Miracle-dz/UniFAR-FTUs huggingface dataset:
A SPECTRAL STUDY OF THE RAPID TRANSITIONS OF TYPE-B QUASI-PERIODIC OSCILLATIONS IN THE BLACK HOLE TRANSIENT XTE J1859+226

The fast transitions of type-B and type-A quasi-periodic oscillations (QPOs) are rarely found, and they are observed at the peak of the outburst in black hole transient (BHT) sources. The associated spectral variations during such events are crucial to understand the origin and location of such QPOs in the accretion disk. During the 1999 outburst of XTE J1859+226, on four occasions a rapid transition of type-B/A QPOs was noted. We performed broadband spectral analysis on these four observations to unveil the responsible spectral parameter causing the rapid transitions. After invoking simple spectral models, it was observed that disk parameters were consistently varying along with disk and power-law fluxes, and almost no change was noted in the power-law index parameter. Though using a complex physical model showed consistent results, the spectral parameter variations across the transitions were not significant. It was observed that the type-B QPO was always associated with an inner disk front which is closer to the BH. In one observation, a type-A QPO appeared as the source count rate suddenly dropped, and the power-law index as well as disk normalization parameter considerably changed during this transition. The spectral changes in this particular observation were similar to the changes observed in XTE J1817-330, indicating a common underlying mechanism. We have also examined a similar observation of BHT source GX 339-4, where a sudden transition of a type-A/B QPO was noted. Similar spectral study again revealed that the disk parameters were changing. We discuss the results in the framework of a truncated disk model and conclude that the movement of the coupled inner disk-corona region is responsible for such rapid transitions of type-B QPOs.
On Automating Finger-Cap Array Synthesis with Optimal Parasitic Matching for Custom SAR ADC

Due to its excellent power efficiency, the successive-approximation-register (SAR) analog-to-digital converter (ADC) is an attractive design choice for low-power ADC implements. In analog layout design, the parasitics induced by interconnecting wires and elements affect the accuracy and performance of the device. Due to the requirement of low-power and high-speed, series of very small lateral metal-metal capacitor units are usually adopted as the architecture of capacitor array. Besides power consumption and area reduction, the parasitic capacitance would significantly affect the matching properties and settling time of capacitors. This work presents a framework to synthesize good-quality binary-weighted capacitors for custom SAR ADC. Also, this work proposes a parasitic-aware ILP-based weight-dynamic network routing algorithm to generate a layout considering parasitic capacitance and capacitance ratio mis-match simultaneously. The experimental result shows that the effective number of bits (ENOB) of the layout generated by our approach is comparable to or better than that of manual design and other auto-mated works, closing the gap between pre-sim and post-sim results.
RVComp: Analog Variation Compensation for RRAM-based In-Memory Computing

Resistive Random Access Memory (RRAM) has shown great potential in accelerating memory-intensive computation in neural network applications. However, RRAM-based computing suffers from significant accuracy degradation due to the inevitable device variations. In this paper, we propose RVComp, a fine-grained analog Compensation approach to mitigate the accuracy loss of in-memory computing incurred by the Variations of the RRAM devices. Specifically, weights in the RRAM crossbar are accompanied by dedicated compensation RRAM cells to offset their programming errors with a scaling factor. A programming target shifting mechanism is further designed with the objectives of reducing the hardware overhead and minimizing the compensation errors under large device variations. Based on these two key concepts, we propose double and dynamic compensation schemes and the corresponding support architecture. Since the RRAM cells only account for a small fraction of the overall area of the computing macro due to the dominance of the peripheral circuitry, the overall area overhead of RVComp is low and manageable. Simulation results show RVComp achieves a negligible 1.80% inference accuracy drop for ResNet18 on the CIFAR-10 dataset under 30% device variation with only 7.12% area and 5.02% power overhead and no extra latency.
Accelerating Convolutional Neural Networks in Frequency Domain via Kernel-sharing Approach

Convolutional neural networks (CNN s) are typically computationally heavy. Fast algorithms such as fast Fourier transforms (FFTs), are promising in significantly reducing computation complexity by replacing convolutions with frequency-domain element-wise multiplication. However, the increased high memory access overhead of complex weights counteracts the computing benefit, because frequency-domain convolutions not only pad weights to the same size as input maps, but also have no sharable complex kernel weights. In this work, we pro-pose an FFT - based kernel-sharing technique called FS-Conv to reduce memory access. Based on FS-Conv, we derive the sharable complex weights in frequency-domain convolution-s, which has never been solved. FS-Conv includes a hybrid padding approach, which utilizes the inherent periodic char-acteristic of FFT transformation to provide sharable complex weights for different blocks of complex input maps. We in addition build a frequency-domain inference accelerator (called Yixin) that can utilize the sharable complex weights for CNN accelerations. Evaluation results demonstrate the significant performance and energy efficiency benefits compared with the state-of-the-art baseline.
Automatic Test Pattern Generation and Compaction for Deep Neural Networks

Deep Neural Networks (DNNs) have gained considerable attention lately due to their excellent performance on a wide range of recognition and classification tasks. Accordingly, fault detection in DNNs and their implementations plays a crucial role in the quality of DNN implementations to ensure that their post-mapping and in-field accuracy matches with model accuracy. This paper proposes a functional-level automatic test pattern generation approach for DNNs. This is done by generating inputs which causes misclassification of the output class label in the presence of single or multiple faults. Furthermore, to obtain a smaller set of test patterns with full coverage, a heuristic algorithm as well as a test pattern clustering method using K-means were implemented. The experimental results showed that the proposed test patterns achieved the highest label misclassification and a high output deviation compared to state-of-the-art approaches.
Code-line-level Bugginess Identification: How Far have We Come, and How Far have We Yet to Go?

Background. Code-line-level bugginess identification (CLBI) is a vital technique that can facilitate developers to identify buggy lines without expending a large amount of human effort. Most of the existing studies tried to mine the characteristics of source codes to train supervised prediction models, which have been reported to be able to discriminate buggy code lines amongst others in a target program. Problem. However, several simple and clear code characteristics, such as complexity of code lines, have been disregarded in the current literature. Such characteristics can be acquired and applied easily in an unsupervised way to conduct more accurate CLBI, which also can decrease the application cost of existing CLBI approaches by a large margin. Objective. We aim at investigating the status quo in the field of CLBI from the perspective of (1) how far we have really come in the literature, and (2) how far we have yet to go in the industry, by analyzing the performance of state-of-the-art (SOTA) CLBI approaches and tools, respectively. Method. We propose a simple heuristic baseline solution GLANCE (aiminG at controL- ANd ComplEx-statements) with three implementations (i.e., GLANCE-MD, GLANCE-EA, and GLANCE-LR). GLANCE is a two-stage CLBI framework: first, use a simple model to predict the potentially defective files; second, leverage simple code characteristics to identify buggy code lines in the predicted defective files. We use GLANCE as the baseline to investigate the effectiveness of the SOTA CLBI approaches, including natural language processing (NLP) based, model interpretation techniques (MIT) based, and popular static analysis tools (SAT). Result. Based on 19 open-source projects with 142 different releases, the experimental results show that GLANCE framework has a prediction performance comparable or even superior to the existing SOTA CLBI approaches and tools in terms of 8 different performance indicators. Conclusion. The results caution us that, if the identification performance is the goal, the real progress in CLBI is not being achieved as it might have been envisaged in the literature and there is still a long way to go to really promote the effectiveness of static analysis tools in industry. In addition, we suggest using GLANCE as a baseline in future studies to demonstrate the usefulness of any newly proposed CLBI approach.
ML to the Rescue: Reliability Estimation from Self-Heating and Aging in Transistors all the Way up Processors

With increasingly confined 3D structures and newly-adopted mate-rials of higher thermal resistance, transistor self-heating has risen to a critical reliability threat in state-of-the-art and emerging process nodes. One of the challenges of transistor self-heating is acceler-ated transistor aging, which leads to earlier failure of the chip if not considered appropriately. Nevertheless, adequate consideration of accelerated aging effects, induced by self-heating, throughout a large circuit design is profoundly challenging due to the large gap between where self-heating does originate (i.e., at the transis-tor level) and where its ultimate effect occurs (i.e., at the circuit and system levels). In this work, we demonstrate an end-to-end workflow starting from self-heating and aging effects in individual transistors all the way up to large circuits and processor designs. We demonstrate that with our accurately estimated degradations, the required timing guardband to ensure reliable operation of circuits is considerably reduced by up to 96 % compared to otherwise worst-case estimations that are conventionally employed.
Reusing GEMM Hardware for Efficient Execution of Depthwise Separable Convolution on ASIC-based DNN Accelerators

Deep learning (DL) accelerators are optimized for standard convolution. However, lightweight convolutional neural networks (CNNs) use depthwise convolution (DwC) in key layers, and the structural difference between DwC and standard convolution leads to signif-icant performance bottleneck in executing lightweight CNNs on such platforms. This work reuses the fast general matrix-vector multiplication (GEMM) core of DL accelerators by mapping DwC to channel-wise parallel matrix-vector multiplications. An analytical framework is developed to guide pre-RTL hardware choices, and new hardware modules and software support are developed for end-to-end evaluation of the solution. This GEMM-based DwC exe-cution strategy offers substantial performance gains for lightweight CNNs: 7× speedup and 1.8× lower off-chip communication for MobileNet-v1 over a conventional DL accelerator, and 74× speedup over a CPU, and even 1.4× speedup over a power-hungry GPU.
Computational assessment of phytochemicals of medicinal plants from Mexico as potential inhibitors of Salmonella enterica efflux pump AcrB protein

Abstract The AcrAB-TolC efflux pump (EP) confers multidrug resistance to Salmonella enterica, a major etiological agent of foodborne infections. Phytochemicals that inhibit the functions of AcrAB-TolC EP present ideal candidates for reversal of antibiotic resistance. Progressive technological advancements, have facilitated the development of computational methods that offer a rapid low-cost approach to screen and identify phytochemicals with inhibitory potential against EP. In this study, 71 phytochemicals derived from plants used for medicinal purposes in Mexico were screened for their potential as inhibitors of Salmonella AcrB protein using in silico approaches including molecular docking and molecular dynamics (MD) simulation. Consequently, naringenin, 5-methoxypsoralen, and licarin A were identified as candidate inhibitors of AcrB protein. The three phytochemicals bound distal/deep pocket (DP) and hydrophobic trap (HPT) residues of AcrB protein critical for interactions with inhibitors, with estimated binding free energies of −95.5 kJ/mol, −97.4 kJ/mol, and −143.8 kJ/mol for naringenin, 5-methoxypsoralen, and licarin A, respectively. Data from the 50 ns MD simulation study revealed stability of the protein-ligand complex and alterations in the AcrB protein DP conformation upon binding of phytochemicals to the DP and HPT regions. Based on the estimated binding free energy and interactions with three out of five residues lining the hydrophobic trap, licarin A demonstrated the highest inhibitory potential, supporting its further application as a candidate for overcoming drug resistance in pathogens. Communicated by Ramaswamy H. Sarma
The autophagy receptor NBR1 directs the clearance of photodamaged chloroplasts

The ubiquitin-binding NBR1 autophagy receptor plays a prominent role in recognizing ubiquitylated protein aggregates for vacuolar degradation by macroautophagy. Here, we show that upon exposing Arabidopsis plants to intense light, NBR1 associates with photodamaged chloroplasts independently of ATG7, a core component of the canonical autophagy machinery. NBR1 coats both the surface and interior of chloroplasts, which is then followed by direct engulfment of the organelles into the central vacuole via a microautophagy-type process. The relocalization of NBR1 into chloroplasts does not require the chloroplast translocon complexes embedded in the envelope but is instead greatly enhanced by removing the self-oligomerization mPB1 domain of NBR1. The delivery of NBR1-decorated chloroplasts into vacuoles depends on the ubiquitin-binding UBA2 domain of NBR1 but is independent of the ubiquitin E3 ligases SP1 and PUB4, known to direct the ubiquitylation of chloroplast surface proteins. Compared to wild-type plants, nbr1 mutants have altered levels of a subset of chloroplast proteins and display abnormal chloroplast density and sizes upon high light exposure. We postulate that, as photodamaged chloroplasts lose envelope integrity, cytosolic ligases reach the chloroplast interior to ubiquitylate thylakoid and stroma proteins which are then recognized by NBR1 for autophagic clearance. This study uncovers a new function of NBR1 in the degradation of damaged chloroplasts by microautophagy.
Single fluorogen imaging reveals distinct environmental and structural features of biomolecular condensates

Recent computations suggest that biomolecular condensates that form via macromolecular phase separation are network fluids featuring spatially inhomogeneous organization of the underlying molecules. Computations also point to unique conformations of molecules at condensate interfaces. Here, we test these predictions using high-resolution structural characterizations of condensates formed by intrinsically disordered prion-like low complexity domains (PLCDs). We leveraged the localization and orientational preferences of freely diffusing fluorogens and the solvatochromic effect whereby specific fluorogens are turned on in response to the physico-chemical properties of condensate microenvironments to facilitate single-molecule tracking and super-resolution imaging. We deployed three different fluorogens to probe internal microenvironments and molecular organization of PLCD condensates. The spatiotemporal resolution and environmental sensitivity afforded by single-fluorogen imaging shows that the internal environments of condensates are more hydrophobic than coexisting dilute phases. Molecules within condensates are organized in a spatially inhomogeneous manner featuring slow-moving nanoscale molecular clusters or hubs that coexist with fast-moving molecules. Finally, molecules at interfaces of condensates are found to have distinct orientational preferences when compared to the interiors. Our findings, which affirm computational predictions, help provide a structural basis for condensate viscoelasticity and dispel the notion of protein condensates being isotropic liquids defined by uniform internal densities.
Structure of the PfRCR complex which bridges the malaria parasite and erythrocyte during invasion

The symptoms of malaria occur during the blood stage of infection, when parasites invade and replicate within human erythrocytes. The five-component PfPCRCR complex, containing PfRH5, PfCyRPA, PfRIPR, PfCSS and PfPTRAMP, is essential for erythrocyte invasion by the deadliest human malaria parasite, Plasmodium falciparum. Invasion can be prevented by antibodies or nanobodies against each of these five conserved proteins, making them the leading blood stage malaria vaccine candidates. However, little is known about the molecular mechanism by which PfPCRCR functions during invasion. Here we present the structure of the PfRCR complex, containing PfRH5, PfCyRPA and PfRIPR, determined by cryogenic-electron microscopy. This reveals that PfRIPR consists of an ordered multi-domain core flexibly linked to an elongated tail. We test the hypothesis that PfRH5 opens to insert into the membrane, but instead show that a rigid, disulphide-locked PfRH5 can mediate efficient erythrocyte invasion. Finally, we show that the elongated tail of PfRIPR, which is the target of growth- neutralising antibodies, binds to the PfCSS-PfPTRAMP complex on the parasite membrane. Therefore, a modular PfRIPR is linked to the merozoite membrane through an elongated tail, while its structured core presents PfCyRPA and PfRH5 to interact with erythrocyte receptors. This provides novel insight into the mechanism of erythrocyte invasion and opens the way to new approaches in rational vaccine design.
Population Genomics of the Mostly Thelytokous Diplolepis rosae (Linnaeus, 1758) (Hymenoptera: Cynipidae) Reveals Population-specific Selection for Sex

In Hymenoptera, arrhenotokous parthenogenesis (arrhenotoky) is a common reproductive mode. Thelytokous parthenogenesis (thelytoky), when virgin females produce only females, is less common and is found in several taxa. In our study, we assessed the efficacy of recombination and the effect of thelytoky on the genome structure of Diplolepis rosae, a gall wasp producing bedeguars in dog roses. We assembled a high-quality reference genome using Oxford Nanopore long-read technology and sequenced 17 samples collected in France with high-coverage Illumina reads. We found two D. rosae peripatric lineages that differed in the level of recombination and homozygosity. The first D. rosae lineage showed a recombination rate that was 13.2 times higher and a per-individual heterozygosity that was 1.6 times higher. We inferred that genes under negative selection were enriched in functions related to male traits (‘sperm competition’, ‘insemination’, and ‘copulation’ gene ontology terms) in the more recombining lineage, while in the less recombining form, the same lineage genes showed traces pointing towards balancing or relaxed selection. Thus, although D. rosae reproduces mainly by thelytoky, selection may act to maintain sexual reproduction. Significance Many organisms can alternate between sexual and asexual reproduction in different ways. Sexual reproduction is essential to creating genetic diversity for adaptation to changing environments, whereas asexual reproduction is important in the short term and in stable environments. Using genomic data, we demonstrated the existence of two lineages in the rose bedeguar wasp Diplolepis rosae previously shown to reproduce mainly by thelytokous parthenogenesis, giving almost only females. One of the lineages showed higher recombination, higher heterozygosity, and genes involved in male traits under negative selection. This could be linked to the expected advantages of maintaining sexual reproduction in natural populations.
Electrophysiological signatures of veridical head direction in humans

Information about heading direction is critical for navigation as it provides the means to orient ourselves in space. However, given that veridical head direction signals require physical rotation of the head and most human neuroimaging experiments depend upon fixing the head in position, little is known about how the human brain is tuned to such heading signals. To address this, we asked fifty-two healthy participants undergoing simultaneous EEG and motion tracking recordings (split into two experiments) and ten patients undergoing simultaneous intracranial EEG and motion tracking recordings to complete a series of orientation tasks in which they made physical head rotations to target positions. We then used a series of forward encoding models and linear mixed-effects models to isolate electrophysiological activity that was specifically tuned to heading direction. We identified a robust posterior central signature that predicts changes in veridical head orientation after regressing out confounds including sensory input and muscular activity. Both source localisation and intracranial analysis implicated the medial temporal lobe as the origin of this effect. Subsequent analyses disentangled head direction signatures from signals relating to head rotation and those reflecting location-specific effects. Lastly, when directly comparing head direction and eye gaze-related tuning, we found that the brain maintains both codes while actively navigating, with stronger tuning to head direction in the medial temporal lobe. Together, these results reveal a taxonomy of population-level head direction signals within the human brain that is reminiscent of those reported in the single units of rodents.
The nanoscale organization of reticulon 4 shapes local endoplasmic reticulum structure in situ

Fuentes and Marin et al. show that Rtn4 forms linear-shaped oligomers containing an average of five Rtn4 proteins that localize to the sides of elliptical tubules with orientations parallel to the tubule axis. These oligomers increase local curvature in the ER membrane by increasing local Rtn4 density.
Axonal transport of autophagosomes is regulated by dynein activators JIP3/JIP4 and ARF/RAB GTPases

Neuronal autophagosomes, “self-eating” degradative organelles, form at presynaptic sites in the distal axon and are transported to the soma to recycle their cargo. During transit, autophagic vacuoles (AVs) mature through fusion with lysosomes to acquire the enzymes necessary to breakdown their cargo. AV transport is driven primarily by the microtubule motor cytoplasmic dynein in concert with dynactin and a series of activating adaptors that change depending on organelle maturation state. The transport of mature AVs is regulated by the scaffolding proteins JIP3 and JIP4, both of which activate dynein motility in vitro. AV transport is also regulated by ARF6 in a GTP-dependent fashion. While GTP-bound ARF6 promotes the formation of the JIP3/4-dynein-dynactin complex, RAB10 competes with the activity of this complex by increasing kinesin recruitment to axonal AVs and lysosomes. These interactions highlight the complex coordination of motors regulating organelle transport in neurons. Summary Mature autophagosomes in the axon are transported by the microtubule motor dynein, activated by JNK-interacting proteins 3 and 4 (JIP3/4). This motility is regulated by the small GTPases ARF6 and RAB10. The tight regulation of autolysosomal transport is essential for intracellular recycling to maintain neuronal homeostasis.