Miracle-dz/UniFAR-FTUs huggingface dataset:
On Detection of Multiple Object Instances using Hough Transforms

To detect multiple objects of interest, the methods based on Hough transform use non-maxima supression or mode seeking in order to locate and to distinguish peaks in Hough images. Such postprocessing requires tuning of extra parameters and is often fragile, especially when objects of interest tend to be closely located. In the paper, we develop a new probabilistic framework that is in many ways related to Hough transform, sharing its simplicity and wide applicability. At the same time, the framework bypasses the problem of multiple peaks identification in Hough images, and permits detection of multiple objects without invoking nonmaximum suppression heuristics. As a result, the experiments demonstrate a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category-level (pedestrian) de-
tiNNbre: a timbre-based musical agent

In this paper, we present tiNNbre, a generative music prototype-system that reacts to timbre gestures. By timbre gesture we mean a sonic (as opposed to body) gesture that mainly conveys artistic meaning through timbre rather than other sonic properties. The system is designed and developed to be used in free improvisation and composition. Our prototype is powered by a neural network trained using a supervised learning approach on a set of sonic gestures representing the stimulus (or input) and a correspondent set of sonic gestures representing the reactions (or output). This model is then used to explore and generate new musical material. Here, we present an informal evaluation of our system, based on two use cases. In the first, the system is trained using MFCC analysis, while the second uses Constant-Q Transform spectrum. Participants were asked to rate our system’s generated audio, particularly in respect of timbre. Results showed that although tiNNbre has been tested offline, it fosters timbre-rich interaction with the sonic materials and the co-creation process. Broadly speaking, musicians preferred the results obtained using the second system other than for purely percussive gestures.
On the use of deep learning for computational imaging

Since their inception in the 1930–1960s, the research disciplines of computational imaging and machine learning have followed parallel tracks and, during the last two decades, experienced explosive growth drawing on similar progress in mathematical optimization and computing hardware. While these developments have always been to the benefit of image interpretation and machine vision, only recently has it become evident that machine learning architectures, and deep neural networks in particular, can be effective for computational image formation, aside from interpretation. The deep learning approach has proven to be especially attractive when the measurement is noisy and the measurement operator ill posed or uncertain. Examples reviewed here are: super-resolution; lensless retrieval of phase and complex amplitude from intensity; photon-limited scenes, including ghost imaging; and imaging through scatter. In this paper, we cast these works in a common framework. We relate the deep-learning-inspired solutions to the original computational imaging formulation and use the relationship to derive design insights, principles, and caveats of more general applicability. We also explore how the machine learning process is aided by the physics of imaging when ill posedness and uncertainties become particularly severe. It is hoped that the present unifying exposition will stimulate further progress in this promising field of research.
Questioni di Economia e Finanza

Boom-and-bust cycles in the housing market pose a threat to macroeconomic and financial stability, thus calling for a timely assessment of imbalances. This work sheds light on the drivers of house price dynamics in some euro area economies, investigating whether the increases in house prices underway since 2015 signal a risk of overheating. We show that an Error-Correction-Model (ECM) featuring a long-run relationship between house prices and income and short-run effects of interest rates and housing supply fits the data well in most cases. We then propose a novel model-based misalignment indicator that consists in the difference between actual and predicted house prices once we have removed the component relating to extrapolative house price expectations (bubble-builder component). We find that, in 2021 (the last year in our analysis), prices were slightly undervalued in Italy and Ireland, moderately overvalued in France and Belgium, and significantly overvalued in Spain and Germany. Despite some quantitative differences, our results are broadly in line with the assessment of the European Central Bank.
Complementary‐Relationship‐Based Modeling of Terrestrial Evapotranspiration Across China During 1982–2012: Validations and Spatiotemporal Analyses

Having recognized the limitations in spatial representativeness and/or temporal coverage of (i) current ground ETa observations and (ii) land surface model‐ and remote sensing‐based ETa estimates due to uncertainties in soil and vegetation parameters, a calibration‐free nonlinear complementary relationship (CR) model is employed with inputs of air and dew‐point temperature, wind speed, and net radiation to estimate 0.1°, monthly ETa over China during 1982–2012. The modeled ETa rates were first validated against 13 eddy‐covariance measurements, producing Nash‐Sutcliffe efficiency values in the range of 0.72–0.94. On the basin scale, the modeled ETa values yielded a relative bias of 6%, and a Nash‐Sutcliffe efficiency value of 0.80 in comparison with water‐balance‐derived evapotranspiration rates across 10 major river basins in China, indicating the CR‐simulated ETa rates reliable over China. Further evaluations suggest that the CR‐based ETa product is more accurate than seven other mainstream ETa products. The 31‐year mean annual ETa value decreases from the southeast to the northwest in China, resulting in a country average of 406 ± 15 mm/year. The country‐representative annual ETa rates slightly decreased with a rate of −0.5 mm/year (p = 0.86) during 1982–2012. Annual ETa increased significantly over most parts of western and northeastern China but decreased significantly in many regions of the North China Plain as well as in the eastern and southern coastal regions. The present CR‐based method, with its calibration‐free nature and minimal data requirement, could help future calibrations/verifications of the more complex and more data‐intensive land surface model‐ and remote sensing‐based models.
The Buuer Tree: a New Technique for Optimal I/o-algorithms ?

In this paper we develop a technique for transforming an internal memory tree data structure into an external storage structure. We show how the technique can be used to develop a search-tree-like structure , a priority-queue, a (one-dimensional) range-tree and a segment-tree, and give examples of how these structures can be used to develop ee-cient I/O-algorithms. All our algorithms are either extremely simple or straightforward generalizations of known internal memory algorithms | given the developed external data structures.
Investigating the Role of Information Technology in Building Buyer-Supplier Relationships

A widely held position observed through the lens of transaction cost theory (TCT) has been the role of information technology in decreasing transaction costs between buyers and suppliers and in creating more market based governance structures. However, observations have not supported this contention. In particular, buyer-supplier dyads often engage in cooperative behavior that could offset opportunistic tendencies espoused by TCT. The role of IT in this structure is unclear. This paper examines the relationship between perceived transaction costs and the concept of relationalism within buyer-supplier dyads. The role of IT in mediating this relationship is also examined. Survey data from 203 buyers in the OEM electronics industry is used to test proposed hypotheses. All major constructs, transaction costs, relationalism, and IT use are operationalized using validated multidimensional scales. The results suggest a positive role of IT in partially offsetting the negative relationship between transaction costs and relationalism. The results suggest that the decision to use IT within the dyad can encourage a commitment to establishing relational behavior. Journal of the Association for Information Systems (Volume 3, 2002) 217-245 218 Investigating the Role of IT in Building Buyer-Supplier Relationships by V. Grover, J. T. C. Teng, and K. D. Fiedler
Systems Biology Mosaic: Making Biological Sense of Complex Networks

We present a Cytoscape plugin called Mosaic to support interactive network annotation, partitioning, layout and coloring based on gene ontology or other relevant annotations. Availability: Mosaic is distributed for free under the Apache v2.0 open source license and can be downloaded via the Cytoscape plugin manager. A detailed user manual is available on the Mosaic web site
Communicative Representation Learning on Attributed Molecular Graphs

Constructing proper representations of molecules lies at the core of numerous tasks such as molecular property prediction and drug design. Graph neural networks, especially message passing neural network (MPNN) and its variants, have recently made remarkable achievements in molecular graph modeling. Albeit powerful, the one-sided focuses on atom (node) or bond (edge) information of existing MPNN methods lead to the insufficient representations of the attributed molecular graphs. Herein, we propose a Communicative Message Passing Neural Network (CMPNN) to improve the molecular embedding by strengthening the message interactions between nodes and edges through a communicative kernel. In addition, the message generation process is enriched by introducing a new message booster module. Extensive experiments demonstrated that the proposed model obtained superior performances against state-of-the-art baselines on six chemical property datasets. Further visualization also showed better representation capacity of our model.
Super Resolution Based Image Inpainting

A new algorithm is proposed for removing large objects from digital images. The challenge is to fill in the hole that is left behind in a visually plausible way. This has been addressed by two classes of algorithms one is the “inpainting algorithms” for filling in small image gaps, and second is the “super resolution” techniques” for creating one enhanced resolution image. This paper presents a novel and efficient algorithm that combines the advantages of these two approaches. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. We propose a best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting.
Visual modeling with a hand-held camera MARC

In this paper a complete system to build visual models from camera images is presented. The system can deal with uncalibrated image sequences acquired with a hand-held camera. Based on tracked or matched features the relations between multiple views are computed. From this both the structure of the scene and the motion of the camera are retrieved. The ambiguity on the reconstruction is restricted from projective to metric through self-calibration. A flexible multi-view stereo matching scheme is used to obtain a dense estimation of the surface geometry. From the computed data different types of visual models are constructed. Besides the traditional geometryand image-based approaches, a combined approach with view-dependent geometry and texture is presented. As an application fusion of real and virtual scenes is also shown. keywords: Visual modeling, Structure-from-Motion, Projective reconstruction, Self-calibration, Multi-view stereo matching, Dense reconstruction, 3D reconstruction, Image-based rendering, Augmented video, hand-held camera.
Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning Supplementary Materials

In ablation study, we replace our mCS with max pooling, average pooling, projection metric [3], covariance matrix [9, 1], set kernel [4], and cosine similarity metric [8]. For projection metric, we use the inner product as described in [3]. For covariance matrix, we calculate two covariance matrices and the inner product between the two matrices [10]. Note that we also normalize the calculated similarities as described in [8]. For set kernel, we use Gaussian kernel and multiple kernel learning as described in [5].
Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm

One fundamental issue of learning to rank is the choice of loss function to be optimized. Although the evaluation measures used in Information Retrieval (IR) are ideal ones, in many cases they can't be used directly because they do not satisfy the smooth property needed in conventional machine learning algorithms. In this paper a new method named RankCSA is proposed, which tries to use IR evaluation measure directly. It employs the clonal selection algorithm to learn an effective ranking function by combining various evidences in IR. Experimental results on the LETOR benchmarh datasets demonstrate that RankCSA outperforms the baseline methods in terms of P@n, MAP and NDCG@n.
A Floating-Point Exponentiation Units for Reconfigurable Computing

The high performance and capacity of current FPGAs makes them suitable as acceleration co-processors. This article studies the implementation, for such accelerators, of the floating-point power function xy as defined by the C99 and IEEE 754-2008 standards, generalized here to arbitrary exponent and mantissa sizes. Last-bit accuracy at the smallest possible cost is obtained thanks to a careful study of the various subcomponents: a floating-point logarithm, a modified floating-point exponential, and a truncated floatingpoint multiplier. A parameterized architecture generator in the open-source FloPoCo project is presented in details and evaluated.
Computers in Human Behavior

Big data analytics has recently emerged as an important research area due to the popularity of the Internet and the advent of the Web 2.0 technologies. Moreover, the proliferation and adoption of social media applications have provided extensive opportunities and challenges for researchers and practitioners. The massive amount of data generated by users using social media platforms is the result of the integration of their background details and daily activities. This enormous volume of generated data known as “ big data ” has been intensively re-searched recently. A review of the recent works is presented to obtain a broad perspective of the social media big data analytics research topic. We classify the literature based on important aspects. This study also compares possible big data analytics techniques and their quality attributes. Moreover, we provide a discussion on the applications of social media big data analytics by highlighting the state-of-the-art techniques, methods, and the quality attributes of various studies. Open research challenges in big data analytics are described as well.
The UCSC Repeat Browser allows discovery and visualization of evolutionary conflict across repeat families

Background Nearly half the human genome consists of repeat elements, most of which are retrotransposons, and many of these sequences play important biological roles. However repeat elements pose several unique challenges to current bioinformatic analyses and visualization tools, as short repeat sequences can map to multiple genomic loci resulting in their misclassification and misinterpretation. In fact, sequence data mapping to repeat elements are often discarded from analysis pipelines. Therefore, there is a continued need for standardized tools and techniques to interpret genomic data of repeats. Results We present the UCSC Repeat Browser, which consists of a complete set of human repeat reference sequences derived from the gold standard repeat database RepeatMasker. The UCSC Repeat Browser contains mapped annotations from the human genome to these references, and presents all of them as a comprehensive interface to facilitate work with repetitive elements. Furthermore, it provides processed tracks of multiple publicly available datasets of biological interest to the repeat community, including ChIP-SEQ datasets for KRAB Zinc Finger Proteins (KZNFs) – a family of proteins known to bind and repress certain classes of repeats. Here we show how the UCSC Repeat Browser in combination with these datasets, as well as RepeatMasker annotations in several non-human primates, can be used to trace the independent trajectories of species-specific evolutionary conflicts. Conclusions The UCSC Repeat Browser allows easy and intuitive visualization of genomic data on consensus repeat elements, circumventing the problem of multi-mapping, in which sequencing reads of repeat elements map to multiple locations on the human genome. By developing a reference consensus, multiple datasets and annotation tracks can easily be overlaid to reveal complex evolutionary histories of repeats in a single interactive window. Specifically, we use this approach to retrace the history of several primate specific LINE-1 families across apes, and discover several species-specific routes of evolution that correlate with the emergence and binding of KZNFs.
How Do Developers Utilize Source Code from Stack Over ow?

Technical question and answer Q&A platforms, such as Stack Overow, provide a platform for users to ask and answer questions about a wide variety of programming topics. These platforms accumulate a large amount of knowledge, including hundreds of thousands lines of source code. Developers can bene t from the source code that is attached to the questions and answers on Q&A platforms by copying or learning from (parts of) it. By understanding how developers utilize the source code from Q&A platforms, we can provide insights for researchers which help improve next-generation Q&A platforms to help developers to reuse the source code fast and easily. In this paper, we rst conduct an exploratory study on 95 JavaScript les from 89 projects which contain source code that contains an explicit reference to a Stack Over ow post. In 49.4% of the studied cases, developers need to modify source code to make it work in the required context, with the degree of modi cation varying from renaming variables to rewriting the whole algorithm. Developers sometimes choose to implement an algorithm from scratch based on the descriptions from Stack Over ow answers, even if there is an implementation readily available in the post. To further understand the barriers that prevent code reuse from developers and obtain suggestions for improving the code reuse from Q&A platforms, we conduct a survey on 453 developers on Stack Over ow. We nd that the top 3 barriers that prevent developers from copying code from Stack Over ow are: (1) too much code modi cation required to t in their projects, (2) incomprehensive code, and (3) low code quality. Yuhao Wu · Katsuro Inoue Graduate School of Information Science and Technology, Osaka University, Japan E-mail: {wuyuhao,inoue}@ist.osaka-u.ac. jp Shaowei Wang (B) · Cor-Paul Bezemer SAIL, Queen's University, Canada E-mail: {shaowei,bezemer}@cs.queensu. ca
How Do Developers Utilize Source Code from Stack Over(cid:29)ow?

Technical question and answer Q&A platforms, such as Stack Over(cid:29)ow, provide a platform for users to ask and answer questions about a wide variety of programming topics. These platforms accumulate a large amount of knowledge, including hundreds of thousands lines of source code. Developers can bene(cid:28)t from the source code that is attached to the questions and answers on Q&A platforms by copying or learning from (parts of) it. By understanding how developers utilize source code from Q&A platforms, we can provide insights for researchers which can be used to improve next-generation Q&A platforms to help developers to reuse source code fast and easily. In this paper, we (cid:28)rst conduct an exploratory study on 289 (cid:28)les from 182 open-source projects, which contain source code that contains an explicit reference to a Stack Over(cid:29)ow post. Our goal is to understand how developers utilize code from Q&A platforms and to reveal barriers that may make code reuse more di(cid:30)cult. In 31.5% of the studied (cid:28)les, developers needed to modify source code from Stack Over(cid:29)ow to make it work in their own projects. The degree of required modi(cid:28)cation varied from simply renaming variables to rewriting the whole algorithm. Developers sometimes chose to implement an algorithm from scratch based on the descriptions from Stack Over(cid:29)ow answers, even if there was an implementation readily available in the post. In 35.5% of the studied (cid:28)les, developers used Stack Over(cid:29)ow posts as an information source for later reference. To further understand the barriers of reusing code and to obtain suggestions for improving the code reuse process on Q&A platforms, we conducted a survey of 453 open-source developers who are also on Stack Over(cid:29)ow. We (cid:28)nd that
Relation-Oriented: Toward Causal Knowledge-Aligned AGI

The current relationship modeling paradigm, grounded in the observational i.i. d assumption, fundamentally misaligns with our causal knowledge understanding due to two key oversights: 1) the unobservable relations, which lead to undetectable hierarchical levels of knowledge, driving the need for model generalizability; 2) the cognitive relative timings, which crucially support our structural knowledge comprehension, resulting in inherent biases within the present Observation-Oriented paradigm. Adopting a novel Relation-Oriented perspective, this paper proposes a new framework to unify the various confusions surrounding causality learning, ranging from traditional causal inference to modern language models. Also, relation-indexed representation learning (RIRL) is raised as a baseline implementation method of the proposed new paradigm, alongside comprehensive experiments demonstrating its efficacy in autonomously identifying dynamical effects in relationship learning.
Utility Based Q-learning to Maintain Cooperation in Prisoner's Dilemma Games

This work deals with Q-learning in a multiagent environment. There are many multiagent Q-learning methods, and most of them aim to converge to a Nash equilibrium, which is not desirable in games like the prisoner's dilemma (PD). However, normal Q-learning agents that use a stochastic method in choosing actions to avoid local optima may bring mutual cooperation in PD. Although such mutual cooperation usually occurs singly, it can be maintained if the Q- function of cooperation becomes larger than that of defection after the cooperation. This work derives a theorem on how many times the cooperation is needed to make the Q- function of cooperation larger than that of defection. In addition, from the perspective of the author's previous works that discriminate utilities from rewards and use utilities for learning in PD, this work also derives a corollary on how much utility is necessary to make the Q-function larger by one-shot mutual cooperation.