Miracle-dz/UniFAR-FTUs huggingface dataset:
Dict-NMT: Bilingual Dictionary based NMT for Extremely Low Resource Languages

Neural Machine Translation (NMT) models have been effective on large bilingual datasets. However, the existing methods and techniques show that the model's performance is highly dependent on the number of examples in training data. For many languages, having such an amount of corpora is a far-fetched dream. Taking inspiration from monolingual speakers exploring new languages using bilingual dictionaries, we investigate the applicability of bilingual dictionaries for languages with extremely low, or no bilingual corpus. In this paper, we explore methods using bilingual dictionaries with an NMT model to improve translations for extremely low resource languages. We extend this work to multilingual systems, exhibiting zero-shot properties. We present a detailed analysis of the effects of the quality of dictionaries, training dataset size, language family, etc., on the translation quality. Results on multiple low-resource test languages show a clear advantage of our bilingual dictionary-based method over the baselines.
On Gradient Descent Convergence beyond the Edge of Stability

Gradient Descent (GD) is a powerful workhorse of modern machine learning thanks to its scalability and efficiency in high-dimensional spaces. Its ability to find local minimisers is only guaranteed for losses with Lipschitz gradients, where it can be seen as a ‘bona-fide’ discretisation of an underlying gradient flow. Yet, many ML setups involving overparametrised models do not fall into this problem class, which has motivated research beyond the so-called “Edge of Stability” (EoS), where the step-size crosses the admissibility threshold inversely proportional to the Lipschitz constant above. Perhaps surprisingly, GD has been empirically observed to still converge regardless of local instability and oscillatory behavior. The incipient theoretical analysis of this phenomena has mainly focused in the overparametrised regime, where the effect of choosing a large learning rate may be associated to a ‘SharpnessMinimisation’ implicit regularisation within the manifold of minimisers, under appropriate asymptotic limits. In contrast, in this work we directly examine the conditions for such unstable convergence, focusing on simple, yet representative, learning problems. Specifically, we characterize a local condition involving third-order derivatives that stabilizes oscillations of GD above the EoS, and leverage such property in a teacher-student setting, under population loss. Finally, focusing on Matrix Factorization, we establish a non-asymptotic ‘Local Implicit Bias’ of GD above the EoS, whereby quasi-symmetric initializations converge to symmetric solutions — where sharpness is minimum amongst all minimisers.
A Framework for Building Secure, Scalable, Networked Enclaves

In 2020, Amazon introduced Nitro enclaves—cloud-based secure enclaves that do not share hardware with untrustwor-thy code, therefore promising resistance against side channel attacks, which have plagued Intel’s SGX for years. While their security properties are attractive, Nitro enclaves are dif-ﬁcult to write code for and are not meant to be used as a networked service, which greatly limits their potential. In this paper, we built a framework that allows for convenient and ﬂexible use of Nitro enclaves by abstracting away complex aspects like remote attestation and end-to-end encryption be-tween an enclave and a remote client. We demonstrate the practicality of our framework by designing and implementing two production-grade systems that solve real-world problems: remotely veriﬁable IP address pseudonymization and private telemetry. Our practical experience suggests that our framework enables quick prototyping, is ﬂexible enough to accommodate different use cases, and inherits strong security and performance properties from the underlying Nitro enclaves.
Evaluating Aleatoric Uncertainty via Conditional Generative Models

Aleatoric uncertainty quantification seeks for distributional knowledge of random responses, which is important for reliability analysis and robustness improvement in machine learning applications. Previous research on aleatoric uncertainty estimation mainly targets closed-formed conditional densities or variances, which requires strong restrictions on the data distribution or dimensionality. To overcome these restrictions, we study conditional generative models for aleatoric uncertainty estimation. We introduce two metrics to measure the discrepancy between two conditional distributions that suit these models. Both metrics can be easily and unbiasedly computed via Monte Carlo simulation of the conditional generative models, thus facilitating their evaluation and training. We demonstrate numerically how our metrics provide correct measurements of conditional distributional discrepancies and can be used to train conditional models competitive against existing benchmarks.
Revisiting End-to-End Speech-to-Text Translation From Scratch

End-to-end (E2E) speech-to-text translation (ST) often depends on pretraining its encoder and/or decoder using source transcripts via speech recognition or text translation tasks, without which translation performance drops substantially. However, transcripts are not always available, and how significant such pretraining is for E2E ST has rarely been studied in the literature. In this paper, we revisit this question and explore the extent to which the quality of E2E ST trained on speech-translation pairs alone can be improved. We reexamine several techniques proven beneficial to ST previously, and offer a set of best practices that biases a Transformer-based E2E ST system toward training from scratch. Besides, we propose parameterized distance penalty to facilitate the modeling of locality in the self-attention model for speech. On four benchmarks covering 23 languages, our experiments show that, without using any transcripts or pretraining, the proposed system reaches and even outperforms previous studies adopting pretraining, although the gap remains in (extremely) low-resource settings. Finally, we discuss neural acoustic feature modeling, where a neural model is designed to extract acoustic features from raw speech signals directly, with the goal to simplify inductive biases and add freedom to the model in describing speech. For the first time, we demonstrate its feasibility and show encouraging results on ST tasks.
Applying separative non-negative matrix factorization to extra-financial data

We present here an original application of the non-negative matrix factorization (NMF) method, for the case of extra-financial data. These data are subject to high correlations between co-variables, as well as between observations. NMF provides a much more relevant clustering of co-variables and observations than a simple principal component analysis (PCA). In addition, we show that an initial data separation step before applying NMF further improves the quality of the clustering.
An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction

Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks. DIP represents the image to be recovered as the output of a deep convolutional neural network, and learns the network's parameters such that the model output matches the corrupted observation. Despite its impressive reconstructive properties, the approach is slow when compared to supervisedly learned, or traditional reconstruction techniques. To address the computational challenge, we bestow DIP with a two-stage learning paradigm: (i) perform a supervised pretraining of the network on a simulated dataset; (ii) fine-tune the network's parameters to adapt to the target reconstruction task. We provide a thorough empirical analysis to shed insights into the impacts of pretraining in the context of image reconstruction. We showcase that pretraining considerably speeds up and stabilizes the subsequent reconstruction task from real-measured 2D and 3D micro computed tomography data of biological specimens.
Surface charge-transfer doping a quantum-confined silver monolayer beneath epitaxial graphene

Recently the graphene/SiC interface has emerged as a versatile platform for the epitaxy of otherwise unstable, monoelemental, two-dimensional (2D) layers via intercalation. Intrinsically capped into a van der Waals heterostructure with overhead graphene, they compose a new class of quantum materials with striking properties contrasting their parent bulk crystals. Intercalated silver presents a prototypical example where 2D quantum conﬁnement and inversion symmetry breaking entail a metal-to-semiconductor transition. However, little is known about the associated unoccupied states, and control of the Fermi level position across the bandgap would be desirable. Here, we n -type dope a graphene/2D-Ag/SiC heterostack via in situ potassium deposition and probe its band structure by means of synchrotron-based angle-resolved photoelectron spectroscopy. While the induced carrier densities on the order of 10 14 cm − 2 are not yet suﬃcient to reach the onset of the silver conduction band, the band alignment of graphene changes relative to the rigidly shifting Ag valence band and substrate core levels. We further demonstrate an ordered potassium adlayer (2 × 2 relative to graphene) with free-electron-like dispersion, suppressing plasmaron quasiparticles in graphene via enhanced metalization of the heterostack. Our results establish surface charge-transfer doping as an eﬃcient handle to modify band alignment and electronic properties of a van der Waals heterostructure assembled from graphene and a novel type of monolayered quantum material.
MILP models and metaheuristic for balancing and sequencing of mixed-model two-sided assembly lines

Mixed-model assembly lines are becoming increasingly popular due to flexibility of producing customised products. In a mixed-model assembly line, line balancing and model sequencing problems are tightly interrelated and very important for efficiency. This paper proposes a new assembly line configuration based on paced mixed-model two-sided assembly lines where balancing and sequencing problems are considered simultaneously. Minimal work has been reported considering both problems simultaneously for this type of assembly line configuration. Two mixed-integer linear programming (MILP) models are developed and a restarted SA algorithm with new encoding, decoding and neighbourhood procedures is developed. The parameters of the proposed algorithm are selected based on a statistical technique and the performance of it is tested on a set of new benchmark problems. The computational results demonstrate the effectiveness of the MILP models and the high efficiency of the proposed algorithm. The proposed algorithm outperforms the comparative original SA algorithm. [Received 22 July 2016; Revised 1 December 2016; Accepted 14 February 2017]
MOOCifying Courses: Delivery of a MOOC to Enhance University Course Activities

Since 2012 MOOCs have been heralded as a new way of learning outside of formal university programs of study and there has been much speculation regarding their impact. While MOOCs have provided millions of global learners with access to courses, they failed to deliver the types of learning experiences and completion requirements that were hoped for. One potential iteration of MOOCs might be to blend them with existing courses offered in universities supporting links and connections between study and the outside world. This MOOCification of full-fee courses may provide another next step in the delivery of real and authentic learning. Using an empirical case study design, this project explored the MOOCification of an undergraduate preservice education course at an Australian university. The study presents evidence that blending MOOCs with classroom-based or online learning does provide higher education learners with personalized active learning opportunities. Further research on scaffolded support enabling learners to capitalize on additional aspects of networked learning in MOOCs is needed.
The Vulnerability Ecosystem: Exploring vulnerability discovery and the resulting cyberattacks through agent-based modelling

Software vulnerabilities are a major enabler for cyberattacks, and are therefore responsible for a sizeable portion of the risk for large organisations in cyberspace. Little research has been done on the dynamics underlying this risk. The goal of this paper is to find how organisations and software vendors can influence the overall risk level as a result of vulnerabilities. An agent-based model is developed to capture the behaviour and complexity of the vulnerability ecosystem. Somewhat counter intuitively, the model shows that not necessarily the number of vulnerabilities, but rather the diffusion of the knowledge on vulnerabilities, has a strong influence on the resulting attacks. Instead of trying to fix all vulnerabilities, focussing patching efforts on vulnerabilities that are actually being exploited can significantly reduce the risk of organisations. The model further showed that organisations should improve their own efforts to deploy patches on shorter notice to narrow down the window of vulnerability.
Semantics

Semantics is concerned with meaning: what meanings are, how meanings are assigned to words, phrases and sentences of natural and formal languages, and how meanings can be combined and used for inference and reasoning. The goal of this chapter is to introduce computational linguists and computer scientists to the tools, methods, and concepts required to work on natural language semantics. Semantics, while often paired with pragmatics, is nominally distinct. On a traditional view, semantics concerns itself with the compositional buildup of meaning from the lexicon to the sentence level whereas pragmatics concerns the way in which contextual factors and speaker intentions affect meaning and inference (see, e.g., Potts pear in this volume). Although the semantics-pragmatics distinction is historically important, and continues to be widely adopted, in practice it is not clearcut. Work in semantics inevitably involves pragmatics and vice versa. Furthermore, it is not a distinction which is of much relevance for applications in computational linguistics. This chapter is organized as follows. In sections 2 and 2.2 we introduce foundational concepts and discuss ways of representing the meaning of sentences, and of combining the meaning of smaller expressions to produce sentential meanings. In section 3 we discuss the representation of meaning for larger units, especially with respect to anaphora, and introduce two formal theories that go beyond sentence meaning: Discourse Representation Theory and Dynamic Semantics. Then, in section 4 we discuss temporality, introducing event semantics and describing standard approaches to the semantics of time. Section 5 concerns the tension between the surface-oriented statistical methods characteristic of mainstream computational linguistics and the more abstract methods typical of formal semantics and includes discussion of phenomena for which it seems particularly important to utilize insights from formal semantics. Throughout the chapter we assume familiarity with basic notions from logic (propositional logic and first-order predicate logic), computer science and, to a lesser extent, computational linguistics (e.g., algorithms, parsing, syntactic categories and tree representations).
Simpatico: A Text Simplification System for Senate and House Bills

In the Philippines, public participation in the creation of Senate and the House bills is very minimal. This could be the case because of the lack of accessibility of an ordinary citizen to take part in the lawmaking process and the complexity of the terms and grammar rules being used in legal proceedings called legalese. eParticipation is seen to be a solution to increase public participation but it only solves the process’ accessibility and not the complexity of the bills. Simplification solves this problem by transforming technical jargons and complicated phrases into words or phrases that are easier to understand. Also, existing simplification systems do not cover Philippine Senate and House bills as part of their domain. This research focuses on developing a text simplification system using lexical and syntactic simplification method for Philippine Senate and House bills called Simpatico. It aims to simplify legalese to plain English, which a majority of the Philippine population can understand. The system is based on a mix of previous simplification systems and makes use of different tools in order to accomplish certain tasks for simplification. It is composed of 3 major components: the preprocessing module, lexical simplification module, and the syntactic simplification module.
General Current Control of Six-Phase-Based Non-Isolated Integrated On-Board Charger with Low Order Harmonic Compensation

Electric vehicle charging technology has recently witnessed massive developments due to its significant role in the ever-growing number of electric vehicles on the market. The integrated onboard charger technology (IOBC) represents an effective and attractive solution to reduce EV size, cost, and weight. IOBC technology employs propulsion components, such as the motor and its converter, in the charging process. The main objective of IOBC is to achieve the maximum charging current with zero average/pulsating torque so that mechanical interlocking can be dispensed. Recently, some of the IOBC topologies have adopted machines with six-phase stators to exploit the many advantages of multiphase-based systems. This paper investigates the effect of the winding design, namely, chorded or un-chorded designs, as well as the winding configuration, namely, dual three-phase, asymmetrical, or symmetrical winding configurations, on the current quality of a six-phase-based non-isolated IOBC. The relation between the winding design and the induced low order harmonics in the charging current is first clarified. The required current controller structure is then proposed, which ensures balanced grid line currents with high quality, under either healthy or one-phase fault conditions. Finally, a comparative study between all available designs with the proposed current controller is carried out to validate the theoretical findings.
A distal eff ect of microsomal triglyceride transfer protein defi ciency on the lysosomal recycling of CD 1 d

JEM © The Rockefeller University Press $15.00 Vol. 204, No. 4, April 16, 2007 921–928 www.jem. org/cgi/doi/10.1084/jem.20061568 921 The CD1 family of glycoproteins is composed of conserved MHC-like, β2-microglobulin– associated glycoproteins that specialize in the capture of self and microbial lipid antigens for presentation to T cells (1, 2). Recent studies have revealed that CD1-mediated antigen presentation depended on a set of proteins involved in general lipid metabolism. Thus, lysosomal saposins, which promote the enzymatic degradation of glycosphingolipids (GSLs), also performed essential lipid exchange reactions between membranes and CD1 proteins (3–5). CD1e, a lysosomal member of the human CD1 family, enhanced lipid processing by degrading enzymes (6). Serum VLDL and the surface LDL receptor directly contributed to the transport and uptake of exogenous lipids (7). Disruption of NPC1, a transmembrane protein present in late endosomal membranes, interrupted GSL traffi cking from late endosome to lysosome and impaired CD1-mediated antigen presentation and NKT cell development (8). Several lines of evidence also suggest a role for microsomal triglyceride transfer protein (MTP), an ER-resident protein that functions as a lipid transfer protein and is essential for the loading of apolipoprotein B (apoB) with cholesterol, triacylglycerol, and phospholipids (9). MTP was originally characterized as a heterodimer of protein disulfi de isomerase and a 97-kD subunit in the ER of hepatocytes and enterocytes (10–12), but recent studies have demonstrated weak expression in hemopoietic cells as well, including T cells and dendritic cells (13). Because MTP coprecipitated with CD1d and could transfer lipids onto plate-bound CD1d in a cell-free assay, it was proposed that MTP might assist in loading lipids onto CD1 molecules during biosynthesis in the ER in a manner similar to chaperone-assisted loading of peptides onto nascent MHC class I molecules (13, 14). In the absence of MTP, misfolded CD1d molecules would be retained in the ER, explaining the reduction of surface CD1d and the impaired antigen presentation observed in MTP-defi cient cells. Consistent with this hypothesis, MTP ablation after injection of double-stranded RNA (dsRNA [polyI:C]) in mttpfl /fl Mx1-Cre mice aff orded resistance to diseases mediated by CD1d-restricted NKT cells such as αGalCer-induced hepatitis and oxazolone-induced colitis (14). Here, we have examined the presentation of lipid antigens and studied the dynamics of A distal eff ect of microsomal triglyceride transfer protein defi ciency on the lysosomal recycling of CD1d
Stability-Indicating HPTLC Method for Simultaneous Quantification of Moxonidine and Amlodipine Besylate in Their Combined Pharmaceutical Dosage Form

A sensitive, selective and precise high performance thin layer chromatographic method has been developed and validated for simultaneous determination of moxonidine and amlodipine besylateboth as a bulk drug and in formulation. The method employed TLC aluminium plates pre-coated with silica gel 60 F-254 as stationary phase while the solvent system was methanol: toluene: ethyl acetate: ammonia (10% v/v) (2: 3.5: 5: 1, v/v/v/v). The R f values of moxonidine and amlodipine besylate were observed to be 0.46 ± 0.05 and 0.28 ± 0.03, respectively. The densitometric analysis was carried out in absorbance mode at 237 nm. The linear regression analysis data for the calibration plots showed a good linear relationship for moxonidine and amlodipine besylate over a concentration range of 100-3500 ng/band and 200–3500 ng/band, respectively. The method was validated for precision, robustness and recovery. The limit of detection and limit of quantification for moxonidine and amlodipine besylate were found to be 45.69 and 138.44 ng/band, 27.60 and 83.63ng/band respectively. Moxonidine and amlodipine besylate stock solutions were subjected to acid and alkali hydrolysis, chemical oxidation, dry heat degradation and photo degradation. The degraded product peaks were well resolved from the pure drug peak with significant difference in their R f values. Stressed samples were assayed using developed HPTLC method. Sstatistical analysis showed that the method is repeatable, selective, and precise. Degradation products produced as a result of stress studies did not interfere with the detection of moxonidine and amlodipine besylate and the assay can thus be considered stability-indicating.