nils-herrmann/clean_scirepeval huggingface dataset:
5.6 A 0.13μm fully digital low-dropout regulator with adaptive control and reduced dynamic stability for ultra-wide dynamic range

This paper presents a discrete-time, fully digital, scan-programmable LDO macro in 0.13μm technology featuring greater than 90% current efficiency across a 50× current range, and 8× improvement in transient response time in response to large load steps. The baseline design features a 128b barrel shifter that digitally controls 128 identical power PMOS devices to provide load and line regulation at the node VREG, for a scan-programmable fine-grained synthetic load. A clocked comparator, which eliminates the need for any bias current, controls the direction of shift, D. The programmable mux-select signals, MUX1 and MUX2, provide controllable closed loop gains, KBARREL, of 1 to 3×. Since at any clock edge only 1, 2 or 3 shifts can occur (depending on the gain setting), fine-grained clock gating is enabled by dividing the 128b shifter into four sections and only enabling the clock to the section(s) where the shift occurs.
Application of Machine Translation in Localization into Low-Resourced Languages

This paper evaluates the impact of machine translation on the software localization process and the daily work of professional translators when SMT is applied to low-resourced languages with rich morphology. Translation from English into six low-resourced languages (Czech, Estonian, Hungarian, Latvian, Lithuanian and Polish) from different language groups are examined. Quality, usability and applicability of SMT for professional translation were evaluated. The building of domain and project tailored SMT systems for localization purposes was evaluated in two setups. The results of the first evaluation were used to improve SMT systems and MT platform. The second evaluation analysed a more complex situation considering tag translation and its effects on the translator’s productivity.
Korean text summarization using an aggregate similarity

In this paper, each document is represented by a weighted graph called a text relationship map. In the graph, each node represents a vector of nouns in a sentence, an undirected link connects two nodes if two sentences are semantically related, and a weight on the link is a value of the similarity between a pair of sentences. The vector similarity can be computed as the inner product between corresponding vector elements. The similarity is based on the word overlap between the corresponding sentences. The importance of a node on the map, called an aggregate similarity, is defined as the sum of weights on the links connecting it to other nodes on the map. In this paper, we present a Korean text summarization system using the aggregate similarity. To evaluate our system, we used two test collections: one collection (PAPER-InCon) consists of 100 papers in the domain of computer science; the other collection (NEWS) is composed of 105 articles in the newspapers. Under the compression rate of 20%, we achieved the recall of 46.6% (PAPER-InCon) and 30.5% (NEWS), and the precision of 76.9% (PAPER-InCon) and 42.3% (NEWS). Experiments show that our system outperforms two commercial systems.
People Hearing Without Listening : ” An Introduction To Compressive Sampling

The conventional approach to sampling signals or images follows the celebrated Shannon sampling theorem: the sampling rate must be at least twice the maximum frequency present in the signal (the so-called Nyquist rate). In fact, this principle underlies nearly all signal acquisition protocols used in consumer audio and visual electronics, medical imaging devices, radio receivers, and so on. In the field of data conversion, for example, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation: the signal is uniformly sampled at or above the Nyquist rate. This paper surveys the theory of compressive sampling also known as compressed sensing, or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. The CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use. To make this possible, compressive sampling relies on two tenets, namely, sparsity, which pertains to the signals of interest, and incoherence, which pertains to the sensing modality. • Sparsity expresses the idea that the “information rate” of a continuous time signal may be much smaller than suggested by its bandwidth, or that a discrete-time signal depends on a number of degrees of freedom which is comparably much smaller than its (finite) length. More precisely, compressive sampling exploits the fact that many natural signals are sparse or compressible in the sense that they have concise representations when expressed in the proper basis Ψ. • Incoherence extends the duality between time and frequency and expresses the idea that objects having a sparse representation in Ψ must be spread out in the domain in which they are acquired, just as a Dirac or a spike in the time domain is spread out in the frequency domain. Put differently, incoherence says that unlike the signal of interest, the sampling/sensing waveforms have an extremely dense representation in Ψ.
Region-based strategies for active contour models

The variational method has been introduced by Kass et al. (1987) in the field of object contour modeling, as an alternative to the more traditional edge detection-edge thinning-edge sorting sequence. since the method is based on a pre-processing of the image to yield an edge map, it shares the limitations of the edge detectors it uses. in this paper, we propose a modified variational scheme for contour modeling, which uses no edge detection step, but local computations instead—only around contour neighborhoods—as well as an “anticipating” strategy that enhances the modeling activity of deformable contour curves. many of the concepts used were originally introduced to study the local structure of discontinuity, in a theoretical and formal statement by leclerc & zucker (1987), but never in a practical situation such as this one. the first part of the paper introduces a region-based energy criterion for active contours, and gives an examination of its implications, as compared to the gradient edge map energy of snakes. then, a simplified optimization scheme is presented, accounting for internal and external energy in separate steps. this leads to a complete treatment, which is described in the last sections of the paper (4 and 5). the optimization technique used here is mostly heuristic, and is thus presented without a formal proof, but is believed to fill a gap between snakes and other useful image representations, such as split-and-merge regions or mixed line-labels image fields.
Paraphrase Pattern Acquisition by Diversifiable Bootstrapping

Texts that convey the same or close meaning can be written in many different ways. On the other hand, computer programs are not good at algorithmically processing meaning equivalence of short texts, without relying on knowledge. Toward addressing this problem, researchers have been investigating methods for automatically acquiring paraphrase templates from a corpus. The goal of this thesis work is to develop a paraphrase acquisition framework that can acquire lexically-diverse paraphrase templates, given small (5-20) seed instances and a small (1-10GB) plain monolingual corpus. The framework works in an iterative fashion where the seed instances are used to find paraphrase patterns from the corpus, and the patterns are used to harvest more seed instances to be used in the next iteration. Unlike previous works, lexical diversity of resulting paraphrase patterns can be controlled with a parameter. Our corpus requirement is decent as compared to previous works that require a parallel/comparable corpus or a huge parsed monolingual corpus, which is ideal for languageand domain-portability.
Finding the adequate resolution for grid mapping - Cell sizes locally adapting on-the-fly

For robot mapping occupancy grid maps are the most common representation of the environment. However, most existing algorithms for creating such maps assume a fixed resolution of the grid cells. In this paper we present a novel mapping technique that chooses the resolution of each cell adaptively by merging and splitting cells depending on the measurements. The splitting of the cells is based on a statistical measure that we derive in this paper. In contrast to other approaches the adaption of the resolution is done online during the mapping process itself. Additionally, we introduce the Nd-Tree, a generalization of quadtrees and octrees that allows to subdivide any d-dimensional volume recursively with Nd children per node. Using this data structure our approach can be implemented in a very generic way and allows the creation of 2D, 3D and even higher dimensional maps using the same algorithm. Finally, we show results of our proposed method for 2D and 3D mapping using different kinds of range sensors.
Change detection from remotely sensed images : From pixel-based to object-based approaches

The appetite for up-to-date information about earth’s surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection. 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
Representing Context in Web Search with Ontological User Profiles

One of the key factors for effective personalization of information access is the user context. We propose a framework which integrates several critical elements that make up the user context, namely, the user’s short-term behavior, semantic knowledge from ontologies that provide explicit representations of the domain of interest, and long-term user profiles revealing interests and trends. Our proposed approach involves implicitly building ontological user profiles by assigning interest scores to existing concepts in a domain ontology. These profiles are, therefore, maintained and updated as annotated instances of a reference domain ontology. We propose a spreading activation algorithm for maintaining the interest scores in the user profile based on the user’s ongoing behavior. Our experimental results show that the user context can be effectively utilized for Web search personalization. Specifically, re-ranking the search results based on interest scores derived from the semantic evidence in an ontological user profile provides better search results by proficiently bringing results closer to the top when they are most relevant to the user.
Large Scale Purchase Prediction with Historical User Actions on B2C Online Retail Platform

This paper describes the solution of Bazinga Team for Tmall Recommendation Prize 2014. With real-world user action data provided by Tmall, one of the largest B2C online retail platforms in China, this competition requires to predict future user purchases on Tmall website. Predictions are judged on F1Score, which considers both precision and recall for fair evaluation. The data set provided by Tmall contains more than half billion action records from over ten million distinct users. Such massive data volume poses a big challenge, and drives competitors to write every single program in MapReduce fashion and run it on distributed cluster. We model the purchase prediction problem as standard machine learning problem, and mainly employ regression and classification methods as single models. Individual models are then aggregated in a two-stage approach, using linear regression for blending, and finally a linear ensemble of blended models. The competition is approaching the end but still in running during writing this paper. In the end, our team achieves F1Score 0.0611 and ranks 7th (out of 7,276 teams in total).
Reservoir Computing and Self-Organized Neural Hierarchies

There is a growing understanding that machine learning architectures have to be much bigger and more complex to approach any intelligent behavior. There is also a growing understanding that purely supervised learning is inadequate to train such systems. A recent paradigm of artificial recurrent neural network (RNN) training under the umbrella-name Reservoir Computing (RC) demonstrated that training big recurrent networks (the reservoirs) differently than supervised readouts from them is often better. It started with Echo State Networks (ESNs) and Liquid State Machines ten years ago where the reservoir was generated randomly and only linear readouts from it were trained. Rather surprisingly, such simply and fast trained ESNs outperformed classical fully-trained RNNs in many tasks. While full supervised training of RNNs is problematic, intuitively there should also be something better than a random network. In recent years RC became a vivid research field extending the initial paradigm from fixed random reservoir and trained output into using different methods for training the reservoir and the readout. In this thesis we overview existing and investigate new alternatives to the classical supervised training of RNNs and their hierarchies. First we present a taxonomy and a systematic overview of the RNN training approaches under the RC umbrella. Second, we propose and investigate the use of two different neural network models for the reservoirs together with several unsupervised adaptation techniques, as well as unsupervisedly layer-wise trained deep hierarchies of such models. We rigorously empirically test the proposed methods on two temporal pattern recognition datasets, comparing it to the classical reservoir computing state of art.
Pixel Circuit With Parallel Driving Scheme for Compensating Luminance Variation Based on a-IGZO TFT for AMOLED Displays

This work proposes a new pixel structure based on amorphous indium-gallium-zinc-oxide thin-film transistors (a-IGZO TFTs) and a parallel addressing scheme for high-resolution active-matrix organic light-emitting diode (AMOLED) displays. The proposed circuit compensates for the nonuniformity of luminance that is caused by shifts in the threshold voltage (<inline-formula><tex-math notation="LaTeX">${{V}}_{{\rm{TH}}}$</tex-math></inline-formula>) and mobility of driving TFTs. Measurement results verify that the parallel addressing scheme successfully extends the compensation time and accurately detects the <inline-formula><tex-math notation="LaTeX">${{V}}_{{\rm{TH}}}$</tex-math> </inline-formula> of the driving TFT. Moreover, the proposed circuit reduces the variations of OLED luminance from more than 83% to less than 13% when the <inline-formula><tex-math notation="LaTeX">${{V}}_{{\rm{TH}}}$ </tex-math></inline-formula> and mobility of driving TFT shifts by 1 V and 30%, respectively, and the <inline-formula><tex-math notation="LaTeX">${{V}}_{{\rm{TH}}}$</tex-math></inline-formula> of OLED varies from 0 to 0.9 V.
The dynamic functional connectome: State-of-the-art and perspectives

Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different regions of the brain, which was for a long time assumed to have stationary nature. Only recently was the dynamic behaviour of FC revealed, showing that on top of correlational patterns of spontaneous fMRI signal fluctuations, connectivity between different brain regions exhibits meaningful variations within a typical resting-state fMRI experiment. As a consequence, a considerable amount of work has been directed to assessing and characterising dynamic FC (dFC), and several different approaches were explored to identify relevant FC fluctuations. At the same time, several questions were raised about the nature of dFC, which would be of interest only if brought back to a neural origin. In support of this, correlations with electroencephalography (EEG) recordings, demographic and behavioural data were established, and various clinical applications were explored, where the potential of dFC could be preliminarily demonstrated. In this review, we aim to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that we see as most promising for the future developments of the field. Advantages and pitfalls of dFC analyses are addressed, helping the readers to orient themselves through the complex web of available methodologies and tools.
Quantum computation, quantum theory and AI

The main purpose of this paper is to examine some (potential) applications of quantum computation in AI and to review the interplay between quantum theory and AI. For the readers who are not familiar with quantum computation, a brief introduction to it is provided, and a famous but simple quantum algorithm is introduced so that they can appreciate the power of quantum computation. Also, a (quite personal) survey of quantum computation is presented in order to give the readers a (unbalanced) panorama of the field. The author hopes that this paper will be a useful map for AI researchers who are going to explore further and deeper connections between AI and quantum computation as well as quantum theory although some parts of the map are very rough and other parts are empty, and waiting for the readers to fill in.
Practical Secure Aggregation for Privacy-Preserving Machine Learning

We design a novel, communication-efficient, failure-robust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner (i.e. without learning each user's individual contribution), and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network. We prove the security of our protocol in the honest-but-curious and active adversary settings, and show that security is maintained even if an arbitrarily chosen subset of users drop out at any time. We evaluate the efficiency of our protocol and show, by complexity analysis and a concrete implementation, that its runtime and communication overhead remain low even on large data sets and client pools. For 16-bit input values, our protocol offers $1.73 x communication expansion for 210 users and 220-dimensional vectors, and 1.98 x expansion for 214 users and 224-dimensional vectors over sending data in the clear.
An Amharic Stemmer : Reducing Words to their Citation Forms

Stemming is an important analysis step in a number of areas such as natural language processing (NLP), information retrieval (IR), machine translation(MT) and text classification. In this paper we present the development of a stemmer for Amharic that reduces words to their citation forms. Amharic is a Semitic language with rich and complex morphology. The application of such a stemmer is in dictionary based cross language IR, where there is a need in the translation step, to look up terms in a machine readable dictionary (MRD). We apply a rule based approach supplemented by occurrence statistics of words in a MRD and in a 3.1M words news corpus. The main purpose of the statistical supplements is to resolve ambiguity between alternative segmentations. The stemmer is evaluated on Amharic text from two domains, news articles and a classic fiction text. It is shown to have an accuracy of 60% for the old fashioned fiction text and 75% for the news articles.
Surface EMG pattern recognition for real-time control of a wrist exoskeleton

Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.



Both sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme.



The data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms.



The results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist.
Efficient Genome Editing in Apple Using a CRISPR/Cas9 system

Genome editing is a powerful technique for genome modification in molecular research and crop breeding, and has the great advantage of imparting novel desired traits to genetic resources. However, the genome editing of fruit tree plantlets remains to be established. In this study, we describe induction of a targeted gene mutation in the endogenous apple phytoene desaturase (PDS) gene using the CRISPR/Cas9 system. Four guide RNAs (gRNAs) were designed and stably transformed with Cas9 separately in apple. Clear and partial albino phenotypes were observed in 31.8% of regenerated plantlets for one gRNA, and bi-allelic mutations in apple PDS were confirmed by DNA sequencing. In addition, an 18-bp gRNA also induced a targeted mutation. These CRIPSR/Cas9 induced-mutations in the apple genome suggest activation of the NHEJ pathway, but with some involvement also of the HR pathway. Our results demonstrate that genome editing can be practically applied to modify the apple genome.