Miracle-dz/UniFAR-FTUs huggingface dataset:
CAKE: a Compact and Accurate K-dimensional representation of Emotion

Numerous models describing the human emotional states have been built by the psychology community. Alongside, Deep Neural Networks (DNN) are reaching excellent performances and are becoming interesting features extraction tools in many computer vision tasks.Inspired by works from the psychology community, we first study the link between the compact two-dimensional representation of the emotion known as arousal-valence, and discrete emotion classes (e.g. anger, happiness, sadness, etc.) used in the computer vision community. It enables to assess the benefits -- in terms of discrete emotion inference -- of adding an extra dimension to arousal-valence (usually named dominance). Building on these observations, we propose CAKE, a 3-dimensional representation of emotion learned in a multi-domain fashion, achieving accurate emotion recognition on several public datasets. Moreover, we visualize how emotions boundaries are organized inside DNN representations and show that DNNs are implicitly learning arousal-valence-like descriptions of emotions. Finally, we use the CAKE representation to compare the quality of the annotations of different public datasets.
Out-of-sample extension of graph adjacency spectral embedding

Many popular dimensionality reduction procedures have out-of-sample extensions, which allow a practitioner to apply a learned embedding to observations not seen in the initial training sample. In this work, we consider the problem of obtaining an out-of-sample extension for the adjacency spectral embedding, a procedure for embedding the vertices of a graph into Euclidean space. We present two different approaches to this problem, one based on a least-squares objective and the other based on a maximum-likelihood formulation. We show that if the graph of interest is drawn according to a certain latent position model called a random dot product graph, then both of these out-of-sample extensions estimate the true latent position of the out-of-sample vertex with the same error rate. Further, we prove a central limit theorem for the least-squares-based extension, showing that the estimate is asymptotically normal about the truth in the large-graph limit.
Temporal Poisson Square Root Graphical Models

We propose temporal Poisson square root graphical models (TPSQRs), a generalization of Poisson square root graphical models (PSQRs) specifically designed for modeling longitudinal event data. By estimating the temporal relationships for all possible pairs of event types, TPSQRs can offer a holistic perspective about whether the occurrences of any given event type could excite or inhibit any other type. A TPSQR is learned by estimating a collection of interrelated PSQRs that share the same template parameterization. These PSQRs are estimated jointly in a pseudo-likelihood fashion, where Poisson pseudo-likelihood is used to approximate the original more computationally-intensive pseudo-likelihood problem stemming from PSQRs. Theoretically, we demonstrate that under mild assumptions, the Poisson pseudo-likelihood approximation is sparsistent for recovering the underlying PSQR. Empirically, we learn TPSQRs from Marshfield Clinic electronic health records (EHRs) with millions of drug prescription and condition diagnosis events, for adverse drug reaction (ADR) detection. Experimental results demonstrate that the learned TPSQRs can recover ADR signals from the EHR effectively and efficiently.
Letter to Editor from Dr. Steiner, et al. regarding JVIM_15039

Dear Drs. Hinchcliff and DiBartola, We read with great interest the recent article DOI:10.1111/ jvim.15039, entitled “Evaluation of SNAP cPL, Spec cPL, VetScan cPL Rapid Test, and Precision PSL Assays for the Diagnosis of Clinical Pancreatitis in Dogs” by Cridge et al. and we applaud the authors for their efforts in comparing various diagnostic tests currently available for the diagnosis of canine pancreatitis. However, we would like to highlight some important limitations that should be taken into consideration when reading this manuscript. All of the diagnostic tests utilized in this study were performed under field conditions, except the VetSan cPL Rapid Test, which was not performed patient-side as intended, but was performed at Abaxis Laboratories in Union City, CA, which, to our knowledge, is a research facility rather than a service laboratory. One may consider this to be just a minor matter being that the equipment and the assay kits utilized should be the same as those utilized under field conditions. However, this is not the case, because shipping the kits to veterinary clinics and storage at veterinary facilities under uncontrolled conditions, as specified by the manufacturer of this kit, may have an important impact on assay performance. It should also be noted that no analytical validation data for the VetScan cPL Rapid Test are publically available. Because of this, our laboratory performed an analytical validation study under field conditions. The assay was found to show poor linearity. More importantly, the assay was not precise and had unacceptable reproducibility with a mean inter-assay coefficient of variability of 31.8%. Most samples retested multiple times gave results in two or even three different diagnostic bins (i.e., normal, questionable, or suggestive of pancreatitis). Almost none of the samples had results within 665 mg/L of Spec cPL results as suggested by the manufacturer. Unfortunately, we cannot report more-detailed results of our analytical validation study in this letter as these data are currently under review. However, clearly, determining agreement of an assay with other diagnostic assays when under field conditions the assay fails to agree with itself is concerning. We would therefore like to caution readers that the conditions used for assessing the VetScan cPL Rapid Test described in this manuscript may have led to results that cannot be replicated under field conditions. Sincerely, Joerg Steiner, DMV, PhD, DACVIM (SAIM), DECVIM-CA; Jonathan Lidbury, BVMS, MRCVS, PhD, DACVIM (SAIM), DECVIM-CA; Texas A&M University, College Station, TX.
Accelerating Stochastic Gradient Descent

There is widespread sentiment that it is not possible to effectively utilize fast gradient methods (e.g. Nesterov's acceleration, conjugate gradient, heavy ball) for the purposes of stochastic optimization due to their instability and error accumulation, a notion made precise in d'Aspremont 2008 and Devolder, Glineur, and Nesterov 2014. This work considers these issues for the special case of stochastic approximation for the least squares regression problem, and our main result refutes the conventional wisdom by showing that acceleration can be made robust to statistical errors. In particular, this work introduces an accelerated stochastic gradient method that provably achieves the minimax optimal statistical risk faster than stochastic gradient descent. Critical to the analysis is a sharp characterization of accelerated stochastic gradient descent as a stochastic process. We hope this characterization gives insights towards the broader question of designing simple and effective accelerated stochastic methods for more general convex and non-convex optimization problems.
Cerebrospinal fluid free kappa light chains and kappa index perform equal to oligoclonal bands in the diagnosis of multiple sclerosis

Abstract Background Detection of intrathecal immunoglobulin G (IgG) synthesis by gold standard oligoclonal bands (OCB) or IgG index remains an integral part of multiple sclerosis (MS) diagnostics, although both methods have weaknesses. Emerging evidence suggests that automated detection of free light chains (FLC) in the cerebrospinal fluid (CSF) has diagnostic performance equal to OCB. The objective of this study was to compare the diagnostic performance of CSF FLC with OCB and IgG index in a large cohort of Scandinavian patients referred for MS evaluation. Methods We prospectively included 230 patients suspected for MS. They are composed of patients with MS (n=96), clinically isolated syndrome (n=37), other neurological diseases (OND, n=31) and symptomatic controls (SC, n=66). CSF and serum samples were analyzed for kappa and lambda FLC, OCB and IgG index. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. Results Both the absolute concentration of CSF-kappa and the kappa index had excellent MS diagnostic performances with ROC area under the curve of 0.93 and 0.94 (MS vs. SC+OND). At the 0.42 mg/L cutoff, CSF-kappa had sensitivity and specificity of 93.8% and 85.6%, whereas sensitivity and specificity for OCB was 82.3% and 93.8% (72.9% and 95.9% for IgG index at cutoff 0.64). CSF-lambda and lambda index performed inferior to CSF-kappa and kappa index. Conclusions CSF-kappa and kappa index represent automated, rapid and low-cost alternatives to OCB. Using merely the absolute concentration of CSF-kappa is a logistic advantage in the clinical laboratories.
IoT as a Land of Opportunity for DDoS Hackers

In 2016, Mirai showed the destructive potential of a botnet composed exclusively of Internet of Things devices. To evaluate the current state of anti-DDoS (distributed denial-of-service) protection for such devices, the authors studied webcams—discovered by Shodan—and found that most have little to no firewall protection and, as such, are vulnerable to future direct and reflective DDoS attacks.
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing

This paper proposes a neural semantic parsing approach – Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases. Secondly, by leveraging the powerful representation learning and prediction ability of neural network models, we propose a RNN model which can effectively map sentences to action sequences for semantic graph generation. Experiments show that our method achieves state-of-the-art performance on Overnight dataset and gets competitive performance on Geo and Atis datasets.
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design

Estimating heterogeneous treatment effects from observational data is a central problem in many domains. Because counterfactual data is inac-cessible, the problem differs fundamentally from supervised learning, and entails a more com-plex set of modeling choices. Despite a variety of recently proposed algorithmic solutions, a principled guideline for building estimators of treatment effects using machine learning algorithms is still lacking. In this paper, we provide such guidelines by characterizing the fundamental limits of estimating heterogeneous treatment effects, and establishing conditions under which these limits can be achieved. Our analysis reveals that the relative importance of the different aspects of observational data vary with the sample size. For instance, we show that selection bias matters only in small-sample regimes, whereas with a large sample size, the way an algorithm models the control and treated outcomes is what bottlenecks its performance. Guided by our analysis, we build a practical algorithm for estimating treatment effects using a non-stationary Gaussian processes with doubly-robust hyperparameters. Using a standard semi-synthetic simulation setup, we show that our algorithm outperforms the state-of-the-art, and that the behavior of ex-isting algorithms conforms with our analysis.
Interactive Query Refinement using Formal Concept Analysis

Formal Concept Analysis (FCA) of a document corpus yields a concept lattice uniting the powersets of corpus terms and documents. Structural navigation of the directed graph connecting neighbours in this lattice affords interactive query refinement. The starting point for navigation is typically the closure of the conjunctive Boolean query with respect to the corpus. This paper describes the special treatment of “closure” terms – those in this closure but which are not specified by the user – and its implications for implicit structural navigation of the digraph through query editing. This approach, in which the user’s choice of each additional query term is constrained to avoid the null result set, is contrasted with explicit structural navigation. If a term is added which does not co-occur with the terms already specified, the user must either remove the new term or choose which other term(s) to remove. A novel technique is used to present the user with options for those other terms.
Adaptive tracking control of nonlinear systems with dynamic uncertainties using neural network

ABSTRACT In this paper, an adaptive neural tracking control approach is proposed for a class of nonlinear systems with dynamic uncertainties. The radial basis function neural networks (RBFNNs) are used to estimate the unknown nonlinear uncertainties, and then a novel adaptive neural scheme is developed, via backstepping technique. In the controller design, instead of using RBFNN to approximate each unknown function, we lump all unknown functions into a suitable unknown function that is approximated by only a RBFNN in each step of the backstepping. It is shown that the designed controller can guarantee that all signals in the closed-loop system are semi-globally bounded and the tracking error finally converges to a small domain around the origin. Two examples are given to demonstrate the effectiveness of the proposed control scheme.
Session Fingerprinting in Android via Web-to-App Intercommunication

The extensive adoption of mobile devices in our everyday lives, apart from facilitating us through their various enhanced capabilities, has also raised serious privacy concerns. While mobile devices are equipped with numerous sensors which offer context-awareness to their installed apps, they can also be exploited to reveal sensitive information when correlated with other data or sources. Companies have introduced a plethora of privacy invasive methods to harvest users’ personal data for profiling and monetizing purposes. Nonetheless, up till now, these methods were constrained by the environment they operate, e.g., browser versus mobile app, and since only a handful of businesses have actual access to both of these environments, the conceivable risks could be calculated and the involved enterprises could be somehow monitored and regulated. This work introduces some novel user deanonymization approaches for device and user fingerprinting in Android. Having Android AOSP as our baseline, we prove that web pages, by using several inherent mechanisms, can cooperate with installed mobile apps to identify which sessions operate in specific devices and consequently further expose users’ privacy.
Image-level to Pixel-wise Labeling: From Theory to Practice

Conventional convolutional neural networks (CNNs) have achieved great success in image semantic segmentation. Existing methods mainly focus on learning pixel-wise labels from an image directly. In this paper, we advocate tackling the pixel-wise segmentation problem by considering the image-level classification labels. Theoretically, we analyze and discuss the effects of image-level labels on pixel-wise segmentation from the perspective of information theory. In practice, an end-to-end segmentation model is built by fusing the image-level and pixel-wise labeling networks. A generative network is included to reconstruct the input image and further boost the segmentation model training with an auxiliary loss. Extensive experimental results on benchmark dataset demonstrate the effectiveness of the proposed method, where good image-level labels can significantly improve the pixel-wise segmentation accuracy.
Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring

In this paper we present a qualitatively enhanced deep convolution recurrent neural network for computing the quality of a text in an automatic essay scoring task. The novelty of the work lies in the fact that instead of considering only the word and sentence representation of a text, we try to augment the different complex linguistic, cognitive and psycological features associated within a text document along with a hierarchical convolution recurrent neural network framework. Our preliminary investigation shows that incorporation of such qualitative feature vectors along with standard word/sentence embeddings can give us better understanding about improving the overall evaluation of the input essays.
Shaping Cooperation between Humans and Agents with Emotion Expressions and Framing

Emotion expressions can help solve social dilemmas where individual interest is pitted against the collective interest. Building on research that shows that emotions communicate intentions to others, we reinforce that people can infer whether emotionally expressive computer agents intend to cooperate or compete. We further show important distinctions between computer agents that are perceived to be driven by humans (i.e., avatars) vs. by algorithms (i.e., agents). Our results reveal that, when the emotion expression reflects an intention to cooperate, participants will cooperate more with avatars than with agents; however, when the emotion reflects an intention to compete, participants cooperate just as little with avatars as with agents. Finally, we present first evidence that the way the dilemma is described - or framed - can influence people's decision-making. We discuss implications for the design of autonomous agents that foster cooperation with humans, beyond what game theory predicts in social dilemmas.
Optimal Sensing for Patient Health Monitoring

In this paper, we construct a framework for optimally sensing a patient's health state with a wireless body area network (WBAN). In such a resource-constrained paradigm, it is often necessary to use lower performance sensing modes, conditionally reducing system performance to increase efficiency and maximize system lifetime. The optimal control architecture trades between shallow and deep sensing modes according to the estimated patient health state, minimizing the expected costs over future states. We construct an implicit formulation for deriving the optimal sensing policy via dynamic programming, an easily implemented myopic sensing policy, and a useful performance bound for evaluating near-optimal approximate sensing policies. We further provide an Monte Carlo experimental evaluation of how such policies depend on key model parameters.
Climate mediates the biodiversity–ecosystem stability relationship globally

Significance Securing the stable delivery of ecosystem services related to plant biomass (e.g., food, carbon sequestration, and soil fertility) is a pressing issue under ongoing climate change. Biodiversity increases ecosystem stability, but climate change may alter this positive relationship. We coupled a field survey of plant diversity conducted in drylands worldwide with remote sensing estimates of primary productivity to show a strong climate dependency of the biodiversity–ecosystem stability relationship. Our findings suggest that land management should be adapted to the aridity conditions if we aim to secure stable plant production. For instance, promoting higher species richness may represent a simple yet effective strategy to stabilize plant biomass over time in the face of the increasing aridity forecasted for drylands worldwide. The insurance hypothesis, stating that biodiversity can increase ecosystem stability, has received wide research and political attention. Recent experiments suggest that climate change can impact how plant diversity influences ecosystem stability, but most evidence of the biodiversity–stability relationship obtained to date comes from local studies performed under a limited set of climatic conditions. Here, we investigate how climate mediates the relationships between plant (taxonomical and functional) diversity and ecosystem stability across the globe. To do so, we coupled 14 years of temporal remote sensing measurements of plant biomass with field surveys of diversity in 123 dryland ecosystems from all continents except Antarctica. Across a wide range of climatic and soil conditions, plant species pools, and locations, we were able to explain 73% of variation in ecosystem stability, measured as the ratio of the temporal mean biomass to the SD. The positive role of plant diversity on ecosystem stability was as important as that of climatic and soil factors. However, we also found a strong climate dependency of the biodiversity–ecosystem stability relationship across our global aridity gradient. Our findings suggest that the diversity of leaf traits may drive ecosystem stability at low aridity levels, whereas species richness may have a greater stabilizing role under the most arid conditions evaluated. Our study highlights that to minimize variations in the temporal delivery of ecosystem services related to plant biomass, functional and taxonomic plant diversity should be particularly promoted under low and high aridity conditions, respectively.
Distributionally Robust Optimization with Principal Component Analysis

Distributionally robust optimization (DRO) is widely used because it offers a way to overcome the conservativeness of robust optimization without requiring the specificity of stochastic programming. On the computational side, many practical DRO instances can be equivalently (or approximately) formulated as semidefinite programming (SDP) problems via conic duality of the moment problem. However, despite being theoretically solvable in polynomial time, SDP problems in practice are computationally challenging and quickly become intractable with increasing problem sizes. We propose a new approximation method to solve DRO problems with moment-based ambiguity sets. Our approximation method relies on principal component analysis (PCA) for optimal lower dimensional representation of variability in random samples. We show that the PCA approximation yields a relaxation of the original problem and derive theoretical bounds on the gap between the original problem and its PCA approximation. Furthermore, an extensive n.
Enforcing Context-Aware BYOD Policies with In-Network Security

Bring Your Own Device, or BYOD, has become the new norm for many enterprise networks; but it also raises security concerns. We present our vision of programmable in-network security, and sketch an initial system design, Poise. Poise has a high-level policy language that can express a wide range of existing and new security policies. These policies can then be compiled to device configurations to collect device/apps information, as well as switch programs in P4 that enforce security inside the network. Our initial results seem promising—Poise runs with reasonable overhead, and it successfully detects policy violations for seven useful BYOD policies.