Miracle-dz/UniFAR-FTUs huggingface dataset:
Search to Distill: Pearls Are Everywhere but Not the Eyes

Standard Knowledge Distillation (KD) approaches distill the knowledge of a cumbersome teacher model into the parameters of a student model with a pre-defined architecture. However, the knowledge of a neural network, which is represented by the network's output distribution conditioned on its input, depends not only on its parameters but also on its architecture. Hence, a more generalized approach for KD is to distill the teacher's knowledge into both the parameters and architecture of the student. To achieve this, we present a new \textit{Architecture-aware Knowledge Distillation (AKD)} approach that finds student models (pearls for the teacher) that are best for distilling the given teacher model. In particular, we leverage Neural Architecture Search (NAS), equipped with our KD-guided reward, to search for the best student architectures for a given teacher. Experimental results show our proposed AKD consistently outperforms the conventional NAS plus KD approach, and achieves state-of-the-art results on the ImageNet classification task under various latency settings. Furthermore, the best AKD student architecture for the ImageNet classification task also transfers well to other tasks such as million level face recognition and ensemble learning.
Real-time Scene Text Detection with Differentiable Binarization

Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text. However, the post-processing of binarization is essential for segmentation-based detection, which converts probability maps produced by a segmentation method into bounding boxes/regions of text. In this paper, we propose a module named Differentiable Binarization (DB), which can perform the binarization process in a segmentation network. Optimized along with a DB module, a segmentation network can adaptively set the thresholds for binarization, which not only simplifies the post-processing but also enhances the performance of text detection. Based on a simple segmentation network, we validate the performance improvements of DB on five benchmark datasets, which consistently achieves state-of-the-art results, in terms of both detection accuracy and speed. In particular, with a light-weight backbone, the performance improvements by DB are significant so that we can look for an ideal tradeoff between detection accuracy and efficiency. Specifically, with a backbone of ResNet-18, our detector achieves an F-measure of 82.8, running at 62 FPS, on the MSRA-TD500 dataset. Code is available at: https://github.com/MhLiao/DB.
A CNN-RNN Framework for Crop Yield Prediction

Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for crop yield prediction based on environmental data and management practices. The proposed CNN-RNN model, along with other popular methods such as random forest (RF), deep fully connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt (including 13 states) in the United States for years 2016, 2017, and 2018 using historical data. The new model achieved a root-mean-square-error (RMSE) 9% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN has three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information. (2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields.
Understanding Top-k Sparsification in Distributed Deep Learning

Distributed stochastic gradient descent (SGD) algorithms are widely deployed in training large-scale deep learning models, while the communication overhead among workers becomes the new system bottleneck. Recently proposed gradient sparsification techniques, especially Top-$k$ sparsification with error compensation (TopK-SGD), can significantly reduce the communication traffic without an obvious impact on the model accuracy. Some theoretical studies have been carried out to analyze the convergence property of TopK-SGD. However, existing studies do not dive into the details of Top-$k$ operator in gradient sparsification and use relaxed bounds (e.g., exact bound of Random-$k$) for analysis; hence the derived results cannot well describe the real convergence performance of TopK-SGD. To this end, we first study the gradient distributions of TopK-SGD during the training process through extensive experiments. We then theoretically derive a tighter bound for the Top-$k$ operator. Finally, we exploit the property of gradient distribution to propose an approximate top-$k$ selection algorithm, which is computing-efficient for GPUs, to improve the scaling efficiency of TopK-SGD by significantly reducing the computing overhead. Codes are available at: \url{this https URL}.
Reducing agency conflict between bank stakeholders: the role of independent-but-related directors

We examine for a panel of European banks whether having a board structure that includes directors that are independent from insiders but related to non-controlling shareholders is effective in limiting expropriation by insiders, but also prevents excessive risk taking. We find that the inclusion of such “independent-but-related” directors increases bank board effectiveness for both controlled and widely held banks as it reduces the probability of default; it also decreases the cost of equity and results in higher market valuations for controlled banks. However, the inclusion of such directors is more likely to be successful if bank-level governance is accompanied by a strict supervisory regime. JEL Classification: G21, G28, G32
Deterioration Modeling of Subordinate Elements and Element Interaction for Bridge Management Systems

Today, transportation agencies face aging infrastructure, increasing traffic demands and funding limitations, making efficient allocation of maintenance, rehabilitation, and replacement (MR&R) resources increasingly difficult. Many State Departments of Transportation (DOTs) use asset management principles, such as deterioration modeling and future condition prediction, to manage and schedule maintenance for thousands of bridges in an inventory or network. Deterioration modeling is an essential bridge management tool for predicting future condition and for helping allocate MR&R funds. However, existing deterioration modeling methods fail to account for interaction between bridge elements, particularly subordinate deterioration. Subordinate deterioration occurs when element deterioration is impacted by the deterioration of a separate element, for example, bridge joints affecting the condition of bearings, pier caps, beam ends and abutments. Bridge engineers recognize subordinate deterioration exists for certain bridge elements, but it is ignored in current deterioration modeling. This study provides an investigation into the effect of deck joint deterioration on the deterioration of steel bearings and reinforced concrete pier caps for bridges in the Commonwealth of Virginia. However, the techniques developed in this research are general and can be applied to investigate the subordinate interaction of other elements. State DOTs are required by federal law to inspect and assess the condition of bridges by visual inspection. This is captured through element-level inspections, where the condition of individual members and components of a bridge are rated on a numeric scale based on condition definitions. Element-level data from Virginia’s inspection database were used to develop datasets of bridge inspection reports with steel bearing and pier cap elements. First, exploratory statistical analysis, using categorical data methods, was conducted to determine the significance of
Translanguaging and translation

This paper considers the construction of social difference in the interactions of a couple as they communicate at home and work, with one another, their colleagues, and strangers in a superdiverse English city. In our linguistic ethnographic approach we observed, wrote field notes, audio-recorded key participants, took photographs, made video-recordings, and conducted interviews. We documented the role translanguaging and translation played and showed how these social practices varied across the city ’ s spatial realms as different kinds of relationships are brought into play. While the interactions can be thematically characterized as broadly about money, business, and commerce, they can also be said to draw on widely circulating discourses about social and linguistic difference. We found that the construction of difference varied qualitatively by the distance and intimacy of the relationships in play. We also found that a translanguaging repertoire was particularly evident in navigating sensitive cultural activities, attitudes and beliefs. This points to the usefulness of translanguaging to signpost an openness to, and interest in, social and linguistic diversity in the market place, where buying and selling are the order of the day.
Stock Market Liquidity and Incentives for Corporate Innovation

Fang, Tian, and Tice’s (2014) [Does Stock Liquidity Enhance or Impede Firm Innovation? Journal of Finance, 69, 2085-2125] show that low liquid firms are more innovative. We complement this finding by investigating and documenting that aggregate stock liquidity enhances innovation at both firm and aggregate levels. To reconcile the difference, we provide three reasons. First, high stock market liquidity reduces the cost of raising external capital, making it easier for firms, especially for low liquid ones, to raise capital to finance their innovation. Second, high stock market liquidity reduces transaction costs, motivating firms to buy innovation from ones through merger and acquisition activities. The large takeover premium creates strong incentives for low liquid firms to be innovative and become takeover targets. Third, high stock market liquidity encourages innovation by attracting more institutional investors who have better monitoring. Overall, they help explain why although firm stock liquidity appears to cross-sectionally impede firm innovation, aggregate stock liquidity actually encourages innovation at both firm and aggregate levels. JEL classification: G12, G19, G34, G38, O31
Asset Quality and Monetary Transmission in India

This paper assesses the impact of asset quality of banks in India on monetary transmission. This is attempted through a detailed analysis of determinants of net interest margin (NIM). Using quarterly data for the period (Q1:2010-11 to Q1:201718) and dynamic panel data regression, the study finds that credit risk, proxied separately by the gross non-performing assets (NPA) ratio and the stressed assets ratio (NPA plus restructured assets), had a statistically significant and positive impact on the NIM of scheduled commercial banks, suggesting that deterioration in asset quality impeded monetary transmission. The analysis of two sub-periods suggests that monetary transmission was impaired through the interest rate channel when the gross NPA ratio was at a relatively low level but rising. At a high level of gross NPA ratio, NIMs came under pressure as banks were not able to sufficiently increase their spreads in a competitive market to compensate for credit risk. However, banks became risk averse and reduced their loan exposures, which impacted monetary transmission through the lending channel. This was further corroborated by the results of bank group-wise analysis. Overall, it emerges that the deterioration in asset quality has impaired monetary transmission in India.
ALCES VOL. 42, 2006 BEAZLEY ET AL.-RECOVERY PLANNING FOR MOOSE IN NOVA SCOTIA

In 2003, the Eastern moose (Alces alces americana) on mainland Nova Scotia was declared an endangered species under the Nova Scotia Endangered Species Act. Subsequently, as required by the Act, a recovery team was established and the recovery planning process was initiated. Very early in this process, it was recognized that developing a recovery strategy for this moose population was tion size, structure, reproduction, and mortality are not current for the population, and the assessment methodologies are inconsistent. The ability to evaluate potential factors limiting the population is hindered by a lack of information, primarily in the subject areas of genetic structure, health, illegal effect relationships, as well as verifying the potential cumulative and synergistic effects of the factors impacting the moose population. Answering these questions is challenging and will require substantial requisite data. Until the information gaps can be addressed, it is prudent to adopt a precautionary and adaptive approach to the recovery of this species. ALCES VOL. 42: 89-109 (2006)
NBER WORKING PAPER SERIES PREVENTING YOUTH VIOLENCE AND DROPOUT: A RANDOMIZED FIELD EXPERIMENT

This project was supported by the University of Chicago’s Office of the Provost, Center for Health Administration Studies, and the School of Social Service Administration, as well as NICHD award R21HD061757, CDC grant 5U01CE001949-02 to the University of Chicago Center for Youth Violence Prevention, grants from the Joyce, MacArthur, McCormick, Polk, and Spencer foundations, the Exelon corporation, and the Chicago Community Trust, and visiting scholar awards to Jens Ludwig from the Russell Sage Foundation and LIEPP at Sciences Po. We are grateful to the staff of Youth Guidance and World Sport Chicago (the two non-profit organizations that implemented the intervention we study here), to Wendy Fine of Youth Guidance, who designed and implemented required program data systems, to the Chicago Public Schools, to the Illinois Criminal Justice Information Authority for providing Illinois Criminal History Record Information (CHRI) data through an agreement with the Illinois State Police, and to Ellen Alberding, Jon Baron, Dan Black, Laura Brinkman, Carol Brown, Kerwin Charles, Philip Cook, Stephen Coussens, Hon. Richard M. Daley, Christine Devitt Westley, Ken Dodge, Steve Gilmore, Jonathan Guryan, Hon. Curtis Heaston, Ron Huberman, Brian Jacob, Rachel Johnston, Ilyana Kuziemko, Ben Lahey, Ann Marie Lipinski, John MacDonald, Sonya Malunda, Jeanne Marsh, Michael Masters, Michael McCloskey, Al McNally, Ernst Melchior, Douglas Miller, Michelle Morrison, Duff Morton, Sendhil Mullainathan, Mark Myrent, Derek Neal, Stacy Norris, Amy Nowell, Devah Pager, Steve Raudenbush, Sean Reardon, Thomas Rosenbaum, Anuj Shah, David Showalter, Sebastian Sotelo, Laurence Steinberg, Ashley Van Ness, Nina Vinik, Paula Wolff, and Sabrina Yusuf for valuable assistance and suggestions. We also thank seminar participants at the Boeing Corporation, Case Western University, Columbia University, Duke University, Erasmus University, Harvard University, the MacArthur Foundation, National Bureau of Economic Research, New York City Department of Probation,
Investor Attention: Endogenous Allocations, Clientele Effects, and Asset Pricing Implications

This paper investigates investor attention, its determinants, and its effect on investor behavior and asset prices. We find that retail and institutional attention significantly increases following earnings announcements and the release of macroeconomic news, and that the effect is stronger for large firms. When faced with both firm-specific and market-wide information shocks, retail attention to firm-specific information attenuates, especially during periods of greater macro uncertainty, whereas institutional attention does not. Moreover, the presence of macro news significantly lowers the return, volume, and volatility responses to earnings announcements. These results are consistent with the theoretical predictions of investor attention models and suggest that both institutional and retail investor attention are important for asset prices. JEL Classification: G10, G11, G12, G14
Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning

In this article, we propose a structured robust adaptive dictionary pair learning (RA-DPL) framework for the discriminative sparse representation (SR) learning. To achieve powerful representation ability of the available samples, the setting of RA-DPL seamlessly integrates the robust projective DPL, locality-adaptive SRs, and discriminative coding coefficients learning into a unified learning framework. Specifically, RA-DPL improves existing projective DPL in four perspectives. First, it applies a sparse <inline-formula> <tex-math notation="LaTeX">$l_{2,1}$ </tex-math></inline-formula>-norm-based metric to encode the reconstruction error to deliver the robust projective dictionary pairs, and the <inline-formula> <tex-math notation="LaTeX">$l_{2,1}$ </tex-math></inline-formula>-norm has the potential to minimize the error. Second, it imposes the robust <inline-formula> <tex-math notation="LaTeX">$l_{2,1}$ </tex-math></inline-formula>-norm clearly on the analysis dictionary to ensure the sparse property of the coding coefficients rather than using the costly <inline-formula> <tex-math notation="LaTeX">$l_{0}/l_{1}$ </tex-math></inline-formula>-norm. As such, the robustness of the data representation and the efficiency of the learning process are jointly considered to guarantee the efficacy of our RA-DPL. Third, RA-DPL conceives a structured reconstruction weight learning paradigm to preserve the local structures of the coding coefficients within each class clearly in an adaptive manner, which encourages to produce the locality preserving representations. Fourth, it also considers improving the discriminating ability of coding coefficients and dictionary by incorporating a discriminating function, which can ensure high intraclass compactness and interclass separation in the code space. Extensive experiments show that our RA-DPL can obtain superior performance over other state of the arts.
An Analysis of the Price Impact

This paper investigates which component of price impact plays a main role for asset pricing. To find a main component, we decompose the price impacts measured by turnover-version Amihud illiquidity into the permanent and transitory price impacts. We then further decompose each of these price impacts into half-price impact measures that correspond 1) positive and negative return days, and 2) good and bad news days. We find that, among the eight decomposed half-price impact measures, the “transitory half-price impact associated with bad news days” is the main component of the turnover-version Amihud illiquidity measure. Based on this finding, we suggest a new price impact measure, “Net price impact”, defined as the average net ratio of the daily transitory deviation to share turnover. With this suggested measure, we find that the price impact measure that takes into account the investors’ asymmetric response to not only the good and bad news days but also the asymmetric effect of transitory deviation associated with the news is better in explaining future stock
Logic-inspired Deep Neural Networks

Deep neural networks have achieved impressive performance and become de-facto standard in many tasks. However, phenomena such as adversarial examples and fooling examples hint that the generalization they make is ﬂawed. We argue that the problem roots in their distributed and connected nature and propose remedies inspired by propositional logic. Our experiments show that the proposed models are more local and better at resisting fooling and adversarial examples. By means of an ablation analysis, we reveal insights into adversarial examples and suggest a new hypothesis on their origins.
Towards a Unified Evaluation of Explanation Methods without Ground Truth

This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges. The core challenge is that people usually cannot obtain ground-truth explanations of the neural network. To this end, we design four metrics to evaluate explanation results without ground-truth explanations. Our metrics can be broadly applied to nine benchmark methods of interpreting neural networks, which provides new insights of explanation methods.
HOW SOCIAL MEDIA USE BY SALESPEOPLE TRANSLATES INTO SALES PERFORMANCE: THE MEDIATING ROLE

Social media is rapidly transforming the sales role in many B2B organizations with social media being increasingly adopted into the sales process by sales organizations worldwide. Using social media as part of the sales process is also known as social selling. Social selling is defined as “leveraging social media to find the right prospects, build trusted relationships, and ultimately, achieve your sales goals. This sales technique enables better sales lead generation and sales prospecting process and eliminates the need for cold calling” (LinkedIn, n.d.). A recent study by Forrester Research found 77% of B2B organizations have adopted or in the process of implementing social media technologies for use by their sales force (Forrester Research, 2017), and expect social media to be the dominant medium through which salespeople engage with customers and potential customers. However, the same study by Forrester Research found only 20% of sales organizations were implementing social selling effectively, an indication that it takes more than the adoption of social media technology tools for social media use by salespeople to translate in sales performance.
Quantitative Predictive Modelling Approaches to Understanding Rheumatoid Arthritis: A Brief Review

Rheumatoid arthritis is a chronic autoimmune disease that is a major public health challenge. The disease is characterised by inflammation of synovial joints and cartilage erosion, which lead to chronic pain, poor life quality and, in some cases, mortality. Understanding the biological mechanisms behind the progression of the disease, as well as developing new methods for quantitative predictions of disease progression in the presence/absence of various therapies is important for the success of therapeutic approaches. The aim of this study is to review various quantitative predictive modelling approaches for understanding rheumatoid arthritis. To this end, we start by briefly discussing the biology of this disease and some current treatment approaches, as well as emphasising some of the open problems in the field. Then, we review various mathematical mechanistic models derived to address some of these open problems. We discuss models that investigate the biological mechanisms behind the progression of the disease, as well as pharmacokinetic and pharmacodynamic models for various drug therapies. Furthermore, we highlight models aimed at optimising the costs of the treatments while taking into consideration the evolution of the disease and potential complications.