Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid Odor Processing in the Honeybee Antennal Lobe Network

In their natural environment, many insects need to identify and evaluate behaviorally relevant odorants on a rich and dynamic olfactory background. Behavioral studies have demonstrated that bees recognize learned odors within <200 ms, indicating a rapid processing of olfactory input in the sensory pathway. We studied the role of the honeybee antennal lobe network in constructing a fast and reliable code of odor identity using in vivo intracellular recordings of individual projection neurons (PNs) and local interneurons (LNs). We found a complementary ensemble code where odor identity is encoded in the spatio-temporal pattern of response latencies as well as in the pattern of activated and inactivated PN firing. This coding scheme rapidly reaches a stable representation within 50–150 ms after stimulus onset. Testing an odor mixture versus its individual compounds revealed different representations in the two morphologically distinct types of lateral- and median PNs (l- and m-PNs). Individual m-PNs mixture responses were dominated by the most effective compound (elemental representation) whereas l-PNs showed suppressed responses to the mixture but not to its individual compounds (synthetic representation). The onset of inhibition in the membrane potential of l-PNs coincided with the responses of putative inhibitory interneurons that responded significantly faster than PNs. Taken together, our results suggest that processing within the LN network of the AL is an essential component of constructing the antennal lobe population code.
Learning Horizontal Connections in a Sparse Coding Model of Natural Images

It has been shown that adapting a dictionary of basis functio s t the statistics of natural images so as to maximize sparsity in the coefficien ts results in a set of dictionary elements whose spatial properties resemble t hose of V1 (primary visual cortex) receptive fields. However, the resulting spars e coefficients still exhibit pronounced statistical dependencies, thus violating the i nd pendence assumption of the sparse coding model. Here, we propose a model that atte mpts to capture the dependencies among the basis function coefficients by in cludi g a pairwise coupling term in the prior over the coefficient activity stat es. When adapted to the statistics of natural images, the coupling terms learn a com bination of facilitatory and inhibitory interactions among neighboring basis funct ions. These learned interactions may offer an explanation for the function of hori z ntal connections in V1, and we discuss the implications of our findings for physio logical experiments.
The Institute for Integrating Statistics in Decision Sciences

In this paper, we consider a Cost of Quality (CoQ) optimization problem that finds an optimal allocation of prevention and inspection resources to minimize the expected total quality costs under a prevention-appraisal-failure framework, where the quality costs in the proposed model are involved with prevention, inspection, and correction of internal and external failures. Commencing with a simple structure of the problem, we progressively increase the complexity of the problem by accommodating realistic scenarios regarding preventive, appraisal, and corrective actions. The resulting problem is formulated as zero-one polynomial program, which can be solved either directly using a mixed-integer nonlinear programming (MINLP) solver such as BARON, or using a more conventional mixed-integer programming (MILP) solver such as CPLEX after performing an appropriate linearization step. We present a case study from the literature to illustrate how the proposed model can be utilized to find optimal inspection and prevention strategies, and to analyze sensitivity with respect to different cost parameters. We also provide a comparative numerical study of using the aforementioned solvers to optimize the respective model formulations. The results provide insights into the use of such quantitative methods for optimizing the cost of quality, and indicate the efficacy of using the linearized MILP
Revenue-Optimal Dynamic Auctions for Adaptive Ad Sequencing

Mobile in-app advertising has grown exponentially in the last few years. In-app ads are often shown in a sequence of short-lived exposures for the duration of a user’s stay in an app. The current state of both research and practice ignores the dynamics of ad sequencing and instead adopts a myopic framework to serve ads. In this paper, we propose a unified dynamic framework for adaptive ad sequencing that optimizes user engagement in the session, e.g., the number of clicks or length of stay. Our framework comprises of two components – (1) a Markov Decision Process that captures the domain structure and incorporates inter-temporal trade-offs in ad interventions, and (2) an empirical framework that combines machine learning methods such as Extreme Gradient Boosting (XGBoost) with ideas from the causal inference literature to obtain counterfactual estimates of user behavior. We apply our framework to largescale data from the leading in-app ad-network of an Asian country. We document significant gains in user engagement from adopting a dynamic framework. We show that our forward-looking ad sequencing policy outperforms all the existing methods by comparing it to a series of benchmark policies often used in research and practice. Further, we demonstrate that these gains are heterogeneous across sessions: adaptive forward-looking ad sequencing is most effective when users are new to the platform. Finally, we use a descriptive approach to explain the gains from adopting the dynamic framework.
Communications of the Association for Information Systems

: Over the past two decades, the information systems community has become engaged in improving the environmental effects of information systems and technologies, which has given rise to the green IS field. Despite increasing interest, some have suggested that progress toward meaningful solutions for sustainability has been too slow. Responding to these concerns, we examine the development of green IS research using the modernity perspective to understand green IS’s evolution and to present alternative perspectives to motivate future research. From a sample of over 80 green IS papers published over a 15-year period, we identify four main patterns of modernity that are manifest in green IS research. These patterns include the importance of the individual in solving environmental problems; science as the main source of solutions; and the emergence of an artificial science approach, reliance on technology, and growth as businesses’ ultimate goals. Further, our analysis reveals that green IS research has started to demonstrate elements of a hyper-modernity perspective that emphasizes reflexivity. We argue that future green IS research should continue on this path and propose a conceptual framework inspired by hyper-modernity and centered on reflexivity that could serve as a guide for future research.
Guaranteed Matrix Completion via Nonconvex Factorization

Matrix factorization is a popular approach for large-scale matrix completion. In this approach, the unknown low-rank matrix is expressed as the product of two much smaller matrices so that the low-rank property is automatically fulfilled. The resulting optimization problem, even with huge size, can be solved (to stationary points) very efficiently through standard optimization algorithms such as alternating minimization and stochastic gradient descent (SGD). However, due to the non-convexity caused by the factorization model, there is a limited theoretical understanding of whether these algorithms will generate a good solution. In this paper, we establish a theoretical guarantee for the factorization based formulation to correctly recover the underlying low-rank matrix. In particular, we show that under similar conditions to those in previous works, many standard optimization algorithms converge to the global optima of the factorization based formulation, and recover the true low-rank matrix. A major difference of our work from the existing results is that we do not need resampling (i.e., Using independent samples at each iteration) in either the algorithm or its analysis. To the best of our knowledge, our result is the first one that provides exact recovery guarantee for many standard algorithms such as gradient descent, SGD and block coordinate gradient descent.
CrossNorm: Normalization for Off-Policy TD Reinforcement Learning

Off-policy temporal difference (TD) methods are a powerful class of reinforcement learning (RL) algorithms. Intriguingly, deep off-policy TD algorithms are not commonly used in combination with feature normalization techniques, despite positive effects of normalization in other domains. We show that naive application of existing normalization techniques is indeed not effective, but that well-designed normalization improves optimization stability and removes the necessity of target networks. In particular, we introduce a normalization based on a mixture of on- and off-policy transitions, which we call cross-normalization. It can be regarded as an extension of batch normalization that re-centers data for two different distributions, as present in off-policy learning. Applied to DDPG and TD3, cross-normalization improves over the state of the art across a range of MuJoCo benchmark tasks.
Fine-tuning Pre-trained Language Models with Noise Stability Regularization

—The advent of large-scale pre-trained language models has contributed greatly to the recent progress in natural language processing. Many state-of-the-art language models are ﬁrst trained on a large text corpus and then ﬁne-tuned on downstream tasks. Despite its recent success and wide adoption, ﬁne-tuning a pre-trained language model often suffers from over-ﬁtting, which leads to poor generalizability due to the extremely high complexity of the model and the limited training samples from downstream tasks. To address this problem, we propose a novel and effective ﬁne-tuning framework, named Layerwise Noise Stability Regularization (LNSR). Speciﬁcally, we propose to inject the standard Gaussian noise or In-manifold noise and regularize hidden representations of the ﬁne-tuned model. We ﬁrst provide theoretical analyses to support the efﬁcacy of our method. We then demonstrate the advantages of the proposed method over other state-of-the-art algorithms including L 2 -SP [1], Mixout [2] and SMART [3]. While these previous works only verify the effectiveness of their methods on relatively simple text classiﬁcation tasks, we also verify the effectiveness of our method on question answering tasks, where the target problem is much more difﬁcult and more training examples are available. Furthermore, extensive experimental results indicate that the proposed algorithm can not only enhance the in-domain performance of the language models but also improve the domain generalization performance on out-of-domain data.
Acting with Attainment Technologies in Deaf Education Reinventing Monitoring as an Intervention Collaboratory

It continues to puzzle researchers, parents, and educators that educational achievements among deaf learners can often remain quite so persistently behind those of their hearing peers. A landmark study conducted in 1979 by educational psychologist Reuben Conrad had concluded that the average sixteen-year-old deaf learner exited compulsory education in the UK with the reading ability of a nine-year-old (Conrad 1979). I myself contributed to an exhaustive international research literature review commissioned by the then English Department for Education and Employment twenty years later. We noted that there was no convincing evidence to support a different conclusion despite the many educational changes that had taken place since Conrad’s study (Powers, Gregory, and Thoutenhoofd 1999). The overall sense one gets from recent attempts to overview (e.g., Marschark, Rhoten, and Fabich 2007; Thoutenhoofd et al. 2005)
Effectiveness, Explainability and Reliability of Machine Meta-Learning Methods for Predicting Mortality in Patients with COVID-19: Results of the Brazilian COVID-19 Registry

The majority prognostic scores proposed for early assessment of coronavirus disease 19 (COVID-19) patients are bounded by methodological flaws. Our group recently developed a new risk score - ABC2SPH - using traditional statistical methods (least absolute shrinkage and selection operator logistic regression - LASSO). In this article, we provide a thorough comparative study between modern machine learning (ML) methods and state-of-the-art statistical methods, represented by ABC2SPH, in the task of predicting in-hospital mortality in COVID-19 patients using data upon hospital admission. We overcome methodological and technological issues found in previous similar studies, while exploring a large sample (5,032 patients). Additionally, we take advantage of a large and diverse set of methods and investigate the effectiveness of applying meta-learning, more specifically Stacking, in order to combine the methods' strengths and overcome their limitations. In our experiments, our Stacking solutions improved over previous state-of-the-art by more than 26% in predicting death, achieving 87.1% of AUROC and MacroF1 of 73.9%. We also investigated issues related to the interpretability and reliability of the predictions produced by the most effective ML methods. Finally, we discuss the adequacy of AUROC as an evaluation metric for highly imbalanced and skewed datasets commonly found in health-related problems.
A flexible stream architecture for ASR using articulatory features

Recently, speech recognition systems based on articulatory features such as “voicing” or the position of lips and tongue have gained interest, because they promise advantages with respect to robustness and permit new adaptation methods to compensate for channel, noise, and speaker variability. These approaches are also interesting from a general point of view, because their models use phonological and phonetic concepts, which allow for a richer description of a speech act than the sequence of HMM-states, which is the prevalent ASR architecture today. In this work, we present a multi-stream architecture, in which CD-HMMS are supported by detectors for articulatory features, using a linear combination of log-likelihood scores. This multi-stream approach results in a 15% reduction of WER on a read Broadcast-News (BN) task and im-proves performance on a spontaneous scheduling task (ESST) by 7%. The proposed architecture potentially allows for new speaker and channel adaptation schemes, including stream asynchronicity.
Environment, Climate and Society in Roman and Byzantine Butrint

A multiproxy analysis (sedimentology, geochemistry and pollen) of sediments recovered in the Butrint lagoon (Albania) allows us to reconstruct the environmental changes that occurred in the area during the 1st millennium AD. In this paper, we compare these analytical results with the evidence provided by archaeological investigations carried out at the site of the Roman city of Butrint (surrounded by these lagoon waters) and in the city’s hinterlands. From this, we can say that different periods of farming and siltation (AD 400–600 and 700–900) were accompanied by increased run-off and wetter conditions in the region. This coincided with the territorial and economic expansion of the Byzantine empire, suggesting the key role of trade in the profound land use changes experienced in Butrint.
The Effects of Personalized Recommendations with Popularity Information on Sales - A Field Study in Grocery Retailing

In consumer and information systems research, it remains unclear how consumers consider smartphone app recommendations in the course of their decision making process that leads to product choices in the physical store. Moreover, it is unclear which type of information smartphone apps should transport to consumers and if there are any customer segmentation criteria for smartphone app design. With respect to the theoretical and managerial importance of recommendation services in the form of smartphone apps we want to shed some light on this topic. Combining literature from the fields of IS and marketing research, we hypothesize that personalized recommendations via smartphone apps can help to boost sales in physical grocery stores. Furthermore, we hypothesize that additional popularity information (in the form of “stars”) does not amplify the positive effect of personalized recommendations. In addition, we assume that the effects of recommendation usage differ for men and women. We conducted a field study with a European grocery retailer to test our hypotheses. Finally, we discuss first implications as well as central limitations of our research and present the next research steps.
Fast Exact Inference with a Factored Model for Natural Language Parsing

We present a novel generative model for natural language tree structures in which semantic (lexical dependency) and syntactic structures are scored with separate models. This factorization provides conceptual simplicity, straightforward opportunities for separately improving the component models, and a level of performance already close to that of similar, non-factored models. Most importantly, unlike other modern parsing models, the factored model admits an extremely effective A (cid:0) parsing al-gorithm, which makes efﬁcient, exact inference feasible
Beyond Command and Control: Sense Making under Large World Uncertainty

During military endeavors a large amount of information floods in from a variety of sources, both human sources and non-human sources such as autonomous (robotic) vehicles, sensors, etc. Timely evaluation of intelligence with background information is necessary for effective operations, but the sheer volume of incoming data poses a tremendous challenge. Automatically fusing data derived from multiple diverse sources into recognizable patterns of potentially threatening behavior can provide a distinct operational advantage, allowing commanders to react with agile, appropriate responses to an ever-changing situation. This advantage would be even more marked if the model is flexible enough to respond to changing patterns of behavior, and if the time needed for processing was close to real-time. In this article we present an easily modifiable model for threats which use Battle Management Language (BML) as the underlying standardized language for representation and fusion. The model is simple and based upon heuristics, its strength lies in the analysis and quantification of uncertainties in the fusion process. And most importantly, it provides a near realtime solution for first-pass processing of inflowing information to provide early warning of developing threats. 2 The International C2 Journal | Vol 5, No
Learning Embedding Adaptation for Few-Shot Learning

Learning with limited data is a key challenge for visual recognition. Few-shot learning methods address this challenge by learning an instance embedding function from seen classes, and apply the function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them is the target task. In this paper, we propose a novel approach to adapt the embedding model to the target classification task, yielding embeddings that are task-specific and are discriminative. To this end, we employ a type of self-attention mechanism called Transformer to transform the embeddings from task-agnostic to task-specific by focusing on relating instances from the test instances to the training instances in both seen and unseen classes. We verify the effectiveness of our model on both the standard few-shot classification benchmark and four extended few-shot learning settings with essential use cases (i.e. cross-domain, transductive, generalized few-shot learning, and large scale low-shot learning). Our approach archived consistent improvements over baseline models and previous state-of-the-art methods.
Competencies of Exceptional and Non-exceptional Software Engineers Competencies of Exceptional and Non-exceptional Software Engineers Submitted for Publication

The attributes of individual software engineers are perhaps the most important factors in determining the success of software development. Our goal is to identify the professional compe-tencies that are most essential. In particular, we seek to identify the attributes that diierentiate between exceptional and non-exceptional software engineers. Phase 1 of our research is a qualitative study designed to identify competencies to be used in the quantitative analysis performed in Phase 2. In Phase 1, we conduct an in-depth review of ten exceptional and ten non-exceptional software engineers working for a major computing rm. We use biographical data and Myers-Briggs Type Indicator test results to characterize our sample. We conduct Critical Incident Interviews focusing on the subjects experience in software and identify 38 essential competencies of software engineers. Phase 2 of this study surveys 129 software engineers to determine the competencies that are diierential between exceptional and non-exceptional engineers. Years of experience in software is the only biographical predictor of performance. Analysis of the participants Q-Sort of the 38 competencies identiied in Phase 1 reveals that nine of these competencies are diierentially related to engineer performance using a t-test. A ten variable Canonical Discrimination Function consisting of three biographical variables and seven competencies is capable of correctly classifying 81% of the cases. The statistical analyses indicate that exceptional engineers (at the company studied) can be distinguished by behaviors associated with an external focus | behaviors directed at people or objects outside the individual. Exceptional engineers are more likely than non-exceptional engineers to maintain a \big picture", have a bias for action, be driven by a sense of mission, exhibit and articulate strong convictions, play a pro-active role with management, and help other engineers. material is granted provided that the copies are not made or distributed for direct commercial advantage, the copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the author.
Forecasting changes in UK interest rates

Making accurate forecasts of the future direction of interest rates is a vital element when making economic decisions. The focus on central banks as they make decisions about the future direction of interest rates requires the forecaster to assess the likely outcome of committee decisions based on new information since the previous meeting. We characterize this process as a dynamic ordered probit process that uses information to decide between three possible outcomes for interest rates: an increase, decrease or no-change. When we analyze the predictive ability of two information sets, we find that the approach has predictive ability both in-sample and out-of-sample that helps forecast the direction of future rates. ∗Corresponding Author: Paul Mizen, University of Nottingham, University Park, Nottingham, NG7 2RD, UK. email: paul.mizen@nottingham.ac. uk. We are grateful to the Experian Centre of Economic Modelling for funding this research and Tae-Hwan Kim is grateful to the College of Business and Economics for financial support under the project code 2005-1-0244. We thank Mike Artis, Anindya Banerjee, Roel Beetsma, Keith Blackburn, Mick Devereux, Stefan Gerlach, Petra Gerlach-Kristen, Edward Nelson, Denise Osborn, Alan Timmerman, James Yetman and seminar participants at the Hong Kong Monetary Authority, the Universities of Amsterdam, Manchester and the National University of Singapore for helpful comments.