Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Structure and Collective Intelligence in the Diffusion of Innovation

When multiple innovations compete for adoption, historical chance leading to early advantage can generate lock-in effects that allow suboptimal innovations to succeed at the expense of superior alternatives. Research on the diffusion of innovafacetion has identified many possible sources of early advantage, but these mechanisms can benefit both optimal and suboptimal innovations. This paper moves beyond chance-as-explanation to identify structural principles that systematically impact the likelihood that the optimal strategy will spread. A formal model of innovation diffusion shows that the network structure of organizational relationships can systematically impact the likelihood that widely adopted innovations will be payoff optimal. Building on prior diffusion research, this paper focuses on the role of central actors i.e. well-connected people or firms. While contagion models of diffusion highlight the benefits of central actors for spreading innovations further and faster, the present analysis reveals a dark side to this influence: the mere presence of central actors in a network increases rates of adoption but also increases the likelihood of suboptimal outcomes. This effect, however, does not represent a speed-optimality tradeoff, as dense networks are both fast and optimal. This finding is consistent with related research showing that network centralization undermines collective intelligence.
Spectral Maps for Learning on Subgraphs

In graph learning, maps between graphs and their subgraphs frequently arise. For instance, when coarsening or rewiring operations are present along the pipeline, one needs to keep track of the corresponding nodes between the original and modiﬁed graphs. Classically, these maps are represented as binary node-to-node correspondence matrices, and used as-is to transfer node-wise features between the graphs. In this paper, we argue that simply changing this map representation can bring notable beneﬁts to graph learning tasks. Drawing inspiration from recent progress in geometry processing, we introduce a spectral representation for maps that is easy to integrate into existing graph learning models. This spectral representation is a compact and straightforward plug-in replacement, and is robust to topological changes of the graphs. Remarkably, the representation exhibits structural properties that make it interpretable, drawing an analogy with recent results on smooth manifolds. We demonstrate the beneﬁts of incorporating spectral maps in graph learning pipelines, addressing scenarios where a node-to-node map is not well deﬁned, or in the absence of exact isomorphism. Our approach bears practical beneﬁts in knowledge distillation and hierarchical learning, where we show comparable or improved performance at a fraction of the computational cost.
Time out of Mind: Generating Rate of Speech conditioned on emotion and speaker

Voice synthesis has seen significant improvements in the past decade resulting in highly intelligible voices. Further investigations have resulted in models that can produce variable speech, including conditional emotional expression. The problem lies, however, in a focus on phrase-level modifications and prosodic vocal features. Using the CREMA-D dataset we have trained a GAN conditioned on emotion to generate worth lengths for a given input text. These word lengths are relative to neutral speech and can be provided, through speech synthesis markup language (SSML) to a text-to-speech (TTS) system to generate more expressive speech. Additionally, a generative model is also trained using implicit maximum likelihood estimation (IMLE) and a comparative analysis with GANs is included. We were able to achieve better performances on objective measures for neutral speech, and better time alignment for happy speech when compared to an out-of-box model. However, further investigation of subjective evaluation is required.
A Detection Probability Localization Algorithm of Wireless Sensor Networks Based on Distributed Compressed-sensing

With fast development of science and technology, People gradually need more and more information, causing signicant pressure on the sampling. The sampling rate must be two times higher than the highest frequency of the signal based on Nyquist sampling theorem. Compressed Sensing (CS) employs a special sampling method which can capture and represent compressible signals at a rate signicantly below the Nyquist rate. It can relieve the pressure of sampling process in Wireless Sensor Networks. And a cooperative self-localization method based on probability for wireless sensor networks is proposed. The method rstly estimates the initial Position of the located node based on the joint Probability density function of the distance between the located node and the connected reference nodes. Further, based on the renement principle, a cooperative localization method is studied by making the best of the neighbour nodes and giving the neighbour nodes some condence. The method improves the estimation accuracy as well as makes more unknown nodes to be located.
Towards a Defense Against Federated Backdoor Attacks Under Continuous Training

Backdoor attacks are dangerous and difficult to prevent in federated learning (FL), where training data is sourced from untrusted clients over long periods of time. These difficulties arise because: (a) defenders in FL do not have access to raw training data, and (b) a new phenomenon we identify called backdoor leakage causes models trained continuously to eventually suffer from backdoors due to cumulative errors in defense mechanisms. We propose shadow learning, a framework for defending against backdoor attacks in the FL setting under long-range training. Shadow learning trains two models in parallel: a backbone model and a shadow model. The backbone is trained without any defense mechanism to obtain good performance on the main task. The shadow model combines filtering of malicious clients with early-stopping to control the attack success rate even as the data distribution changes. We theoretically motivate our design and show experimentally that our framework significantly improves upon existing defenses against backdoor attacks.
Analysis of the complex light field generated by a deflectable mirror array device

An exact analysis of the scalar coherent monochromatic light field produced by a deflectable mirror array device is presented. The three-dimensional light field is related to the tilt angles of the mirrors. The first Rayleigh-Sommerfeld diffraction formula is used to model the diffraction. The analysis is carried out based on the assumption that the mirrors can be tilted with continuously varying angles, so the field produced by a finite (discrete) set of possible tilt angles is included as a special case.
What is a mammalian omnivore? Insights into terrestrial mammalian diet diversity, body mass and evolution

Mammalian omnivores are a broad group of species that are often treated uniformly in ecological studies. Here, we incorporate omnivorous dietary differences to investigate previously found mammalian macroevolutionary and macroecological trends. We investigate the frequency with which vertebrate prey, invertebrate prey, fibrous plant material and non-fibrous plant material co-occur in the diets of terrestrial mammals. We quantify the body size distributions and phylogenetic signal of different omnivorous diets and use multistate reversible jump Markov chain Monte Carlo methods to assess the transition rates between diets on the mammalian phylogenetic tree. We find omnivores that consume all four food types are relatively rare, as most omnivores consume only invertebrate prey and non-fibrous plants. In addition, omnivores that only consume invertebrate prey, many of which are from Rodentia, are on average smaller than omnivores that incorporate vertebrate prey. Our transition models have high rates from invertivorous omnivory to herbivory, and from vertivory to prey mixing and ultimately invertivory. We suggest prey type is an important aspect of omnivore macroevolution and macroecology, as it is correlated with body mass, evolutionary history and diet-related evolutionary transition rates. Future work should avoid lumping omnivores into one category given the ecological variety of omnivore diets and their strong evolutionary influence.
Do unbiased people act more rationally?—The case of comparative realism and vaccine intention

Within different populations and at various stages of the pandemic, it has been demonstrated that individuals believe they are less likely to become infected than their average peer. This is known as comparative optimism and it has been one of the reproducible effects in social psychology. However, in previous and even the most recent studies, researchers often neglected to consider unbiased individuals and inspect the differences between biased and unbiased individuals. In a mini meta-analysis of six studies (Study 1), we discovered that unbiased individuals have lower vaccine intention than biased ones. In two pre-registered, follow-up studies, we aimed at testing the reproducibility of this phenomenon and its explanations. In Study 2 we replicated the main effect and found no evidence for differences in psychological control between biased and unbiased groups. In Study 3 we also replicated the effect and found that realists hold more centric views on the trade-offs between threats from getting vaccinated and getting ill. We discuss the interpretation and implication of our results in the context of the academic and lay-persons' views on rationality. We also put forward empirical and theoretical arguments for considering unbiased individuals as a separate phenomenon in the domain of self–others comparisons.
The first genetic engineered system for ovothiol biosynthesis in diatoms reveals a mitochondrial localization for the sulfoxide synthase OvoA

Diatoms represent one of the most abundant groups of microalgae in the ocean and are responsible for approximately 20% of photosynthetically fixed CO2 on Earth. Due to their complex evolutionary history and ability to adapt to different environments, diatoms are endowed with striking molecular biodiversity and unique metabolic activities. Their high growth rate and the possibility to optimize their biomass make them very promising ‘biofactories’ for biotechnological applications. Among bioactive compounds, diatoms can produce ovothiols, histidine-derivatives, endowed with unique antioxidant and anti-inflammatory properties, and occurring in many marine invertebrates, bacteria and pathogenic protozoa. However, the functional role of ovothiols biosynthesis in organisms remains almost unexplored. In this work, we have characterized the thiol fraction of Phaeodactylum tricornutum, providing the first evidence of the presence of ovothiol B in pennate diatoms. We have used P. tricornutum to overexpress the 5-histidylcysteine sulfoxide synthase ovoA, the gene encoding the key enzyme involved in ovothiol biosynthesis and we have discovered that OvoA localizes in the mitochondria, a finding that uncovers new concepts in cellular redox biochemistry. We have also obtained engineered biolistic clones that can produce higher amount of ovothiol B compared to wild-type cells, suggesting a new strategy for the eco-sustainable production of these molecules.
European Journal of Political Economy

This paper studies the role of free media in how governments and the public responded to the COVID-19 pandemic. We first document the presence of policy and behavioural responsiveness during the early phase of the pandemic. Using a panel data of daily COVID-19 deaths, lockdown policies, and mobility changes in 155 countries, we find that governments were more likely to impose a lockdown, and citizens to reduce their mobility, as the initial number of deaths increased. To measure the role of media freedom on responsiveness given endogeneity in death reporting, we simulate deaths from a calibrated SEIR model as an instrument for reported deaths. Using this approach, we find evidence that the presence of free media mattered for the timing of early responses to COVID-19. Responsiveness to deaths was limited to citizens in free-media countries, and accounted for 40% of the difference in lockdown decision and mobility changes between free-media and censored-media countries. In support of the role of free media, we show that differences in responsiveness are not explained by a range of other country characteristics such as the level of income, education or democracy. We also find evidence that citizens with access to free media were better informed about the pandemic and had more responsive levels of online searches about COVID-19, supporting the view that free media served to inform the public on the risks of COVID-19.
An SMT-Solver-based Synthesis of NNA-Compliant Quantum Circuits Consisting of CNOT, H and T Gates

It is natural to assume that we can perform quantum operations between only two adjacent physical qubits (quantum bits) to realize a quantum computer for both the current and possible future technologies. This restriction is called the Nearest Neighbor Architecture (NNA) restriction. This paper proposes an SMT-solver-based synthesis of quantum circuits consisting of CNOT, H, and T gates to satisfy the NNA restriction. Although the existing SMT-solver-based synthesis cannot treat H and T gates directly, our method treats the functionality of quantum-specific T and H gates carefully so that we can utilize an SMT-solver to minimize the number of CNOT gates; unlike the existing SMT-solver-based methods, our method considers “Don't Care” conditions in intermediate points of a quantum circuit by exploiting the property of T gates to reduce CNOT gates. Experimental results show that our approach can reduce the number of CNOT gates by 58.11% on average compared to the naive application of the existing method which does not consider the “Don't Care” condition.
ALow-Bitwidth Integer-STBP Algorithm for Efficient Training and Inference of Spiking Neural Networks

Spiking neural networks (SNNs) that enable energy-efficient neuromorphic hardware are receiving growing attention. Training SNNs directly with back-propagation has demonstrated accuracy comparable to deep neural networks (DNNs). However, previous direct-training algorithms require high-precision floating-point operations, which are not suitable for low-power end-point devices. The high-precision operations also require the learning algorithm to run on high-performance accelerator hardware. In this paper, we propose an improved approach that converts the high-precision floating-point operations to low-bitwidth integer operations for an existing direct-training algorithm, i.e., the Spatio-Temporal BackPropagation (STBP) algorithm. The proposed low-bitwidth IntegerSTBP algorithm requires only integer arithmetic for SNN training and inference, which greatly reduces the computational complexity. Experimental results show that the proposed STBP algorithm achieves comparable accuracy and higher energy efficiency than the original floating-point STBP algorithm. Moreover, it can be implemented on low-power end-point devices to provide learning capability during inference, which are mostly supported by fixed-point hardware.
Chiplet Placement for 2.5D IC with Sequence Pair Based Tree and Thermal Consideration

This work develops an efficient chiplet placer with thermal consideration for 2.5D ICs. Combining the sequence-pair based tree, branch-and-bound method, and advanced placement/pruning techniques, the developed placer can find the solution fast with the optimized total wirelength (TWL) on half-perimeter wirelength (HPWL). Additionally, with the post placement procedure, the placer reduces maximum temperatures with slight increase of wirelength. Experimental results show that the placer can not only find better optimized TWL (reducing 1.035% HPWL) but also speed up at most two orders of magnitude than the prior art. With thermal consideration, the placer can reduce the maximum temperature up to 8.214 °C with an average 5.376% increase of TWL.
DECC: Differential ECC for Read Performance Optimization on High-Density NAND Flash Memory

3D NAND flash memory with advanced multi-level-cell technology has been widely adopted due to its high density, but with significantly degraded reliability. To solve the reliability issue, flash memory often adopts the low-density parity-check code (LDPC) as error correction code (ECC) to encode data and provide fault tolerance. For LDPC with a low code rate, it can provide a strong correction capability, but with a high energy cost. To avoid the cost, LDPC with a higher code rate is always adopted. When the accessed data is not successfully decoded, LDPC will rely on read retry operations to improve the error correction capability. However, the read retry operation will induce degraded read performance. In this work, a differential ECC (DECC) method is proposed to improve the read performance. The basic idea of DECC is to adopt LDPC with different code rates for data with different access characteristics. Specifically, when data is hot read and retried due to reliability, LDPC with a low code rate will be adopted to optimize performance. With this approach, the cost from LDPC with a low code rate is minimized and the performance is optimized. Through careful design and real-world workloads evaluation on a 3D triple-level-cell (TLC) NAND flash memory, DECC achieves encouraging read performance optimization.
Efficient System-Level Design Space Exploration for High-level Synthesis using Pareto-Optimal Subspace Pruning

High-level synthesis (HLS) is a rapidly evolving and popular approach to designing, synthesizing, and optimizing embedded systems. Many HLS methodologies utilize design space exploration (DSE) at the post-synthesis stage to find Pareto-optimal hardware implementations for individual components. However, the design space for the system-level Pareto-optimal configurations is orders of magnitude larger than component-level design space, making existing approaches insufficient for system-level DSE. This paper presents Pruned Genetic Design Space Exploration (PG-DSE)—an approach to post-synthesis DSE that involves a pruning method to effectively reduce the system-level design space and an elitist genetic algorithm to accurately find the system-level Pareto-optimal configurations. We evaluate PG-DSE using an autonomous driving application subsystem (ADAS) and three synthetic systems with extremely large design spaces. Experimental results show that PGDSE can reduce the design space by several orders of magnitude compared to prior work while achieving higher quality results (an average improvement of 58.1x).
BufFormer: A Generative ML Framework for Scalable Buffering

Buffering is a prevalent interconnect optimization technique to help timing closure and is often performed after placement. A common buffering approach is to construct a Steiner tree and then buffers are inserted on the tree based on Ginneken-Lillis style algorithm. Such an approach is difficult to scale with large nets. Our work attempts to solve this problem with a generative machine-learning (ML) approach without Steiner tree construction. Our approach can extract and reuse knowledge from high quality samples and therefore has significantly improved scalability. A generative ML framework, BufFormer, is proposed to construct abstract tree topol-ogy while simultaneously determining buffer sizes & locations. A baseline method, FLUTE-based Steiner tree construction followed by Ginneken-Lillis style buffer insertion, is implemented to generate training samples. After training, BufFormer can produce solutions for unseen nets highly comparable to baseline results with a correlation coefficient 0.977 in terms of buffer area and 0.934 for driver-sink delays. On average, BufFormer-generated tree achieves similar de-lays with slightly larger buffer area. And up to 160X speedup can be achieved for large nets when running on a GPU over the baseline on a single CPU thread.
Towards On-Chip Learning for low Latency Reasoning with End-to-End Synthesis

The Software Defined Architectures (SODA) Synthesizer is an open-source compiler-based tool able to automatically generate domain-specialized systems targeting Application-Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) starting from high-level programming. SODA is composed of a frontend, SODA-OPT, which leverages the multilevel intermediate representation (MLIR) framework to interface with productive programming tools (e.g., machine learning frameworks), identify kernels suitable for acceleration, and perform high-level optimizations, and of a state-of-the-art high-level synthesis backend, Bambu from the PandA framework, to generate custom accelerators. One specific application of the SODA Synthesizer is the generation of accelerators to enable ultra-low latency inference and control on autonomous systems for scientific discovery (e.g., electron microscopes, sensors in particle accelerators, etc.). This paper provides an overview of the flow in the context of the generation of accelerators for edge processing to be integrated in transmission electron microscopy (TEM) devices, focusing on use cases from precision material synthesis. We show the tool in action with an example of design space exploration for inference on reconfigurable devices with a conventional deep neural network model (LeNet). Finally, we discuss the research directions and opportunities enabled by SODA in the area of autonomous control for scientific experimental workflows.
EO-Shield: A Multi-function Protection Scheme against Side Channel and Focused Ion Beam Attacks

Smart devices, especially Internet-connected devices, typically in-corporate security protocols and cryptographic algorithms to en-sure the control flow integrity and information security. However, there are various invasive and non-invasive attacks trying to tam-per with these devices. Chip-level active shield has been proved to be an effective countermeasure against invasive attacks, but existing active shields cannot be utilized to counter side-channel attacks (SCAs). In this paper, we propose a multi-function protection scheme and an active shield prototype to against invasive and non-invasive attacks simultaneously. The protection scheme has a complex active shield implemented using the top metal layer of the chip and an information leakage obfuscation module under-neath. The leakage obfuscation module generates its protection patterns based on the operating conditions of the circuit that needs to be protected, thus reducing the correlation between electromag-netic (EM) emanations and cryptographic data. We implement the protection scheme on one Advanced Encryption Standard (AES) circuit to demonstrate the effectiveness of the method. Experiment results demonstrate that the information leakage obfuscation mod-ule decreases SNR below 0.6 and reduces the success rate of SCAs. Compared to existing single-function protection methods against physical attacks, the proposed scheme provides good performance against both invasive and non-invasive attacks.