Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Low-Rank and Sparse Discriminative Correlation Filters for Coarse-to-Fine Visual Object Tracking

Discriminative correlation filter (DCF) has achieved advanced performance in visual object tracking with remarkable efficiency guaranteed by its implementation in the frequency domain. However, the effect of the structural relationship of DCF and object features has not been adequately explored in the context of the filter design. To remedy this deficiency, this paper proposes a Low-rank and Sparse DCF (LSDCF) that improves the relevance of features used by discriminative filters. To be more specific, we extend the classical DCF paradigm from ridge regression to lasso regression, and constrain the estimate to be of low-rank across frames, thus identifying and retaining the informative filters distributed on a low-dimensional manifold. To this end, specific temporal-spatial-channel configurations are adaptively learned to achieve enhanced discrimination and interpretability. In addition, we analyse the complementary characteristics between hand-crafted features and deep features, and propose a coarse-to-fine heuristic tracking strategy to further improve the performance of our LSDCF. Last, the augmented Lagrange multiplier optimisation method is used to achieve efficient optimisation. The experimental results obtained on a number of well-known benchmarking datasets, including OTB2013, OTB50, OTB100, TC128, UAV123, VOT2016 and VOT2018, demonstrate the effectiveness and robustness of the proposed method, delivering outstanding performance compared to the state-of-the-art trackers.
Fiscal Spending and Crowding out Effect: A Comparison between Developed and Developing Countries

This paper evaluates the effect of disaggregated fiscal spending (consumption, capital formation and budget deficit) on private investment in both developed and developing countries using a panel data over the period of 2000-09. The results indicate that the elasticity of private investment with respect to government capital formation expenditure is positive in both groups (crowd in effect), but this complementary effect is greater than in the developed countries. Likewise, the elasticity of private investment with respect to government consumption spending is significantly negative in both groups (crowd out effect), but this substitution effect is larger in developed countries. Furthermore, the effect of budget deficit on private investment in developed countries is negative (crowd out effect), while this effect is positive in developing countries (crowd in effect). However, these effects are marginal in both groups.
LIFO Adoption by Industry

Prior studies have shown the cluster of Last-In-First-Out (LIFO) adoptions by firms in the same industry. However, it is not clear which factors affect the variation in the use of LIFO among different industries. In this paper, we uses proxies representing different aspects of industries' production/investment opportunity set in an effort to gain insight regarding the variation in the use of LIFO among diverse industries. Among all industry-specific variables, we find that industry-average effective tax rate (both GAAP ETR and cash ETR) significantly affect the tendency of adopting LIFO in industry level. We further find that firms in industry with lower inventory turnover rate and increasing price level are more likely to apply LIFO. In addition, because IFRS prohibit firms from using LIFO on their inventory valuation, we find that firms in the industry with more global business are less likely to use LIFO. Lastly, consistent with positive theory, we find evidence suggesting that firms in industry with higher leverage ratio are less likely to adopt LIFO while firms in industry that rely heavily on CEO compensation are more likely to adopt LIFO.
Comparison of Physical Activity in Small-Sided Basketball Games versus Full-Sided Games

Organized youth sports leagues attempt to meet many goals. Three primary needs for a youth sports league are to meet the participants' desires, provide quality learning experiences, and develop the competencies needed for continued participation. Possessing the ball, shooting more, and being more involved with a team's offense lead to more enjoyment and feelings of competence. Therefore, increasing the involvement of each player through more ball contacts should be a goal of youth sports leagues. Twelve male basketball players (age=15) from one high-school basketball team participated in this study. This study used paired t-tests to examine the differences between three-on-three basketball games and five-on-five basketball games that lasted for eight minutes in terms of average heart rate, moderate to vigorous intensity physical activity, vigorous intensity activity, and ball contacts. There were no significant differences between average heart rate, moderate-to-vigorous intensity activity, or vigorous intensity activity in the two conditions, but there were significantly more ball contacts on average in the three-on-three games. These results suggest that three-on-three leagues may be an appropriate sport for the initial exposure to basketball for youth players.
Agri market Marketing Communication Strategy through agriculture-based Online Shopping Application

This study aims to find out the communication marketing strategy form through one of the agriculture-based online shopping applications. This study used a qualitative approach. The descriptive research method with the data collection techniques is literature study and online data search. Then it is analyzed according to the descriptive method. This research is done by discussing the current marketing communication strategies by utilizing technology, paying attention to market share and consumer needs which are the important points in online Agri market business; with an agriculture-based online shopping application, it can make marketing easier and get customers. The result of this research is to identify whether the online Agri market has a good promotional strategy and can compete with other online shopping applications that have different bases. Along with technological advances, the online Agri market improves the quality of sales and purchases of fresh agricultural products from producers to consumers.
Edinburgh Research Explorer Static compression of Fe4N to 77 GPa and its implications for nitrogen in the deep Earth

Compression and decompression experiments on face-centred cubic (fcc) γʹ-Fe 4 N to 77 GPa 3 at room temperature were conducted in a diamond anvil cell with in-situ X-ray diffraction 4 (XRD) in order to examine its stability under high pressure. In the investigated pressure range, 5 γʹ-Fe 4 N did not show any structural transitions. However, a peak broadening was observed in 6 the XRD pattern above 60 GPa. The obtained pressure-volume data to 60 GPa were fitted to 7 the third-order Birch-Murnaghan equation of state (EoS), which yielded the following elastic 8 parameters: K 0 = 169 (6) GPa, Kʹ = 4.1 (4), with a fixed V 0 = 54.95 Å at 1 bar. A quantitative 9 Schreinemakers’ web was obtained at 15-60 GPa and 300-1600 K by combining the EoS for 10 γʹ-Fe 4 N with reported phase stability data at low pressures. The web indicates the existence of 11 an invariant point at 41 GPa and 1000 K where γʹ-Fe 4 N, hexagonal closed-packed (hcp) ε- 12 Fe 7 N 3 , double hexagonal closed-packed β-Fe 7 N 3 , and hcp Fe phases are stable. From the 13 invariant point, a reaction γʹ-Fe 4 N = β-Fe 7 N 3 + hcp Fe originates towards the high pressure 14 side, which determines the high-pressure stability of γʹ-Fe 4 N at 56 GPa and 300 K. Therefore, 15 the γʹ-Fe 4 N phase observed in the experiments beyond this pressure must be metastable. The 16 obtained results support the existing idea that β-Fe 7 N 3 would be the most nitrogen-rich iron 17 compound under core conditions. An iron carbonitride Fe 7 (C,N) 3 found as a mantle-derived 18 diamond inclusion implies that β-Fe 7 N 3 and Fe 7 C 3 may form a continuous solid solution in 19 the mantle deeper than 1000 km depth. Diamond formation may be related to the presence of 20 fluids in the mantle and dehydration reactions of high-pressure hydrous phase D might have supplied free fluids in the mantle at depths greater than 1000 km. As such, the existence of
Inverse Raman scattering : theory

This chapter reports on one of the nonlinear spectral features, the inverse Raman scattering (IRS), observed upon the interaction of ultrafast‐pulsed lasers in a Raman‐ active medium. Hereby, a comprehensive theoretical description of the IRS is exposed. Furthermore, the investigation carried out on synthetic eumelanin dispersions is addressed to show how the transient absorption measurements can be influenced by the IRS, if probing at energies close to Stokes and anti‐Stokes vibrational modes of the medium. A thorough analysis demonstrates that the IRS affects the sign of dynamics but not relaxation times. A specific kind of spectroscopy based on the IRS effect (ultrafast Raman loss spectroscopy) is eventually illustrated as valuable tool to characterize the structure of molecules and to investigate their dynamics during chemical reactions, even occurring at ultrafast timescales.
ON THE RELATION BETWEEN CATEGORIES OF (m, n)-ARY HYPERMODULES AND (m, n)-ARY MODULES

We introduce the category of R(m, n)-hypermodules over a Krasner (m, n)-hyperring R and obtain some categorical objects in this category such as product and coproduct. We apply the fundamental relations * and * on, M and, R respectively to construct fundamental functor from the category of R(m, n)-hypermodules into category of R/*-modules. In particular we consider the fundamental relation on (m, n)-hypermodules, and construct functor from the category of (m, n)-hypermodules to the category of (m, n)-modules. Then, we find the relations between hom, product, coproduct and fundamental functor.
Effect of thermotherapy duration, virus type and cultivar interactions on elimination of potato viruses X and S in infected seed stocks

Virus infection in potato reduces yield through seed degeneration. This can be reduced by use of virus-free seed tubers. However, novel approaches are required to ensure availability of virus-free stocks, especially in developing countries where the seed potato system are evolving. Consequently, a laboratory experiment was conducted at Kachwekano ZARDI in Uganda to evaluate and determine appropriate procedures for cleaning valuable potato cultivars infected with the most prevalent potato viruses; potato virus X (PVX) and potato virus S (PVS) which are frequent in the potato farming system. Thus, 20 in-vitro potato plantlets from cultivars Victoria, Kinigi and Rwangume each batch infected with either PVX or PVS in three replicates were grown in a thermotherapy chamber for two, three and four weeks at 37 to 40°C in 16 h of light and 30 to 34°C for 8 h of darkness per day. An equal number plantlets and replicates of the same cultivars and virus infection combinations were grown in a standard tissue culture (TC) growth room at 16 to 18°C with 16 h of light without thermotherapy as controls. Results indicated that plantlet survival after in-vitro thermotherapy decreased with increasing duration of heat treatment. Virus elimination efficiency significantly (P ≤ 0.05) differed between heat-treated plantlets and the controls but not between the duration of thermotherapy treatment. However, the highest proportion of virus-free plantlets was obtained after three weeks of thermotherapy. Significant (P ≤ 0.05) interactions were observed between thermotherapy duration and virus type on virus elimination efficiency where significantly (P ≤ 0.05) more PVS-free than PVX-free plantlets were obtained at the same thermotherapy duration. Three weeks of thermotherapy of virus-infected in-vitro plantlets, particularly for PVS, offered an equilibrium duration for adequate plantlet survival and maximum meri-clone regeneration to obtain the highest proportion of virus-free plantlets. The use of thermotherapy to obtain a high proportion of PVX-free plantlets from infected potato seed stock showed no clear trend and needs further investigation. Key words: Latent virus infection, meristem tip culture, plant virus heat therapy, recalcitrant viruses.
Introduction to the Special Section

Machine Learning (ML) is exploding in popularity. What little more than a decade ago was the realm of specialists is now becoming a commonplace tool in the development of technology products. For example, as of January 2019, Amazon has sold more than 100 million devices running its Alexa software—whose speech synthesis, recognition, and dialogue systems are all built with ML [Bohn 2019]. Alongside this growth in the application of ML, universities are investing massive resources in hiring faculty who are experts in artificial intelligence, ML, or data science, with approximately 35% of recent faculty searches in top 100 Ph.D.-granting computing departments targeting such faculty [Wills 2019]. These faculty, in turn, are teaching an ever-larger number of ML courses, thereby preparing a wave of software engineers to use ML in their work. These in-person university courses are complemented by online ML education offerings from Coursera, Udacity, EdX, and others that tout enrollments in the millions. ML-focused industry job advertisements are also are rapidly growing in number. The number of industry job postings mentioning ML or artificial intelligence on Indeed rose about 30% between 2018 and 2019, after doubleand triple-digit growth in the preceding 2 years [Indeed Editorial Team 2019]. This rapid growth in ML-based products, ML faculty positions, and ML engineering jobs is propelled by one basic phenomenon: ML is capable of leveraging ever-increasing amounts of data to inform computational tasks such as decision making, reasoning, and even design and creation of new systems. This means that systems created with ML can be far better than those created with traditional programming approaches at capturing the nuances of many phenomena that matter to people: human bodies, human communication, human societies, and the natural and human-built physical world. Some examples illustrate this point:
A CUBIC MARS METHOD FOR FOURTH-, SIXTH-, AND EIGHTH-ORDER INTERFACE TRACKING OF AN ARBITRARY NUMBER OF MATERIALS IN TWO DIMENSIONS

This work is a further development and the culmination along our line of research on explicit interface tracking in two dimensions [Zhang and Liu, J. Comput. Phys. 27 (2008): 4063-4088] [Zhang, SIAM J. Numer. Anal. 51 (2013): 2822-2850] [Zhang and Fogelson, SIAM J. Sci. Comput. 38 (2014): A2369-A2400] [Zhang and Fogelson, SIAM J. Numer. Anal. 54 (2016): 530-560]. Previously, we have proposed an analytic framework for explicit interface tracking and a fourth-order interface tracking method. In this paper, we continue our approach to propose a new method called the cubic MARS method that features (1) a solid theoretical foundation consisting of well developed concepts from distinct branches of mathematics, (2) a representation of the interface with cubic splines, (3) a novel algorithm for Boolean operations on linear/curvilinear polygons, (4) 4th-, 6th-, and 8th-order accuracy via a refined control of the relation between the Eulerian grid size h and a Lagrangian length scale hL of interface markers, and (5) a straightforward generalization to an arbitrary number of phases without accuracy deterioration. For the single-vortex test and the deformation test, the proposed method achieves machine precision on an Eulerian grid with h = 1 128 , thus making it unnecessary to devote exclusive algorithmic steps to volume conservation.
Topographic Controls on Southern California Ecosystem Function and Post-fire Recovery: a Satellite and Near-surface Remote Sensing Approach

Author(s): Azzari, George | Advisor(s): Goulden, Michael L | Abstract: Southern Californian wildfires can influence climate in a variety of ways, including changes in surface albedo, emission of greenhouse gases and aerosols, and the production of tropospheric ozone. Ecosystem post-fire recovery plays a key role in determining the strength, duration, and relative importance of these climate forcing agents. Southern California's ecosystems vary markedly with topography, creating sharp transitions with elevation, aspect, and slope. Little is known about the ways topography influences ecosystem properties and function, particularly in the context of post-fire recovery. We combined images from the USGS satellite Landsat 5 with flux tower measurements to analyze pre- and post-fire albedo and carbon exchanged by Southern California's ecosystems in the Santa Ana Mountains. We reduced the sources of external variability in Landsat images using several correction methods for topographic and bidirectional effects. We used time series of corrected images to infer the Net Ecosystem Exchange and surface albedo, and calculated the radiative forcing due to CO2 emissions and albedo changes. We analyzed the patterns of recovery and radiative forcing on north- and south-facing slopes, stratified by vegetation classes including grassland, coastal sage scrub, chaparral, and evergreen oak forest. We found that topography strongly influenced post-fire recovery and radiative forcing. Field observations are often limited by the difficulty of collecting ground validation data. Current instrumentation networks do not provide adequate spatial resolution for landscape-level analysis. The deployment of consumer-market technology could reduce the cost of near-surface measurements, allowing the installation of finer-scale instrument networks. We tested the performance of the Microsoft Kinect sensor for measuring vegetation structure. We used Kinect to acquire 3D vegetation point clouds in the field, and used these data to compute plant height, crown diameter, and volume. We found good agreement between Kinect-derived and manual measurements.
Detecting (Unusual) Events in Urban Areas using Bike-Sharing Data

Social media, traffic sensors, GPS trajectories, and location-based social network data provide diverse Spatio temporal information sources that help to detect and analysis Spatio temporal events. Nowadays, bike sharing systems are active all over the world in major cities, and collecting a large amount of data regarding trips taken by users and status of the stations. Through analysis of the data aggregated by bike sharing systems, one can gain an understanding of crowd/commuter movements and behaviors. However, no one has used only the bike sharing data for generic event detection. In this paper, we propose a clustering-based detection method to identify Spatio temporal events that deviate from normal or regular everyday life using publicly available bike sharing data. In particular, we apply spectral clustering on bike station and bike flow data as evolving graphs and monitor changes of the bike share network (edge/node values) over time. Our proposed method decides whether a cluster is expected or anomalous (unusual). When a cluster is anomalous, there is an unusual event occurring at that time instance. Preliminary results on 6-months of data from Philadelphia and Washington DC are used to show the feasibility of our proposed method. In particular, our preliminary results show that some signatures of local (and less prominent) events (e.g., university events/activities in an urban area) can show up when bike sharing data is utilized for generic event detection.
Coordinating Clinical Teams

Patient safety errors account for over 400,000 preventable deaths annually in US in hospitals alone, 70% of which are caused by team communication breakdowns, stemming from hierarchical structures and asymmetrical power dynamics between physicians, nurses, patients, and others. Nurses are uniquely positioned to identify and prevent these errors, but they are often penalized for speaking up, particularly when physicians are responsible. Nevertheless, empowering nurses and building strong interdisciplinary teams can lead to improved patient safety and outcomes. Thus, our group has been developing a series of intelligent systems that support teaming in safety critical settings, Robot-Centric Team Support System (RoboTSS), and recently developed a group detection and tracking system for collaborative robots. In this paper, we explore how RoboTSS can be used to empower nurses in interprofessional team settings, through a three month long, collaborative design process with nurses across five US-based hospitals. The main findings and contributions of this paper are as follows. First, we found that participants envisioned using a robotic crash cart to guide resuscitation procedures to improve efficiency and reduce errors. Second, nurses discussed how RoboTSS can generate choreography for efficient spatial reconfigurations in co-located clinical teams, which is particularly important in time-sensitive situations such as resuscitation. Third, we found that nurses want to use RoboTSS to "stop the line," and disrupt power dynamics by policing unsafe physician behavior, such as avoiding safety protocols using a robotic crash cart. Fourth, nurses envisioned using our system to support real-time error identification, such as breaking the sterile field, and then communicating those errors to physicians, to relieve them of responsibility. Finally, based on our findings, we propose robot design implications that capture how nurses envision utilizing RoboTSS. We hope this work promotes further exploration in how to design technology to challenge authority in asymmetrical power relationships, particularly in healthcare, as strong teams save lives.
A Machine Learning Approach to Predicting Alcohol Consumption in Adolescents From Historical Text Messaging Data

A Machine Learning Approach to Predicting Alcohol Consumption in Adolescents From Historical Text Messaging Data by Adrienne Melissa Martin Bergh Techniques based on artificial neural networks represent the current state-of-the-art in machine learning due to the availability of improved hardware and large data sets. Here we employ doc2vec, an unsupervised neural network, to capture the semantic content of text messages sent by adolescents during high school, and encode this semantic content as numeric vectors. These vectors e↵ectively condense the text message data into highly leverageable inputs to a logistic regression classifier in a matter of hours, as compared to the tedious and often quite lengthy task of manually coding data. Using our machine learning approach, we are able to train a logistic regression model to predict adolescents’ engagement in substance abuse during distinct life phases with accuracy ranging from 76.5% to 88.1%. We show the e↵ects of grade level and text message aggregation strategy on the e cacy of document embedding generation with doc2vec. Additional examination of the vectorizations for specific terms extracted from the text message data adds quantitative depth to this analysis. We demonstrate the ability of the method used herein to overcome traditional natural language processing concerns related to unconventional orthography. These results suggest that the approach described in this thesis is a competitive and e cient alternative to existing methodologies for predicting substance abuse behaviors. This work reveals the potential for the application of machine learning-based manipulation of text messaging data to development of automatic intervention strategies against substance abuse and other adolescent challenges.