Miracle-dz/UniFAR-FTUs huggingface dataset:
Exposure Inequality in People Recommender Systems: The Long-Term Effects

People recommender systems may affect the exposure that users receive in social networking platforms, influencing attention dynamics and potentially strengthening pre-existing inequalities that disproportionately affect certain groups. In this paper we introduce a model to simulate the feedback loop created by multiple rounds of interactions between users and a link recommender in a social network. This allows us to study the long-term consequences of those particular recommendation algorithms. Our model is equipped with several parameters to control: (i) the level of homophily in the network, (ii) the relative size of the groups, (iii) the choice among several state-of-the-art link recommenders, and (iv) the choice among three different stochastic user behavior models, that decide which recommendations are accepted or rejected. Our extensive experimentation with the proposed model shows that a minority group, if homophilic enough, can get a disproportionate advantage in exposure from all link recommenders. Instead, when it is heterophilic, it gets underexposed. Moreover, while the homophily level of the minority affects the speed of the growth of the disparate exposure, the relative size of the minority affects the magnitude of the effect. Finally, link recommenders strengthen exposure inequalities at the individual level, exacerbating the“rich-get-richer” effect: this happens for both the minority and the majority class and independently of their level of homophily.
The global landscape of phase retrieval II: quotient intensity models

A fundamental problem in phase retrieval is to reconstruct an unknown signal from a set of magnitude-only measurements. In this work we introduce three novel quotient intensitybased models (QIMs) based a deep modification of the traditional intensity-based models. A remarkable feature of the new loss functions is that the corresponding geometric landscape is benign under the optimal sampling complexity. When the measurements ai ∈ R n are Gaussian random vectors and the number of measurements m ≥ Cn, the QIMs admit no spurious local minimizers with high probability, i.e., the target solution x is the unique global minimizer (up to a global phase) and the loss function has a negative directional curvature around each saddle point. Such benign geometric landscape allows the gradient descent methods to find the global solution x (up to a global phase) without spectral initialization.
New Static Var Compensator (SVC) based damping control using remote generator speed signal

Rapid advances in Wide-Area Measurement (WAM) and communication technologies have made the use of remote system data/signals for effective control and operation of power systems possible. This paper presents a new concept of damping control of Static Var Compensator (SVC) based on rotor speeds of remotely located generators. The concept is demonstrated in three study systems: a single machine infinite bus system, a two-area four-machine 11-bus system, and the Western System Coordinating Council (WSCC) three-machine nine-bus meshed system. The performance of the proposed remote generator speed signal is compared with the traditionally utilised local signals for SVC damping control through nonlinear time domain simulations. It is shown that the rotor speed of the remotely located generator that significantly participates in the lightly or negatively damped electromechanical mode of oscillation is the most effective SVC control signal in damping electromechanical oscillations in a power system.
Fix your Models by Fixing your Datasets

The quality of underlying training data is very crucial for building performant machine learning models with wider generalizabilty. However, current machine learning (ML) tools lack streamlined processes for improving the data quality. So, getting data quality insights and iteratively pruning the errors to obtain a dataset which is most representative of downstream use cases is still an ad-hoc manual process. Our work addresses this data tooling gap, required to build improved ML workflows purely through data-centric techniques. More specifically, we introduce a systematic framework for (1) finding noisy or mislabelled samples in the dataset and, (2) identifying the most informative samples, which when included in training would provide maximal model performance lift. We demonstrate the efficacy of our framework on public as well as private enterprise datasets of two Fortune 500 companies, and are confident this work will form the basis for ML teams to perform more intelligent data discovery and pruning.
Visually Guided UGV for Autonomous Mobile Manipulation in Dynamic and Unstructured GPS Denied Environments

A robotic solution for the unmanned ground vehicles (UGVs) to execute the highly complex task of object manipulation in an autonomous mode is presented. This paper primarily focuses on developing an autonomous robotic system capable of assembling elementary blocks to build the large 3D structures in GPS-denied environments. The key contributions of this system paper are i) Designing of a deep learning-based unified multi-task visual perception system for object detection, part-detection, instance segmentation, and tracking, ii) an electromagnetic gripper design for robust grasping, and iii) system integration in which multiple system components are integrated to develop an optimized software stack. The entire mechatronic and algorithmic design of UGV for the above application is detailed in this work. The performance and efficacy of the overall system are reported through several rigorous experiments.
Learning to Retrieve Passages without Supervision

Dense retrievers for open-domain question answering (ODQA) have been shown to achieve impressive performance by training on large datasets of question-passage pairs. In this work we ask whether this dependence on labeled data can be reduced via unsupervised pretraining that is geared towards ODQA. We show this is in fact possible, via a novel pretraining scheme designed for retrieval. Our “recurring span retrieval” approach uses recurring spans across passages in a document to create pseudo examples for contrastive learning. Our pretraining scheme directly controls for term overlap across pseudo queries and relevant passages, thus allowing to model both lexical and semantic relations between them. The resulting model, named Spider, performs surprisingly well without any labeled training examples on a wide range of ODQA datasets. Specifically, it significantly outperforms all other pretrained baselines in a zero-shot setting, and is competitive with BM25, a strong sparse baseline. Moreover, a hybrid retriever over Spider and BM25 improves over both, and is often competitive with DPR models, which are trained on tens of thousands of examples. Last, notable gains are observed when using Spider as an initialization for supervised training.
Fast Computation of Generalized Eigenvectors for Manifold Graph Embedding

Our goal is to efficiently compute low-dimensional latent coordinates for nodes in an input graph—known as graph embedding—for subsequent data processing such as clustering. Focusing on finite graphs that are interpreted as uniform samples on continuous manifolds (called manifold graphs), we leverage existing fast extreme eigenvector computation algorithms for speedy execution. We first pose a generalized eigenvalue problem for sparse matrix pair (A, B), where A = L – μQ + ∊I is a sum of graph Laplacian L and disconnected two-hop difference matrix Q. Eigenvector v minimizing Rayleigh quotient $\tfrac{\mathbf{v}^ \top \mathbf{Av}}{\mathbf{v}^ \top \mathbf{v}}$ thus minimizes 1-hop neighbor distances while maximizing distances between disconnected 2-hop neighbors, preserving graph structure. Matrix B = diag({bi}) that defines eigenvector orthogonality is then chosen so that boundary/interior nodes in the sampling domain have the same generalized degrees. K-dimensional latent vectors for the N graph nodes are the first K generalized eigenvectors for (A, B), computed in $\mathcal{O}(N)$ using LOBPCG, where K ≪ N. Experiments show that our embedding is among the fastest in the literature, while producing the best clustering performance for manifold graphs.
Hybrid inspection system for LCD color filter panels

We developed a hybrid color filter inspection system which contributes to increasing the production yield of color filters. As LCD production consists of many processes, the following topics are required: further decrease of the inspection time; grain defect repairement; photomask inspection accuracy improvement; quick feedback of the results for each process; and defect analysis. In addition to this, TFT panel production also requires automatic inspection system and repair system. <<ETX>>
Multimodal Classification of Mild Traumatic Brain Injury Reveals Local Coupling Between Structural and Functional Connectomes

Background: Following mild traumatic brain injury (mTBI) compromised white matter structural integrity can result in alterations in functional connectivity of large-scale brain networks and may manifest in functional deficit including cognitive dysfunction . Advanced magnetic resonance neuroimaging techniques, specifically diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI), have demonstrated an increased sensitivity for detecting microstructural changes associated with mTBI. Identification of novel imaging biomarkers can facilitate early detection of these changes for effective treatment. In this study, we hypothesize that feature selection combining both structural and functional connectivity increases classification accuracy. Methods: 16 subjects with mTBI and 20 healthy controls underwent both DTI and resting state functional imaging. Structural connectivity matrices were generated from white matter tractography from DTI sequences. Functional connectivity was measured through pairwise correlations of rs-fMRI between brain regions. Features from both DTI and rs-fMRI were selected by identifying five brain regions with the largest group differences and were used to classify the generated functional and structural connectivity matrices, respectively. Classification was performed using linear support vector machines and validated with leave-one-out cross validation. Results: Group comparisons revealed increased functional connectivity in the temporal lobe and cerebellum as well as decreased structural connectivity in the temporal lobe. After training on structural connections only, a maximum classification accuracy of 78% was achieved when structural connections were selected based on their corresponding functional connectivity group differences. After training on functional connections only, a maximum classification accuracy of 69% was achieved when functional connections were selected based on their structural connectivity group differences. After training on both structural and functional connections, a maximum classification accuracy of 69% was achieved when connections were selected based on their structural connectivity. Conclusions: Our multimodal approach to ROI selection achieves at highest, a classification accuracy of 78%. Our results also implicate the temporal lobe in the pathophysiology of mTBI. Our findings suggest that white matter tractography can serve as a robust biomarker for mTBI when used in tandem with resting state functional connectivity.
Listening to older voices: results of a cross-sectional survey of older patient-reported experiences of facility-based healthcare in Nouna, Burkina Faso

Abstract Background: Ensuring responsive healthcare which meets patient expectations and generates trust is important to increase rates of access and retention. This need is important for aging populations where non-communicable diseases (NCDs) are a growing cause of morbidity and mortality. Methods: We performed a cross-sectional household survey including socio-demographic, morbidities, and health system utilization, responsiveness, and quality outcomes in individuals over age 40 in northwestern Burkina Faso. We describe results and use exploratory factor analysis to derive a contextually appropriate grouping of health system responsiveness (HSR) variables. We used linear or logistic regression to explore associations between socio-demographics, morbidities, and the grouped-variable, then between these variables and health system quality outcomes. Results: Of 2,639 eligible respondents, 26.8% had least one NCD, 56.3% were frail or pre-frail and 23.9% had a recent visit, including only 1/3 of those with an NCD. Highest ratings of care experience (excellent/very good) included ease of following instructions (86.1%) and trust in provider skills (81.1%). The HSR grouping with the greatest factor loading included involvement in decision-making, clarity in communication, trust in the provider, and confidence in provider skills, termed as Shared Understanding and Decision Making (SUDM). In multivariable analysis, higher quality of life (OR 1.02,95%CI 1.01-1.04), frailty (OR 1.47,95%CI 1.00-2.16), and SUDM (OR 1.06,95%CI 1.05-1.09) were associated with greater health system trust and confidence. SUDM was associated with overall positive assessment of the healthcare system (OR 1.02,95%CI 1.01-1.03) and met healthcare needs (OR 1.09,95%CI 1.08-1.11). Younger age and highest wealth quintile were also associated with higher met needs. Conclusions: Recent healthcare access was low for people with existing NCDs, and SUDM was the most consistent factor associated with higher health system quality outcomes. Results highlight the need to increase continuity of care for aging populations with NCDs and explore strengthening SUDM to achieve this goal.
The Future Will Not Be Calculated: Neural Nets, Neoliberalism, and Reactionary Politics

This article traces the relationship between neoliberal thought and neural networks through the work of Friedrich Hayek, Donald O. Hebb, and Frank Rosenblatt. For all three, networked systems could accomplish acts of evolution, change, and learning impossible for individual neurons or subjects—minds, machines, and economies could therefore all autonomously evolve and adapt without government. These three figures, I argue, were also symptoms of a broader reconceptualization of reason, decision making, and “freedom” in relation to the state and technology that occurred throughout the 1950s–1970s. I also argue that this genealogy of decision-making underpins contemporary relations between machine learning, reactionary politics, and neoliberal economics.
Suppressor Mutants: History and Today’s Applications

For decades, biologist have exploited the near boundless advantages that molecular and genetic tools and analysis provide for our ability to understand biological systems. One of these genetic tools, suppressor analysis, has proven invaluable in furthering our understanding of biological processes and pathways and in discovering unknown interactions between genes and gene products. ABSTRACT For decades, biologist have exploited the near boundless advantages that molecular and genetic tools and analysis provide for our ability to understand biological systems. One of these genetic tools, suppressor analysis, has proven invaluable in furthering our understanding of biological processes and pathways and in discovering unknown interactions between genes and gene products. The power of suppressor analysis lies in its ability to discover genetic interactions in an unbiased manner, often leading to surprising discoveries. With advancements in technology, high-throughput approaches have aided in large-scale identification of suppressors and have helped provide insight into the core functional mechanisms through which suppressors act. In this review, we examine some of the fundamental discoveries that have been made possible through analysis of suppressor mutations. In addition, we cover the different types of suppressor mutants that can be isolated and the biological insights afforded by each type. Moreover, we provide considerations for the design of experiments to isolate suppressor mutants and for strategies to identify intergenic suppressor mutations. Finally, we provide guidance and example protocols for the isolation and mapping of suppressor mutants.
Understanding the Performance Guarantee of Physical Topology Design for Optical Circuit Switched Data Centers

As a first step of designing O ptical-circuit-switched D ata C enters (ODC), physical topology design is critical as it determines the scalability and the performance limit of the entire ODC. However, prior works on ODC have not yet paid much attention to physical topology design, and the adopted physical topologies either scale poorly, or lack performance guarantee. We offer a mathematical foundation for the design and performance analysis of ODC physical topologies in this paper. We introduce a new performance metric β(G ) to evaluate the gap between a physical topology G and the ideal physical topology. We develop a coupling technique that bypasses a significant amount of computational complexity of calculating β(G). Using β(G ) and the coupling technique, we study four physical topologies that are representative of those in literature, analyze their scalabilities and prove their performance guarantees. Our analysis may provide new guidance for network operators to design better physical topologies for their ODCs.
Small Antenna와 관련된 논제들

복사구조(안테나 구조)에서 물리적 구조의 크기가 작아질수록 안테나 구조로부터 실제 복사전력은 점차 줄어들고 리액티브 축적(reactive stored) 에너지가 증가하게 되면서 안테나 구조로서의 복사효율이 매우 열화된다는 것은 널리 알려진 사실이다. 하지만 양자역학 분야에서의 quantum radiation의 예로서 라이만, 발미, 파셴 계열의 복사를 생각해 보면, 이러한 quantum radiation의 경우, 복사체인 원자구조의 크기는 빛 파장의 수백분의 일에 지나지 않으면서도 복사가 잘 일어난다. 이러한 사실로부터 우리는 보다 작은 소형 안테나 구조를 기대할 수 있지 않을까? 본 글에서는 이러한 문제에 대하여 최근에 제기된 질문들과 이와 관련된 문제들에 대하여 간략히 살펴보고자 한다.
An FPGA-Based Data Pre-Processing Architecture to Accelerate De-Novo Genome Assembly

Genome assembly is a field of bioinformatics which refers to the process of taking small fragments of genetic material and putting them back together in order to reconstruct the original DNA sequence from which the fragments originated. As the DNA genome assembly input datasets in most cases have a very large amount of data, it is important to develop custom architectures in order to speed up these processes and gain significant execution time reduction. In this paper we present the Reads Matching Filter (RMF), an input dataset prefiltering process, based on string matching and implemented on Field Programmable Gate Array (FPGA) technology, in order to reduce the genome assembly execution time. The outputs of the RMF running on the FPGA as well as the original input dataset are given as input to the Velvet genome assembler which produces the assembly of the input sequences. The Velvet genome assembler is based on the manipulation of de Bruijn graphs, and produces its output via the removal of errors and the simplication of repeated regions. The FPGA-based RMF pre-filtering process manages to speedup the entire genome assembly processing, including I/O, by up to 6 times, while maintaining the quality of the output sequence contigs (i.e. the series of overlapping DNA sequences).
An LSTM-based Approach Towards Automated Meal Detection from Continuous Glucose Monitoring in Type 1 Diabetes Mellitus

Technological advancements in glucose sensing, insulin pumps, and closed-loop glucose control algorithms open new opportunities towards the realization of Artificial Pancreas (AP). However, the effective management of meal disturbances in these systems still remains a challenge. Meal detection algorithms eliminate the need for meal announcements and enable the shift to more automated and reliable AP systems. The aim of the present study is to develop and evaluate a personalized approach for the detection of meal disturbances in patients with Type 1 Diabetes Mellitus (T1DM). Long Short Term Memory Neural Networks (LSTM)'s inherent ability to efficiently handle sequential data is leveraged within an ensemble learning strategy towards the development of different versions of ensemble models. The models receive as input sequences of Continuous Glucose Monitoring (CGM) measurements (glucose profiles) of a 120-min duration and classify them as positive or negative for the onset of an ingested meal. In silico evaluation is performed using the UVA-PADOVA T1DM Simulator. All ensembles achieve acceptable discriminative performance (mean c-statistic: 75.12%-79.52%) and are able to detect meals in a timely manner (mean detection time: 7.08-12.84 min). Statistical analysis demonstrates the superiority of the simple averaging combination scheme over the other schemes in terms of the c-statistic.
Design of a Double Break Current Injection DC Circuit Breaker

With the rapid development of DC power system in recent years, more stringent requirements have been put forward for the rapid reliability of DC circuit breakers. At present, series and parallel breakers are commonly used in medium-voltage DC circuit breakers to improve the breaking capacity, but there are some problems such as poor uniformity of breakers, large size of circuit breakers and high cost. In this paper, a double-break DC circuit breaker structure based on reactance coupler is proposed. In the process of current breaking, two vacuum switches in series operate simultaneously, and the capacitive discharge and reactance coupler are used to realize rapid current commutation, which improves the voltage bearing capacity of the circuit breaker. Connecting IGCT in the commutator branch can shorten the break time and make reclosing easier. The working principle of the circuit breaker is analyzed, and the rationality of the proposed scheme is verified by simulation calculation.
Research on User Stickiness of Mobile Games

User stickiness with the development of information technology and social change, digital games have become important cultural and entertainment consumer products, especially the rapid growth of mobile game users. Due to the competitive incentive of the mobile game industry, user retention has become the key issue of competition. According to the characteristics of game and user behavior, combined with rational behavior theory, technology acceptance model, flow theory, game stickiness, and other theories and related research foundation, the correlation model between game stickiness and user-perceived quality, switching cost, perceived ease of use and other factors is established. The structural equation model is used to analyze and verify the relationship between social factors, payment mechanism, user interface, and plot Game stickiness has a positive effect. Finally, combined with the questionnaire survey and the actual game data, the paper puts forward the systematic strategy to improve user stickiness.
A 5-bit, 1.6ps resolution true time delay optical beamforming network for 4-element antenna arrays

Next-generation mobile networks will use mmWave frequencies as part of their strategy to meet the ever increasing wireless data rate demands. To mitigate the challenging propagation characteristics at these high frequencies, beamforming will be key. In this paper, we discuss a true time delay optical beamforming network (OBFN) based on switchable optical delay lines for $\mathrm{a}4 \times 1$ antenna array. This OBFN is implemented on a Si-Photonics platform and the delay lines are capable of discretely tuning the delay difference between elements with a resolution of 1.6 ps up to a maximum delay of 49.6 ps.