Miracle-dz/UniFAR-FTUs huggingface dataset:
Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels

Extreme Classification (XC) refers to supervised learning where each training/test instance is labeled with small subset of relevant labels that are chosen from a large set of possible target labels. The framework of XC has been widely employed in web applications such as automatic labeling of web-encyclopedia, prediction of related searches, and recommendation systems. While most state-of-the-art models in XC achieve high overall accuracy by performing well on the frequently occurring labels, they perform poorly on a large number of infrequent (tail) labels. This arises from two statistical challenges, (i) missing labels, as it is virtually impossible to manually assign every relevant label to an instance, and (ii) highly imbalanced data distribution where a large fraction of labels are tail labels. In this work, we consider common loss functions that decompose over labels, and calculate unbiased estimates that compensate missing labels according to Natarajan et al. [26]. This turns out to be disadvantageous from an optimization perspective, as important properties such as convexity and lower-boundedness are lost. To circumvent this problem, we use the fact that typical loss functions in XC are convex surrogates of the 0-1 loss, and thus propose to switch to convex surrogates of its unbiased version. These surrogates are further adapted to the label imbalance by combining with label-frequency-based rebalancing. We show that the proposed loss functions can be easily incorporated into various different frameworks for extreme classification. This includes (i) linear classifiers, such as DiSMEC, on sparse input data representation, (ii) attention-based deep architecture, AttentionXML, learnt on dense Glove embeddings, and (iii) XLNet-based transformer model for extreme classification, APLC-XLNet. Our results demonstrate consistent improvements over the respective vanilla baseline models, on the propensity-scored metrics for precision and nDCG.
Adapting to Context-Aware Knowledge in Natural Conversation for Multi-Turn Response Selection

Virtual assistants aim to build a human-like conversational agent. However, current human-machine conversations still cannot make users feel intelligent enough to build a continued dialog over time. Some responses from agents are usually inconsistent, uninformative, less-engaging and even memoryless. In recent years, most researchers have tried to employ conversation context and external knowledge, e.g. wiki pages and knowledge graphs, into the model which only focuses on solving some special conversation problems in local perspectives. Few researchers are dedicated to the whole capability of the conversational agent which is endowed with abilities of not only passively reacting the conversation but also proactively leading the conversation. In this paper, we first explore the essence of conversations among humans by analyzing real dialog records. We find that some conversations revolve around the same context and topic, and some require additional information or even move on to a new topic. Base on that, we conclude three conversation modes shown in Figure 1 and try to solve how to adapt to them for a continuous conversation. To this end, we define “Adaptive Knowledge-Grounded Conversations” (AKGCs) where the knowledge is to ground the conversation within a multi-turn context by adapting to three modes. To achieve AKGC, a model called MNDB is proposed to model natural dialog behaviors for multi-turn response selection. To ensure a consistent response, MNDB constructs a multi-turn context flow. Then, to mimic user behaviors of incorporating knowledge in natural conversations, we design a ternary-grounding network along with the context flow. In this network, to gain the ability to adapt to diversified conversation modes, we exploit multi-view semantical relations among response candidates, context and knowledge. Thus, three adaptive matching signals are extracted for final response selection. Evaluation results on two benchmarks indicate that MNDB can significantly outperform state-of-the-art models.
Demystifying Illegal Mobile Gambling Apps

Mobile gambling app, as a new type of online gambling service emerging in the mobile era, has become one of the most popular and lucrative underground businesses in the mobile app ecosystem. Since its born, mobile gambling app has received strict regulations from both government authorities and app markets. However, to the best of our knowledge, mobile gambling apps have not been investigated by our research community. In this paper, we take the first step to fill the void. Specifically, we first perform a 5-month dataset collection process to harvest illegal gambling apps in China, where mobile gambling apps are outlawed. We have collected 3,366 unique gambling apps with 5,344 different versions. We then characterize the gambling apps from various perspectives including app distribution channels, network infrastructure, malicious behaviors, abused third-party and payment services. Our work has revealed a number of covert distribution channels, the unique characteristics of gambling apps, and the abused fourth-party payment services. At last, we further propose a “guilt-by-association” expansion method to identify new suspicious gambling services, which help us further identify over 140K suspicious gambling domains and over 57K gambling app candidates. Our study demonstrates the urgency for detecting and regulating illegal gambling apps.
MQuadE: a Unified Model for Knowledge Fact Embedding

The task of knowledge graph embedding (KGE) tries to find appropriate representations for entities and relations and appropriate mathematical computations between the representations to approximate the symbolic and logical relationships between entities. One major challenge for KGE is that the relations in real-world knowledge bases exhibit complex behaviors: they can be injective (1-1) or non-injective (1-N, N-1, or N-N), symmetry or skew-symmetry; one relation may be the inversion of another relation; one relation may be the composition of other two relations (where the composition can be either Abelian or non-Abelian). To our knowledge, there has not been any theoretical guarantee that these complex behaviors can be modeled by existing KGE methods. This paper proposes a method called MQuadE to tackle the challenge in KGE modeling. In MQuadE, we represent a fact triple (h, r, t), that is, (head entity, relation, tail entity), in the knowledge graph with a matrix quadruple , where H and T are the representations of h and t respectively and is the pair of representation of r. MQuadE projects the head entity into HR and the tail entity into , then assumes that HR and are similar for true facts and dissimilar for false facts. We prove that MQuadE, as a unified model for KGE, is able to model the generally concerned types of relations (symmetric, skew-symmetric, injective, non-injective, inversion, Abelian composition, non-Abelian composition). Experiments on link prediction and triple classification show that MQuadE outperforms many previous knowledge graph embedding methods, especially on 1-N, N-1, and N-N relations.
Variable Interval Time Sequence Modeling for Career Trajectory Prediction: Deep Collaborative Perspective

In today’s fast-evolving job market, the timely and effective understanding of the career trajectories of talents can help them quickly develop necessary skills and make the right career transitions at the right time. However, it is a non-trivial task for developing a successful career trajectory prediction method, which should have the abilities for finding the right timing for job-hopping, identifying the right companies, and matching the right positions for the candidates. While people have been trying to develop solutions for providing some of the above abilities, there is no total solution or complete framework to integrate all these abilities together. To this end, in this paper, we propose a unified time-aware career trajectory prediction framework, namely TACTP, which is capable of jointly providing the above three abilities for better understanding the career trajectories of talents. Along this line, we first exploit a hierarchical deep sequential modeling network for career embedding and extract latent talent factors from multiple networks, which are designed with different functions of handling related issues of the timing, companies, and positions for job-hopping. Then, we perform collaborative filtering for generating personalized predictions. Furthermore, we propose a temporal encoding mechanism to handle dynamic temporal information so that TACTP is capable of generating time-aware predictions by addressing the challenges for variable interval time sequence modeling. Finally, we have conducted extensive experiments on large-scale real-world data to evaluate TACTP against the state-of-the-art baselines, and the results show that TACTP has advantages over baselines on all targeted tasks for career trajectory prediction.
Analyzing European Migrant-related Twitter Deliberations

Machine-driven topic identification of online contents is a prevalent task in the natural language processing (NLP) domain. Social media deliberation reflects society's opinion, and a structured analysis of these contents allows us to decipher the same. We employ an NLP-based approach for investigating migration-related Twitter discussions. Besides traditional deep learning-based models, we have also considered pre-trained transformer-based models for analyzing our corpus. We have successfully classified multiple strands of public opinion related to European migrants. Finally, we use 'BertViz' to visually explore the interpretability of better performing transformer-based models.
Incrementality Testing in Programmatic Advertising: Enhanced Precision with Double-Blind Designs

Measuring the incremental value of advertising (incrementality) is critical for financial planning and budget allocation by advertisers. Running randomized controlled experiments is the gold standard in marketing incrementality measurement. Current literature and industry practices to run incrementality experiments focus on running placebo, intention-to-treat (ITT), or ghost bidding based experiments. A fundamental challenge with these is that the serving engine as treatment administrator is not blind to the user treatment assignment. Similarly, ITT and ghost bidding solutions provide greatly decreased precision since many experiment users never see ads. We present a novel randomized design solution for incrementality testing based on ghost bidding with improved measurement precision. Our design provides faster and cheaper results including double-blind, to the users and to the serving engine, post-auction experiment execution without ad targeting bias. We also identify ghost impressions in open ad exchanges by matching the bidding values or ads sent to external auctions with held-out bid values. This design leads to larger precision than ITT or current ghost bidding solutions. Our proposed design has been fully deployed in a real production system within a commercial programmatic ad network combined with a Demand Side Platform (DSP) that places ad bids in third-party ad exchanges. We have found reductions of up to 85% of the advertiser budget to reach statistical significance with typical ghost bids conversion and winner rates. Moreover, the highest statistical power at 50% control size design of this current practice is reached at 8% of our proposed design. By deploying this design, for an advertiser in the insurance industry, to measure the incrementality of display and native programmatic advertising, we have found conclusive evidence that the last-touch attribution framework (current industry standard) undervalues these channels by 87% when compared to the incremental conversions derived from the experiment.
Spotify and the democratisation of music

Abstract The corporate rhetoric of streaming platforms often assumes a tight link between their scale-making ambitions on the one hand and the creative interests of musicians on the other. In practice, most musicians recognise that claims of musical ‘democratisation’ are deeply flawed. The creative ambivalence this produces is an understudied pillar in scholarship on digital music platforms and suggests that these systems can be more creatively constrictive than empowering. Based on ethnographic research among Spotify engineers, record labels and musicians, this article explores how music recommendation systems become inculcated with a corporate rhetoric of ‘scalability’ and considers, following Anna Tsing, how this impacts musical creativity further down the value chain. I argue that the ‘creative ambivalence’ that these technologies produce should be more fully understood as woven into a complex web of social relations and corporate interests than prevailing claims of technological objectivity and ‘democratisation’ suggest.
Lisura superficial de resinas compostas com nanopartículas após protocolos de acabamento e polimento

The aim of this study was to evaluate the smoothness surface of 3 composites by nanoparticles (Filtek Z350 XT, Premise e Charisma Diamond), after being subjected to three different finishing and polishing systems. Were obtained 40 specimens of each resin selected and randomly divided into 4 experimental groups (n=10): C (control) no treatment; SOF Abrasive discs Soflex; ENH – silicon tips Enhance; and F / FF diamond tips fine/extra-fine granulation + felt disc and diamond paste. Twenty-four hours after the preparation and storage of the specimens in distilled water at 37oC, the finishing and polishing systems were applied. The smooth surface of the composites was evaluated using a rugosimeter, where five readings were taken on each specimen. The mean surface roughness (um) were analyzed by ANOVA, two factors, followed by Tukey’s test, with significance of 1%. For all resins tested, statistical difference was found between finishing and polishing systems tested (p<0,01). To the resin Charisma roughness values were: C – 0,10a; SOF – 0,24b; ENH – 0,23b; F/FF – 0,32c. For the premise : C – 0,12a; SOF – 0,16ab; ENH – 0,22b; F/FF – 0,30c. For Filtek : C – 0,08a; SOF – 0,14ab; ENH – 0,20bc; F/FF – 0,26c. The control group obtained better results of surface smoothness for all composites tested. Regarding the treatments groups, SOF and ENH resulted on better smoothness surface, while the group F / FF resulted in surfaces with higher roughness. Descriptors: composite resins; nanotechnology; dental polishing Artigo 06B Lisura superficial de resinas compostas com nanopartículas fluxo 879.indd 224 08/11/13 14:30
The experimental study of CFRP interlayer of dissimilar joint AA7075-T651/Ti-6Al-4V alloys by friction stir spot welding on mechanical and microstructural properties

Abstract The present study focused on two dissimilar metal alloys: AA7075-T651 and Ti-6Al-4V alloys with additional carbon fiber-reinforced polymer (CFRP) as an interlayer were welded together by friction stir spot welding (FSSW). The effect of welding parameters (rotational speed and dwell time) and carbon fiber-reinforced polymer on mechanical and microstructural properties of a weld joint was investigated. The obtained results explore the parametric effects on mechanical properties of the weld joint. The maximum tensile shear load 2597.8 N was achieved at the rotational speed of 2,000 rpm and dwell time of 10 s. While at the same rotational speed, 54.7% reduction in the tensile shear load was attained at shorter dwell time of 5 s. Therefore, dwell time plays an important role in the tensile shear load of a weld joint. The scanning electron microscope (SEM-EDS) results show the formation of intermetallic compound of Ti3Al and Ti-Al-C that significantly affect the mechanical and microstructural properties of the weld joint. Moreover, the effect of the rotational speed on micro-hardness was found at significant than dwell time. The micro-hardness of the weld joint increase by 18.90% in the keyhole rather than the stir zone and the thermomechanical affected zone, which might be due to the presence of ternary (Ti-Al-C) intermetallic compound. Graphical abstract
Development, validation, and application of the ribosome separation and reconstitution system for protein translation in vitro

The conventional view regarding regulation of gene expression is based on transcription control. However, a growing number of recent studies has revealed the important additional impact of translational regulation. Eukaryotic translational machinery appears to be capable of reprogramming mRNA translation to generate proteins required to maintain a healthy cellular proteostasis under particular physiological conditions or to adapt to stress. Although the mechanisms of such remarkable regulation are beginning to emerge, recent studies have identified the ribosome as one of the major constituents of translation-dependent control of gene expression that is especially important during stress. Built of RNA and proteins, ribosomes are susceptible to environmental and intracellular stresses. How stress-modified ribosomes regulate translation and whether they play a role in stress-induced gene expression remain largely elusive. This knowledge gap is likely due to the lack of an appropriate experimental system. Canonical approaches based on exposing cells or cell-free extracts to stressors provide inconclusive results due to off-target effects of modifying agents. Here we describe a robust and simple in vitro assay that allows separation of yeast ribosomes from other translational machinery constituents, followed by reconstitution of the translation reaction. This ribosome separation and reconstitution assay (RSR) is highly advantageous, as it allows modification of ribosomes without compromising other key translational components, followed by supplementing the ribosomes back into translation reactions containing undamaged, translationally-competent yeast lysate. Besides addressing the impact of ribosome-derived stress on translation, RSR can also be used to characterize mutated ribosomes and ribosomes devoid of associated factors.
PGFinder, a novel analysis pipeline for the consistent, reproducible, and high-resolution structural analysis of bacterial peptidoglycans

Many software solutions are available for proteomics and glycomics studies, but none are ideal for the structural analysis of peptidoglycan, the essential and major component of bacterial cell envelopes. It is comprised of glycan chains and peptide stems, both containing unusual amino acids and sugars. This has forced the field to rely on manual analysis approaches, which are time-consuming, labour-intensive, and prone to error. The lack of automated tools has hampered the ability to perform high-throughput analyses and prevented the adoption of a standard methodology. Here, we describe a novel tool called PGfinder for the analysis of peptidoglycan structure and demonstrate that it represents a powerful tool to quantify PG fragments and discover novel structural features. Our analysis workflow, which relies on open-access tools, is a breakthrough towards a consistent and reproducible analysis of bacterial peptidoglycans. It represents a significant advance towards peptidoglycomics as a full-fledged discipline.
CamelliA-based simultaneous imaging of Ca2+ dynamics in subcellular compartments

As a universal second messenger, calcium (Ca2+) transmits specific cellular signals via a spatiotemporal signature generated from its extracellular source and internal stores. Our knowledge of the mechanisms underlying generation of a Ca2+ signature is hampered by limited tools enabling simultaneous monitoring of the dynamics of Ca2+ levels in multiple subcellular compartments. To overcome the limitation and to further improve spatiotemporal resolutions, here we have assembled a molecular toolset (the CamelliA lines) in Arabidopsis that enables simultaneous and high-resolution monitoring of Ca2+ dynamics in multiple subcellular compartments through imaging analyses of different single-colored GECIs (Genetically Encoded Calcium Indicators). Indeed, the uncovering of the previously unrecognized Ca2+ signatures in three types of Arabidopsis cells in response to internal and external cues is a testimony to the wide applicability of the newly generated toolset for elucidating the subcellular sources contributing to the Ca2+signatures in plants. One sentence summary A toolset for simultaneous imaging of Ca2+ dynamics in subcellular compartments has uncovered unrecognized Ca2+ signatures in Arabidopsis cells in response to developmental and external cues.
MRS-BILE AGAR: ITS SUITABILITY FOR THE ENUMERATION OF MIXED PROBIOTIC CULTURES IN CULTURED DAIRY PRODUCTS

This paper contains a study on the suitability of MRS-bile agar medium for enumeration of mixed probiotic cultures in cultured dairy products, beside an analysis of the morphological characteristics of probiotic colonies using SDM (stereo digital microscopy) method. Three probiotic cultures (Lactobacillus acidophilus, Lactobacillus casei and bifidobacteria) were enumerated in various mixed culture compositions, i.e. AY (L. acidophilus and yogurt bacteria), CY (L. casei and yogurt bacteria), BY (bifidobacteria and yogurt bacteria), ABY (L. acidophilus, bifidobacteria and yogurt bacteria), ACY (L. acidophilus, L. casei and yogurt bacteria) and BCY (bifidobacteria, L. casei and yogurt bacteria). MRS-bile agar showed a good suitability for differential enumeration of probiotics in ACY culture composition under both aerobic and anaerobic conditions, differential and selective enumeration of probiotics in BCY culture composition (anaerobiosis and aerobiosis, respectively) and selective enumeration in ABY culture composition only for L. acidophilus under aerobiosis. Furthermore, by using the subtractive enumeration method (SEM) proposed in this article based on the subtraction of the colonies growth under aerobic condition (only L. acidophilus) from the colonies growth under anaerobic condition (both L. acidophilus and bifidobacteria), the number of bifidobacteria in ABY culture composition could also be well determined.
Novel Switched Reluctance Inverter with Fast De-energizing Capability for Four-Phase Switched Reluctance Drive

Abstract This paper proposes novel switched reluctance (SR) inverter for four-phase SR drive which generates high output torque without compromise in efficiency for medium and high speed operations. In proposed SR inverter drive, the recovered energy of phase windings circulates with help of two boost capacitors. The circulation of energy between two alternate working phase winding provides charging of boost capacitors without phase current overlapping. The de-energization of phase windings makes either of boost capacitor and dc-bus voltage in series. Thus, the phase windings are de-energized with twice negative dc-bus voltage during entire process until SR drive working under absolute torque region. The de-energizing current reduces to zero at faster rate, and therefore proposed SR inverter drive operates at high dwell time as well as utilization of effective torque region. So, the extension of constant torque range generates high output torque without compromise in efficiency. It requires two less no. of IGBTs and additional two capacitors and four diodes as compare to conventional SR inverter. The analysis of working states is presented. The high output torque of proposed SR inverter drive without compromise in efficiency is justified from comparison of both simulation and experimental results with conventional SR inverter drive.