Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Semantics for Visual Relationship Detection

Scene graph construction / visual relationship detection from an image aims to give a precise structural description of the objects (nodes) and their relationships (edges). The mutual promotion of object detection and relationship detection is important for enhancing their individual performance. In this work, we propose a new framework, called semantics guided graph relation neural network (SGRN), for effective visual relationship detection. First, to boost the object detection accuracy, we introduce a source-target class cognoscitive transformation that transforms the features of the co-occurent objects to the target object domain to refine the visual features. Similarly, source-target cognoscitive transformations are used to refine features of objects from features of relations, and vice versa. Second, to boost the relation detection accuracy, besides the visual features of the paired objects, we embed the class probability of the object and subject separately to provide high level semantic information. In addition, to reduce the search space of relationships, we design a semantics-aware relationship filter to exclude those object pairs that have no relation. We evaluate our approach on the Visual Genome dataset and it achieves the state-of-the-art performance for visual relationship detection. Additionally, Our approach also significantly improves the object detection performance (i.e. 4.2\% in mAP accuracy).
MEMORANDUM No 11 / 2004

We study climate policy when there are technology spillovers within and across countries, and the technology externalities within each country are corrected through a domestic subsidy of R&D investments. We compare the properties of international climate agreements when the inter-country externalities from R&D are not regulated through the climate agreement. With an international agreement controlling abatements directly through emission quotas, the equilibrium R&D subsidy is lower that the socially optimal subsidy. The equilibrium subsidy is even lower if the climate agreement does not specify emission levels directly, but instead imposes a common carbon tax. Social costs are higher under a tax agreement than under a quota agreement. Moreover, for a reasonable assumption on the abatement cost function, R&D investments and abatement levels are lower under a tax agreement than under a quota agreement. Total emissions may be higher or lower in a second-best optimal quota agreement than in the first-best optimum.
Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives

People often share personal narratives in order to seek advice from others. To properly infer the narrator’s intention, one needs to apply a certain degree of common sense and social intuition. To test the capabilities of NLP systems to recover such intuition, we introduce the new task of inferring what is the advice-seeking goal behind a personal narrative. We formulate this as a cloze test, where the goal is to identify which of two advice-seeking questions was removed from a given narrative. The main challenge in constructing this task is finding pairs of semantically plausible advice-seeking questions for given narratives. To address this challenge, we devise a method that exploits commonalities in experiences people share online to automatically extract pairs of questions that are appropriate candidates for the cloze task. This results in a dataset of over 20,000 personal narratives, each matched with a pair of related advice-seeking questions: one actually intended by the narrator, and the other one not. The dataset covers a very broad array of human experiences, from dating, to career options, to stolen iPads. We use human annotation to determine the degree to which the task relies on common sense and social intuition in addition to a semantic understanding of the narrative. By introducing several baselines for this new task we demonstrate its feasibility and identify avenues for better modeling the intention of the narrator.
SAS Macros for Multivariate Two-Sample Tests in the Case of Incomplete Data

Multivariate two-sample tests offer the opportunity to condense the statistical comparison between two samples containing data on several variables into a single p-value. This approach is preferable in decision-oriented situations, for example, in clinical trials when the evaluation of therapeutic superiority of one treatment over the other is based on more than one endpoint describing treatment efficacy. In such situations, Hotelling's T2 statistic detects any departure from the global null hypothesis of no difference between the samples with respect to all variables, whereas O'Brien's OLSand GLS-test looks for equidirected alternatives. In the paper, a SAS macro collection is introduced to address the computational realization of those multivariate two-sample tests within the SAS software. The statistical methods implemented in this collection incorporate the case of incomplete data using all available information from the observed variables. The statistical background of the test procedures is briefly summarized, the syntactical requirements for the usage of the macros are delineated, and a worked example of their application to a data set is presented.
Identifying Patient Groups based on Frequent Patterns of Patient Samples

Grouping patients meaningfully can give insights about the different types of patients, their needs, and the priorities. Finding groups that are meaningful is however very challenging as background knowledge is often required to determine what a useful grouping is. In this paper we propose an approach that is able to find groups of patients based on a small sample set of positive examples given by domain experts. Because of that, the approach relies on very limited efforts by the domain experts. The approach groups based on the activities and diagnostic/billing codes within health pathways of patients. To define such a grouping based on the sample of patients efficiently, frequent patterns of activities are discovered and used to measure the similarity between the care pathways of other patients to the patients in the sample group. This approach results in an insightful definition of the group. The proposed approach is evaluated using several datasets obtained from a large university medical center. The evaluation shows F1-scores of around 0.7 for grouping kidney injury and around 0.6 for diabetes.
On the Formalization of Importance Measures Using HOL Theorem Proving

Importance measures provide a systematic approach to scrutinize critical system components, which are extremely beneficial in making important decisions, such as prioritizing reliability improvement activities, identifying weak-links and effective usage of given resources. The importance measures are then in turn used to obtain a criticality value for each system component and to rank the components in descending manner. Simulations tools are generally used to perform importance measure based analysis, but they require expensive computations and thus they are not suitable for large systems. A more scalable approach is to utilize the importance measures to obtain all the necessary conditions by proving a generic relationship describing the relative importance between any pair of components in a system. In this paper, we propose to use higher-order-logic (HOL) theorem proving to verify such relationships and thus making sure that all the essential conditions are accompanied by the proven property. In particular, we formalize the commonly used importance measures, such as Birnbaum and Fussell-Vesely, and conduct a formal importance measure analysis of a railway signaling system at a Moroccan level crossing as an application for illustration purpose.
The VAST Survey - II. Orbital motion monitoring of A-type star multiples

As a part of our ongoing Volume-limited A-Star (VAST) adaptive optics survey, we have obtained observations of 26 binary systems with projected separations <100 AU, 13 of which have sufficient historical measurements to allow for refinem ent of their orbital elements. For each system with an estimated orbit, the dynamical system mass obtained was compared with the system mass estimated from mass-magnitude relations. Discrepancies between the dynamical and theoretical system mass can be explained by the presence of a previously unresolved spectroscopic component, or by a non-solar metallicity of the system. Using this approach to infer the presence of additional companions, a lower limit to the fraction of binaries, triples, and quadruples can be estimated as 39, 46, and 15 per cent, for systems with at least one companion within 100 AU. The fraction of multiple systems with three or more components shows a relative increase compared to the fraction for Solar-type primaries resolved in previous volume-limited surveys. The observations have also revealed a pair of potentially young (<100 Myr) M-dwarf companions, which would make an ideal benchmark for the theoretical models during the pre-Main Sequence contraction phase for M-dwarfs. In addition to those systems with orbit fits, we report 13 systems for which furthe r orbital monitoring observations are required, 11 of which are newly resolved as a part of the VAST survey.
Exponentially convergent stochastic k-PCA without variance reduction

We present Matrix Krasulina, an algorithm for online k-PCA, by generalizing the classic Krasulina's method (Krasulina, 1969) from vector to matrix case. We show, both theoretically and empirically, that the algorithm naturally adapts to data low-rankness and converges exponentially fast to the ground-truth principal subspace. Notably, our result suggests that despite various recent efforts to accelerate the convergence of stochastic-gradient based methods by adding a O(n)-time variance reduction step, for the k-PCA problem, a truly online SGD variant suffices to achieve exponential convergence on intrinsically low-rank data.
Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction

We present MonoPSR, a monocular 3D object detection method that leverages proposals and shape reconstruction. First, using the fundamental relations of a pinhole camera model, detections from a mature 2D object detector are used to generate a 3D proposal per object in a scene. The 3D location of these proposals prove to be quite accurate, which greatly reduces the difficulty of regressing the final 3D bounding box detection. Simultaneously, a point cloud is predicted in an object centered coordinate system to learn local scale and shape information. However, the key challenge is how to exploit shape information to guide 3D localization. As such, we devise aggregate losses, including a novel projection alignment loss, to jointly optimize these tasks in the neural network to improve 3D localization accuracy. We validate our method on the KITTI benchmark where we set new state-of-the-art results among published monocular methods, including the harder pedestrian and cyclist classes, while maintaining efficient run-time.
Persistent Memory I/O Primitives

I/O latency and throughput is one of the major performance bottlenecks for disk-based database systems. Upcoming persistent memory (PMem) technologies, like Intel's Optane DC Persistent Memory Modules, promise to bridge the gap between NAND-based flash (SSD) and DRAM, and thus eliminate the I/O bottleneck. In this paper, we provide one of the first performance evaluations of PMem in terms of bandwidth and latency. Based on the results, we develop guidelines for efficient PMem usage and two essential I/O primitives tuned for PMem: log writing and block flushing.
DYNAMIC SLIDING MANIFOLDS FOR REALIZATION OF HIGH INDEX DIFFERENTIAL‐ALGEBRAIC SYSTEMS

Differential‐algebraic equation (DAE) systems present a number of difficult problems in nonlinear simulation and control. One of the key difficulties is that DAEs are not expressed in an explicit state space form required by many simulation and control design methods. In this paper, the problem is addressed using a new approach that constructs an explicit state space approximation of the DAEs using a sliding controller. The state space model can in turn be used with existing nonlinear control and simulation methods. This procedure, known as realization, is achieved by developing a boundary layer sliding controller with a dynamic sliding manifold. The approach builds on previous realization methods proposed by the author that employ a static sliding control surface. The approach is generalized by employing a dynamic sliding manifold which allows much greater freedom in determining optimality, robustness, and convergence of the realization than previous methods allow. The necessary criteria for key properties such as convergence, stability, and controllability of this new method are proven using a special type of sliding normal form. Furthermore, the important property of observability for sliding realizations is established for the first time by analyzing the convergence of local eigenvectors of the approximation. Together these results establish a new general framework for realization of a large class of nonlinear high index DAE systems.
Valley Filtering in Spatial Maps of Coupling between Silicon Donors and Quantum Dots

Exchange coupling is a key ingredient for spin-based quantum technologies since it can be used to entangle spin qubits and create logical spin qubits. However, the influence of the electronic valley degree of freedom in silicon on exchange interactions is presently the subject of important open questions. Here we investigate the influence of valleys on exchange in a coupled donor/quantum dot system, a basic building block of recently proposed schemes for robust quantum information processing. Using a scanning tunneling microscope tip to position the quantum dot with sub-nm precision, we find a near monotonic exchange characteristic where lattice-aperiodic modulations associated with valley degrees of freedom comprise less than 2~\% of exchange. From this we conclude that intravalley tunneling processes that preserve the donor's $\pm x$ and $\pm y$ valley index are filtered out of the interaction with the $\pm z$ valley quantum dot, and that the $\pm x$ and $\pm y$ intervalley processes where the electron valley index changes are weak. Complemented by tight-binding calculations of exchange versus donor depth, the demonstrated electrostatic tunability of donor/QD exchange can be used to compensate the remaining intravalley $\pm z$ oscillations to realise uniform interactions in an array of highly coherent donor spins.
Zakat expenditure and economic growth in the Federal Territory of Malaysia

This research paper aims to examine the impact of zakat distribution on growth in the Federal Territory Malaysia. Specifically, an econometric study is carried out to examine the ability of zakat expenditure to affect real economic growth in the Federal Territory Malaysia by employing various econometric procedures such as the unit root tests, the cointegration tests, the vector error-correction model (VECM), and the Granger causality tests. The findings of the study suggest that zakat expenditure has a positive relationship with real GDP in the long-run. The Granger causality test indicates that zakat spending causes real economic growth with no feedback. In other words, zakat expenditure could boost GDP in the Federal Territory Malaysia both in the short-run and long-run.
Categorical Mixture Models on VGGNet activations

In this project, I use unsupervised learning techniques in order to cluster a set of yelp restaurant photos under meaningful topics. In order to do this, I extract layer activations from a pre-trained implementation of the popular VGGNet convolutional neural network. First, I explore using LDA with the activations of convolutional layers as features. Secondly, I explore using the object-recognition powers of VGGNet trained on ImageNet in order to extract meaningful objects from the photos, and then perform LDA to group the photos under topic-archetypes. I find that this second approach finds meaningful archetypes, which match the human intuition for photo topics such as restaurant, food, and drinks. Furthermore, these clusters align well and distinctly with the actual yelp photo labels.
How Rational Is the Reluctance to Be Vaccinated against COVID-19? An Economic Approach

This paper considers a static game in which a player makes a decision in the first moment of whether or not to be vaccinated against COVID-19 and then nature acts. In making the decision the player takes into consideration expected costs and benefits of being inoculated in terms of income. The relative speed with which the first vaccines have reached the market has caused some parts of the population to be concerned about the possible side effects of vaccination and, as a result, a part of the population is reluctant to take the vaccine. The papers finds that according to statistics on the pandemic’s evolution and certain assumptions about the likelihood of virus sequelae and vaccine side-effects, the decision to be vaccinated appears to be optimal. Low probability of contagion and a very low vaccine effectiveness rate are needed to make the decision to be vaccinated non-optimal.
Malware Analysis by Combining Multiple Detectors and Observation Windows

Malware developers continually attempt to modify the execution pattern of malicious code hiding it inside apparent normal applications, which makes its detection and classification challenging. This article proposes an ensemble detector, which exploits the capabilities of the main analysis algorithms proposed in the literature designed to offer greater resilience to specific evasion techniques. In particular, the article presents different methods to optimally combine both generic and specialized detectors during the analysis process, which can be used to increase the unpredictability of the detection strategy, as well as improve the detection rate in presence of unknown malware families and provide better detection performance in the absence of a constant re-training of detector needed to cope with the evolution of malware. The paper also presents an alpha-count mechanism that explores how the length of the observation time window can affect the detection accuracy and speed of different combinations of detectors during the malware analysis. An extended experimental campaign has been conducted on both an open-source sandbox and an Android smartphone with different malware datasets. A trade-off among performance, training time, and mean-time-to-detect is presented. Finally, a comparison with other ensemble detectors is also presented.
Lipid lowering effect of Eurycoma longifolia Jack aqueous root extract in hepatocytes

An aqueous extract of Eurycoma longifolia Jack, or natively known in Malay as Tongkat Ali, roots is traditionally used to overcome plethora of health conditions especially andrological maladies. As compiling evidences corroborate the therapeutic effects of this plant extract as efficacious concoction, it is noteworthy that novel potential for the use of the plant extract in regulating fat metabolism is being unravelled. Due to studies that investigated the effects of Tongkat Ali on in vivo models and clinical subjects where the measurable parameters were the end stage of response, this study adopts an in vitro model to investigate whether Tongkat Ali root extract possesses lipid lowering potential or the changes was a side-effect of the supplementation. Human hepatic cell line WRL-68 was used in this study and treated with various concentrations (1, 2, 4 and 8 µg/ml) of the aqueous Tongkat Ali root extract were conducted on fatty liver cells. Results demonstrated reduced intracellular fatty acids build up, up to ~72% reduction, in treated cells when compared to those untreated control cells. This study therefore, unravelled the novel potential of intracellular lipid lowering effect of Tongkat Ali. The finding therefore, warrants further studies to elucidate the mechanisms that explain the phenomenon.
Coastal Management Using UAS and High-Resolution Satellite Images for Touristic Areas

Coastline change and human activities in shoreline zones are two factors indicating the vulnerability and the quality of a coastal environment. In this article, coastline evolution and spatiotemporal differences on coastal touristic infrastructure are presented as two case studies. Both case studies have increasing interest among scientists monitoring sensitive coastal areas, and for stakeholders evolved in the tourist industry. The study is twofold: monitors the shoreline evolution and examines how the shoreline behavior affects the seasonal anthropogenic touristic infrastructure. Shoreline detection methodology integrates unmanned aerial systems (UAS) or high-resolution satellite images for data acquisition, and geographic object-based image analysis (GEOBIA) for the shoreline recognition and the infrastructure change detection. The methodology used produced robust results in the aspect of mapping and detecting coastline changes, coastal erosion and the human pressure due to specific activities.
Deep near-infrared spectroscopy of submillimetre-selected galaxies

We present the results of deep near-infrared spectroscopy of seven submillimetre-selected galaxies from the Submillimetre Common User Bolometer Array (SCUBA) 8-mJy Survey and Canada–UK Deep Submillimetre Survey (CUDSS). These galaxies were selected because they are too faint to be accessible to optical spectrographs on large telescopes. We obtain a spectroscopic redshift for one object, and likely redshifts for two more, based on a combination of marginal emission-line detections and the shape of the continuum. All three redshifts broadly agree with estimates from their radio/submm spectral energy distributions. From the emission-line strengths of these objects, we infer star formation rates of 10–25 M⊙ yr−1, while the lack of detections in the other objects imply even lower rates. By comparing our results with those of other authors, we conclude it is likely that the vast majority (more than 90 per cent) of the star formation in these objects is completely extinguished at rest-frame optical wavelengths, and the emission lines originate in a relatively unobscured region. Finally, we look at future prospects for making spectroscopic redshift determinations of submm galaxies.