Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallelization of the Number Theoretic Transform

The Fast Fourier Transform algorithm is an efficient algorithm for computing the Discrete Fourier Transform (DFT) of an object, converting between its frequency and spatial domains. FFT and its variants, such as the Number Theoretic Transform (NTT), are essential to many areas of computing, including signal processing and computer arithmetic. In the context of post-quantum cryptography, the Ring Learning with Errors (RLWE) assumption enables the use NTT to perform fast polynomial operations, making lattice-based cryptography the leading candidate for efficient post-quantum cryptosystems. Our experiments seek to analyze the parallelization of the NTT algorithm, and in particular the extent to which different parallelization techniques can provide empirical speedups when combined with pre-existing mathematical optimizations.
EXPLORING THE EFFECTS OF CELLULAR STRESS ON BREAST CANCER METASTASIS By

Cancer is characterized by two major features. The first is uncontrolled cell proliferation and the second is metastasis, which is the main cause of cancer related deaths. The causes of uncontrolled cell growth have been studied thoroughly and consequently the underlying mechanisms are fairly well understood. In contrast, the causes of metastasis are significantly less well understood. We are suggesting that metastasis occurs as a result of cancer cells removing themselves from the inhospitable environment inside tumors by employing normal cellular defense mechanisms. We grew MCF-7 and MDA-MB-468 breast cancer cells in cell culture and exposed them to heat and hypoxia stress. Western blot analysis of the cell lysates showed an increase in furin expression in the heat and hypoxia stress conditions. The MCF-7 cells showed a decrease in PACE4 expression in the stress conditions. Invasion assay analysis of the MDA-MB468 and MCF-7 cells under our experimental cell culture condition showed an increase in cell migration with the stress exposed cells. The Western blot results paired with the invasion assay findings suggest that heat and hypoxia stress can promote increased invasiveness of the breast cancer cells and may be an underlying mechanism that drives cancer metastasis. Further exploration of this hypothesis using 3-dimensional cell culture and an alternative method of testing MMP activity or expression would bolster our understanding of the biological mechanisms involved in cancer metastasis, leading to the identification of novel diagnostic or therapeutic targets in treating aggressive cancers.
THE REPRESENTATION OF RELATIONAL ALGEBRAS

1. Summary A family of binary relations, or sets of ordered pairs, which is a boolean algebra, which is closed under the operations of forming converses and relative products, and which contains an identity relation, constitutes what Tarski has called a proper relational algebra. Tarski has given a set of axioms for abstract algebras of this type, and has raised the question whether every abstract algebraic system satisfying these axioms is isomorphic to a proper relational algebra. It follows from the results established below that this is not the case. In Part One we obtain, after some preliminaries, a set C of conditions which are necessary and sufficient for a finite algebra to be isomorphic to a proper relational algebra. A finite model is exhibited which satisfies all of Tarski's axioms, but which does not satisfy all the conditions C, and hence is not representable. The conditions C, which are infinite in number and non-finitary in the sense that they contain bound variables, are necessary for any relational algebra, finite or infinite, that is complete as a boolean algebra, to be representable. In Part Two it is shown, by means of an infinite model, that no set of finitary axioms suffices to characterize the class of representable relational algebras. The relation of these results to some unsolved problems is discussed in the last section (? 15).
School Fees and Vouchers when Quality of Education Matters

The aim of this paper is to analyse the role that quantity and quality of education may play in the design of public policies. In our model, educated people enjoy a premium on their incomes, and education generates a positive externality for the society. Households live in two areas with different socio-economic characteristics. Altruistic parents choose both the amount and the quality of schooling they want for their children, ignoring the external effect. The latter is, instead, taken into account by the government, which is assumed to provide a composite education service that has a quantity as well as a quality dimension, and is financed mainly via taxes on the income of the parents. We investigate the effects on welfare of balanced-budget policy reforms aimed at introducing or raising i) school fees, and ii) vouchers meant to compensate the costs of attending high-quality schools. We show that, if the external effects are not considered, school fees improve private parental welfare while vouchers do not. Parental altruism is not enough to support high levels of quantity and quality of education. The consideration of social welfare, i.e. inclusive of the impact of the externality, somewhat mitigates the above results, by favouring lower (albeit positive) fees and allowing vouchers to increase social welfare under some circumstances.
Design of Miniaturized Microstrip Antenna with Semi-Fractal Structure For GPS/GLONASS/Galileo Applications

Article History: Received 08 November 2022 Reviewed 24 December 2022 Revised 08 February 2023 Accepted 01 March 2023 Background and Objectives: Microstrip patch antennas are widely used due to their advantages of compact size and easy fabrication compared to other types. However, they have low-performance parameters. As a result, several techniques are used to improve performance parameters in newly designed microstrip antennas. In this study, a novel miniaturized microstrip antenna with circular polarization (CP) is proposed for GNSS applications. Methods: In the design process, the semi-fractal structure is used to reduce the antenna size. Circular polarization is generated using a three-feed configuration with 120 phase shift. The CP value is increased by use of perturbing slots and also removing the corners. The novel design of the feeding network and also considering the ground size same as the patch layer, keep the antenna size small. The co-axial probe is used in the feeding network and it is printed on Taconic RF43 substrate with a low loss tangent of 0.0033. Numerical simulation is applied via CST commercial software to evaluate the antenna performance. The simulations are repeated in two other software, HFSS and FEKO, to validate the study. Results: The proposed antenna has a compact size of 17.56 cm. The single-layer structure of the designed antenna leads to easy fabrication feature. The proposed antenna has a bandwidth of 55 MHz (1.558-1.614 GHz). It can operate at GPS L1 (1575 MHz), GLONASS G1 (1602 MHz), Galileo E1 (1589 MHz), and E2 (1561 MHz) bands. Results show a high front-to-back ratio (FBR) of 40 dB, RHCP gain of 3.45 dB, and pure CP with axial ratio (AR) beamwidth of 108. Furthermore, the phase center variation (PCV) is less than 0.16 mm. Conclusion: Key features of the proposed antenna are its novel fractal structure that leads to compact size, high front-to-back ratio, wide RHCP beamwidth with desirable bandwidth, and axial ratio beamwidth.
Exploring the Logical and Mathematical Capabilities of the BERT Embedding Space using Contrastive Learning

Natural language understanding is a critical challenge in the field of artificial intelligence. BERT is a widely used model for natural language processing tasks, but its ability to perform logical and mathematical reasoning is not well understood. In this project, we investigate whether BERT’s embedding space can learn logical and mathematical relationships and perform well on benchmarks designed for such tasks, using contrastive learning methods to measure similarity and improve embeddings. Our main goal is to look at the potential and limitations of BERT-like models for these types of tasks. We also aim to compare the performance of BERT models trained with contrastive learning to those trained with other techniques, and potentially extend our investigation to the domain of mathematical reasoning. Our main findings will dependant on the performance of BERT models trained with contrastive learning on logical and mathematical reasoning tasks. We hope to demonstrate the potential of contrastive learning to improve the performance of BERT on these tasks and provide insights into the capabilities of BERT-like models for logical and mathematical aspects of natural language understanding.
Nano Backpack Language Model on Chinese Characters

This project focuses on the Backpack Language Model proposed in the paper "Backpack Language Models" [1], which aims to improve lexical representations and non-contextual controllability in language modeling while maintaining contextual performance. To facilitate comprehension by beginners, we reproduced the Backpack model in a NanoGPT-styled [2] implementation and pretrained it on a character-based Chinese corpus. Our implementation achieves comparable or superior performance to a matching-sized GPT2 [3][4] model on perplexity and word prediction accuracy tasks, demonstrating the potential of the Backpack model in character-based languages. We further evaluate the lexical interpretability of sense vectors and experiment with basic character-level interventions for controllable generations.
PiGGyBacking off of PEGASUS: Pre-training with Gap-sentences for Government Bills

Congressional bills are notoriously long and difficult to understand, limiting citizen engagement. As a result, summarizing legislation in a low-resource setting is a foundational step in making our democracy more inclusive. In this project, we propose a novel approach to bill summarization, PIGASUS, that is based off of Google’s state-of-the-art summarizer, PEGASUS. PIGASUS makes government bills more accessible by creating quality summaries in low resource settings. Our model differs from PEGASUS by using a state-of-the-art extractive summarization technique, TextRank, to select masked sentences for pretraining. To experiment with this, we implement a second "pretraining" stage that builds off the pretrained PEGASUS model, mimicking the effect of pretraining a large summarization model from scratch. We find that in low-resource settings, PIGASUS is not only more efficient in learning, but also generates higher quality summaries that capture more important information than PEGASUS does in the same settings. This suggests that using TextRank to mask sentences during pretraining may improve the quality of summarization models.
Trilingual Journal of African Languages and Cultures Revue trilingue des langues et cultures africaines Dreisprachige Zeitschrift für afrikanische Sprachen und Kulturen Moraic mismatches in Somali phonology: coda consonants reconsidered

This paper reconsiders the moraic status of coda consonants in Somali. It is argued that Somali joins a growing list of languages presenting a challenge to the Moraic Uniqueness Hypothesis. Several phenomena are explored that suggest that Somali exhibits moraic mismatches whereby moras associated with segments of different types contribute to, or “count” differently for particular phonological processes. Evidence in support of this proposal is drawn from the literature on tonology and poetic metrics, but also from word shape and minimality requirements, and from the distribution of syllable shapes of different types in Somali words. It is argued that an approach to Somali phonology that permits reference to moras associated with different segment types offers a unified and more transparent account of the language’s segmental and tonal phenomena. Notably, such an approach precludes the assumption of “early” coda consonant moraicity followed by a global dissociation of consonantal moras by rule before high tone assignment, as argued for in earlier work. The findings presented here illustrate that standing points of view on the role of the mora in Somali phonology must be reconfigured.
BERT for Sentiment Analysis, Paraphrase Detection and Semantic Textual Similarity with Cosine Similarity

BERT, which stands for Bidirectional Encoder Representations from Transformers (Devlin et al., 2018), is a language model based on the transformer architecture that produces contextual word representations. For the initial phase of this project, sentiment analysis was conducted on two datasets - Stanford Sentiment Treebank (SST) and CFIMDB. In the extended phase of the project, a multitask implementation of minBERT was trained to perform sentiment analysis, paraphrase detection
Patient Expenditure Prediction in Healthcare Sectors

: Predicting the expenses for patient expenditure in health sectors became an important task with various applications such as provider profiling, accountable management, and medical payment Adjustment. Previous approaches mainly deals with manually designed features and linear regression-based models, which require massive medical domain knowledge and show limited predictive performance. This paper gives us a multi-view deep learning framework which can help to predict upcoming healthcare expenses at the individual level which are based on historical data. Our multi-view approach can accurately model the mixed information, including patient demographic features, medical codes, drug usages, and facility utilization. We conducted forecasting of expense tasks on a day-to-day pediatric dataset that contains more than 390,000 patients. The empirical results displays that our proposed method outperforms all baselines for medical expenses calculation. These findings help toward better preventive care and accountable care in the healthcare domain.
Boredom Classifier Predicting Boredom Of Users Based On Mobile Phone Usage

The project is based around empathy based data analysis, and aims to investigate the correlation between users’ emotional states and their mobile phone usage. This study’s core objective is to identify the moments of boredom in a mobile phone user and build a model that can subsequently help find opportune moments for the delivery of notifications. The primary motive is to utilize moments of boredom to identify whether the user would interact with the notification based on his mobile phone usage. A Machine Learning approach was taken to be able to utilize maximum features, and the machine learning algorithms were used to train a model that was then applied to a different dataset to predict boredom based on mobile phone usage patterns. For evaluation of the machine learning models, Confusion Matrix, AUC-ROC curve, recall, precision, accuracy and F1-Score were used. It was possible to build a boredom classifier based on a user’s mobile phone usage patterns from one dataset and use it to classify moments of boredom on users in a different dataset. Amongst the three Machine Learning Models used, Random Forest Classifier performed the best with an F1 score of 0.93 and Accuracy of 0.88. From the 5 features considered for building the ML models, Application Type gave the best predictions of boredom on the WeAreUs dataset with 11960 instances of boredom detected.
Learning Task-Agnostic 3D Representations of Objects by De-Rendering

State-of-the-art unsupervised representation learning meth-ods typically do not exploit the physical properties of objects, such as geometry, albedo, lighting, and camera view, and, when they do, multi-view images are often needed for training. We show that de-rendering, a way to reverse the rendering process to recover these properties from single images without supervision, can also be used to learn task-agnostic representations, which we dub physically disentangled representations (PDRs). While de-renderers predict distinct physical properties, the features learned in the process may not be disentangled. To ensure meaningful features are encoded by de-rendering and thus prevent overreliance on decoders, we propose a novel Leave-One-Out, Cycle Contrastive loss (LOOCC) to improve feature disentanglement w.r. t. physical properties, which leads to higher downstream accuracy . We evaluate PDRs on downstream clustering tasks, including car classification and face identification. We perform a comparison of our method with other generative representation learning methods for these tasks and find PDRs consistently yield higher accuracy, outperforming evaluated baselines by as much as 18%. Code is available here.
A Nodal Basis for C Piecewise Polynomials

A basis for the space of C piecewise polynomials in two variables of degree n > 5 is constructed. The basis is parametrized by "nodal variables," namely, the values and derivatives of the basis functions at a discrete set of points. Let u be a bounded domain in R2 such that 9n consists of a finite number of nonintersecting polygonal arcs (by a polygonal arc, we mean a continuous curve consisting of a finite number of line segments), and let T be a rectilinear triangulation of n (i.e., a triangulation with straight edges). Denote by Sn = Sn(tt, T) the subspace of Cl(U) consisting of functions whose restriction to each triangle is a polynomial of degree < n (the space of C1 piecewise polynomials of degree n). Strang [3] conjectured a formula for the dimension of Sn (in terms of the number of triangles, edges, and vertices in T), and we mention later some results of our study [2] of the dimension of Sn. But the main purpose of this note is to describe the structure of Sn completely for « > 5 by exhibiting a nodal basis for Sn. This basis makes Sn convenient to use for the approximation of solutions of differential equations via the finite element method (see, e.g., [1], [4]). By a nodal basis, we mean that the functions in Sn are determined by their values and derivatives at points in n (these are called nodal values). The nodal values describing Sn are (1) the value and x and y derivatives at each vertex in T, (2) the value at each of « — 5 distinct points in the interior of each edge, (3) the (edge) normal derivative at each of « 4 distinct points in the interior of each edge (the points may coincide with those of (2)), and (4) the value at xh(n — 4)(« 5) distinct points in the interior of each triangle, chosen so that if a polynomial of degree « 6 vanishes at all the points, it vanishes identically. The remaining nodal values are more complicated to describe, as they involve second derivatives at the vertices. Suppose ex is an edge in T, u is one of its vertices and/G Sn. By the "second ex derivative of/at v", we mean 9e be (f\r)(v), where 9 is differentiation in the ex direction away from v and r is a triangle with ex as an edge. There are two such triangles, and the valúe 9e 9e (/|t)(u) is independent of the choice for/G Sn. Similarly, if ex and e2 are two edges of a triangle r in T, we define the "ex, e2 cross derivative of/at u" to be 9e 9e (/|r)(u) (here v = ex n e2). Referring to Fig. 1, we have V^|T)(U) = delde2<f\ToM = *exàe2if\TlM Received August 30, 1974 AMS (MOS) subject classifications (1970). Primary 65N30. Copyright © 1975, American Mathematical Society 736 A NODAL BASIS FOR C1 PIECEWISE POLYNOMIALS 737
SuperHF: Supervised Finetuning from Human Feedback

As AI models become more capable and play a larger role in our lives and economy, it will become increasingly important to ensure they are aligned with human values. To date, the dominant approach incorporating human preference data in large language models (LLMs) has been by reinforcement learning from human feedback (RLHF) which inherits the instability of reinforcement learning and potential for reward hacking. We propose SuperHF: Supervised Fine-Tuning from Human Preferences to optimize a similar reward as in RLHF. Initial progress suggests SuperHF can significantly improve model reward, though it perhaps overfits to and games the reward model, and future experiments will more rigorously compare it to the RLHF baseline. We hope this method leads to performant, preference-aligned LLMs without the need for explicit reinforcement learning.
EXTENSION OF K4-INTERSECTING FAMILIES OF GRAPHS

The problem on the largest triangle-intersecting family of graphs on n labeled vertices was proposed by Simonovits and Sós in 1976. They conjectured the upper bound to be 2( n 2)−3, which is obtained by taking all graphs containing some fixed triangle. This was proven by Ellis, Filmus, and Friedgut, who also conjectured that their results extend to the Kt-intersecting families. Berger and Zhao recently proved the case for t = 4. We present current progress to prove the case for t = 5, including reductions and optimizations in the computational process. Moreover, we extend these conjectures to hypergraph K (3) 4 -intersecting families of graphs.
Improving minBERT Performance on Multiple Tasks through In-domain Pretraining, Negatives Ranking Loss Learning, and Hyperparameter Optimization

BERT models have seen a recent explosion in use cases, but an understanding of how to optimize BERT for various tasks is developing. The present study aims to improve the performance of minBERT, a smaller version of the original BERT model, on a variety of sentence-level tasks (sentiment classification, paraphrase detection, and semantic contextual similarity) simultaneously. To do so, we employ an in-domain pretraining strategy in which minBERT is pretrained on a Masked Language Model (MLM) objective on the datasets which it performs tasks on. We also employ Negatives Ranking Loss Learning to improve baseline BERT embed-dings. Both strategies, along with optimal learning rate selection, significantly improved minBERT’s performance.
Robust Control of Flapping-Wing in Micro Aerial Vehicle to have a Smooth Flapping Motion

This paper in first sections, will give a brief overview of both the purpose and the challenges facing the actuator and structure of Micromechanical Flying Insects (MFIs) and, in the last sections, an appropriate controller will developed for flapping motion. A hierarchical architecture that divides the control unit into three main levels is introduced. This approach break a complex control problem into a multi-level set of smaller control schemes, each of which is responsible for a clearly defined task. Also, the controller at each level can be designed independently of those in other levels. A fourbar mechanism for the wing displacement amplification, and a new system for fourbar mechanism actuation (wing actuation) is developed. We will develop a flexible beam with piezoelectric actuators and sensor (called Smart Beam) that will used to excite the fourbar mechanism for flapping mode of flight. The Frequency Response Function (FRF) of the smart beam was obtained from a Finite Element (FE) model and experimental system identification. The corresponding transfer function was derived from the mu synthesis and several robust controllers were then designed to control the beam to reach a smooth flapping motion. Besides excitation of the fourbar mechanism, the Smart beam will be used to control of noise and disturbance in the structure of the wing system.