Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural-Network Guided Expression Transformation

Optimizing compilers, as well as other translator systems, often work by rewriting expressions according to equivalence preserving rules. Given an input expression and its optimized form, finding the sequence of rules that were applied is a non-trivial task. Most of the time, the tools provide no proof, of any kind, of the equivalence between the original expression and its optimized form. In this work, we propose to reconstruct proofs of equivalence of simple mathematical expressions, after the fact, by finding paths of equivalence preserving transformations between expressions. We propose to find those sequences of transformations using a search algorithm, guided by a neural network heuristic. Using a Tree-LSTM recursive neural network, we learn a distributed representation of expressions where the Manhattan distance between vectors approximately corresponds to the rewrite distance between expressions. We then show how the neural network can be efficiently used to search for transformation paths, leading to substantial gain in speed compared to an uninformed exhaustive search. In one of our experiments, our neural-network guided search algorithm is able to solve more instances with a 2 seconds timeout per instance than breadth-first search does with a 5 minutes timeout per instance.
Solving moving-blocks problems

In this thesis, we study the class of moving-blocks problems. A moving-blocks problem consists of k movable blocks placed on a grid-square maze where there is an additional movable block called the man, which is the only block that can be moved directly. In particular, each moving-blocks problem is defined by the set of moves available, by the goal description and by what happens when the man attempts to move a block. Sokoban is the best known and researched moving-blocks problem. We study moving-blocks problems in theory and practice. We investigate the computational complexity of problems of moving-blocks. Prior to this thesis, most of the scientific literature addressed moving-blocks problems with PUSH moves only, in most of the cases proving that these problems are PSPACE-complete. We consider two sets of problems: PULL moves only, and PUSH and PULL moves combined. Our reductions are from Nondeterministic Constraint Logic. We prove that many problems with PULL moves only are PSPACE-complete. In addition, we prove that the entire set of PUSH and PULL moves is PSPACE-complete. Our contribution in this research line is to enhance the knowledge on the complexity landscape of moving-blocks problems. Our main objective in this thesis is to optimally solve moving-blocks problems with a focus on Sokoban. Methods based on heuristic search and abstraction heuristics such as pattern databases are the most effective approaches to optimally solve these problems. We make many contributions in this research line. We introduce novel heuristic functions using pattern databases with the idea of intermediate goal states. We propose a technique based on pattern databases to detect deadlocks. We propose tie-breaking rules that exploit the structure of the problem. Using these heuristic functions and tie-breaking rules we increase the number of optimally solved instances of Sokoban and other problems compared to previous methods.
Exploration of Performance and Energy Trade-offs for Heterogeneous Multicore Architectures

Energy-efficiency has become a major challenge in modern computer systems. To address this challenge, candidate systems increasingly integrate heterogeneous cores in order to satisfy diverse computation requirements by selecting cores with suitable features. In particular, single-ISA heterogeneous multicore processors such as ARM big.LITTLE have become very attractive since they offer good opportunities in terms of performance and power consumption trade-off. While existing works already showed that this feature can improve system energy-efficiency, further gains are possible by generalizing the principle to higher levels of heterogeneity. The present paper aims to explore these gains by considering single-ISA heterogeneous multicore architectures including three different types of cores. For this purpose, we use the Samsung Exynos Octa 5422 chip as baseline architecture. Then, we model and evaluate Cortex A7, A9, and A15 cores using the gem5 simulation framework coupled to McPAT for power estimation. We demonstrate that varying the level of heterogeneity as well as the different core ratio can lead to up to 2.3x gains in energy efficiency and up to 1.5x in performance. This study further provides insights on the impact of workload nature on performance/energy trade-off and draws recommendations concerning suitable architecture configurations. This contributes in fine to guide future research towards dynamically reconfigurable HSAs in which some cores/clusters can be disabled momentarily so as to optimize certain metrics such as energy efficiency. This is of particular interest when dealing with quality-tunable algorithms in which accuracy can be then traded for compute effort, thereby enabling to use only those cores that provide the best energy-efficiency for the chosen algorithm.
Deep CSI Learning for Gait Biometric Sensing and Recognition

Gait is a person's natural walking style and a complex biological process that is unique to each person. Recently, the channel state information (CSI) of WiFi devices have been exploited to capture human gait biometrics for user identification. However, the performance of existing CSI-based gait identification systems is far from satisfactory. They can only achieve limited identification accuracy (maximum $93\%$) only for a very small group of people (i.e., between 2 to 10). To address such challenge, an end-to-end deep CSI learning system is developed, which exploits deep neural networks to automatically learn the salient gait features in CSI data that are discriminative enough to distinguish different people Firstly, the raw CSI data are sanitized through window-based denoising, mean centering and normalization. The sanitized data is then passed to a residual deep convolutional neural network (DCNN), which automatically extracts the hierarchical features of gait-signatures embedded in the CSI data. Finally, a softmax classifier utilizes the extracted features to make the final prediction about the identity of the user. In a typical indoor environment, a top-1 accuracy of $97.12 \pm 1.13\%$ is achieved for a dataset of 30 people.
Design and Implementation of Location and Activity Monitoring System Based on LoRa

The location and human activity are usually used as one of the important parameters to monitor the health status in healthcare devices. However, nearly all existing location and monitoring systems have the limitation of short-range communication and high power consumption. In this paper, we propose a new mechanism to collect and transmit monitoring information based on LoRa technology. The monitoring device with sensors can collect the real-time activity and location information and transmit them to the cloud server through LoRa gateway. The user can check all his history and current information through the specific designed mobile applications. Experiment was carried out to verify the communication, power consumption and monitoring performance of the entire system. Experimental results demonstrate that this system can collect monitoring and activity information accurately and provide the long rang coverage with low power consumption.
Adversarially Learning a Local Anatomical Prior: Vertebrae Labelling with 2D reformations

Robust localisation and identification of vertebrae, jointly termed vertebrae labelling, in computed tomography (CT) images is an essential component of automated spine analysis. Current approaches for this task mostly work with 3D scans and are comprised of a sequence of multiple networks. Contrarily, our approach relies only on 2D reformations, enabling us to design an end-to-end trainable, standalone network. Our contribution includes: (1) Inspired by the workflow of human experts, a novel butterfly-shaped network architecture (termed Btrfly net) that efficiently combines information across sufficiently-informative sagittal and coronal reformations. (2) Two adversarial training regimes that encode an anatomical prior of the spine's shape into the Btrfly net, each enforcing the prior in a distinct manner. We evaluate our approach on a public benchmarking dataset of 302 CT scans achieving a performance comparable to state-of-art methods (identification rate of $>$88%) without any post-processing stages. Addressing its translation to clinical settings, an in-house dataset of 65 CT scans with a higher data variability is introduced, where we discuss refinements that render our approach robust to such scenarios.
Finite-Sample Analysis for SARSA and Q-Learning with Linear Function Approximation

Though the convergence of major reinforcement learning algorithms has been extensively studied, the finite-sample analysis to further characterize the convergence rate in terms of the sample complexity for problems with continuous state space is still very limited. Such a type of analysis is especially challenging for algorithms with dynamically changing learning policies and under non-i.i. d. \ sampled data. In this paper, we present the first finite-sample analysis for the SARSA algorithm and its minimax variant (for zero-sum Markov games), with a single sample path and linear function approximation. To establish our results, we develop a novel technique to bound the gradient bias for dynamically changing learning policies, which can be of independent interest. We further provide finite-sample bounds for Q-learning and its minimax variant. Comparison of our result with the existing finite-sample bound indicates that linear function approximation achieves order-level lower sample complexity than the nearest neighbor approach.
TzK: Flow-Based Conditional Generative Model

We formulate a new class of conditional generative models based on probability flows. Trained with maximum likelihood, it provides efficient inference and sampling from class-conditionals or the joint distribution, and does not require a priori knowledge of the number of classes or the relationships between classes. This allows one to train generative models from multiple, heterogeneous datasets, while retaining strong prior models over subsets of the data (e.g., from a single dataset, class label, or attribute). In this paper, in addition to end-to-end learning, we show how one can learn a single model from multiple datasets with a relatively weak Glow architecture, and then extend it by conditioning on different knowledge types (e.g., a single dataset). This yields log likelihood comparable to state-of-the-art, compelling samples from conditional priors.
Information Flow in Computational Systems

We develop a theoretical framework for defining and identifying flows of information in computational systems. Here, a computational system is assumed to be a directed graph, with “clocked” nodes that send transmissions to each other along the edges of the graph at discrete points in time. We are interested in a definition that captures the dynamic flow of information about a specific message, and which guarantees an unbroken “information path” between appropriately defined inputs and outputs in the directed graph. Prior measures, including those based on Granger Causality and Directed Information, fail to provide clear assumptions and guarantees about when they correctly reflect information flow about a message. We take a systematic approach—iterating through candidate definitions and counterexamples—to arrive at a definition for information flow that is based on conditional mutual information, and which satisfies desirable properties, including the existence of information paths. Finally, we describe how information flow might be detected in a noiseless setting, and provide an algorithm to identify information paths on the time-unrolled graph of a computational system.
Equal Opportunity in Online Classification with Partial Feedback

We study an online classification problem with partial feedback in which individuals arrive one at a time from a fixed but unknown distribution, and must be classified as positive or negative. Our algorithm only observes the true label of an individual if they are given a positive classification. This setting captures many classification problems for which fairness is a concern: for example, in criminal recidivism prediction, recidivism is only observed if the inmate is released; in lending applications, loan repayment is only observed if the loan is granted. We require that our algorithms satisfy common statistical fairness constraints (such as equalizing false positive or negative rates -- introduced as "equal opportunity" in Hardt et al. (2016)) at every round, with respect to the underlying distribution. We give upper and lower bounds characterizing the cost of this constraint in terms of the regret rate (and show that it is mild), and give an oracle efficient algorithm that achieves the upper bound.
Bot Electioneering Volume: Visualizing Social Bot Activity During Elections

It has been widely recognized that automated bots may have a significant impact on the outcomes of national events. It is important to raise public awareness about the threat of bots on social media during these important events, such as the 2018 US midterm election. To this end, we deployed a web application to help the public explore the activities of likely bots on Twitter on a daily basis. The application, called Bot Electioneering Volume (BEV), reports on the level of likely bot activities and visualizes the topics targeted by them. With this paper we release our code base for the BEV framework, with the goal of facilitating future efforts to combat malicious bots on social media.
Blind Signal Classification Analysis and Impact on User Scheduling and Power Allocation in Nonorthogonal Multiple Access

For a massive number of devices, nonorthogonal multiple access (NOMA) has been recognized as a promising technology for improving the spectral efficiency compared to orthogonal multiple access (OMA). However, it is difficult for a base station (BS) to provide all of the information about NOMA signals via a high layer owing to signaling overhead concerns. This paper studies blind signal classification, which determines whether or not the received NOMA signal requires successive interference cancellation (SIC) without a priori signal information. In this paper, two types of blind signal classification errors are analyzed: 1) the signal is classified as one that does not require SIC on the SIC user side and 2) the signal is classified as one for which SIC is necessary on the non-SIC user side. In addition, we formulate the joint optimization problem for user scheduling and power allocation, which maximizes the sum-rate gain of NOMA over OMA with constraints on the maximum classification error probability and minimum data rate. The proposed algorithm iteratively finds solutions for user scheduling and power allocation. Simulation results show that the proposed scheme outperforms existing user scheduling methods.
SDN on BLE: Controlling Resource Constrained Mesh Networks

Wireless connectivity for the Internet of Things (IoT) requires combining low latency and high reliability with power and cost constraints. On top of that, diverse applications and end-to-end services with very different performance requirements should ideally share the same network infrastructure. Software defined networks (SDN) were defined to allow this coexistence, but they have seen very limited adoption in IoT systems due to the inherent resource constrained nature of the wireless devices. Additionally, the low-power nature of IoT communication standards does not usually allow for physically separated control and data channels, which is a basic requirement for real-time network slicing. In this work, we propose a SDN implementation for Bluetooth Low Energy (BLE), which has become the de-facto technology for IoT applications. The proposed BLE mesh structure is able to use different protocols for different services, ensuring a clear separation between control and data channels while still being sent over the wireless medium. A proof-of-concept for the proposed SDN implementation is given in a real BLE mesh testbed, where measurements show how the system is able to automatically detect and recover from network congestion by identifying the nodes responsible of such situation and reconfiguring their parameters over the air.
State Estimation over Worst-Case Erasure and Symmetric Channels with Memory

Worst-case models of erasure and symmetric channels are investigated, in which the number of channel errors occurring in each sliding window of a given length is bounded. Upper and lower bounds on their zero-error capacities are derived, with the lower bounds revealing a connection with the topological entropy of the channel dynamics. Necessary and sufficient conditions for linear state estimation with bounded estimation errors via such channels are then obtained, by extending previous results for non-stochastic memoryless channels to those with finite memory. These estimation conditions involve the topological entropies of the linear system and the channel.
A Distributed Self-Organization Approach to Minimize the Signaling and Delay Caused by Mobility Management Function in Cellular Networks

To manage mobility, RAN nodes in both 4G and 5G are grouped into a hierarchy of geographical areas. We demonstrate a 4G/5G compliant Network Level Mobility Management Optimization solution based on User Equipment (UE) Mobility to minimize signaling (i.e., handover signaling, paging and tracking area updates) and handover latency by dynamically reconfiguring the association between nodes in the Radio Access Network (RAN) and nodes (e.g. Mobility Management Entity), functions (e.g. Access and Mobility Management Function) and Location Regions (e.g. Tracking Area, Registration Area, Tracking Area List) in the core network.
Complex Leadership in Healthcare: A Scoping Review

Background: Nowadays, health systems are generally acknowledged to be complex social systems. Consequently, scholars, academics, practitioners, and policy-makers are exploring how to adopt a complexity perspective in health policy and system research. While leadership and complexity has been studied extensively outside health, the implications of complexity theories for the study of leadership in healthcare have received limited attention. We carried out a scoping review of complex leadership (CL) in healthcare to investigate how CL in healthcare has been defined, theorised and conceptualised and to explore how ‘CL’ has been applied in healthcare settings. Methods: We followed the methodological steps proposed by (Arksey and O’Malley, 2005): (1) specifying the research question, (2) identifying relevant studies, (3) study selection, (4) charting the data, (5) collating and summarizing the findings, and (6) reporting the results. We searched using Medline, Psychinfo, Wiley online library, and Google Scholar. Our inclusion criteria were: publication type (peer reviewed articles, theses, and book chapters); phenomenon of interest: complex leadership; context: healthcare and period of publication: between 2000 and 2016. Results: Our search and selection resulted in 37 papers (16 conceptual papers, 14 empirical studies and 7 advocacy papers). We note that empirical studies on CL are few and almost all research reported by these papers was carried out in the North (mainly in USA and UK). We found that there is some variation in definitions of CL. Furthermore, the research papers adopt mostly an explorative or explanatory approach and do not focus on assessing effectiveness of CL approaches. Finally, we found that the majority of researchers seem to adhere to the mathematical complexity perspective. Conclusion: Complexity concepts derived from natural sciences may not automatically fit management of health services. Further research into how social complexity theories may offer researchers useful grounds to empirically test CL theories in health settings is warranted. Specific attention should be paid to the multi-layered nature of leadership.
The effects of genomic germline variant reclassification on clinical cancer care

The last two decades have provided an astounding amount of novel information about the human genome. Translating germline genomic data into clinically actionable findings is reliant on the annotation and laboratory classification of specific variants. Variant classification helps providers and patients determine if genomic findings can inform clinical management. In germline hereditary cancer predisposition testing, variants of uncertain significance (VUS) are routinely misunderstood. By definition, they cannot be classified by the testing laboratory as either problematic mutations or benign variants. Many VUS undergo category reclassifications over time (from months to years after initial classification) as more information is known about normal human genomic diversity, especially among underrepresented minority populations. When VUS are reclassified, it has been shown that they are often downgraded. Likewise, some variants originally thought to be actionable mutations are downgraded to VUS or benign variants. Rarely but importantly, VUS may be reclassified in a manner that increases their initial clinical significance. Here, we discuss the insights gained from the study of variant reclassification. We provide a case series to highlight the potential impact that variant reclassifications can have on individual and family cancer management, risk counseling, and screening.
Chronic Lymphocytic Leukemia Presenting as a Subcortical Watershed Infarct

Internal watershed infarcts (WI) involve white matter between deep and superficial arterial systems of middle cerebral artery. These infarcts are considered to be either from low blood flow or microembolism. Anemia is an extremely rare cause of watershed infarcts. Very few cases of hemolytic anemia causing watershed cerebral infarcts have been reported. Chronic lymphocytic leukemia (CLL) is frequently complicated with secondary autoimmune cytopenia such as autoimmune hemolytic anemia (AIHA), immune thrombocytopenia (ITP), and pure red cell aplasia. AIHA is present in about 7–10% of patients with CLL. AIHA from CLL presenting as WI is an extremely rare phenomenon with no previously published case reports to the best of our knowledge.
Computational Approach for Structural Feature Determination of Grapevine NHX Antiporters

Plant NHX antiporters are responsible for monovalent cation/H+ exchange across cellular membranes and play therefore a critical role for cellular pH regulation, Na+ and K+ homeostasis, and salt tolerance. Six members of grapevine NHX family (VvNHX1-6) have been structurally characterized. Phylogenetic analysis revealed their organization in two groups: VvNHX1-5 belonging to group I (vacuolar) and VvNHX6 belonging to group II (endosomal). Conserved domain analysis of these VvNHXs indicates the presence of different kinds of domains. Out of these, two domains function as monovalent cation-proton antiporters and one as the aspartate-alanine exchange; the remaining are not yet with defined function. Overall, VvNHXs proteins are typically made of 11-13 putative transmembrane regions at their N-terminus which contain the consensus amiloride-binding domain in the 3rd TM domain and a cation-binding site in between the 5th and 6th TM domain, followed by a hydrophilic C-terminus that is the target of several and diverse regulatory posttranslational modifications. Using a combination of primary structure analysis, secondary structure alignments, and the tertiary structural models, the VvNHXs revealed mainly 18 α helices although without β sheets. Homology modeling of the 3D structure showed that VvNHX antiporters are similar to the bacterial sodium proton antiporters MjNhaP1 (Methanocaldococcus jannaschii) and PaNhaP (Pyrococcus abyssi).