Miracle-dz/UniFAR-FTUs huggingface dataset:
Piracy and New Product Creation: A Bollywood Story

While copyright research in the decade following Napster focused mostly on whether file sharing undermines demand, research has more recently asked how piracy and other aspects of digitization affect the supply of new products. Although revenue has declined sharply, evidence that weakened effective copyright protection undermines creation has been elusive. Instead, because of cost-reducing effects of digitization, the number of new recorded music products – and their apparent quality – has increased. This study examines movie production in India during a period of technological change that facilitated large-scale piracy. The diffusion of the VCR and cable television in India between 1985 and 2000 created substantial opportunities for unpaid movie consumption. We use this episode to study possible impacts of piracy on supply. We first document, from narrative sources, conditions conducive to piracy as these technologies diffused. We then provide strong circumstantial evidence of piracy in diminished appropriability: movies’ revenues fell by a third to a half, conditional on their ratings by movie-goers and their ranks in their annual revenue distributions. Weaker effective demand undermined creative incentives. While the number of new movies released had grown steadily from 1960 to 1985, it fell markedly between 1985 and 2000, suggesting a supply elasticity in the range of 0.2-0.7. Thus, our study provides affirmative evidence on a central tenet of copyright policy, that stronger effective copyright protection effects more creation. We contrast our findings with evidence from other contexts.
Movement Correction in Instructed Fitness Training: Design Recommendations and Opportunities

The use of technology to assist in instructed fitness training in collocated social settings is underexplored. Here we focus on how technology can be designed to fit within, leverage, and be part of the strategies and tools that fitness trainees and instructors use to detect and correct performance errors. Drawing on ethnomethodological approaches and using the concept of correction to focus our analysis, we scrutinize the interaction between instructor and trainees in two fitness activities, AntiGravity Fitness and Pilates. We identify social configurations and resources employed in instances of correction. We also present an analytical tool useful for deconstructing such correction processes, highlighting the strategies and resources used by the social actors, and their impact on performance. Based on insights gained from our analyses, we propose design recommendations and identify design opportunities that capitalize on existing tools and collaborative correction strategies, such as scaffolding the correction process.
Prediction and bounds on shared cache demand from memory access interleaving

Cache in multicore machines is often shared, and the cache performance depends on how memory accesses belonging to different programs interleave with one another. The full range of performance possibilities includes all possible interleavings, which are too numerous to be studied by experiments for any mix of non-trivial programs. This paper presents a theory to characterize the effect of memory access interleaving due to parallel execution of non-data-sharing programs. The theory uses an established metric called the footprint (which can be used to calculate miss ratios in fully-associative LRU caches) to measure cache demand, and considers the full range of interleaving possibilities. The paper proves a lower bound for footprints of interleaved traces, and then formulates an upper bound in terms of the footprints of the constituent traces. It also shows the correctness of footprint composition used in a number of existing techniques, and places precise bounds on its accuracy.
Convergence Rates for Projective Splitting

Projective splitting is a family of methods for solving inclusions involving sums of maximal monotone operators. First introduced by Eckstein and Svaiter in 2008, these methods have enjoyed significant innovation in recent years, becoming one of the most flexible operator splitting frameworks available. While weak convergence of the iterates to a solution has been established, there have been few attempts to study convergence rates of projective splitting. The purpose of this manuscript is to do so under various assumptions. To this end, there are three main contributions. First, in the context of convex optimization, we establish an $O(1/k)$ ergodic function convergence rate. Second, for strongly monotone inclusions, strong convergence is established as well as an ergodic $O(1/\sqrt{k})$ convergence rate for the distance of the iterates to the solution. Finally, for inclusions featuring strong monotonicity and cocoercivity, linear convergence is established.
A new Miocene pinniped Allodesmus (Mammalia: Carnivora) from Hokkaido, northern Japan

A nearly complete pinniped skeleton from the middle Miocene Okoppezawa Formation (ca 16.3–13.9 Ma), Hokkaido, northern Japan, is described as the holotype of Allodesmus uraiporensis sp. nov. The new species is distinguishable from other species of the genus by having the palatine fissure (incisive foramen) that is located anterior to the canine, an anteriorly located supraorbital process of the frontal, and by having the calcaneum with a developed peroneal tubercle. Our phylogenetic analysis suggests that the subfamily Allodesminae are represented by two genera, Atopotarus and Allodesmus, and the latter genus is represented by at least six species; Al. kernensis, Al. sinanoensis, Al. naorai, Al. packardi, Al. demerei and Al. uraiporensis sp. nov. Allodesmus uraiporensis sp. nov. is one of the oldest and the northernmost record of the genus in the western North Pacific, and it suggests that the diversification of the genus in the western North Pacific was synchronous to the time of their diversification in the eastern North Pacific.
Modeling the Dunning-Kruger Effect: A Rational Account of Inaccurate Self-Assessment

Self-assessment, or the evaluation of one’s ability on a task, is widely perceived as a fundamental skill, yet in most studies, people are found to be poorly calibrated to their own abilities. Some results seem to show poorer calibration for low performers than for high performers. This effect has been explained in multiple ways: it could indicate worse metacognitive ability among the low performers (the “Dunning-Kruger” effect), or simply regression to the mean. To tease apart these explanations we develop a Bayesian model of self-assessment and evaluate its predictions in two experiments. Our results suggest that poor self-assessment is caused by the influence of prior beliefs and imperfect skill at determining whether a problem was solved correctly or not, and offer only weak support for of a relationship between metacognitive ability and performance.
Fisher-KPP equations and applications to a model in medical sciences

This paper is devoted to a class of reaction-diffusion equations with nonlinearities depending on time modeling a cancerous process with chemotherapy. We begin by considering nonlinearities periodic in time. For these functions, we investigate equilibrium states, and we deduce the large time behavior of the solutions, spreading properties and the existence of pulsating fronts. Next, we study nonlinearities asymptotically periodic in time with perturbation. We show that the large time behavior and the spreading properties can still be determined in this case.
Enhancing PHY Security of MISO NOMA SWIPT Systems with a Practical Non-Linear EH Model

Non-orthogonal multiple-access (NOMA) and simultaneous wireless information and power transfer (SWIPT) are promising techniques to improve spectral efficiency and energy efficiency. However, the security of NOMA SWIPT systems has not received much attention in the literature. In this paper, an artificial noise-aided beamforming design problem is studied to enhance the security of a multiple-input single-output NOMA SWIPT system where a practical non-linear energy harvesting model is adopted. The problem is non-convex and challenging to solve. Two algorithms are proposed to tackle this problem based on semidefinite relaxation (SDR) and successive convex approximation. Simulation results show that a performance gain can be obtained by using NOMA compared to the conventional orthogonal multiple access. It is also shown that the performance of the algorithm using a cost function is better than the algorithm using SDR at the cost of a higher computation complexity.
Prioritization of Candidate Genes in QTL Regions for Physiological and Biochemical Traits Underlying Drought Response in Barley (Hordeum vulgare L.)

Drought is one of the most adverse abiotic factors limiting growth and productivity of crops. Among them is barley, ranked fourth cereal worldwide in terms of harvested acreage and production. Plants have evolved various mechanisms to cope with water deficit at different biological levels, but there is an enormous challenge to decipher genes responsible for particular complex phenotypic traits, in order to develop drought tolerant crops. This work presents a comprehensive approach for elucidation of molecular mechanisms of drought tolerance in barley at the seedling stage of development. The study includes mapping of QTLs for physiological and biochemical traits associated with drought tolerance on a high-density function map, projection of QTL confidence intervals on barley physical map, and the retrievement of positional candidate genes (CGs), followed by their prioritization based on Gene Ontology (GO) enrichment analysis. A total of 64 QTLs for 25 physiological and biochemical traits that describe plant water status, photosynthetic efficiency, osmoprotectant and hormone content, as well as antioxidant activity, were positioned on a consensus map, constructed using RIL populations developed from the crosses between European and Syrian genotypes. The map contained a total of 875 SNP, SSR and CGs, spanning 941.86 cM with resolution of 1.1 cM. For the first time, QTLs for ethylene, glucose, sucrose, maltose, raffinose, α-tocopherol, γ-tocotrienol content, and catalase activity, have been mapped in barley. Based on overlapping confidence intervals of QTLs, 11 hotspots were identified that enclosed more than 60% of mapped QTLs. Genetic and physical map integration allowed the identification of 1,101 positional CGs within the confidence intervals of drought response-specific QTLs. Prioritization resulted in the designation of 143 CGs, among them were genes encoding antioxidants, carboxylic acid biosynthesis enzymes, heat shock proteins, small auxin up-regulated RNAs, nitric oxide synthase, ATP sulfurylases, and proteins involved in regulation of flowering time. This global approach may be proposed for identification of new CGs that underlies QTLs responsible for complex traits.
Neural Proximal Gradient Descent for Compressive Imaging

Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information. Learning a good inverse mapping from training data faces severe challenges, including: (i) scarcity of training data; (ii) need for plausible reconstructions that are physically feasible; (iii) need for fast reconstruction, especially in real-time applications. We develop a successful system solving all these challenges, using as basic architecture the repetitive application of alternating proximal and data fidelity constraints. We learn a proximal map that works well with real images based on residual networks with recurrent blocks. Extensive experiments are carried out under different settings: (a) reconstructing abdominal MRI of pediatric patients from highly undersampled k-space data and (b) super-resolving natural face images. Our key findings include: 1. a recurrent ResNet with a single residual block (10-fold repetition) yields an effective proximal which accurately reveals MR image details. 2. Our architecture significantly outperforms conventional non-recurrent deep ResNets by 2dB SNR; it is also trained much more rapidly. 3. It outperforms state-of-the-art compressed-sensing Wavelet-based methods by 4dB SNR, with 100x speedups in reconstruction time.
An Annotated Portfolio on Doing Postphenomenology Through Research Products

In this paper, we argue for framing the crafting and studying of research products as doing philosophy through things. We do this by creating an annotated portfolio of such Research through Design (RtD) artifact inquiries as postphenomenological inquiries. In our annotated portfolio, we first provide an account of the postphenomenological commitments of 1) taking empirical work as the basis of the inquiry, 2) analyzing structures of human-technology relations and 3) studying technological mediation. Secondly, we trace these commitments across six RtD artifact inquiries. We conclude with a discussion on how research products can be seen as an experimental way of doing postphenomenology and how HCI design researchers can work with that. As a result, the presented philosophical framing can be leveraged in HCI research to form a deeper and more dimensional understanding of the human-technology relations we craft and study. This also adds a methodological path to moving beyond foci of use, utility, interaction, and human-centeredness.
Temporal Aggregation of Visual Features for Large-Scale Image-to-Video Retrieval

In this research we study the specific task of image-to-video retrieval, in which static pictures are used to find a specific timestamp or frame within a collection of videos. The inner temporal structure of video data consists of a sequence of highly correlated images or frames, commonly reproduced at rates of 24 to 30 frames per second. To perform large-scale retrieval, it is necessary to reduce the amount of data to be processed by exploiting the redundancy between these highly correlated images. In this work, we explore several techniques to aggregate visual temporal information from video data based on both standard local features and deep learning representations with the focus on the image-to-video retrieval task.
IGCT Switching Behaviour Under Low Current Conditions

As MVDC technology is slowly finding its way into the area of energy distribution, there is a need for reliable, efficient, compact and low maintenance medium voltage DC-DC converters, as a counterpart to AC transformers. Given the typical operating conditions and the design practices, IGCT manufacturers have paid, so far, little attention to the switch's behaviour under low loads (below 10% of the device rated current) and this information is mostly unavailable in the datasheets. Low current turn-off performance of the IGCT, typical to some resonant converters, is explored throughout this paper, supported by TCAD simulations and test results obtained from a dedicated test setup. These tests show the prolonged duration of the turn-off process as the turn-off current decreases, which must be correctly taken into account during converter design.
Long Term Land Use Effects of New Rail Investment: Lessons from San Diego

In recent decades, U.S. cities have invested in rail transit for reasons beyond supplying alternatives to driving. Increasingly, rail investments are specifically promoted to reshape the built environment for property-led economic development. In these cases, new investment in rail transit is claimed to facilitate particular types of land use changes, mostly in the form of dense multi-family residential and mixed-use developments. Although rail’s effects on land use are widely claimed, scholarly evaluations offer mixed results. This paper examines two potential reasons for these mixed results. First, as most empirical examinations tend to be conducted shortly after new transit investment opens analysis is often criticized on the basis that short time frames may not allow land use changes to materialize. The second is that rail investment often includes changes to local zoning and land use regulations, creating opportunities for types of development that were previously outlawed. This paper evaluates these two critiques through an analysis of long-term land use effects associated with new rail transit service in San Diego, California. The results suggest that even after three decades of development cycles, rail transit has not led to consistent regulatory patterns of increased density or new mixed-use development.
Implication for treatment : GABA A receptors in aging , Down syndrome and Alzheimer ' s disease

In addition to progressive dementia, Alzheimer's disease (AD) is characterized by increased incidence of seizure activity. Although originally discounted as a secondary process occurring as a result of neurodegeneration, more recent data suggest that alterations in excitatory-inhibitory (E/I) balance occur in AD and may be a primary mechanism contributing AD cognitive decline. In this study, we discuss relevant research and reports on the GABAA receptor in developmental disorders, such as Down syndrome, in healthy aging, and highlight documented aberrations in the GABAergic system in AD. Stressing the importance of understanding the subunit composition of individual GABAA receptors, investigations demonstrate alterations of particular GABAA receptor subunits in AD, but overall sparing of the GABAergic system. In this study, we review experimental data on the GABAergic system in the pathobiology of AD and discuss relevant therapeutic implications. When developing AD therapeutics that modulate GABA it is important to consider how E/I balance impacts AD pathogenesis and the relationship between seizure activity and cognitive decline.
EMGuitar: Assisting Guitar Playing with Electromyography

Mastering fine motor tasks, such as playing the guitar, takes years of time-consuming practice. Commonly, expensive guidance by experts is essential for adjusting the training program to the student's proficiency. In our work, we showcase the suitability of Electromyography to detect fine-grained hand and finger postures in an exemplary guitar tutor scenario. We present EMGuitar, an interactive guitar tutoring system, that assists students by reporting on play correctness and adjusts playback tempi automatically. We report person-dependent classification utilizing a ring of electrodes around the forearm with an F1 score of up to 0.89 on recorded calibration data. Furthermore, our system was received well by neither diminishing ease of use nor being disruptive for the participants. Based on the received comments, we identified the need for detailed play accuracy feedback down to individual chords, for which we suggest an adapted visualization and an algorithmic approach.
The Effect of Network Width on the Performance of Large-batch Training

Distributed implementations of mini-batch stochastic gradient descent (SGD) suffer from communication overheads, attributed to the high frequency of gradient updates inherent in small-batch training. Training with large batches can reduce these overheads; however, large batches can affect the convergence properties and generalization performance of SGD. In this work, we take a first step towards analyzing how the structure (width and depth) of a neural network affects the performance of large-batch training. We present new theoretical results which suggest that--for a fixed number of parameters--wider networks are more amenable to fast large-batch training compared to deeper ones. We provide extensive experiments on residual and fully-connected neural networks which suggest that wider networks can be trained using larger batches without incurring a convergence slow-down, unlike their deeper variants.
Data Synthesis based on Generative Adversarial Networks

Privacy is an important concern for our society where sharing data with partners or releasing data to the public is a frequent occurrence. Some of the techniques that are being used to achieve privacy are to remove identifiers, alter quasi-identifiers, and perturb values. Unfortunately, these approaches suffer from two limitations. First, it has been shown that private information can still be leaked if attackers possess some background knowledge or other information sources. Second, they do not take into account the adverse impact these methods will have on the utility of the released data. In this paper, we propose a method that meets both requirements. Our method, called table-GAN , uses generative adversarial networks (GANs) to synthesize fake tables that are statistically similar to the original table yet do not incur information leakage. We show that the machine learning models trained using our synthetic tables exhibit performance that is similar to that of models trained using the original table for unknown testing cases. We call this property model compatibility . We believe that anonymization/perturbation/synthesis methods without model compatibility are of little value. We used four real-world datasets from four different domains for our experiments and conducted indepth comparisons with state-of-the-art anonymization, perturbation, and generation techniques. Throughout our experiments, only our method consistently shows balance between privacy level and model compatibility.
I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application

As online platforms are striving to get more users, a critical challenge is user churn, which is especially concerning for new users. In this paper, by taking the anonymous large-scale real-world data from Snapchat as an example, we develop ClusChurn , a systematic two-step framework for interpretable new user clustering and churn prediction, based on the intuition that proper user clustering can help understand and predict user churn. Therefore, ClusChurn firstly groups new users into interpretable typical clusters, based on their activities on the platform and ego-network structures. Then we design a novel deep learning pipeline based on LSTM and attention to accurately predict user churn with very limited initial behavior data, by leveraging the correlations among users' multi- dimensional activities and the underlying user types. ClusChurn is also able to predict user types, which enables rapid reactions to different types of user churn. Extensive data analysis and experiments show that ClusChurn provides valuable insight into user behaviors, and achieves state-of-the-art churn prediction performance. The whole framework is deployed as a data analysis pipeline, delivering real-time data analysis and prediction results to multiple relevant teams for business intelligence uses. It is also general enough to be readily adopted by any online systems with user behavior data.
Learning Acoustic Word Embeddings with Temporal Context for Query-by-Example Speech Search

We propose to learn acoustic word embeddings with temporal context for query-by-example (QbE) speech search. The temporal context includes the leading and trailing word sequences of a word. We assume that there exist spoken word pairs in the training database. We pad the word pairs with their original temporal context to form fixed-length speech segment pairs. We obtain the acoustic word embeddings through a deep convolutional neural network (CNN) which is trained on the speech segment pairs with a triplet loss. Shifting a fixed-length analysis window through the search content, we obtain a running sequence of embeddings. In this way, searching for the spoken query is equivalent to the matching of acoustic word embeddings. The experiments show that our proposed acoustic word embeddings learned with temporal context are effective in QbE speech search. They outperform the state-of-the-art frame-level feature representations and reduce run-time computation since no dynamic time warping is required in QbE speech search. We also find that it is important to have sufficient speech segment pairs to train the deep CNN for effective acoustic word embeddings.