Miracle-dz/UniFAR-FTUs huggingface dataset:
Bidirectional Predation Between Larvae of the Hoverfly Episyrphus balteatus (Diptera: Syrphidae) and the Fall Armyworm Spodoptera frugiperda (Lepidoptera: Noctuidae)

Abstract The fall armyworm, Spodoptera frugiperda (J. E. Smith), a newly invasive pest, has natural insect enemies that hold promise as biological control agents. Here we analyzed predation rates between natural enemy insect, the syrphid Episyrphus balteatus (De Geer) and S. frugiperda in all paired combinations of all immature stages for each insect in petri dishes. The 2nd and 3rd instars E. balteatus larvae consumed 1st and 2nd instars S. frugiperda larvae, and 3rd and higher larval instars of S. frugiperda preyed on all instar larvae of E. balteatus. The 2nd and 3rd instars larvae of E. balteatus preyed on 1st and 2nd larval instars of S. frugiperda, consistent with the Holling type III response in petri dishes, with a theoretical maximum predation of 77 and 71 individuals in 24 h. The 5th and 6th instars S. frugiperda larvae consumed E. balteatus larvae, also with the Holling type III response, with a theoretical maximum predation on 1st instar E. balteatus larvae were 29 and 36 individuals, respectively. In a plant cage trial study, predation results were similar to those in petri dishes but with a lower predation number. None of the S. frugiperda larvae that fed on E. balteatus larvae developed to adulthood, and only about 20% of E. balteatus larvae that fed on S. frugiperda larvae became adults which had a significantly shorter lifespan than those who consume aphids. This two-way predation study revealed the complexity of S. frugiperda invasion and provided new insights into relationship between pests and natural enemies.
Distributed Fuzzy Optimal Consensus Control of State-Constrained Nonlinear Strict-Feedback Systems

This article investigates the distributed fuzzy optimal consensus control problem for state-constrained nonlinear strict-feedback systems under an identifier-actor–critic architecture. First, a fuzzy identifier is designed to approximate each agent’s unknown nonlinear dynamics. Then, by defining multiple barrier-type local optimal performance indexes for each agent, the optimal virtual and actual control laws are obtained, where two fuzzy-logic systems working as the actor network and critic network are used to execute control behavior and evaluate control performance, respectively. It is proved that the proposed control protocol can drive all agents to reach consensus without violating state constraints, and make the local performance indexes reach the Nash equilibrium simultaneously. Simulation studies are given to verify the effectiveness of the developed fuzzy optimal consensus control approach.
The Handmaid’s Tale: An Intertextual Transformation through Storytelling

ABSTRACT This article analyzes how Offred, the protagonist of The Handmaid’s Tale, reconstructs her fragmented self through storytelling in a dialogic thought process that is connected to the intertextual references. She recollects her memories and engages in a parodic critique of Gileadean propagandistic discourse. This implies a process of transformation that involves both her body and her mind and critically deconstructs the role the regime has assigned to her. The readers are invited to take a stand if this is not the world they want to live in. The novel challenges the narrative of Gilead in an attempt to rewrite it from a female point of view. This process is connected to the disruptive and heterogeneous disposition of the novel, which Kristeva calls semiotic and links to the maternal chora. The dichotomous view is therefore denied at the root and a multifaceted perspective is proposed.
Recognition of Alzheimer's Disease Using Structural MRI Based on Smooth Group L1/2

Accurate classification of Alzheimer's disease (AD) is helpful for timely taking relevant measures in the early stage of AD, controlling the incidence rate of AD in key population and delaying the deterioration of AD disease. In this study, the calibration support vector machine (c-SVM) model based on smooth group L1/2 (SGL1/2) was used to select the key features of key brain regions, so as to realize the prediction and auxiliary diagnosis of AD. In the experiment, this method is applied to structured magnetic resonance imaging (s-MRI) datasets for training and testing. Compared with other group level regularization methods, the classification model of SGL1/2 combined with c-SVM has better effect on AD recognition. The conclusion of this study provides an objective reference for the automatic diagnosis of AD in the future.
The Formation of Computed Tomography Images from Compressed Sampled One-dimensional Reconstructions

Compressive Sensing (CS) algorithms are widely adopted for the reconstruction of Magnetic Resonance images (MRI). Owing to differences in the nature of the measurements acquisition processes, these techniques are still not often employed for X-ray Computed Tomography (CT) imaging. However, CS has the potential of reducing the amount of emitted radiation during the CT acquisition process. This study establishes a structure, based on one-dimensional reconstructions, to build CT images using numerical optimization with direct methods, as opposed to traditional indirect methods, such as Conjugate Gradient. The structure was evaluated with regard to its ideal measurements and obtained better results, in terms of signal-to-noise ratio, with respect the reconstruction based on a Filtered Back Projection (FBP) algorithm.
Store-n-Learn: Classification and Clustering with Hyperdimensional Computing across Flash Hierarchy

Processing large amounts of data, especially in learning algorithms, poses a challenge for current embedded computing systems. Hyperdimensional (HD) computing (HDC) is a brain-inspired computing paradigm that works with high-dimensional vectors called hypervectors. HDC replaces several complex learning computations with bitwise and simpler arithmetic operations at the expense of an increased amount of data due to mapping the data into high-dimensional space. These hypervectors, more often than not, cannot be stored in memory, resulting in long data transfers from storage. In this article, we propose Store-n-Learn, an in-storage computing solution that performs HDC classification and clustering by implementing encoding, training, retraining, and inference across the flash hierarchy. To hide the latency of training and enable efficient computation, we introduce the concept of batching in HDC. We also present on-chip acceleration for HDC encoding in flash planes. This enables us to exploit the high parallelism provided by the flash hierarchy and encode multiple data points in parallel in both batched and non-batched fashion. Store-n-Learn also implements a single top-level FPGA accelerator with novel implementations for HDC classification training, retraining, inference, and clustering on the encoded data. Our evaluation over 10 popular datasets shows that Store-n-Learn is on average 222× (543×) faster than CPU and 10.6× (7.3×) faster than the state-of-the-art in-storage computing solution, INSIDER for HDC classification (clustering).
TAB: Unified and Optimized Ternary, Binary, and Mixed-precision Neural Network Inference on the Edge

Ternary Neural Networks (TNNs) and mixed-precision Ternary Binary Networks (TBNs) have demonstrated higher accuracy compared to Binary Neural Networks (BNNs) while providing fast, low-power, and memory-efficient inference. Related works have improved the accuracy of TNNs and TBNs, but overlooked their optimizations on CPU and GPU platforms. First, there is no unified encoding for the binary and ternary values in TNNs and TBNs. Second, existing works store the 2-bit quantized data sequentially in 32/64-bit integers, resulting in bit-extraction overhead. Last, adopting standard 2-bit multiplications for ternary values leads to a complex computation pipeline, and efficient mixed-precision multiplication between ternary and binary values is unavailable. In this article, we propose TAB as a unified and optimized inference method for ternary, binary, and mixed-precision neural networks. TAB includes unified value representation, efficient data storage scheme and novel bitwise dot product pipelines on CPU/GPU platforms. We adopt signed integers for consistent value representation across binary and ternary values. We introduce a bitwidth-last data format that stores the first and second bits of the ternary values separately to remove the bit extraction overhead. We design the ternary and binary bitwise dot product pipelines based on Gated-XOR using up to 40% fewer operations than State-Of-The-Art (SOTA) methods. Theoretical speedup analysis shows that our proposed TAB-TNN is 2.3× fast as the SOTA ternary method RTN, 9.8× fast as 8-bit integer quantization (INT8), and 39.4× fast as 32-bit full-precision convolution (FP32). Experiment results on CPU and GPU platforms show that our TAB-TNN has achieved up to 34.6× speedup and 16× storage size reduction compared with FP32 layers. TBN, Binary-activation Ternary-weight Network (BTN), and BNN in TAB are up to 40.7×, 56.2×, and 72.2× as fast as FP32. TAB-TNN is up to 70.1% faster and 12.8% more power-efficient than RTN on Darknet-19 while keeping the same accuracy. TAB is open source as a PyTorch Extension1 for easy integration with existing CNN models.
Beijing’s Institutionalised Economic Statecraft Towards Brazil: A Case Study

This study adopts an institutional approach in the case analysis of China's economic statecraft towards Brazil. In light of institutionalisation theory, it examines the institutional arrangements between Beijing and Brasilia for the purpose of facilitating bilateral economic cooperation and advancing strategic partnership. As a descriptive effort, it yields some preliminary findings: first, the institutionalisation of China's economic statecraft towards Brazil is incremental, driven largely by the desire for and belief in long-term planning; second, a set of norms, values, and principles is instituted alongside designated agencies, point persons, operational protocols, and exchange mechanisms, creating a form of institutional governance based on a multi-actor, multilevel, and network-based steering mode; third, governance remains so centralised that it falls short in empowering strategic participation; and, fourth and finally, Beijing's institution-building proves useful in the management of relations with Brazil, suggesting that economic statecraft may benefit from institutionalisation.
Fit and Pressure Comfort Evaluation on a Virtual Prototype of a Tight-Fit Cycling Shirt

Abstract Graduated compression is widely used for medical application to prevent perioperative venous thromboembolism, but other applications such as sportswear can potentially also benefit from it. A tight-fit cycling shirt meant to ensure the correct position during cycling and prevent injuries was designed. The aim of this study was to improve garment pattern design from the aspect of clothing pressure for providing support and enhancing comfort to the user. This paper investigates the suitability of pressure maps from 3D fashion design software CLO 3D for design and in particular its capability to discriminate between various materials and cycling postures. Moreover, the impact of the mechanical properties of fabric was analyzed. In particular, virtual prototyping tool CLO 3D and pressure mapping were employed to achieve the required graduated compression while ensuring fit and comfort. Pattern adjustments were iteratively performed until stress, strain, and pressure maps showed adequate fit and pressure of the cycling garment on the virtual cyclist in static and dynamic cycling positions. The impact of fabric types on garment fit has been shown by generating the stress, strain, and pressure maps with a virtual simulation. It was found that the visualized pressure on the human body model shows distributions that are related to contact between body and garment, and large compression stresses occur in the lower parts of the two shirts. Evident garment deformation was shown at hip level, upper arm, lower front side seam, and front neck, which can reduce garment wear comfort and freedom of movement. The output was found to be sufficiently accurate to optimize the garments based on material and cycling posture.
Evolution of Daphnia population dynamics following invasion by a non-native predator

Predators are frequently observed to cause evolutionary responses in prey phenotypes, which may, in turn, translate into evolutionary shifts in prey population dynamics. Although a link between predation and population growth has been demonstrated in experimental evolution studies, insights from natural populations are lacking. Here we tested for evolutionary changes in the population dynamics of the herbivorous water flea Daphnia pulicaria in response to the invasion of the predatory spiny water flea (Bythotrephes longimanus) in the Great Lakes region, USA. Using a resurrection ecological approach and a 3-month population growth experiment (in the absence of predation) we compared population dynamics in daphnia from pre- and post-invasion time periods. Post-invasion daphnia were able to maintain an overall higher population abundance throughout the growth experiment, both in terms of the number of individuals (28% higher) and total population biomass (33% higher). Estimation of population dynamics parameters from a theta-logistic model suggested that this was achieved through an increase in intrinsic population growth rate as well as increased carrying capacity. The observed difference in intrinsic rate of increase could not be predicted based on previous measurements of life-history traits in these clones. This indicates that care should be taken when extrapolating from a few life history traits measured in isolated individuals under controlled conditions to population dynamics. Whereas previous experimental evolution studies of predator-prey interactions have demonstrated that genotypes that have evolved under predation have inferior population growth when the predator is absent, this was not the case for the Daphnia. We suggest that complexities in ecological interactions of natural ecosystems, such as the potential for spatial and temporal avoidance of predation, makes it challenging to provide general predictions about evolutionary responses in population dynamics to predators.
Neural Collective Matrix Factorization for Integrated Analysis of Heterogeneous Biomedical Data

Motivation In many biomedical studies, there arises the need to integrate data from multiple directly or indirectly related sources. Collective matrix factorization (CMF) and its variants are models designed to collectively learn from arbitrary collections of matrices. The latent factors learnt are rich integrative representations that can be used in downstream tasks such as clustering or relation prediction with standard machine learning models. Previous CMF-based methods have numerous modeling limitations. They do not adequately capture complex non-linear interactions and do not explicitly model varying sparsity and noise levels in the inputs, and some cannot model inputs with multiple datatypes. These inadequacies limit their use on many biomedical datasets. Results To address these limitations, we develop Neural Collective Matrix Factorization (NCMF), the first fully neural approach to CMF. We evaluate NCMF on two relation prediction tasks, gene-disease association prediction and adverse drug event prediction, using multiple datasets. In each case, data is obtained from heterogeneous publicly available databases, and used to learn representations to build predictive models. NCMF is found to outperform previous CMF-based methods and state-of-the-art graph embedding methods for representation learning in our experiments. Our experiments illustrate the versatility and efficacy of NCMF for seamless integration of heterogeneous data. Availability https://github.com/ncmfsrc/ncmf Contact vaibhav.rajan@nus.edu. sg
An Asymptotically Optimal Algorithm for Maximum Matching in Dynamic Streams

We present an algorithm for the maximum matching problem in dynamic (insertion-deletions) streams with *asymptotically optimal* space complexity: for any $n$-vertex graph, our algorithm with high probability outputs an $\alpha$-approximate matching in a single pass using $O(n^2/\alpha^3)$ bits of space. A long line of work on the dynamic streaming matching problem has reduced the gap between space upper and lower bounds first to $n^{o(1)}$ factors [Assadi-Khanna-Li-Yaroslavtsev; SODA 2016] and subsequently to $\text{polylog}{(n)}$ factors [Dark-Konrad; CCC 2020]. Our upper bound now matches the Dark-Konrad lower bound up to $O(1)$ factors, thus completing this research direction. Our approach consists of two main steps: we first (provably) identify a family of graphs, similar to the instances used in prior work to establish the lower bounds for this problem, as the only"hard"instances to focus on. These graphs include an induced subgraph which is both sparse and contains a large matching. We then design a dynamic streaming algorithm for this family of graphs which is more efficient than prior work. The key to this efficiency is a novel sketching method, which bypasses the typical loss of $\text{polylog}{(n)}$-factors in space compared to standard $L_0$-sampling primitives, and can be of independent interest in designing optimal algorithms for other streaming problems.
Assisting Data Retrieval with a Drug Knowledge Graph

The Normandy health data warehouse EDSaN integrates the medication orders from the University Hospital of Rouen (France). This study aims at describing the design and the evaluation of an information retrieval system founded on a complex and semantically augmented knowledge graph dedicated to EDSaN drugs' prescriptions. The system is intended to help the selection of drugs in the search process by health professionals. The manual evaluation of the relevance of the returned drugs showed encouraging results as expected. A deeper analysis in order to improve the ranking method is needed and will be performed in a future work.
Excluding PH Pessiland

Heuristica and Pessiland are “worlds” of average-case complexity [Impagliazzo95] that are considered unlikely but that current techniques are unable to rule out. Recently, [Hirahara20] considered a PH (Polynomial Hierarchy) analogue of Heuristica, and showed that to rule it out, it would be sufficient to prove the NP -completeness of the problem GapMINKT PH of estimating the PH -oracle time-bounded Kolmogorov complexity of a string. In this work, we analogously define “PH Pessiland” to be a world where PH is hard on average but PH -computable pseudo-random generators do not exist. We unconditionally rule out PH-Pessiland in both non-uniform and uniform settings, by showing that the distributional problem of computing PH -oracle time-bounded Kolmogorov complexity of a string over the uniform distribution is complete for an (error-prone) average-case analogue of PH . Moreover, we show the equivalence between error-prone average-case hardness of PH and the existence of PH -computable pseudorandom generators. 2012
Pre-Constrained Encryption

In all existing encryption systems, the owner of the master secret key has the ability to decrypt all ciphertexts. In this work, we propose a new notion of pre-constrained encryption (PCE) where the owner of the master secret key does not have “full” decryption power. Instead, its decryption power is constrained in a pre-specified manner during the system setup . We present formal definitions and constructions of PCE, and discuss societal applications and implications to some well-studied cryptographic primitives
Correlation Detection in Trees for Planted Graph Alignment

Motivated by alignment of correlated sparse random graphs, we introduce a hypothesis testing problem of deciding whether or not two random trees are correlated. We obtain sufficient conditions under which this testing is impossible or feasible. We propose MPAlign, a message-passing algorithm for graph alignment inspired by the tree correlation detection problem. We prove MPAlign to succeed in polynomial time at partial alignment whenever tree detection is feasible. As a result our analysis of tree detection reveals new ranges of parameters for which partial alignment of sparse random graphs is feasible in polynomial time. We then conjecture that graph alignment is not feasible in polynomial time when the associated tree detection problem is impossible. If true, this conjecture together with our sufficient conditions on tree detection impossibility would imply the existence of a hard phase for graph alignment, i.e. a parameter range where alignment cannot be done in polynomial time even though it is known to be feasible in non-polynomial time.
Dynamic Matching Algorithms Under Vertex Updates

Dynamic graph matching algorithms have been extensively studied, but mostly under edge updates . This paper concerns dynamic matching algorithms under vertex updates, where in each update step a single vertex is either inserted or deleted along with its incident edges. A basic setting arising in online algorithms and studied by Bosek et al. [FOCS’14] and Bernstein et al. [SODA’18] is that of dynamic approximate maximum cardinality matching (MCM) in bipartite graphs in which one side is fixed and vertices on the other side either arrive or depart via vertex updates. In the BASIC-incremental setting, vertices only arrive, while in the BASIC-decremental setting vertices only depart. When vertices can both arrive and depart, we have the BASIC-dynamic setting. In this paper we also consider the setting in which both sides of the bipartite graph are dynamic. We call this the MEDIUM-dynamic setting, and MEDIUM-decremental is the restriction when vertices can only depart. The GENERAL-dynamic setting is when the graph is not necessarily bipartite and the vertices can both depart and arrive.
The Role of Feedback in Two-Way Full-Duplex Secure Communication: Revisit

In this paper, the role of feedback in two-way full-duplex Gaussian wiretap channel (TW-FD-GWTC) is revisited. Traditionally, in the TW-FD-GWTC, one direction channel (feedback channel) is used to transmit a secret key which ensures the secure communication over the other direction channel (feedforward channel), i.e., the transmission rate of the feedback channel is sacrificed to ensure the physical layer security (PLS) in the feedforward channel. Can both directions achieve positive secrecy rates? We answer this question by proposing a noisy feedback scheme for the TW-FD-GWTC. In this proposed new scheme, both directions achieve positive secrecy rates, and the maximum secrecy sum rate approaches that in the literature. The study of this paper offers a new option for the enhancement of the physical layer security in two-way full-duplex wiretap channel.
Artificial Intelligence Enabled Self-healing for Mobile Network Automation

This paper presents an artificial intelligence enabled self-healing framework for cell outage detection and compensation in radio access networks. The developed framework consists of three modules, namely cell outage detection, cell outage compensation, and continuous optimization that work in closed-loop to detect outages, trigger recovery actions, and network optimization to minimize the impact of outages on user experience. The outage detection module is based on machine learning algorithms aimed to detect anomalies in the network performance data. Likewise, the cell outage compensation module uses fuzzy logic to determine compensation actions after an outage cell has been detected. The continuous optimization module is tasked with making incremental improvements to the network configuration through a heuristic approach. The developed self-healing framework is validated using a network simulator ns-3 based test environment. Results show the framework is capable of fully recovering from the outage in terms of accessibility and coverage. In addition, the cell edge reference signal received power is recovered by 45%, thereby significantly improving the network performance once the outage is detected.