Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Power SDR Design on an FPGA for Intersatellite Communications

Small satellite systems make space missions for communication, navigation, and scientific research more realizable and diversified. Small satellites flying in large cluster or constellation formation as a network can provide an economical access to accomplish more complex missions, such as distributed computation, high-resolution imaging, and spacecraft maintenance. An increasing number of satellites operating on lower earth orbit for complex missions require a wireless communication system that is both reliable and flexible. This paper presents a complete software-defined radio (SDR) model for intersatellite communications (ISCs) and its implementation on a field-programmable gate array (FPGA). The proposed SDR for transmitter and receiver only has a power consumption of 2.1 and 3.2 W, respectively, which is suitable for power-limited small satellite systems. Algorithms and parameters of each block are optimized aiming at reducing hardware resource utilization. A low-density parity-check code constructed by the Euclidean geometry method is adopted as the channel code for forward error correction. Implementations of the synchronization, demodulation, and decoding algorithms are optimized for hardware efficiency. The low-power SDR designs are implemented on an FPGA-based experimental platform and successful demonstrated by over-the-air transmissions.
A Generic Inverted Index Framework for Similarity Search on the GPU

We propose a novel generic inverted index framework on the GPU (called GENIE), aiming to reduce the programming complexity of the GPU for parallel similarity search of different data types. Not every data type and similarity measure are supported by GENIE, but many popular ones are. We present the system design of GENIE, and demonstrate similarity search with GENIE on several data types along with a theoretical analysis of search results. A new concept of locality sensitive hashing (LSH) named tau-ANN search, and a novel data structure c-PQ on the GPU are also proposed for achieving this purpose. Extensive experiments on different real-life datasets demonstrate the efficiency and effectiveness of our framework. The implemented system has been released as open source: https://github.com/SeSaMe-NUS/genie.
Influence of Gender and School Location on Senior Secondary School Student’s Achievement in Biology Inagbani Education Zone of Enugu State, Nigeria

The study examined the influence of gender and school location on senior secondary school student’s achievement in biology in Agbani Education Zone of Enugu State. 328 students were sampled (164 males and 164 females respectively) from Four (4) co-education schools and four intact SS3 classes both from rural and urban schools were used. The sampling technique used was multiple stage sampling technique. The research design used was ex-post facto design since the students used for the study has been assigned to appropriate tends of gender (male and female) and school location (rural and urban) and cannot be manipulated by the researcher. Biology Achievement Test (BAT) was used as the instrument for data collection. Two (2) research questions and two (2) research hypotheses were formulated. Research questions were answered using mean, simple percentage while the hypotheses were answered using z-test at 0.05 level of significance. The findings showed that there was a significant difference in the mean achievement score of male and female students. Male students achieved higher than their female counterparts in Biology Achievement Test. Findings further revealed that there was significant difference in the achievement mean scores of students in rural and urban school located areas. The urban students achieved higher than rural students. Based on the findings, it was recommended that government should recruit more qualified biology teachers and distribute them equitably to schools irrespective of location and also females should be provided with adequate motivation to make them perform better in sciences. Keywords: Gender, School location, Achievement, Biology
Design of Hybrid Excited Asymmetric-Stator-Pole Doubly Salient Machine

Doubly salient permanent magnet machine (DSPMM) enjoys the advantages of high torque density, simple rotor structure, high reliability, etc. However, the air-gap flux density is difficult to regulate due to that the air-gap magnetic field is produced only by permanent magnets (PMs). Thus, this paper proposes a hybrid excited asymmetric-stator-pole doubly salient machine (HE-ASPDSM) and its air-gap flux density can be regulated easily by controlling the excitation current of field winding. The topologies and operation principle of the HE-ASPDSM are studied firstly based on modern winding theory and two-dimension finite element analysis (2D FEA). Then, the comparison of no-load flux linkage, back electric magnetomotive force (EMF) and air-gap flux density of different topologies is made. Besides, the expression and harmonic components of airgap flux density are also derived based on mathematical analysis. And then, the influence of key design parameters on the cogging torque, flux regulation ability, electromagnetic torque, losses and efficiency is investigated in details. Finally, the analysis of main performance indexes is performed based on the proposed 12/7 (stator/rotor poles) HE-ASPDSM. It indicates that the proposed 12/7 HE-ASPDSM has strong air-gap flux regulation ability.
An Encoder-Decoder Approach to the Paradigm Cell Filling Problem

The Paradigm Cell Filling Problem in morphology asks to complete word inflection tables from partial ones. We implement novel neural models for this task, evaluating them on 18 data sets in 8 languages, showing performance that is comparable with previous work with far less training data. We also publish a new dataset for this task and code implementing the system described in this paper.
Adversarial Evaluation of Multimodal Machine Translation

The promise of combining language and vision in multimodal machine translation is that systems will produce better translations by leveraging the image data. However, the evidence surrounding whether the images are useful is unconvincing due to inconsistencies between text-similarity metrics and human judgements. We present an adversarial evaluation to directly examine the utility of the image data in this task. Our evaluation tests whether systems perform better when paired with congruent images or incongruent images. This evaluation shows that only one out of three publicly available systems is sensitive to this perturbation of the data. We recommend that multimodal translation systems should be able to pass this sanity check in the future.
Dual Fixed-Size Ordinally Forgetting Encoding (FOFE) for Competitive Neural Language Models

In this paper, we propose a new approach to employ the fixed-size ordinally-forgetting encoding (FOFE) (Zhang et al., 2015b) in neural languages modelling, called dual-FOFE. The main idea of dual-FOFE is that it allows to use two different forgetting factors so that it can avoid the trade-off in choosing either a small or large values for the single forgetting factor. In our experiments, we have compared the dual-FOFE based neural network language models (NNLM) against the original FOFE counterparts and various traditional NNLMs. Our results on the challenging Google Billion word corpus show that both FOFE and dual FOFE yield very strong performance while significantly reducing the computational complexity over other NNLMs. Furthermore, the proposed dual-FOFE method further gives over 10% improvement in perplexity over the original FOFE model.
SiC-based three-phase Quasi-Z-Source Inverter versus the two-stage topology - a comparison

The paper presents an in-depth experimental comparative study between a three-phase Quasi-Z-Source Inverter (qZSI) and a Boost Converter with Voltage-Source Inverter (BC+VSI). Both converters obtain identical output phase voltage value of 230 V RMS from DC input voltage in range of 400 V ÷ 550 V at a similar value of input inductor current ripple. The quality factors investigated are: power losses as well as both input inductor current and output voltage quality (considering both low- and high-frequency harmonics). Moreover, four variants of modulation methods are investigated. At switching frequency of 100 kHz results of experiments using SiC-based 6-kW laboratory models show that at identical conditions the BC+VSI configuration has lower losses and better quality of the output voltage, while qZSI has better quality of the input inductor current.
Multilayer Flat Spiral Resonators for Low Frequency Wireless Power Transfer

This paper presents a procedure to characterize a multilayer and multiturn flat spiral resonator through 2D finite element simulations and numerical calculations. The procedure is general and valid for both rectangular and square shapes. It considers all the parasitic effects occurring at low frequencies (from kHz to a few MHz) in complex structures (i.e., skin effect, crowding current density, proximity effect, stray capacitance and dielectric losses). The validation is carried out for two resonators with different shapes and dimensions and variable number of layers and turns. The characterization is supported by comparisons with electromagnetic software and allows the self-resonant frequency and quality factor to be predicted with good accuracy.
Do explanations make VQA models more predictable to a human?

A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable ‘explanations’ of their decision process, especially for interactive tasks like Visual Question Answering (VQA). In this work, we analyze if existing explanations indeed make a VQA model — its responses as well as failures — more predictable to a human. Surprisingly, we find that they do not. On the other hand, we find that human-in-the-loop approaches that treat the model as a black-box do.
On the complexity of class group computations for large degree number fields

In this paper, we examine the general algorithm for class group computations, when we do not have a small defining polynomial for the number field. Based on a result of Biasse and Fieker, we simplify their algorithm, improve the complexity analysis and identify the optimal parameters to reduce the runtime. We make use of the classes $\mathcal D$ defined in [GJ16] for classifying the fields according to the size of the extension degree and prove that they enable to describe all the number fields.
Incremental Computation of Infix Probabilities for Probabilistic Finite Automata

In natural language processing, a common task is to compute the probability of a phrase appearing in a document or to calculate the probability of all phrases matching a given pattern. For instance, one computes affix (prefix, suffix, infix, etc.) probabilities of a string or a set of strings with respect to a probability distribution of patterns. The problem of computing infix probabilities of strings when the pattern distribution is given by a probabilistic context-free grammar or by a probabilistic finite automaton is already solved, yet it was open to compute the infix probabilities in an incremental manner. The incremental computation is crucial when a new query is built from a previous query. We tackle this problem and suggest a method that computes infix probabilities incrementally for probabilistic finite automata by representing all the probabilities of matching strings as a series of transition matrix calculations. We show that the proposed approach is theoretically faster than the previous method and, using real world data, demonstrate that our approach has vastly better performance in practice.
Two-Converters Based Synchronous Operation and Control of a Brushless Doubly-Fed Reluctance Machine.

This paper discusses the synchronous operation of a brushless doubly fed reluctance machine (BDFRM). Basic operation theory of a ducted rotor BDFRM is reviewed, and mathematical analysis relating to synchronous operation is presented. Corresponding control strategy for synchronous speed operation is discussed. A prototype machine is built and implemented as a motor drive. Subsequently, the BDFRM drive is tested in synchronous mode of operation at varying speed levels and loads.
Emergence of Invariance and Disentanglement in Deep Representations

Using established principles from Information Theory and Statistics, we show that in a deep neural network invariance to nuisance factors is equivalent to information minimality of the learned representation, and that stacking layers and injecting noise during training naturally bias the network towards learning invariant representations. We then show that, in order to avoid memorization, we need to limit the quantity of information stored in the weights, which leads to a novel usage of the Information Bottleneck Lagrangian on the weights as a learning criterion. This also has an alternative interpretation as minimizing a PAC-Bayesian bound on the test error. Finally, we exploit a duality between weights and activations induced by the architecture, to show that the information in the weights bounds the minimality and Total Correlation of the layers, therefore showing that regularizing the weights explicitly or implicitly, using SGD, not only helps avoid overfitting, but also fosters invariance and disentangling of the learned representation. The theory also enables predicting sharp phase transitions between underfitting and overfitting random labels at precise information values, and sheds light on the relation between the geometry of the loss function, in particular so-called “flat minima,” and generalization.
OFF-State-Stress-Induced Instability in Switching Polycrystalline Silicon Thin-Film Transistors and Its Improvement by a Bridged-Grain Structure

In this letter, an off-state stress is proposed to simulate the operation conditions of switching polycrystalline thin-film transistors in active-matrix displays for the first time. A dynamic hot carrier (HC) effect, dependent on data pulse falling time, dominates the device degradation. Incorporated with the transient simulations, the device degradation mechanism is tentatively discussed. Finally, a bridged-grain structure, which can effectively shares the stress voltage drop via multiple reverse junctions, is employed to relieve such off-state-stress-induced dynamic HC degradation.
Conversational Decision-Making Model for Predicting the King’s Decision in the Annals of the Joseon Dynasty

Styles of leaders when they make decisions in groups vary, and the different styles affect the performance of the group. To understand the key words and speakers associated with decisions, we initially formalize the problem as one of predicting leaders’ decisions from discussion with group members. As a dataset, we introduce conversational meeting records from a historical corpus, and develop a hierarchical RNN structure with attention and pre-trained speaker embedding in the form of a, Conversational Decision Making Model (CDMM). The CDMM outperforms other baselines to predict leaders’ final decisions from the data. We explain why CDMM works better than other methods by showing the key words and speakers discovered from the attentions as evidence.
A Neural Local Coherence Model for Text Quality Assessment

We propose a local coherence model that captures the flow of what semantically connects adjacent sentences in a text. We represent the semantics of a sentence by a vector and capture its state at each word of the sentence. We model what relates two adjacent sentences based on the two most similar semantic states, each of which is in one of the sentences. We encode the perceived coherence of a text by a vector, which represents patterns of changes in salient information that relates adjacent sentences. Our experiments demonstrate that our approach is beneficial for two downstream tasks: Readability assessment, in which our model achieves new state-of-the-art results; and essay scoring, in which the combination of our coherence vectors and other task-dependent features significantly improves the performance of a strong essay scorer.
Combined Aerobic and Resistance Exercise Training Improve Hypertension Associated With Menopause

The prevalence of hypertension sharply increases in menopausal women. Recent studies have demonstrated that aerobic or resistance training may help control hypertension. In this study, we report that combining aerobic and resistance training may provide an effective therapeutic approach for hypertension control, attenuating inflammation and oxidative stress in ovariectomized rats. Female Wistar and spontaneous hypertensive rats (SHR) were distributed into four groups: sedentary control (C), sedentary hypertensive (HR), sedentary hypertensive ovariectomized (HR-O), and combined trained hypertensive ovariectomized (T-HR-O). Combined exercise training was performed on a motor treadmill (aerobic training) and on a ladder adapted to rats (resistance training), in alternate days for 8 weeks. Direct arterial pressure was recorded and oxidative stress and inflammation were evaluated in cardiac and renal tissue. Ovariectomy increases increased mean arterial blood pressure, sympathetic modulation, and oxidative stress in SHR. Combining aerobic and resistance training reduced mean arterial blood pressure (12% vs. HR-O), heart rate (8% vs. HR-O), vascular sympathetic modulation (40% vs. HR-O), and improved baroreflex sensitivity. Combined training reduced cardiac inflammation (TNF and IL-6) and cardiac and renal lipoperoxidation (59% and 57%, respectively vs. HR-O). It also enhanced cardiac (71%) and renal (76%) total antioxidant capacity when compared to HR-O group. In conclusion, combining aerobic and resistance training improves mean arterial blood pressure, cardiovascular autonomic control, preventing cardiac and renal oxidative stress and inflammation in an experimental hypertension model with surgical menopause induced with ovariectomy.
RNS Smith-Waterman Accelerator based on the moduli set 2n, 2n-1, 2n-1-1

The Smith-Waterman algorithm is very sensitive but computationally intensive on general purpose CPUs. On the other hand, the FPGA has proven to be an excellent platform for accelerating the algorithm in addition to its low cost and power consumption. This paper therefore proposes the use of the parallelism and carry-free propagation properties of Residue Number Systems (RNS) to accelerate the algorithm on an FPGA; a deviation from the usual binary/decimal implementations. Compared to the only previous known work that uses RNS for this task, our implementation is (1) the first to employ the use of Linear Systolic Arrays (LSA), (2) avoid the use of Lookup Tables, (3) has a larger dynamic range and (4) aligns long sequences. Implemented on a modest hardware and on a Kintex7 FPGA, we achieved a performance improvement of 169 times over the general-purpose processor implementation. This result was obtained using the naïve implementation of LSAs, indicating the positive effects of using RNS.
A Network Model of Goals Boosts Convergent Creativity Performance

To increase employee creativity is critical for organizational success, and yet we still know very little about what organizational contexts promote creative performance. Our research proposes that goal regulation in the workplace may have consequences for creativity. While there is an increasing trend for organizations and workers to visualize the structure of their goals (e.g., management hierarchy, concept-map, flowchart), prior research suggests the visualization approaches differ as one of the three types: hierarchical, network, and sequential models. Because a network model (vs. hierarchical and sequential models) highlights multiple connections between goals and reveals unobvious connections between them, we hypothesized that the use of a network goal model might increase people’s ability to integrate seemingly unrelated ideas, even on subsequent unrelated tasks, leading to higher (convergent) creative performance. To test the hypothesis, we conducted an experiment in 2017 manipulating participants’ goal models (hierarchical, network, sequential; N = 191, median age = 19) and measured their creativity. Results suggest that those in the network model condition performed better in the kind of creativity task that requires meaningful integration of unrelated ideas (i.e., convergent creativity); in contrast, there was no difference between goal model conditions on divergent creative performance. These findings thus illuminate how goal models may influence creativity, providing new insights into situational inductions that can boost creative performance. Theoretical and practical implications, limitations, and future directions of the work are discussed.
Shortest Path Distance Approximation Using Deep Learning Techniques

Computing shortest path distances between nodes lies at the heart of many graph algorithms and applications. Traditional exact methods such as breadth-first-search (BFS) do not scale up to contemporary, rapidly evolving today's massive networks. Therefore, it is required to find approximation methods to enable scalable graph processing with a significant speedup. In this paper, we utilize vector embeddings learnt by deep learning techniques to approximate the shortest paths distances in large graphs. We show that a feedforward neural network fed with embeddings can approximate distances with relatively low distortion error. The suggested method is evaluated on the Facebook, BlogCatalog, Youtube and Flickr social networks.
A Light-Powered Smart Camera With Compressed Domain Gesture Detection

This paper presents an ultralow power smart camera with gesture detection. Low power is achieved by directly extracting gesture features from the compressed measurements, which are the block averages and the linear combinations of the image sensor’s pixel values. We present two classifier techniques to allow low computational and storage requirements. The system has been implemented on an analog devices BlackFin ULP vision processor. By enabling ultralow energy consumption, we demonstrate that the system is powered by ambient light harvested through photovoltaic cells whose output is regulated by TI’s dc–dc buck converter with maximum power point tracking. Measured data reveals that with only 400 compressed measurements (<inline-formula> <tex-math notation="LaTeX">$768\times $ </tex-math></inline-formula> compression ratio) per frame, the system is able to recognize key wake-up gestures with greater than 80% accuracy and only <inline-formula> <tex-math notation="LaTeX">$95mJ$ </tex-math></inline-formula> of energy per frame. Owing to its fully self-powered operation, the proposed system can find wide applications in “always-on” vision systems, such as in surveillance, robotics, and consumer electronics with touch-less operation.
Content Selection in Deep Learning Models of Summarization

We carry out experiments with deep learning models of summarization across the domains of news, personal stories, meetings, and medical articles in order to understand how content selection is performed. We find that many sophisticated features of state of the art extractive summarizers do not improve performance over simpler models. These results suggest that it is easier to create a summarizer for a new domain than previous work suggests and bring into question the benefit of deep learning models for summarization for those domains that do have massive datasets (i.e., news). At the same time, they suggest important questions for new research in summarization; namely, new forms of sentence representations or external knowledge sources are needed that are better suited to the sumarization task.