.04 mm2, and achieves 7x reduction in accumulated jitter (at 1500th cycle) as compared to a oscillator without VAF.
Lifetime analysis of an automotive electrical motor with hairpin wound stator

The electrical motors adopted in electric vehicles are subjected to specific stress conditions which determine their insulation lifetime. However, the qualification of automotive electrical motors does not have a specific standard and follows the one commonly used for other electrical machines. In this work, the insulation of a hairpin wound stator has been evaluated employing a multi-factorial aging procedure, with the simultaneous application of thermal and mechanical stresses. Data here presented suggest that the turn to turn insulation is the weakest point of the motor. Moreover, an extrapolation method to obtain the system lifetime at the operating temperature has been used and discussed. The resulting motor reliability of 99.6% for 10000 working hours appears fairly acceptable. Eventually, this work opens to a discussion on a proper qualification standard for automotive electrical motors.
From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero

Despite the recent successes of deep neural networks in various fields such as image and speech recognition, natural language processing, and reinforcement learning, we still face big challenges in bringing the power of numeric optimization to symbolic reasoning. Researchers have proposed different avenues such as neural machine translation for proof synthesis, vectorization of symbols and expressions for representing symbolic patterns, and coupling of neural back-ends for dimensionality reduction with symbolic front-ends for decision making. However, these initial explorations are still only point solutions, and bear other shortcomings such as lack of correctness guarantees. In this paper, we present our approach of casting symbolic reasoning as games, and directly harnessing the power of deep reinforcement learning in the style of Alpha(Go) Zero on symbolic problems. Using the Boolean Satisfiability (SAT) problem as showcase, we demonstrate the feasibility of our method, and the advantages of modularity, efficiency, and correctness guarantees.
A NOTE ON CONTRACTIVE MAPPINGS

The problem of defining a family of functions F= {a(x, y)} satisfying 0^a(x, y)<l, sup a(x, y) = 1 and such that Banach's theorem holds when the constant a is replaced with any a(x, y)EF, was suggested to the author by Professor H. Hanani, and two theorems to that effect are proved in the present paper. In the first, additional conditions requiring compactness of the mapping are imposed; the second holds for any complete metric space, irrespective of the compactness of the mapping. Definition 1. A mapping A of a metric space X into itself is said to be contractive if for every two distinct points x, y EX
A Book Dewarping System by Boundary-Based 3D Surface Reconstruction

Non-contact imaging devices such as digital cameras and overhead scanners can convert hardcopy books to digital images without cutting them to individual pages. However, the captured images have distinct distortions. A book dewarping system is proposed to remove the perspective and geometric distortions automatically from single images. A book boundary model is extracted, and a 3D book surface is reconstructed. And then the horizontal and vertical metrics of each column are restored from it. Experimental results show the good dewarping and speed performance. Since no additional equipments and no restrictions to specific book layouts or contents are needed, the proposed system is very practical in real applications.
Development and prospect of unmanned aerial vehicle technologies for agricultural production management

Unmanned aerial vehicles have been developed and applied to support agricultural production management. Compared with piloted aircraft, an Unmanned Aerial Vehicle (UAV) can focus on small crop fields at lower flight altitudes than regular aircraft to perform site-specific farm management with higher precision. They can also “fill in the gap” in locations where fixed winged or rotary winged aircraft are not readily available. In agriculture, UAVs have primarily been developed and used for remote sensing and application of crop production and protection materials. Application of fertilizers and chemicals is frequently needed at specific times and locations for site-specific management. Routine monitoring of crop plant health is often required at very high resolution for accurate site-specific management as well. This paper presents an overview of research involving the development of UAV technology for agricultural production management. Technologies, systems and methods are examined and studied. The limitations of current UAVs for agricultural production management are discussed, as well as future needs and suggestions for development and application of the UAV technologies in agricultural production management.
Business process mining: An industrial application

Contemporary information systems (e.g., WFM, ERP, CRM, SCM, and B2B systems) record business events in so-called event logs. Business process mining takes these logs to discover process, control, data, organizational, and social structures. Although many researchers are developing new and more powerful process mining techniques and software vendors are incorporating these in their software, few of the more advanced process mining techniques have been tested on real-life processes. This paper describes the application of process mining in one of the provincial offices of the Dutch national public works department, responsible for the construction and maintenance of the road and water infrastructure. Using a variety of process mining techniques, we analyzed the processing of invoices sent by the various subcontractors and suppliers from three different perspectives: (1) the process perspective, (2) the organizational perspective and (3) the case perspective. For this purpose, we used some of the tools developed in the context of the ProM framework. The goal of this paper is to demonstrate the applicability of process mining in general and our algorithms and tools in particular.
Strategic Surge Pricing and Forecast Communication on On-Demand Service Platforms

On-demand service platforms (e.g., Uber, Lyft) match consumers with independent workers nearby at short notice. To manage fluctuating supply and demand conditions across market locations (zones), many on-demand platforms provide market forecasts to workers and practice surge pricing, wherein the price in a particular zone is temporarily raised above the regular price. We jointly analyze the strategic role of surge pricing and forecast communication explicitly accounting for workers’ incentives to move between zones and the platform’s incentive to share forecasts truthfully. Conventional wisdom suggests that surge pricing is useful in zones where demand for workers exceeds their supply. However, we show that when the platform relies on independent workers to serve consumers across different zones, surge pricing is also useful in zones where supply exceeds demand. Because individual workers do not internalize the competitive externality that they impose on other workers, too few workers may move from a zone with excess supply to an adjacent zone requiring additional workers. Moreover, the platform may have an incentive to misreport market forecasts to exaggerate the need for workers to move. We show how and why distorting the price in a zone with excess supply through surge pricing can increase total platform profit across zones, by incentivizing more workers to move and by making forecast communication credible. Our analysis offers insights for effectively managing on-demand platforms through surge pricing and forecast sharing, and the resulting implications for consumers and workers.
The dawning era of polymer therapeutics

As we enter the twenty-first century, research at the interface of polymer chemistry and the biomedical sciences has given rise to the first nano-sized (5–100 nm) polymer-based pharmaceuticals, the 'polymer therapeutics'. Polymer therapeutics include rationally designed macromolecular drugs, polymer–drug and polymer–protein conjugates, polymeric micelles containing covalently bound drug, and polyplexes for DNA delivery. The successful clinical application of polymer–protein conjugates, and promising clinical results arising from trials with polymer–anticancer-drug conjugates, bode well for the future design and development of the ever more sophisticated bio-nanotechnologies that are needed to realize the full potential of the post-genomic age.
A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation

In this paper, we adopt 3D CNNs to segment the pancreas in CT images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D applications due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarseto-fine framework for volumetric pancreas segmentation to tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial information along all three axes. We conduct experiments on two datasets which include healthy and pathological pancreases respectively, and achieve the state-of-the-art in terms of Dice-Sørensen Coefficient (DSC). Moreover, the worst case of DSC on the NIH dataset was improved by 7% to reach almost 70%, which indicates the reliability of our framework in clinical applications.