Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Comparison of Statistical Methods for Analyzing Human Metabolomics Data

Emerging technologies now allow for mass spectrometry-based profiling of thousands of small molecule metabolites (‘metabolomics’) in an increasing number of biosamples. While offering great promise for insight into the pathogenesis of human disease, standard approaches have not yet been established for statistically analyzing increasingly complex, high-dimensional human metabolomics data in relation to clinical phenotypes, including disease outcomes. To determine optimal approaches for analysis, we formally compare traditional and newer statistical learning methods across a range of metabolomics dataset types. In simulated and experimental metabolomics data derived from large population-based human cohorts, we observe that with an increasing number of study subjects, univariate compared to multivariate methods result in an apparently higher false discovery rate as represented by substantial correlation between metabolites directly associated with the outcome and metabolites not associated with the outcome. Although the higher frequency of such associations would not be considered false in the strict statistical sense, it may be considered biologically less informative. In scenarios wherein the number of assayed metabolites increases, as in measures of nontargeted versus targeted metabolomics, multivariate methods performed especially favorably across a range of statistical operating characteristics. In nontargeted metabolomics datasets that included thousands of metabolite measures, sparse multivariate models demonstrated greater selectivity and lower potential for spurious relationships. When the number of metabolites was similar to or exceeded the number of study subjects, as is common with nontargeted metabolomics analysis of relatively small cohorts, sparse multivariate models exhibited the most-robust statistical power with more consistent results. These findings have important implications for metabolomics analysis in human disease.
The Power of Color: A Study on the Effective Use of Colored Light in Human-Robot Interaction

In times of more and more complex interaction techniques, we point out the powerfulness of colored light as a simple and cheap feedback mechanism. Since it is visible over a distance and does not interfere with other modalities, it is especially interesting for mobile robots. In an online survey, we asked 56 participants to choose the most appropriate colors for scenarios that were presented in the form of videos. In these scenarios a mobile robot accomplished tasks, in some with success, in others it failed because the task is not feasible, in others it stopped because it waited for help. We analyze in what way the color preferences differ between these three categories. The results show a connection between colors and meanings and that it depends on the participants' technical affinity, experience with robots and gender how clear the color preference is for a certain category. Finally, we found out that the participants' favorite color is not related to color preferences.
Improving the Diffie-Hellman Key Exchange Algorithm by Proposing the Multiplicative Key Exchange Algorithm

Exchanging cryptographic keys has been a problem with respect to security. Whitfield Diffie and Martin Hellman proposed the Diffie-Hellman key exchange algorithm to overcome the problem. Since then, the concept of key exchange over an unsecured network has completely been revolutionized. The algorithm is based on using arithmetic calculations for transmission of the shared session keys. The purpose of this algorithm is to enable users to securely exchange keys which can be used for later encryptions. This ability to securely exchange session keys dynamically and publicly between a group of users has become the foundation for secure group applications such as distributed computing, distributed databases and conference calls. Man-in-the-middle attacks are better secured using the Diffie-Hellman algorithm. Over time, Diffie-Hellman algorithm has been altered several times by various authors. However, some limitations to the Diffie-Hellman algorithm still persist. One of the limitations of the Diffie-Hellman algorithm is that it is computationally intensive thereby increasing the time complexity when generating public keys. The proposed algorithm has similar grounds with the Diffie-Hellman algorithm, and a new technique is used for sharing session keys which overcome the time complexity limitation of the Diffie-Hellman algorithm. The proposed “Multiplicative Key Exchange Algorithm” uses simple arithmetic equations to generate and exchange keys over an insecure network. General Terms Security, Diffie-Hellman, Key Exchange Algorithm, Encryption, Decryption.
Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising

In real-time display advertising, ad slots are sold per impression via an auction mechanism. For an advertiser, the campaign information is incomplete --- the user responses (e.g, clicks or conversions) and the market price of each ad impression are observed only if the advertiser's bid had won the corresponding ad auction. The predictions, such as bid landscape forecasting, click-through rate (CTR) estimation, and bid optimisation, are all operated in the pre-bid stage with full-volume bid request data. However, the training data is gathered in the post-bid stage with a strong bias towards the winning impressions. A common solution for learning over such censored data is to reweight data instances to correct the discrepancy between training and prediction. However, little study has been done on how to obtain the weights independent of previous bidding strategies and consequently integrate them into the final CTR prediction and bid generation steps. In this paper, we formulate CTR estimation and bid optimisation under such censored auction data. Derived from a survival model, we show that historic bid information is naturally incorporated to produce Bid-aware Gradient Descents (BGD) which controls both the importance and the direction of the gradient to achieve unbiased learning. The empirical study based on two large-scale real-world datasets demonstrates remarkable performance gains from our solution. The learning framework has been deployed on Yahoo! 's real-time bidding platform and provided 2.97% AUC lift for CTR estimation and 9.30% eCPC drop for bid optimisation in an online A/B test.
Music Playlist Recommendation Using Acoustic-Feature Transition Inside the Songs

Previously, we proposed a playlist recommendation method that recommends a music sequence that has smooth transitions of the acoustic features. Our previous method uses the last two songs in a playlist to make recommendations for following songs that have a smooth transition of acoustic features from the current songs. However, in this previous method, if two or more songs are not given, it is not possible to recommend a suitable next song. Also, there is insufficient correspondence to songs which changing characteristics, such as a tune that gradually rises with a quiet melody. In this study, we propose a new method of recommending music so that the transition becomes smooth by considering the acoustic features that change inside the song. Our preliminary evaluations show that the proposed new method is more effective compared with previous methods.
Blackthorn: Large-Scale Interactive Multimodal Learning

This paper presents Blackthorn, an efficient interactive multimodal learning approach facilitating analysis of multimedia collections of up to 100 million items on a single high-end workstation. Blackthorn features efficient data compression, feature selection, and optimizations to the interactive learning process. The Ratio-64 data representation introduced in this paper only costs tens of bytes per item yet preserves most of the visual and textual semantic information with good accuracy. The optimized interactive learning model scores the Ratio-64-compressed data directly, greatly reducing the computational requirements. The experiments compare Blackthorn with two baselines: Conventional relevance feedback, and relevance feedback using product quantization to compress the features. The results show that Blackthorn is up to 77.5$\times$ faster than the conventional relevance feedback alternative, while outperforming the baseline with respect to the relevance of results: It vastly outperforms the baseline on recall over time and reaches up to 108% of its precision. Compared to the product quantization variant, Blackthorn is just as fast, while producing more relevant results. On the full YFCC100M dataset, Blackthorn performs one complete interaction round in roughly 1 s while maintaining adequate relevance of results, thus opening multimedia collections comprising up to 100 million items to fully interactive learning-based analysis.
Clausal temporal resolution

In this article, we examine how clausal resolution can be applied to a specific, but widely used, nonclassical logic, namely discrete linear temporal logic. Thus, we first define a normal form for temporal formulae and show how arbitrary temporal formulae can be translated into the normal form, while preserving satisfiability. We then introduce novel resolution rules that can be applied to formulae in this normal form, provide a range of examples, and examine the correctness and complexity of this approach. Finally, we describe related work and future developments concerning this work.
The Effect of Classroom Capacity on Vocal Fatigue as Quantified by the Vocal Fatigue Index

Objective: Previous research has concluded that teachers are at a higher-than-normal risk for voice issues that can cause occupational limitations. While some risk factors have been identified, there are still many unknowns. Patients and Methods: A survey was distributed electronically with 506 female teacher respondents. The survey included questions to quantify three aspects of vocal fatigue as captured by the Vocal Fatigue Index (VFI): (1) general tiredness of voice (performance), (2) physical discomfort associated with voicing (pain), and (3) improvement of symptoms with rest (recovery). The effect of classroom capacity on US teachers’ self-reported experience of vocal fatigue was analyzed. Results: The results indicated that a classroom’s capacity significantly affected teachers’ reported amounts of vocal fatigue, while a teacher’s age also appeared to significantly affect the reported amount of vocal fatigue. A quadratic rather than linear effect was seen, with the largest age effect occurring at around 40–45 years in all three factors of the VFI. Conclusion: Further factors which may affect vocal fatigue must be explored in future research. By understanding what increases the risk for vocal fatigue, educators and school administrators can take precautions to mitigate the occupational risk of short- and long-term vocal health issues in school teachers.
ORDER: Object centRic DEterministic Replay for Java

Deterministic replay systems, which record and replay non-deterministic events during program execution, have many applications such as bug diagnosis, intrusion analysis and fault tolerance. It is well understood how to replay native (e.g., C) programs on multi-processors, while there is little work for concurrent java applications on multicore. State-of-the-art work for Java either assumes data-race free execution, or relies on static instrumentation, which leads to missing some necessary nondeterministic events. This paper proposes the ORDER framework to record and reproduce non-deterministic events inside Java virtual machine (JVM). Based on observations of good locality at object level for threads and frequent object movements due to garbage collection, ORDER records and replays non-deterministic data accesses by logging and enforcing the order in which threads access objects. This essentially eliminates unnecessary dependencies introduced by address changes of objects during garbage collection and enjoys good locality as well as less contention, which may result in scalable performance on multicore. Further, by dynamically instrumenting Java code in the JVM compilation pipeline, ORDER naturally covers non-determinism in dynamically loaded classes. We have implemented ORDER based on Apache Harmony. Evaluation on SPECjvm2008, PseudoJBB2005, and JRuby shows that ORDER only incurs 108% performance overhead on average and scales well on a 16-core Xeon testbed. Evaluation with a real-world application, JRuby, shows that several real-world concurrency bugs can be successfully reproduced.
Unusual social behavior in HPC‐1/syntaxin1A knockout mice is caused by disruption of the oxytocinergic neural system

HPC‐1/syntaxin1A (STX1A), a neuronal soluble N‐ethylmaleimide‐sensitive fusion attachment protein receptor, contributes to neural function in the CNS by regulating transmitter release. Recent studies reported that STX1A is associated with human neuropsychological disorders, such as autism spectrum disorder and attention deficit hyperactivity disorder. Previously, we showed that STX1A null mutant mice (STX1A KO) exhibit neuropsychological abnormalities, such as fear memory deficits, attenuation of latent inhibition, and unusual social behavior. These observations suggested that STX1A may be involved in the neuropsychological basis of these abnormalities. Here, to study the neural basis of social behavior, we analyzed the profile of unusual social behavior in STX1A KO with a social novelty preference test, which is a useful method for quantification of social behavior. Interestingly, the unusual social behavior in STX1A KO was partially rescued by intracerebroventricular administration of oxytocin (OXT). In vivo microdialysis studies revealed that the extracellular OXT concentration in the CNS of STX1A KO was significantly lower compared with wild‐type mice. Furthermore, dopamine‐induced OXT release was reduced in STX1A KO. These results suggested that STX1A plays an important role in social behavior through regulation of the OXTergic neural system.
Data intensive review mining for sentiment classification across heterogeneous domains

The automatic detection of orientation and emotions in texts is becoming increasingly important in the Web 2.0 scenario. There is a considerable need for innovative techniques and tools capable of identifying and detecting the attitude of unstructured text. The paper tackles two crucial aspects of the sentiment classification problem: first, the computational complexity of the deployed framework; second, the ability of the framework itself to operate effectively in heterogeneous commercial domains. The proposed approach adopts empirical learning to implement the sentiment-classification technology, and uses a distance-based predictive model to combine computational efficiency and modularity. A suitably designed semantic-based metric is the cognitive core that measures the distance between two user reviews, according to the sentiment they communicate. The framework ultimately nullifies the training process; at the same time, it takes advantage of a classification procedure whose computational cost increases linearly when the training corpus increases. To attain an objective measurement of the actual accuracy of the sentiment classification method, a campaign of tests involved a pair of complex, real-world scoring domains; the goal was to compare the predicted sentiment scores with actual scores provided by human assessors. Experimental results confirmed that the overall approach attained satisfactory performances in terms of both cross-domain classification accuracy and computational efficiency.
Biosensing of cardiac biomarkers using single polyanine nanowires

In this thesis, we explore innovative methods of fabricating single polymer nanowires and applying the fabricated single polymer nanowires for biosensors. The fabrication of single polymer nanowire was carried out via electrochemical deposition growth method, which deposits ionized molecules in a pre-patterned nanochannel between two Au electrodes and forms a nanowire. For biosensing application, we employ polyaniline (PANI) in this research since it has advantages of biocompatibility, easy synthesis, and broad range of electrical conductivity. The single PANI nanowires-based biosensor show high sensitivity and good sensing reliability due to the high surface to volume ratio and single nanowires. Using the fabricated single PANI nanowires, the biosensors were developed to detect cardiac biomarkers such as myoglobin (Myo), cardiac troponin I (cTnI), creatine kinase-MB (CK-MB), and B-type natriuretic peptide (BNP). The single PANI nanowires are functionalized by surface immobilization method that was developed in our laboratory using 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) and N-Hydroxysuccinimde (NHS) to attach monoclonal antibodies (mAbs) of biomarkers without pre-chemical or physical treatments. Lastly, microfluidic devices including channels and solution infusion/withdrawal system were integrated on the biosensor in order to develop an all-in-one biosensor. Our single PANI nanowire-based biosensor is unique in terms of the functionalization on specific area; the mAbs are immobilized on the only surface of PANI nanowires because PANI has superior biocompatibility to mAbs unlike SiO2 layer or Au electrodes. This advantage reduces functionalization steps comparing to inorganic nanowire biosensors and frees the interfered signals from non-nanowire areas. In addition, the microfluidic channel helps achieving stable electrical signals, high sensitivity, safe sample handling, and minimal damage of nanowire. The integrated single PANI nanowire biosensors with microfluidic devices detect very low concentrations of Myo (100 pg/mL), cTnI (250 fg/mL), CK-MB (150 fg/mL), and BNP (50 fg/mL) with good specificity. The remarkable specificity value in cardiac biomarker sensing is over 106 fold, that the specificity value is defined as the ratio of [the highest concentration of non-specific protein showing ignorable or non-response signal] to [the lowest concentration of specific protein showing significant signal change], in the test of bovine serum albumin (BSA) or other cardiac markers. The single PANI nanowire biosensors have shown linear sensing profiles along different concentration from hundreds fg/mL to tens ng/mL depending on the mAbs of the specific biomarkers, and exhibit fast response in a few minutes satisfying reference values of Myo, cTnI, CK-MB, and BNP to diagnose heart failure and determine the patient’s stage of disease.
Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors-based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability. Recently, online social networks and user-generated content provide diverse sources for recommendation beyond ratings. Although social matrix factorization (Social MF) and topic matrix factorization (Topic MF) successfully exploit social relations and item reviews, respectively; both of them ignore some useful information. In this article, we investigate the effective data fusion by combining the aforementioned approaches. First, we propose a novel model MR3 to jointly model three sources of information (i.e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics. Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility. We achieve more accurate rating prediction on real-life datasets over various state-of-the-art methods. Furthermore, we measure the contribution from each of the three data sources and the impact of implicit feedback from ratings, followed by the sensitivity analysis of hyperparameters. Empirical studies demonstrate the effectiveness and efficacy of our proposed model and its extension.
Analysis of phonetic markedness and gestural effort measures for acoustic speech-based depression classification

While acoustic-based links between clinical depression and abnormal speech have been established, there is still however little knowledge regarding what kinds of phonological content is most impacted. Moreover, for automatic speech-based depression classification and depression assessment elicitation protocols, even less is understood as to what phonemes or phoneme transitions provide the best analysis. In this paper we analyze articulatory measures to gain further insight into how articulation is affected by depression. In our investigative experiments, by partitioning acoustic speech data based on lower to high densities of specific phonetic markedness and gestural effort, we demonstrate improvements in depressed/non-depressed classification accuracy and F1 scores.
Aligned discriminative pose robust descriptors for face and object recognition

Face and object recognition in uncontrolled scenarios due to pose and illumination variations, low resolution, etc. is a challenging research area. Here we propose a novel descriptor, Aligned Discriminative Pose Robust (ADPR) descriptor, for matching faces and objects across pose which is also robust to resolution and illumination variations. We generate virtual intermediate pose subspaces from training examples at a few poses and compute the alignment matrices of those subspaces with the frontal subspace. These matrices are then used to align the generated subspaces with the frontal one. An image is represented by a feature set obtained by projecting its low-level feature on these aligned subspaces and applying a discriminative transform. Finally, concatenating all the features we generate the ADPR descriptor. We perform experiments on face and object databases across pose, pose and resolution, and compare with state-of-the-art methods including deep learning approaches to show the effectiveness of our descriptor.
Design of Novel Mycobacterium tuberculosis Pantothenate Synthetase Inhibitors: Virtual Screening, Synthesis and In Vitro Biological Activities

Pantothenate synthetase (PS) enzyme involved in the pantothenate biosynthetic pathway is essential for the virulence and persistent growth of Mycobacterium tuberculosis (MTB). It is encoded by the panC gene, and has become an appropriate target for developing new therapeutics for tuberculosis. Here we report new inhibitors active against MTB PS developed using energy based pharmacophore modelling of the available proteininhibitor complex (3IVX) and virtual screening of a large commercial library. The e‐pharmacophore model consisted of a ring aromatic (R), negative ionizable (N) and acceptor (A) sites. Compounds 5 and 10 emerged as promising hits with IC50s 2.18 µM and 6.63 µM respectively. Further structural optimization was attempted to optimize lead 10 using medicinal chemistry approach and six compounds were found to exhibit better enzyme inhibition compared to parent compound lead 10 (<6 µM).