Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Models with Uniform Performance via Distributionally Robust Optimization

A common goal in statistics and machine learning is to learn models that can perform well against distributional shifts, such as latent heterogeneous subpopulations, unknown covariate shifts, or unmodeled temporal effects. We develop and analyze a distributionally robust stochastic optimization (DRO) framework that learns a model providing good performance against perturbations to the data-generating distribution. We give a convex formulation for the problem, providing several convergence guarantees. We prove finite-sample minimax upper and lower bounds, showing that distributional robustness sometimes comes at a cost in convergence rates. We give limit theorems for the learned parameters, where we fully specify the limiting distribution so that confidence intervals can be computed. On real tasks including generalizing to unknown subpopulations, fine-grained recognition, and providing good tail performance, the distributionally robust approach often exhibits improved performance.
Clonal Glial Response in a Multiple Sclerosis Mouse Model

Multiple sclerosis (MS) is an autoimmune disease causing central nervous system (CNS) demyelination and axonal injury. In the last years the importance of astrocytes in MS is rapidly increasing, recognizing astrocytes as highly active players in MS pathogenesis. Usually the role assigned to astrocytes in MS lesions has been the formation of the glial scar, but now their implication during lesion formation and the immune response increasingly recognized. Since astrocytes are a heterogeneous cell population with diverse roles in the CNS, the aim of this study was to analyze the putative clonal response of astrocytes in a demyelinating scenario. To undertake this aim, we used the induced experimental autoimmune encephalomyelitis (EAE) as a murine model for MS in previously electroporated mice with in vivo multicolor lineage tracing system, the StarTrack methodology. Our data revealed a variety of morphological changes that were different among distinct clones. In many cases, cells of the same clone responded equally to the injury, while in other cases clonally-related cells responded differently to the injury. Therefore, whereas some clones exhibited a strong morphological alteration, other clones located at similar distances to the lesion were apparently unresponsive. Thus, at present there is no compelling evidences that clonal relationship influences the position or function of astrocytes in the EAE model. Further, the coexistence of different astroglial clonal responses to the bran injury reveals the significance of development to determine the astrocyte features that respond to brain injuries.
Proportionality Degree of Multiwinner Rules

An instance of an approval-based multiwinner election consists of a set of alternatives, a population of voters---each voter approves a subset of alternatives, and the desired committee size k; the goal is to select a committee (a subset) of k alternatives according to the preferences of the voters. We investigate a number of election rules and ask whether the committees that they return represent the voters proportionally. In contrast to the classic literature, we employ quantitative techniques that allow to measure the extent to which the considered rules are proportional. This allows us to arrange the rules in a clear hierarchy. For example, we find that Proportional Approval Voting (PAV) has better proportionality guarantees than its sequential counterpart, and that Phragmen's Sequential Rule is worse than Sequential PAV. Yet, the loss of proportionality for the two sequential rules is moderate and in some contexts can be outweighed by their other appealing properties. Finally, we measure the tradeoff between proportionality and utilitarian efficiency for a broad subclass of committee election rules.
A Clustering Based Social Matrix Factorization Technique for Personalized Recommender Systems

Recently, a new paradigm of social network based recommendation approach has emerged wherein struc-tural features from social network turned out to be an effective measure to improve the efficacy of the algorithms. However, these approaches assume a user is impacted by all his social connections and com-pletely ignore their preferential similarity, which is crucial for personalized recommendations. Herein, we address this pivotal issue and propose a two-stage clustering based matrix-factorization algorithm, “ C luster RE finement on P reference E mbedded MF (CREPE MF)” using a subgraph of social network that integrates the preferential similarity score. Clustering has been applied first on the user followed by the item based on ratings. The proposed algorithm has been systematically evaluated with state-of-the-art algorithms in terms of prediction accuracy and runtime complexity using real-world Yelp dataset. Gratifyingly, our approach outperforms the state-of-the-art algorithms with up to 12.97% and 29.60% improvements in RMSE and runtime, respectively.
Information Systems Design Considerations for Reducing Alarm Fatigue in ICU

Medical device alarms are a mechanism for notifying clinicians about deterioration patient conditions. However, alarm fatigue, which refers to the desensitization of clinicians to device alarms, is a major issue in hospitals and especially in intensive care facilities where a high number of medical devices are typically used to monitor critical patients(Ruskin & Hueske-Kraus, 2015). Alleviating alarm fatigue is an important problem since it can lead to lower response times and ultimately effect patient safety (Ruskin & HueskeKraus, 2015). A number of approaches have been proposed for alleviating the problem of alarm fatigue (Winters et al., 2018). Many of the potential solutions are in the domain of information systems and include the design of new algorithms, interfaces, and information routing policies. Given the low rate of accuracy in many types of device alarms, there is a significant scope to improve the accuracy using machine learning and data fusion techniques (Shrestha & Sarnikar, 2014). Another mechanism for alleviating alarm fatigue is role-based routing of alarms based on the nature of the alarm. While new machine learning algorithms can potentially reduce false alarms in aggregate, machine learning algorithms are seldom 100% accurate and are likely to filter out true alarms as well. Moreover, the performance of alarms can vary significantly across different contexts. In order to assess the potential for implementing either the new algorithmic or the role-based routing approaches, it is first necessary to study the relative advantage (Yang, Kankanhalli, Ng, & Lim, 2013) of such systems impact varying performance of such algorithms and identify the threshold at which implementation of new machine learning based algorithms can be considered a viable option. In this research, we use modeling approaches to study the workflow and cognitive processes involved in ICU management (Gupta & Sharda, 2013) and the impact of the performance of machine learning algorithms, alarm routing policies and staffing models on patient safety and identify minimum thresholds for machine learning performance and optimal routing policies for alarms under various staffing models to improve patient safety. The results of this research will help design information system solutions that include software (machine learning algorithms and performance thresholds), people (staffing models) and procedures (routing policies) for solving the alarm fatigue problem.
Identification and Evaluation of the Face System of a Child Android Robot Affetto for Surface Motion Design

Faces of android robots are one of the most important interfaces to communicate with humans quickly and effectively, as they need to match the expressive capabilities of the human face, it is no wonder that they are complex mechanical systems containing inevitable non-linear and hysteresis elements derived from their non-rigid components. Identifying the input-output response properties of this complex system is necessary to design surface deformations accurately and precisely. However, to date, android faces have been used without careful system identification and thus remain black boxes. In this study, the static responses of three-dimensional displacements were investigated for 116 facial surface points against a discrete trapezoidal input provided to each actuator in the face of a child-type android robot Affetto. The results show that the response curves can be modeled with hysteretical sigmoid functions, and that the response properties of the face actuators, including sensitivity, hysteresis, and dyssynchrony, were quite different. The paper further proposes a design methodology for surface motion patterns based on the obtained response models. Design results thus obtained indicate that the proposed response properties enable us to predict the design results, and that the proposed design methodology can cancel the differences among the response curves of the actuators. The proposed identification and quantitative evaluation method can be applied to advanced android face studies instead of conventional qualitative evaluation methodologies.
Designing Incentive-Pricing Mechanisms to Promote Health Information Exchange

The central motivation for a Health Information Exchange (HIE) system is enabling healthcare providers to share and access patients’ electronic medical information(Vest et al. 2015). While patients and insurance companies receive tangible benefits from using HIE, healthcare providers do not have financial motivation to use the HIE system. The financial concern of healthcare providers has been recognized by health policy makers, and thus a few incentive payment programs have been implemented to address that (Williams et al. 2012). However, these programs are not enough enforceable and after they end, physicians are no longer motivated to continue using the system. To address this gap, we design an economic framework for the HIE platform to share the financial savings from HIE usage between engaged entities. We consider two scenarios: (A) the HIE system is freely available for healthcare providers and the flow of payments is among three entities: patients, healthcare providers, and insurance companies; (B) the HIE system is not free and the flow of payments is among the HIE organization, healthcare providers and insurance companies. In scenario A, we recommend that the insurance companies either directly incentivize healthcare providers or indirectly through patients to realize incentive compatible usage by all concerned parties. In both mechanisms in this scenario, patients pay a fee to their healthcare providers for HIE usage. In scenario B, we recommend that healthcare providers are incentivized for using HIE by either insurance companies or the HIE organization. In addition, the HIE organization charges either healthcare providers or insurance companies. Thus, we develop two mechanisms for scenario B as well: (i) direct incentive mechanism in which the HIE organization directly incentivizes healthcare providers to use HIE and insurance companies pay the fee to the HIE organization. (ii) indirect incentive mechanism in which insurance companies incentivize healthcare providers for using HIE and healthcare providers pay the fee for accessing HIE. In each scenario, our objective is to determine a feasible set of values for the variables incentive and fee, under each mechanism, so that the benefits for all three engaged entities increase as a result of HIE usage. Graphical representations corresponding to each mechanism under each scenario have been analyzed. There is always a feasible set for variables incentive and fee so that every entity benefit from HIE usage. While our proposed framework is designed for the basic HIE system, it could be extended for other HIE models who provide more services. We further suggest that to promote HIE, entities should negotiate on sharing the total benefit from HIE usage. This study has significant implications for HIE business.
Adaptive predistortion algorithm based on the multilinear multiplier fusion

Aiming at solving the problem of the insufficiency in the predistortion structure and low accuracy of the memory nonlinear power amplifier in the wireless communication system, an adaptive predistortion method for dual loop learning structure based on the fusion of multilinear multiplier model recognition is put forward. On the basis of the real number delay multilinear multiplier model, an improved quantum behaved optimization (QBO) algorithm is adopted in this method to carry out indirect learning structure off‐line training of the multilinear multiplier, so as to determine the model parameters as an initial value of the predistorter. And then, the least square method (LSM) algorithm is used to carry out direct learning structure on‐line micro adjustment of the parameters of the predistorter and to conduct fitting on the nonlinearity and memory effect of the power amplifier. The method has the advantages of simple structure, fast convergence, and high precision, which has avoided the local optimum. The experimental results show that the adjacent channel power in this scheme is improved by approximately 7 dB compared with that of the classical dual loop structure predistortion method, and the linearization performance of the amplifier has been significantly improved, thus its feasibility has been verified.
Reduced Chlorine in Drinking Water Distribution Systems Impacts Bacterial Biodiversity in Biofilms

In drinking water distribution systems (DWDS), a disinfectant residual is usually applied to limit bacterial regrowth. However, delivering water with no or reduced chlorine residual could potentially decrease the selection for antimicrobial resistant microorganisms, favor bacterial regrowth and result in changes in bacterial populations. To evaluate the feasibility of water reduction in local DWDS while ensuring water safety, water quality was measured over 2 months in two different networks, each of them harboring sub-areas with normal and reduced chlorine. Water quality remained good in chlorine reduced samples, with limited development of total flora and absence of coliforms. Furthermore, 16S rRNA amplicon-based metagenomics was used to investigate the diversity and the composition of microbial communities in the sub-networks. Taxonomic classification of sequence reads showed a reduced bacterial diversity in sampling points with higher chlorine residuals. Chlorine disinfection created more homogeneous bacterial population, dominated by Pseudomonas, a genus that contains some major opportunistic pathogens such as P. aeruginosa. In the absence of chlorine, a larger and unknown biodiversity was unveiled, also highlighted by a decreased rate of taxonomic classification to the genus and species level. Overall, this experiment in a functional DWDS will facilitate the move toward potable water delivery systems without residual disinfectants and will improve water taste for consumers.
Fast multidimensional reduction and broadcast operations on GPU for machine learning

Reduction and broadcast operations are commonly used in machine learning algorithms for different purposes. They widely appear in the calculation of the gradient values of a loss function, which are one of the core structures of neural networks. Both operations are implemented naively in many libraries usually for scalar reduction or broadcast; however, to our knowledge, there are no optimized multidimensional implementations available. This fact limits the performance of machine learning models requiring these operations to be performed on tensors. In this work, we address the problem and propose two new strategies that extend the existing implementations to perform on tensors. We introduce formal definitions of both operations using tensor notations, investigate their mathematical properties, and exploit these properties to provide an efficient solution for each. We implement our parallel strategies and test them on a CUDA enabled Tesla K40 m GPU accelerator. Our performant implementations achieve up to 75% of the peak device memory bandwidth on different tensor sizes and dimensions. Significant speedups against the implementations available in the Knet Deep Learning framework are also achieved for both operations.
Limits on All Known (and Some Unknown) Approaches to Matrix Multiplication

We study the known techniques for designing Matrix Multiplication algorithms. The two main approaches are the Laser method of Strassen, and the Group theoretic approach of Cohn and Umans. We define a generalization based on zeroing outs which subsumes these two approaches, which we call the Solar method, and an even more general method based on monomial degenerations, which we call the Galactic method. We then design a suite of techniques for proving lower bounds on the value of omega, the exponent of matrix multiplication, which can be achieved by algorithms using many tensors T and the Galactic method. Some of our techniques exploit 'local' properties of T, like finding a sub-tensor of T which is so 'weak' that T itself couldn't be used to achieve a good bound on omega, while others exploit 'global' properties, like T being a monomial degeneration of the structural tensor of a group algebra. Our main result is that there is a universal constant ℓ>2 such that a large class of tensors generalizing the Coppersmith-Winograd tensor CW_q cannot be used within the Galactic method to show a bound on omega better than ell, for any q. We give evidence that previous lower-bounding techniques were not strong enough to show this. We also prove a number of complementary results along the way, including that for any group G, the structural tensor of C[G] can be used to recover the best bound on omega which the Coppersmith-Winograd approach gets using CW_|G|-2 as long as the asymptotic rank of the structural tensor is not too large.
Enriched Interpretation

The theory introduced, presented and developed in this paper, is concerned with an enriched extension of the theory of Rough Sets pioneered by Zdzislaw Pawlak. The enrichment discussed here is in the sense of valuated categories as developed by F.W. Lawvere. This paper relates Rough Sets to an abstraction of the theory of Fuzzy Sets pioneered by Lotfi Zadeh, and provides a natural foundation for "soft computation". To paraphrase Lotfi Zadeh, the impetus for the transition from a hard theory to a soft theory derives from the fact that both the generality of a theory and its applicability to real-world problems are substantially enhanced by replacing various hard concepts with their soft counterparts. Here we discuss the corresponding enriched notions for indiscernibility, subsets, upper/lower approximations, and rough sets. Throughout, we indicate linkages with the theory of Formal Concept Analysis pioneered by Rudolf Wille. We pay particular attention to the all-important notion of a "linguistic variable" - developing its enriched extension, comparing it with the notion of conceptual scale from Formal Concept Analysis, and discussing the pragmatic issues of its creation and use in the interpretation of data. These pragmatic issues are exemplified by the discovery, conceptual analysis, interpretation, and categorization of networked information resources in WAVE, the Web Analysis and Visualization Environment currently being developed for the management and interpretation of the universe of resource information distributed over the World-Wide Web.
Engineering Non-cellulosic Polysaccharides of Wood for the Biorefinery

Non-cellulosic polysaccharides constitute approximately one third of usable woody biomass for human exploitation. In contrast to cellulose, these substances are composed of several different types of unit monosaccharides and their backbones are substituted by various groups. Their structural diversity and recent examples of their modification in transgenic plants and mutants suggest they can be targeted for improving wood-processing properties, thereby facilitating conversion of wood in a biorefinery setting. Critical knowledge on their structure-function relationship is slowly emerging, although our understanding of molecular interactions responsible for observed phenomena is still incomplete. This review: (1) provides an overview of structural features of major non-cellulosic polysaccharides of wood, (2) describes the fate of non-cellulosic polysaccharides during biorefinery processing, (3) shows how the non-cellulosic polysaccharides impact lignocellulose processing focused on yields of either sugars or polymers, and (4) discusses outlooks for the improvement of tree species for biorefinery by modifying the structure of non-cellulosic polysaccharides.
The Influence of Goal Orientation, Goal Commitment, and Implementation Mindset on Project Success

While IT project management approaches have been applied and studied for several decades, a large number of projects continue to fail. For instance, Gulla (2012) suggests that 25% of technology projects fail outright, while 50% usually require massive rework by the time they are delivered. Academic literature suggests that such failures can be attributed to expectation misalignment, organizational resistance, system misfit, cultural barriers, and erroneous project management approaches (Dwivedi et al, 2015). To better understand factors that impact project success, we propose an examination of goal orientation, implementation mindset, and goal commitment on project performance. While the impact of goal orientation and its impact on organizational commitment has been studied extensively (e.g., Lee et al. 2010), little is known regarding the impact of goal orientation on goal commitment. In addition, literature has established that multiple forms of commitment (affective, continuance, and normative) have a positive effect on intentions to continue and performance (success) (Korzaan & Brooks, 2015; Lee et al., 2010). However, little is known about the antecedents to these facets of commitment in the context of an information system project. This study seeks to address these gaps by investigating the influence of goal orientations on commitment and further considers the potential intervening psychological states that help explain the relationship between goal orientations and commitment. One such psychological state of interest to this study is implementation mindset (Armor & Taylor, 2003). This is an important research domain as even with extensive literature and a well-trained workforce, a substantial number of IT project implementations continue to fail to meet stakeholder expectations. This project explores the following research questions: RQ1: Does goal orientation impact psychological states such as implementation mindset? RQ2: Do psychological states such as implementation mindset impact project goal commitment? RQ3: Does goal commitment impact outcome variables such as intention to continue and project success? This TREO talk invites discussion for this research-inprogress.
The Role of Mental Factors for the Design of a Virtual Memory Palace

The mnemonic strategy called Method of Loci has been studied for several decades, not only from educators but also by neuroscientists, psychologists and researchers from the domain of information systems (IS). The idea of the Method of Loci is to mentally place the learning content in a familiar environment. This procedure fosters an association process that combines specific items with spatial cues or places. Due to this association, it is easier to recall the information later. Researchers started to evaluate the potential lying in the idea to support the Method of Loci with a virtual environment as a template for the users mental Memory Palace. This research approach intends to optimize the design of a so called Virtual Memory Palace by evaluating the users’ mental factors spatial ability and creativity. It is hypothesized that these factors allow a user-centric and therefore optimized design in order to foster the methods effectiveness.