Miracle-dz/UniFAR-FTUs huggingface dataset:
APOSTLE: Asynchronously Parallel Optimization for Sizing Analog Transistors using DNN Learning

Analog circuit sizing is a high-cost process in terms of the man-ual effort invested and the computation time spent. With rapidly developing technology and high market demand, bringing auto-mated solutions for sizing has attracted great attention. This pa-per presents APOSTLE, an asynchronously parallel optimization method for sizing analog transistors using Deep Neural Network (DNN) learning. This work introduces several methods to minimize real-time of optimization when the sizing task consists of several different simulations with varying time costs. The key contributions of this paper are: (1) a batch optimization framework, (2) a novel deep neural network architecture for exploring design points when the existed solutions are not always fully evaluated, (3) a ranking approximation method based on cheap evaluations and (4) a theo-retical approach to balance between the cheap and the expensive simulations to maximize the optimization efficiency. Our method shows high real-time efficiency compared to other black-box op-timization methods both on small building blocks and on large industrial circuits while reaching similar or better performance.
Client-Specific Upgrade Compatibility Checking via Knowledge-Guided Discovery

Modern software systems are complex, and they heavily rely on external libraries developed by different teams and organizations. Such systems suffer from higher instability due to incompatibility issues caused by library upgrades. In this article, we address the problem by investigating the impact of a library upgrade on the behaviors of its clients. We developed CompCheck, an automated upgrade compatibility checking framework that generates incompatibility-revealing tests based on previous examples. CompCheck first establishes an offline knowledge base of incompatibility issues by mining from open source projects and their upgrades. It then discovers incompatibilities for a specific client project, by searching for similar library usages in the knowledge base and generating tests to reveal the problems. We evaluated CompCheck on 202 call sites of 37 open source projects and the results show that CompCheck successfully revealed incompatibility issues on 76 call sites, 72.7% and 94.9% more than two existing techniques, confirming CompCheck’s applicability and effectiveness.
Hardware-Software Co-Design for On-Chip Learning in AI Systems

Spike-based convolutional neural networks (CNNs) are empowered with on-chip learning in their convolution layers, enabling the layer to learn to detect features by combining those extracted in the previous layer. We propose ECHELON, a generalized design template for a tile-based neuromorphic hardware with on-chip learning capabilities. Each tile in ECHELON consists of a neural processing units (NPU) to implement convolution and dense layers of a CNN model, an on-chip learning unit (OLU) to facilitate spike-timing dependent plasticity (STDP) in the convolution layer, and a special function unit (SFU) to implement other CNN functions such as pooling, concatenation, and residual computation. These tile resources are interconnected using a shared bus, which is segmented and configured via the software to facilitate parallel communication inside the tile. Tiles are themselves interconnected using a classical Network-on-Chip (NoC) interconnect. We propose a system software to map CNN models to ECHELON, maximizing the performance. We integrate the hardware design and software optimization within a co-design loop to obtain the hardware and software architectures for a target CNN, satisfying both performance and resource constraints. In this preliminary work, we show the implementation of a tile on a FPGA and some early evaluations. Using 8 STDP-enabled CNN models, we show the potential of our co-design methodology to optimize hardware resources.
System-Level Exploration of In-Package Wireless Communication for Multi-Chiplet Platforms

Multi-Chiplet architectures are being increasingly adopted to support the design of very large systems in a single package, facilitating the integration of heterogeneous components and improving manufacturing yield. However, chiplet-based solutions have to cope with limited inter-chiplet routing resources, which complicate the design of the data interconnect and the power delivery network. Emerging in-package wireless technology is a promising strategy to address these challenges, as it allows to implement flexible chiplet interconnects while freeing package resources for power supply connections. To assess the capabilities of such an approach and its impact from a full-system perspective, herein we present an exploration of the performance of in-package wireless communication, based on dedicated extensions to the gem5-X simulator. We consider different Medium Access Control (MAC) protocols, as well as applications with different runtime profiles, showcasing that current in-package wireless solutions are competitive with wired chiplet interconnects. Our results show how in-package wireless solutions can outperform wired alternatives when running artificial intelligence workloads, achieving up to a 2.64x speed-up when running deep neural networks (DNNs) on a chiplet-based system with 16 cores distributed in four clusters.
A Course on Hex-Mesh Generation and Processing

In this article, we provide a detailed survey of techniques for hexahedral mesh generation. We cover the whole spectrum of alternative approaches to mesh generation, as well as post processing algorithms for connectivity editing and mesh optimization. For each technique, we highlight capabilities and limitations, also pointing out the associated unsolved challenges. Recent relaxed approaches, aiming to generate not pure-hex but hex-dominant meshes, are also discussed. The required background, pertaining to geometrical as well as combinatorial aspects, is introduced along the way.
Source extraction in audio via background learning

Source extraction in audio is an important problem in the study of blind source separation (BSS) with many practical applications. It is a challenging problem when the foreground sources to be extracted are weak compared to the background sources. Traditional techniques often do not work in this setting. In this paper we propose a novel technique for extracting foreground sources. This is achieved by an interval of silence for the foreground sources. Using this silence interval one can learn the background information, allowing the removal or suppression of background sources. Very effective optimization schemes are proposed for the case of two sources and two mixtures.
Improving the Robustness and Efficiency of PIM-based Architecture by SW/HW Co-design

Processing-in-memory (PIM) based architecture shows great potential to process several emerging artificial intelligence workloads, including vision and language models. Cross-layer optimizations could bridge the gap between computing density and the available resources by reducing the computation and memory cost of the model and improving the model's robustness against non-ideal hard-ware effects. We first introduce several hardware-aware training methods to improve the model robustness to the PIM device's non-ideal effects, including stuck-at-fault, process variation, and thermal noise. Then, we further demonstrate a software/hardware (SW /HW) co-design methodology to efficiently process the state-of-the-art attention-based model on PIM-based architecture by performing sparsity exploration for the attention-based model and circuit-architecture co-design to support the sparse processing.
CompaSeC: A Compiler-assisted Security Countermeasure to Address Instruction Skip Fault Attacks on RISC-V

Fault-injection attacks are a risk for any computing system executing security-relevant tasks, such as a secure boot process. While hardware-based countermeasures to these invasive attacks have been found to be a suitable option, they have to be implemented via hardware extensions and are thus not available in most Commonly used Off-The-Shelf (COTS) components. Software Implemented Hardware Fault Tolerance (SIHFT) is therefore the only valid option to enhance a COTS system's resilience against fault attacks. Established SIHFT techniques usually target the detection of random hardware errors for functional safety and not targeted attacks. Using the example of a secure boot system running on a RISC-V processor, in this work we first show that when the software is hardened by these existing techniques from the safety domain, the number of vulnerabilities in the boot process to single, double, triple, and quadruple instruction skips cannot be fully closed. We extend these techniques to the security domain and propose Compiler-assisted Security Countermeasure (CompaSeC). We demonstrate that CompaSeC can close all vulnerabilities for the studied secure boot system. To further reduce performance and memory overheads we additionally propose a method for CompaSeC to selectively harden individual vulnerable functions without compromising the security against the considered instruction skip faults.
A Multi-Task Graph Neural Network with Variational Graph Auto-Encoders for Session-Based Travel Packages Recommendation

Session-based travel packages recommendation aims to predict users’ next click based on their current and historical sessions recorded by Online Travel Agencies (OTAs). Recently, an increasing number of studies attempted to apply Graph Neural Networks (GNNs) to the session-based recommendation and obtained promising results. However, most of them do not take full advantage of the explicit latent structure from attributes of items, making learned representations of items less effective and difficult to interpret. Moreover, they only combine historical sessions (long-term preferences) with a current session (short-term preference) to learn a unified representation of users, ignoring the effects of historical sessions for the current session. To this end, this article proposes a novel session-based model named STR-VGAE, which fills subtasks of the travel packages recommendation and variational graph auto-encoders simultaneously. STR-VGAE mainly consists of three components: travel packages encoder, users behaviors encoder, and interaction modeling. Specifically, the travel packages encoder module is used to learn a unified travel package representation from co-occurrence attribute graphs by using multi-view variational graph auto-encoders and a multi-view attention network. The users behaviors encoder module is used to encode user’ historical and current sessions with a personalized GNN, which considers the effects of historical sessions on the current session, and coalesce these two kinds of session representations to learn the high-quality users’ representations by exploiting a gated fusion approach. The interaction modeling module is used to calculate recommendation scores over all candidate travel packages. Extensive experiments on a real-life tourism e-commerce dataset from China show that STR-VGAE yields significant performance advantages over several competitive methods, meanwhile provides an interpretation for the generated recommendation list.
Remittances, unemployment, growth and development: a panel cointegration approach

ABSTRACT This paper analyses the short-run and long-run dynamics of the relationship between remittances, unemployment, economic growth and human development in developing countries. Vector Autoregressive Model methodology (VECM) was used to examine the short-run dynamics and direction of causality in three separate panels of 23 low-income, 42 lower middle-income and 48 upper middle-income countries for the 1990 and 2019 period. For the estimation of long-run elasticities of remittance flows, Fully Modified (FMOLS) and Dynamic Ordinary Least Squares (DOLS) estimation techniques were used. According to the findings of this study, in low-income economies, the direction of causality is from GDP to remittances in the short run. The unemployment rate has a positive long-run effect on HDI in low-income countries, indicating the importance of underutilized labour force in low-income countries. Although remittances affect human development more in low-income economies compared to other developing countries, unemployment in the country of origin has no significant relationship between remittances and development in low-income countries.
Vibration Suppression in Structures Using Cable Actuators

We investigate the use of cable tension for active vibration control in frame structures. A general formulation for this class of systems is developed using finite elements, which includes the dynamics of the structure and the effects of cable-structure interactions. It is found that the cable tension has two distinct effects on the structure. The first is a parametric effect in which the cable tension changes the stiffness of the structure, and the second is a direct effect that provides an external force on the structure. Based on this model, a general control scheme is developed that uses cable actuation to take advantage of these effects, both separately and together. The control scheme for all cases is based on modal amplitudes, and it applies and releases tension in such a manner that vibration energy is removed from the modes of the structure over a prescribed frequency range that depends on the bandwidth(s) of the actuator(s). The stability of the controlled systems is proven using nonlinear control theory. In addition, a method is developed for determining the optimal placement of cables for parametric stiffness control, which is verified via simulations. Finally, an experimental realization of the direct force control is tested on a frame structure and compared with simulations, demonstrating its effectiveness.
The International Criminal Court at 25

Abstract On July 17, 2023, the International Criminal Court (ICC) will mark the 25th anniversary of the adoption of the Rome Statute, its founding treaty. The Statute constituted a remarkable transfer of authority from sovereign states to an international institution: The ICC is the first permanent court charged with prosecuting individuals, including senior political and military leaders, for atrocity crimes. Per the Statute, the ICC was designed with the goals of ending impunity for these crimes, contributing to their prevention, and delivering justice to victims. To what extent has the ICC achieved these and other goals in the Rome Statute? The ICC’s upcoming anniversary provides an opportune moment to examine this question and take stock of the Court’s performance. This special issue of the Journal of Human Rights addresses this question from an empirical perspective, focusing on two themes: (1) the ICC’s relations with states, which critically condition its operations and impact, (2) the Court’s effectiveness in achieving the goals outlined in the Rome Statute, specifically ending impunity and mitigating violence.
Myeloperoxidase promotes a tumorigenic microenvironment in non-small cell lung cancer

Myeloperoxidase (MPO) is a heme peroxidase that is mainly expressed and secreted by neutrophils. MPO’s role in inflammatory diseases has been highlighted in recent years, but its role in tumor development remains unclear. Therefore, we investigated the role of MPO in non-small cell lung cancer (NSCLC). In silico analysis revealed a survival benefit in patients with NSCLC and low MPO expression. Furthermore, a syngeneic tumor model using MPO knockout (KO) mice revealed that mice lacking MPO had lower tumor growth than controls. The reduction in tumor size was accompanied by an increase in lymphoid populations, including natural killer cells and CD8+ T cells, suggesting a shift to a more anti-tumorigenic immune environment in MPO-KO mouse tumors. The T cell induced interferon-gamma (IFN-γ) expression was increased in MPO-KO tumors, indicating increased tumoricidal activity. CD8 depletion abolished the previously observed reduction in tumor size in MPO-KO mice, indicating that CD8+ T cells play an important role. In vitro, T cells treated with MPO showed reduced proliferation and IFN-γ expression. Furthermore, MPO could be internalized into T cells. Heparin pretreatment of T cells blocked MPO binding and internalization into T cells and reversed MPO-induced proliferation reduction. Interestingly, MPO+ lymphocytes were found in tumor samples from patients with NSCLC. Our findings suggest that MPO plays an immunosuppressive role in NSCLC. One Sentence Summary High myeloperoxidase (MPO) expression in non-small cell lung cancer patients is a predictor for adverse outcome and mice lacking MPO showed enhanced anti-tumorigenic leukocyte infiltration, suggesting a pro-tumorigenic role of MPO.
ChromDL: A Next-Generation Regulatory DNA Classifier

Motivation Predicting the regulatory function of non-coding DNA using only the DNA sequence continues to be a major challenge in genomics. With the advent of improved optimization algorithms, faster GPU speeds, and more intricate machine learning libraries, hybrid convolutional and recurrent neural network architectures can be constructed and applied to extract crucial information from non-coding DNA. Results Using a comparative analysis of the performance of thousands of Deep Learning (DL) architectures, we developed ChromDL, a neural network architecture combining bidirectional gated recurrent units (BiGRU), convolutional neural networks (CNNs), and bidirectional long short-term memory units (BiLSTM), which significantly improves upon a range of prediction metrics compared to its predecessors in transcription factor binding site (TFBS), histone modification (HM), and DNase-I hypersensitive site (DHS) detection. Combined with a secondary model, it can be utilized for accurate classification of gene regulatory elements. The model can also detect weak transcription factor (TF) binding with higher accuracy as compared to previously developed methods and has the potential to accurately delineate TF binding motif specificities. Availability The ChromDL source code can be found at https://github.com/chrishil1/ChromDL.
Can snare removal safeguard protected areas caught in the Southeast Asian snaring crisis? Promises and perspectives

Unsustainable snaring is causing biodiversity declines across tropical protected areas, resulting in species extinctions and jeopardizing the health of forest ecosystems. Here, we used 11 years of ranger collected data to assess the impact of intensive snare removal in two protected areas in Viet Nam. Snare removal resulted in significant declines in snare occupancy (36.9, 95% BCI [4.6, 59.0] reduction in percent area occupied), but snaring levels nonetheless remained high (31.4, [23.6, 40.8] percent area occupied), and came with a substantial financial cost. Our results indicate that snare removal remains an important component of efforts to protect tropical protected areas, but by itself, is likely insufficient to address this threat. To achieve the target of zero snaring in protected areas, a multifaceted approach will be necessary that combines short-term reactive snare removal with long-term proactive programs that address the underlying drivers behind snaring.
Germline TFAM levels regulate mitochondrial DNA copy number and mutant heteroplasmy in C. elegans

The mitochondrial genome (mtDNA) is packaged into discrete protein-DNA complexes called nucleoids. mtDNA packaging factor TFAM (mitochondrial transcription factor-A) promotes nucleoid compaction and is required for mtDNA replication. Here, we investigate how changing TFAM levels affects mtDNA in the Caenorhabditis elegans germ line. We show that increasing germline TFAM activity boosts mtDNA number and significantly increases the relative proportion of a selfish mtDNA mutant, uaDf5. We conclude that TFAM levels must be tightly controlled to ensure appropriate mtDNA composition in the germ line.
Glutathionylation of Pyruvate Dehydrogenase Complex E2 and Inflammatory Cytokine Production During Acute Inflammation Are Magnified By Mitochondrial Oxidative Stress

Lipopolysaccharide (LPS) is a known inducer of inflammatory signaling which triggers generation of reactive oxygen species (ROS) and cell death in responsive cells like THP-1 promonocytes and freshly isolated human monocytes. A key LPS-responsive metabolic pivot point is the 9 megadalton mitochondrial pyruvate dehydrogenase complex (PDC), which provides pyruvate dehydrogenase (E1), lipoamide-linked transacetylase (E2) and lipoamide dehydrogenase (E3) activities to produce acetyl-CoA from pyruvate. While phosphorylation-dependent decreases in PDC activity following LPS treatment or sepsis have been deeply investigated, redox-linked processes have received less attention. Data presented here demonstrate that LPS-induced reversible oxidation within PDC occurs in PDCE2 in both THP-1 cells and primary human monocytes. Knockout of PDCE2 by CRISPR and expression of FLAG-tagged PDCE2 in THP-1 cells demonstrated that LPS-induced glutathionylation is associated with wild type PDCE2 but not mutant protein lacking the lipoamide-linking lysine residues. Moreover, the mitochondrially-targeted electrophile MitoCDNB, which impairs both glutathione-and thioredoxin-based reductase systems, elevates ROS similar to LPS but does not cause PDCE2 glutathionylation. However, LPS and MitoCDNB together are highly synergistic for PDCE2 glutathionylation, ROS production, and cell death. Surprisingly, the two treatments together had differential effects on cytokine production; pro-inflammatory IL-1β production was enhanced by the co-treatment, while IL-10, an important anti-inflammatory cytokine, dropped precipitously compared to LPS treatment alone. This new information may expand opportunities to understand and modulate PDC redox status and activity and improve the outcomes of pathological inflammation. Highlights PDCE2 is glutathionylated (-SSG) during acute inflammation in monocytes Lipopolysaccharide-induced PDCE2-SSG occurs in THP1 cells and human monocytes. Lipoamide-deficient PDCE2 lowers LPS-induced PDCE2-SSG and ROS production. MitoCDNB leads to ROS production without PDCE2-SSG, but is synergistic with LPS MitoCDNB causes the LPS-stimulated cytokine profile to become more proinflammatory Graphical Abstract
Immunoreactivity against SLC3A2 in high grade gliomas displays positive correlation with glioblastoma patient survival: Potential target for glioma diagnosis and therapy

Immunity against cancer cells is at the forefront of fight against highly malignant tumors like glioblastomas (GBM). Autoantibodies and their autoantigen cooperators are one of the promising biomarkers linked to the immune responses and have been reported as having an initiative or prognostic role for certain types of paraneoplastic disorders. Nevertheless, immunoreactivity against antigens expressed in GBM are poorly studied. To date, autoantibodies were identified by pursuing targeted approaches. By contrast, in this study, we collected human GBM tissue and sera samples and by applying proteomics analysis, we determined autoantigen candidates for GBM. Subsequent immunohistochemistry and immunoprecipitation experiments revealed that a solute carrier; SLC3A2 is widely overexpressed in GBMs and therefore, immunoreactivity against SLC3A2 is present in high grade GBM, and SLC3A2 expression is altered in GBMs. No antibody interaction was detected in SLC3A2 expressors of low grade gliomas. Furthermore, autoantibody presence was correlated with prolonged survival of GBM patients. Taken together, for the first time, we reported that the SLC3A2 immunoreaction exists in high grade gliomas with a distinct GBM profile. In conclusion, our findings may open up new avenues for our understanding of glioma prognosis in the context of autoimmunity. This may eventually lead to diagnostic and therapeutic inventions that can be utilized for prevention of the disease progression.