Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nigerian Pidgin

Nigerian Pidgin is arguably the most widely spoken language in Nigeria. Variants of this language are also spoken across West and Central Africa, making it a very important language. This work aims to establish supervised and unsupervised neural machine translation (NMT) baselines between English and Nigerian Pidgin. We implement and compare NMT models with different tokenization methods, creating a solid foundation for future works.
The Influence of Reward on the Social Valence of Interactions

Throughout the years, social norms have been promoted as an informal enforcement mechanism for achieving beneficial collective outcomes. Among the most used methods to foster interactions, framing the context of a situation or setting in-game rules have shown strong results as mediators on how an individual interacts with their peers. Nevertheless, we found that there is a lack of research regarding the use of incentives such as scores to promote social interactions differing in valence. Weighing how incentives influence in-game behavior, we propose the use of rewards to promote interactions varying in valence, i.e. positive or negative, in a two-player scenario. To do so, we defined social valence as a continuous scale with two poles represented by Complicate and Help. Then, we performed user tests where participants where asked to play a game with two reward-based systems to test on whether the scoring system influenced the social interaction valence. The results indicate that the developed reward-based systems were able to foster interactions diverging in social valence scores, providing insights on how factors such as incentives overlap individual’s established social norms. These findings empower game developers and designers with a low-cost and effective policy tool that is able to promote in-game behavior changes.
Countering Language Drift with Seeded Iterated Learning

Pretraining on human corpus and then finetuning in a simulator has become a standard pipeline for training a goal-oriented dialogue agent. Nevertheless, as soon as the agents are finetuned to maximize task completion, they suffer from the so-called language drift phenomenon: they slowly lose syntactic and semantic properties of language as they only focus on solving the task. In this paper, we propose a generic approach to counter language drift called Seeded iterated learning (SIL). We periodically refine a pretrained student agent by imitating data sampled from a newly generated teacher agent. At each time step, the teacher is created by copying the student agent, before being finetuned to maximize task completion. SIL does not require external syntactic constraint nor semantic knowledge, making it a valuable task-agnostic finetuning protocol. We evaluate SIL in a toy-setting Lewis Game, and then scale it up to the translation game with natural language. In both settings, SIL helps counter language drift as well as it improves the task completion compared to baselines.
Towards Automating the AI Operations Lifecycle

Today's AI deployments often require significant human involvement and skill in the operational stages of the model lifecycle, including pre-release testing, monitoring, problem diagnosis and model improvements. We present a set of enabling technologies that can be used to increase the level of automation in AI operations, thus lowering the human effort required. Since a common source of human involvement is the need to assess the performance of deployed models, we focus on technologies for performance prediction and KPI analysis and show how they can be used to improve automation in the key stages of a typical AI operations pipeline.
Joint Orthogonal Band and Power Allocation for Energy Fairness in WPT System with Nonlinear Logarithmic Energy Harvesting Model

Wireless power transmission (WPT) is expected to play an important role in the Internet of Things services by providing the perpetual operation of IoT sensors. However, to prolong the IoT network's lifetime, the efficient resource allocation algorithm is required, in particular, the energy fairness issue among IoT sensors has been a critical challenge of the WPT system. In this paper, considering energy fairness as the minimum received energy of all energy poverty IoT sensors (EPISs), we allocate orthogonal frequency bands to several EPISs and transfer the RF power on each orthogonal band, using energy beamforming. Based on the energy poverty, we propose orthogonal frequency bands assignment rule, granting the priority to the EPISs with less received energy. We also formulate two transmission power allocation problems, incorporated the nonlinear logarithm-energy harvesting (EH) model. First, the total received power maximization (TRPM) problem is presented and solved by combining the well-known Karush-Kuhn-Tucker (KKT) conditions with the modified water-filling algorithm. Second, the common received power maximization (CRPM) problem is formulated and the optimal solution is derived using the iterative bisection search method. To apply the bisection search method to the problem, this paper proposes a method of specifying the scope of the solution for the objective function defined by the sum of monotonous functions. In numerical results, assuming the mobility of EPISs by the one-dimensional random walk model, the effectiveness of the mobility of EPISs on the minimum received energy of all EPISs is presented. Finally, the performance of the proposed resource allocation schemes is verified by comparing other resources allocation schemes, such as Round robin and equal power distribution
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions

Learning with limited data is a key challenge for visual recognition. Many few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them leads to the target task. In this paper, we propose a novel approach to adapt the instance embeddings to the target classification task with a set-to-set function, yielding embeddings that are task-specific and are discriminative. We empirically investigated various instantiations of such set-to-set functions and observed the Transformer is most effective --- as it naturally satisfies key properties of our desired model. We denote this model as FEAT (few-shot embedding adaptation w/ Transformer) and validate it on both the standard few-shot classification benchmark and four extended few-shot learning settings with essential use cases, i.e., cross-domain, transductive, generalized few-shot learning, and low-shot learning. It archived consistent improvements over baseline models as well as previous methods, and established the new state-of-the-art results on two benchmarks.
An accurate reconstruction of CMB E-mode signal over large angular scales using prior information of CMB covariance matrix in ILC algorithm

Recently, the internal-linear-combination (ILC) method was investigated extensively in the context of reconstruction of Cosmic Microwave Background (CMB) temperature anisotropy signal using observations obtained by WMAP and Planck satellite missions. In this article, we, for the first time, apply the ILC method to reconstruct the large-scale CMB E mode polarization signal, which could probe the ionization history, using simulated observations of 15 frequency CMB polarization maps of future generation Cosmic Origin Explorer (COrE) satellite mission. We find that the clean power spectra, from the usual ILC, are strongly biased due to non-zero CMB-foregrounds chance correlations. In order to address the issues of bias and errors we extend and improve the usual ILC method for CMB E-mode reconstruction by incorporating prior information of theoretical E-mode angular power spectrum while estimating the weights for linear combination of input maps (Sudevan & Saha 2018b). Using the E-mode covariance matrix effectively suppresses the CMB-foreground chance correlation power leading to an accurate reconstruction of cleaned CMB E-mode map and its angular power spectrum. We compare the performance of the usual ILC and the new method over large angular scales and show that the later produces significantly statistically improved results than the former. The new E-mode CMB angular power spectrum contains neither any significant negative bias at the low multipoles nor any positive foreground bias at relatively higher mutltipoles. The error estimates of the cleaned spectrum agree very well with the cosmic variance induced error.
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data

This article investigates the optimization of yaw control inputs of a nine-turbine wind farm. The wind farm is simulated using the high-fidelity simulator SOWFA. The optimization is performed with a modifier adaptation scheme based on Gaussian processes. Modifier adaptation corrects for the mismatch between plant and model and helps to converge to the actual plan optimum. In the case study the modifier adaptation approach is compared with the Bayesian optimization approach. Moreover, the use of two different covariance functions in the Gaussian process regression is discussed. Practical recommendations concerning the data preparation and application of the approach are given. It is shown that both the modifier adaptation and the Bayesian optimization approach can improve the power production with overall smaller yaw misalignments in comparison to the Gaussian wake model.
Optimal Behavior Planning for Autonomous Driving: A Generic Mixed-Integer Formulation

Mixed-Integer Quadratic Programming (MIQP) has been identified as a suitable approach for finding an optimal solution to the behavior planning problem with low runtimes. Logical constraints and continuous equations are optimized alongside. However, it has only been formulated for a straight road, omitting common situations such as taking turns at intersections. This has prevented the model from being used in reality so far. Based on a triple integrator model formulation, we compute the orientation of the vehicle and model it in a disjunctive manner. That allows us to formulate linear constraints to account for the non-holonomy and collision avoidance. These constraints are approximations, for which we introduce the theory. We show the applicability in two benchmark scenarios and prove the feasibility by solving the same models using nonlinear optimization. This new model will allow researchers to leverage the benefits of MIQP, such as logical constraints, or global optimality.
Far from equilibrium dynamics of tracer particles embedded in a growing multicellular spheroid

Local stresses on the cancer cells (CCs) have been measured by embedding inert tracer particles (TPs) in a growing multicellular spheroid. The utility of the experiments requires that the TPs do not alter the CC microenvironment. We show, using theory and extensive simulations, that proliferation and apoptosis of the CCs, drive the dynamics of the TPs far from equilibrium. On times less than the CC division times, the TPs exhibit sub-diffusive behavior (the mean square displacement, with βTP < 1). Surprisingly, in the long-time limit, the motion of the TPs is hyper-diffusive ( with αTP > 2) due to persistent directed motion for a number of CC division times. In contrast, CC proliferation randomizes their motion resulting from jamming at short times to super-diffusive behavior, with αCC exceeding unity, at long times. Surprisingly, the effect of the TPs on CC dynamics and radial pressure is negligible, suggesting that the TPs are reliable reporters of the CC microenvironment.
Temporal Network Representation Learning via Historical Neighborhoods Aggregation

Network embedding is an effective method to learn low-dimensional representations of nodes, which can be applied to various real-life applications such as visualization, node classification, and link prediction. Although significant progress has been made on this problem in recent years, several important challenges remain, such as how to properly capture temporal information in evolving networks. In practice, most networks are continually evolving. Some networks only add new edges or nodes such as authorship networks, while others support removal of nodes or edges such as internet data routing. If patterns exist in the changes of the network structure, we can better understand the relationships between nodes and the evolution of the network, which can be further leveraged to learn node representations with more meaningful information. In this paper, we propose the Embedding via Historical Neighborhoods Aggregation (EHNA) algorithm. More specifically, we first propose a temporal random walk that can identify relevant nodes in historical neighborhoods which have impact on edge formations. Then we apply a deep learning model which uses a custom attention mechanism to induce node embeddings that directly capture temporal information in the underlying feature representation. We perform extensive experiments on a range of real-world datasets, and the results demonstrate the effectiveness of our new approach in the network reconstruction task and the link prediction task.
HAND/PERIPHERAL NERVE Anatomical Repair of Zone 1 Flexor Tendon Injuries

East Grinstead, United Kingdom Background: Repair and rehabilitation of the flexor digitorum profundus tendon in zone I may be demanding. The aim of the authors’ study was to assess a new technique for reinsertion of the distal flexor digitorum profundus tendon. Methods: The authors’ series consisted of 18 patients who required primary (n 10) or secondary (n 8) repair of the flexor digitorum profundus tendon in zone I. A half-Bruner incision was extended into the distal volar skin to expose the insertion site. Two drill holes were made through the base of the distal phalanx obliquely from the insertion of the profundus tendon in a dorsolateral direction. A modified Kessler suture was passed through the tendon and then through these holes and tied anteriorly, providing transosseous, internal fixation. Range of movement was assessed according to Moiemen’s categories. Results: Fourteen patients had excellent or good results, two patients had fair results, and one patient had a poor result. One patient failed to complete physiotherapy and was lost to follow-up. No tendon rupture was documented during a mean follow-up period of 8 months. Conclusions: The authors’ technique anchors the flexor digitorum profundus tendon or the graft in an anatomical position on the distal phalanx, without the need for external sutures or additional incisions. Furthermore, this is accomplished with minimal morbidity to the surrounding highly specialized tissue. The authors’ results compare favorably with those of other techniques in the literature. (Plast. Reconstr. Surg. 123: 617, 2009.)
Unsupervised Sequence Forecasting of 100,000 Points for Unsupervised Trajectory Forecasting

Predicting the future is a crucial first step to effective control, since systems that can predict the future can select plans that lead to desired outcomes. In this work, we study the problem of future prediction at the level of 3D scenes, represented by point clouds captured by a LiDAR sensor, i.e., directly learning to forecast the evolution of >100,000 points that comprise a complete scene. We term this Scene Point Cloud Sequence Forecasting (SPCSF). By directly predicting the densest-possible 3D representation of the future, the output contains richer information than other representations such as future object trajectories. We design a method, SPCSFNet, evaluate it on the KITTI and nuScenes datasets, and find that it demonstrates excellent performance on the SPCSF task. To show that SPCSF can benefit downstream tasks such as object trajectory forecasting, we present a new object trajectory forecasting pipeline leveraging SPCSFNet. Specifically, instead of forecasting at the object level as in conventional trajectory forecasting, we propose to forecast at the sensor level and then apply detection and tracking on the predicted sensor data. As a result, our new pipeline can remove the need of object trajectory labels and enable large-scale training with unlabeled sensor data. Surprisingly, we found our new pipeline based on SPCSFNet was able to outperform the conventional pipeline using state-of-the-art trajectory forecasting methods, all of which require future object trajectory labels. Finally, we propose a new evaluation procedure and two new metrics to measure the end-to-end performance of the trajectory forecasting pipeline. Our code will be made publicly available at this https URL
Analytical Model of Memory-Bound Applications Compiled with High Level Synthesis

The increasing demand of dedicated accelerators to improve energy efficiency and performance has highlighted FPGAs as a promising option to deliver both. However, programming FPGAs in hardware description languages requires long time and effort to achieve optimal results, which discourages many programmers from adopting this technology. High Level Synthesis tools improve the accessibility to FPGAs, but the optimization process is still time expensive due to the large compilation time, between minutes and days, required to generate a single bitstream. Whereas placing and routing take most of this time, the RTL pipeline and memory organization are known in seconds. This early information about the organization of the upcoming bitstream is enough to provide an accurate and fast performance model. This paper presents a performance analytical model for HLS designs focused on memory bound applications. With a careful analysis of the generated memory architecture and DRAM organization, the model predicts the execution time with a maximum error of 9.2% for a set of representative applications. Compared with previous works, our predictions reduce on average at least $2\times$ the estimation error.
Faster Approximation Algorithms for Geometric Set Cover

We improve the running times of $O(1)$-approximation algorithms for the set cover problem in geometric settings, specifically, covering points by disks in the plane, or covering points by halfspaces in three dimensions. In the unweighted case, Agarwal and Pan [SoCG 2014] gave a randomized $O(n\log^4 n)$-time, $O(1)$-approximation algorithm, by using variants of the multiplicative weight update (MWU) method combined with geometric data structures. We simplify the data structure requirement in one of their methods and obtain a deterministic $O(n\log^3 n\log\log n)$-time algorithm. With further new ideas, we obtain a still faster randomized $O(n\log n(\log\log n)^{O(1)})$-time algorithm. For the weighted problem, we also give a randomized $O(n\log^4n\log\log n)$-time, $O(1)$-approximation algorithm, by simple modifications to the MWU method and the quasi-uniform sampling technique.
Parallel Batch-Dynamic k-Clique Counting

In this paper, we study new batch-dynamic algorithms for $k$-clique counting, which are dynamic algorithms where the updates are batches of edge insertions and deletions. We study this problem in the parallel setting, where the goal is to obtain algorithms with low (poly-logarithmic) depth. Our first result is a new parallel batch-dynamic triangle counting algorithm with $O(\Delta\sqrt{\Delta+m})$ amortized work and $O(\log^* (\Delta+m))$ depth with high probability (w.h.p.), and $O(\Delta+m)$ space for a batch of $\Delta$ edge insertions or deletions. Our second result is a simple parallel batch-dynamic $k$-clique counting algorithm that uses a newly developed parallel $k$-clique counting algorithm to bootstrap itself, by enumerating smaller cliques, and intersecting them with the batch. Instantiating this idea gives a simple batch-dynamic algorithm running in $O(\Delta(m+\Delta)\alpha^{k-4})$ expected work and $O(\log^{k-2} n)$ depth w.h. p., all in $O(m + \Delta)$ space. Our third result is an algebraic algorithm based on parallel fast matrix multiplication. Assuming that a parallel fast matrix multiplication algorithm exists with parallel matrix multiplication constant $\omega_p$, the same algorithm solves dynamic $k$-clique counting with $O\left(\min\left(\Delta m^{\frac{(2k - 1)\omega_p}{3(\omega_p + 1)}}, (\Delta+m)^{\frac{2(k + 1)\omega_p}{3(\omega_p + 1)}}\right)\right)$ amortized work, $O(\log (\Delta+m))$ depth, and $O\left((\Delta+m)^{\frac{2(k + 1)\omega_p}{3(\omega_p + 1)}}\right)$ space.
Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing

Using printed photograph and replaying videos of biometric modalities, such as iris, fingerprint and face, are common attacks to fool the recognition systems for granting access as the genuine user. With the growing online person-to-person shopping (e.g., Ebay and Craigslist), such attacks also threaten those services, where the online photo illustration might not be captured from real items but from paper or digital screen. Thus, the study of anti-spoofing should be extended from modality-specific solutions to generic-object-based ones. In this work, we define and tackle the problem of Generic Object Anti-Spoofing (GOAS) for the first time. One significant cue to detect these attacks is the noise patterns introduced by the capture sensors and spoof mediums. Different sensor/medium combinations can result in diverse noise patterns. We propose a GAN-based architecture to synthesize and identify the noise patterns from seen and unseen medium/sensor combinations. We show that the procedure of synthesis and identification are mutually beneficial. We further demonstrate the learned GOAS models can directly contribute to modality-specific anti-spoofing without domain transfer. The code and GOSet dataset are available at cvlab.cse. msu.edu/project-goas.html.
IoT Blockchain Solution for Air Quality Monitoring in SmartCities

IoT cloud enabled societal applications have dramatically increased in the recent past due to the thrust for innovations, notably through startup initiatives, in various sectors such as agriculture, healthcare, industry, and so forth. The existing IoT cloud solutions have led practitioners or researchers to a haphazard clutter of serious security hazards and performance inefficiencies. This paper proposes a blockchain enabled IoT cloud implementation to tackle the existing issues in smart cities. It particularly highlights the implementation of chaincodes for air quality monitoring systems in SmartCities; the proposed architecture named as IoT enabled Blockchain for Air Quality Monitoring System (IB-AQMS) is illustrated using experiments. Experimental results were carried out and the findings were disclosed in the paper.
The specificity of the interactions of electroweak gauge bosons coming from extra dimensions

We discuss the specificity of the interactions of the electroweak gauge boson excitations in models with warped extra dimensions. In particular, we show that the couplings of the gauge boson excitations $W^{\prime}$, $Z^{\prime}$, and $\gamma^\prime$ to the SM gauge bosons treated as the zero modes of the 5D gauge fields are either exactly equal to zero or very much suppressed. In the former case, the three-particle and four-particle interaction Lagrangians of the SM gauge bosons and their lowest excitations are found explicitly. Meanwhile, the couplings of $W^{\prime}$, $Z^{\prime}$, and $\gamma^\prime$ to the SM fermions are non-zero allowing for their production and decays. These are the characteristic features of the gauge boson excitations in models with warped extra dimensions, which distinguish them from the gauge boson excitations in other models beyond the SM.