Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical Detectability for Persistent Lock-Free Data Structures

Persistent memory (PM) is an emerging class of storage technology that combines the benefits of DRAM and SSD. This characteristic inspires research on persistent objects in PM with fine-grained concurrency control. Among such objects, persistent lock-free data structures (DSs) are particularly interesting thanks to their efficiency and scalability. One of the most widely used correctness criteria for persistent lock-free DSs is durable linearizability (Izraelevitz et. al., DISC 2016). However, durable linearizability is insufficient to use persistent DSs for fault-tolerant systems requiring exactly-once semantics for storage systems, because we may not be able to detect whether an operation is performed when a crash occurs. We present a practical programming framework for persistent lock-free DSs with detectability. In contrast to the prior work on such DSs, our framework supports (1) primitive detectable operations such as space-efficient compare-and-swap, insertion, and deletion; (2) systematic transformation of lock-free DSs in DRAM into those in PM requiring modest efforts; (3) comparable performance with non-detectable DSs by DRAM scratchpad optimization; and (4) recovery from both full system and thread crashes. The key idea is memento objects serving as a lightweight, precise, and per-thread checkpoints in PM. As a case study, we implement lock-free and combining queues and hash tables with detectability that outperform (and perform comparably) the state-of-the-art DSs with (and without, respectively) detectability.
Pairwise Valid Instruments

Finding valid instruments is difficult. We propose Validity Set Instrumental Variable (VSIV) estimation, a method for estimating local average treatment effects (LATEs) in heterogeneous causal effect models when the instruments are partially invalid. We consider settings with pairwise valid instruments, that is, instruments that are valid for a subset of instrument value pairs. VSIV estimation exploits testable implications of instrument validity to remove invalid pairs and provides estimates of the LATEs for all remaining pairs, which can be aggregated into a single parameter of interest using researcher-specified weights. We show that the proposed VSIV estimators are asymptotically normal under weak conditions and remove or reduce the asymptotic bias relative to standard LATE estimators (that is, LATE estimators that do not use testable implications to remove invalid variation). We evaluate the finite sample properties of VSIV estimation in application-based simulations and apply our method to estimate the returns to college education using parental education as an instrument.
Panoptic SwiftNet: Pyramidal Fusion for Real-time Panoptic Segmentation

Dense panoptic prediction is a key ingredient in many existing applications such as autonomous driving, automated warehouses, or remote sensing. Many of these applications require fast inference over large input resolutions on affordable or even embedded hardware. We proposed to achieve this goal by trading off backbone capacity for multi-scale feature extraction. In comparison with contemporaneous approaches to panoptic segmentation, the main novelties of our method are efficient scale-equivariant feature extraction, cross-scale upsampling through pyramidal fusion and boundary-aware learning of pixel-to-instance assignment. The proposed method is very well suited for remote sensing imagery due to the huge number of pixels in typical city-wide and region-wide datasets. We present panoptic experiments on Cityscapes, Vistas, COCO, and the BSB-Aerial dataset. Our models outperformed the state-of-the-art on the BSB-Aerial dataset while being able to process more than a hundred 1MPx images per second on an RTX3090 GPU with FP16 precision and TensorRT optimization.
Inverse Problems: A Deterministic Approach using Physics-Based Reduced Models

These lecture notes summarize various summer schools that I have given on the topic of solving inverse problems (state and parameter estimation) by combining optimally measurement observations and parametrized PDE models. After defining a notion of optimal performance in terms of the smallest reconstruction error that any reconstruction algorithm can achieve, the notes present practical numerical algorithms based on nonlinear reduced models for which one can prove that they can deliver a performance close to optimal. We also discuss algorithms for sensor placement with the approach. The proposed concepts may be viewed as exploring alternatives to Bayesian inversion in favor of more deterministic notions of accuracy quantification.
Experimental Investigation of Heat Transfer and Friction Characteristics of Arc-Shaped Roughness Elements Having Central Gaps on the Absorber Plate of Solar Air Heater

Thermal performance of solar air heater does not take into account the energy loss due to friction for propelling air through the duct. In this work, an experimental investigation has been carried out to study the effect of heat transfer and friction characteristics of turbulent flow of air passing through rectangular duct which is roughened by circular arcs having gaps of 2 mm in between arranged in angular fashion, and the roughened wall is uniformly heated. The thermal and friction characteristics are governed by duct aspect ratio (W/H), hydraulic diameter (D), and relative roughness pitch (P/e), angle of attack of angular arc (α), and Reynolds number (Re). Experiments encompassed the Reynolds number ranges from 3600 to 15,100, P/e ranges from 6 to 20, and angle of attack of angular arc of flow over the protrusions ranges from 15 deg to 75 deg. The results have also been compared to W-shaped roughness inclined at 45 deg. The maximum enhancement in heat transfer and friction factor is 3.15 and 3.93 times as compared with smooth duct. Arc with gaps have also been observed to be better than their W-shaped counterparts. These experimental results have been used to study their influence on Nusselt number and friction factor, and empirical relations have been derived using regression analysis.
MTP: Multi-Task Pruning for Efficient Semantic Segmentation Networks

This paper focuses on channel pruning for semantic segmen-tation networks. Previous methods to compress and acceler-ate deep neural networks in the classification task cannot be straightforwardly applied to the semantic segmentation net-work that involves an implicit multi-task learning problem via pre-training. To identify the redundancy in segmentation networks, we present a multi-task channel pruning approach. The importance of each convolution filter w.r. t. the channel of an arbitrary layer will be simultaneously determined by the classification and segmentation tasks. In addition, we de-velop an alternative scheme for optimizing importance scores of filters in the entire network. Experimental results on sev-eral benchmarks illustrate the superiority of the proposed al-gorithm over the state-of-the-art pruning methods. Notably, we can obtain an about 2 x FLOPs reduction on DeepLabv3 with only an about 1 % mIoU drop on the PASCAL VOC 2012 dataset and an about 1.3% mIoU drop on Cityscapes dataset, respectively.
Wind Tunnel Investigation of Grid Fin and Conventional Planar Control Surfaces

The present wind tunnel investigation is part of a project studying the effectiveness of grid fin controls on missiles and projectiles. As part of this program, wind tunnel tests were conducted over a range of Mach numbers from 0.5 to 3.0, and aerodynamic coefficients were generated for various body configurations. A second model, with conventional planar surface controls, was also studied under the same conditions. Increase axial force, reduced normal force, and a marked reduction in static stability for the grid fins configuration over the conventional one were the results of this comparison. Another interesting result from the grid fin model, which was shown during the build-up component study, is that the two elevators generated almost 100% of the normal force of the complete body.
Minimum viable device drivers for ARM trustzone

While TrustZone can isolate IO hardware, it lacks drivers for modern IO devices. Rather than porting drivers, we propose a novel approach to deriving minimum viable drivers: developers exercise a full driver and record the driver/device interactions; the processed recordings, dubbed driverlets, are replayed in the TEE at run time to access IO devices. Driverlets address two key challenges: correctness and expressiveness, for which they build on a key construct called interaction template. The interaction template ensures faithful reproduction of recorded IO jobs (albeit on new IO data); it accepts dynamic input values; it tolerates nondeterministic device behaviors. We demonstrate driverlets on a series of sophisticated devices, making them accessible to Trust-Zone for the first time to our knowledge. Our experiments show that driverlets are secure, easy to build, and incur acceptable overhead (1.4×-2.7× compared to native drivers). Driverlets fill a critical gap in the TrustZone TEE, realizing its long-promised vision of secure IO.
On boundedness of characteristic class via quasi-morphism

In this paper, we characterize the second bounded characteristic classes of foliated bundles in terms of the non-descendible quasi-morphisms on the universal covering of the structure group. As its application, we study the boundedness of obstruction classes for (contact) Hamiltonian fibrations and show the non-existence of foliated structures on some Hamiltonian fibrations. Moreover, for any closed symplectic manifold, we show the non-triviality of the second bounded cohomology group of the Hamiltonian diffeomorphism group.
Dawn of the Transformer Era in Speech Emotion Recognition: Closing the Valence Gap

Recent advances in transformer-based architectures have shown promise in several machine learning tasks. In the audio domain, such architectures have been successfully utilised in the field of speech emotion recognition (SER). However, existing works have not evaluated the influence of model size and pre-training data on downstream performance, and have shown limited attention to generalisation, robustness, fairness, and efficiency. The present contribution conducts a thorough analysis of these aspects on several pre-trained variants of wav2vec 2.0 and HuBERT that we fine-tuned on the dimensions arousal, dominance, and valence of MSP-Podcast, while additionally using IEMOCAP and MOSI to test cross-corpus generalisation. To the best of our knowledge, we obtain the top performance for valence prediction without use of explicit linguistic information, with a concordance correlation coefficient (CCC) of. 638 on MSP-Podcast. Our investigations reveal that transformer-based architectures are more robust compared to a CNN-based baseline and fair with respect to gender groups, but not towards individual speakers. Finally, we show that their success on valence is based on implicit linguistic information, which explains why they perform on-par with recent multimodal approaches that explicitly utilise textual information. To make our findings reproducible, we release the best performing model to the community.
SATr: Slice Attention with Transformer for Universal Lesion Detection

Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by multi-slice-input detection approaches which model 3D context from multiple adjacent CT slices, but such methods still experience difficulty in obtaining a global representation among different slices and within each individual slice since they only use convolution-based fusion operations. In this paper, we propose a novel Slice Attention Transformer (SATr) block which can be easily plugged into convolution-based ULD backbones to form hybrid network structures. Such newly formed hybrid backbones can better model long-distance feature dependency via the cascaded self-attention modules in the Transformer block while still holding a strong power of modeling local features with the convolutional operations in the original backbone. Experiments with five state-of-the-art methods show that the proposed SATr block can provide an almost free boost to lesion detection accuracy without extra hyperparameters or special network designs.
Phases of MANES: Multi-Asset Non-Equilibrium Skew Model of a Strongly Non-Linear Market with Phase Transitions

: This paper presents an analytically tractable and practically-oriented model of non-linear dynamics of a multi-asset market in the limit of a large number of assets. The asset price dynamics are driven by money ﬂows into the market from external investors, and their price impact. This leads to a model of a market as an ensemble of interacting non-linear oscillators with the Langevin dynamics. In a homogeneous portfolio approximation, the mean ﬁeld treatment of the resulting Langevin dynamics produces the McKean-Vlasov equation as a dynamic equation for market returns. Due to the strong non-linearity of the McKean-Vlasov equation, the resulting dynamics give rise to ergodicity breaking and ﬁrst- or second-order phase transitions under variations of model parameters. Using a tractable potential of the Non-Equilibrium Skew (NES) model previously suggested by the author for a single-stock case, the new Multi-Asset NES (MANES) model enables an analytically tractable framework for a multi-asset market. The equilibrium expected market log-return is obtained as a self-consistent mean ﬁeld of the McKean-Vlasov equation, and derived in closed form in terms of parameters that are inferred from market prices of S&P 500 index options. The model is able to accurately ﬁt the market data for either a benign or distressed market environments, while using only a single volatility parameter.
Snowmass2021 Cosmic Frontier Dark Matter Direct Detection to the Neutrino Fog

We present a summary of future prospects for direct detection of dark matter within the GeV/c2 to TeV/c2 mass range. This is paired with a new definition of the neutrino fog in order to better quantify the rate of diminishing returns on sensitivity due to irreducible neutrino backgrounds. A survey of dark matter candidates predicted to fall within this mass range demonstrates that fully testing multiple well-motivated theo-ries will require expanding the currently-funded generation of experiments down to and past the neutrino fog. We end with the status and plans for next-generation exper-iments and novel R&D concepts which will get us there.
Melnikov-method-based broadband mechanism and necessary conditions of nonlinear rotating energy harvesting using piezoelectric beam

Nonlinearity can be used to enhance broadband rotating piezoelectric vibration energy harvesting, but how to construct a proper nonlinear rotating harvester is a challenging problem in engineering applications. This article presents a Melnikov-theory-based method to explore broadband mechanism and necessary conditions of nonlinear rotating piezoelectric vibration energy harvesting system. First, a perturbed state-space representation of nonlinear rotating energy harvesting system is built based on its dynamic model. It can be seen that bi-stability of the unperturbed nonlinear system is the physical basis of achieving broadband and low-frequency rotating energy harvesting. Second, the Melnikov function is defined to derive two necessary conditions of homoclinic bifurcation and chaotic motions. Then simulations are performed to identify the key parameters and their effects on the Melnikov conditions, including distance, rotating frequency, and excitations. It can be seen that homoclinic bifurcation and chaotic motions can occur in nonlinear rotating energy harvesting systems under single-frequency and broadband excitations. Finally, the experiments are carried out to validate the two necessary conditions. The results demonstrate that the proposed method can provide important guidelines for optimally designing nonlinear rotating piezoelectric energy harvesters in practice.
The Physics of Light Relics

Many well-motivated extensions of the Standard Model predict the existence of new light species that may have been produced in the early universe. Prominent examples include axions, sterile neutrinos, gravitinos, dark photons, and more. The gravitational influence of light relics leaves imprints in the cosmic microwave background fluctuations, the large-scale structure of the universe and the primordial element abundances. In this paper, we detail the physics of cosmological light relics, and describe how measurements of their relic density and mass serve as probes of physics beyond the Standard Model. A measurement of the light relic density at the precision of upcoming cosmological surveys will point the way toward new physics or severely constrain the range of viable extensions to the Standard Model.
Learning What Not to Segment: A New Perspective on Few-Shot Segmentation

Recently few-shot segmentation (FSS) has been extensively developed. Most previous works strive to achieve generalization through the meta-learning framework derived from classification tasks; however, the trained models are biased towards the seen classes instead of being ideally class-agnostic, thus hindering the recognition of new concepts. This paper proposes a fresh and straightforward insight to alleviate the problem. Specifically, we apply an additional branch (base learner) to the conventional FSS model (meta learner) to explicitly identify the targets of base classes, i.e., the regions that do not need to be segmented. Then, the coarse results output by these two learners in parallel are adaptively integrated to yield precise segmentation prediction. Considering the sensitivity of meta learner, we further introduce an adjustment factor to estimate the scene differences between the input image pairs for facilitating the model ensemble forecasting. The substantial performance gains on PASCAL-5i and COCO-20i verify the effectiveness, and surprisingly, our versatile scheme sets a new state-of-the-art even with two plain learners. Moreover, in light of the unique nature of the proposed approach, we also extend it to a more realistic but challenging setting, i.e., generalized FSS, where the pixels of both base and novel classes are required to be determined. The source code is available at github.com/chunbolang/BAM.
ScienceWorld: Is your Agent Smarter than a 5th Grader?

We present ScienceWorld, a benchmark to test agents’ scientific reasoning abilities in a new interactive text environment at the level of a standard elementary school science curriculum. Despite the transformer-based progress seen in question-answering and scientific text processing, we find that current models cannot reason about or explain learned science concepts in novel contexts. For instance, models can easily answer what the conductivity of a known material is but struggle when asked how they would conduct an experiment in a grounded environment to find the conductivity of an unknown material. This begs the question of whether current models are simply retrieving answers by way of seeing a large number of similar examples or if they have learned to reason about concepts in a reusable manner. We hypothesize that agents need to be grounded in interactive environments to achieve such reasoning capabilities. Our experiments provide empirical evidence supporting this hypothesis – showing that a 1.5 million parameter agent trained interactively for 100k steps outperforms a 11 billion parameter model statically trained for scientific question-answering and reasoning from millions of expert demonstrations.
Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models

Natural language processing models learn word representations based on the distributional hypothesis, which asserts that word context (e.g., co-occurrence) correlates with meaning. We propose that n-grams composed of random character sequences, or garble, provide a novel context for studying word meaning both within and beyond extant language. In particular, randomly generated character n-grams lack meaning but contain primitive information based on the distribution of characters they contain. By studying the embeddings of a large corpus of garble, extant language, and pseudowords using CharacterBERT, we identify an axis in the model’s high-dimensional embedding space that separates these classes of n-grams. Furthermore, we show that this axis relates to structure within extant language, including word part-of-speech, morphology, and concept concreteness. Thus, in contrast to studies that are mainly limited to extant language, our work reveals that meaning and primitive information are intrinsically linked.
Simultaneous View of FRB 180301 with FAST and NICER during a Bursting Phase

FRB 180301 is one of the most actively repeating fast radio bursts (FRBs) that has shown polarization angle changes in its radio burst emission, an indication for their likely origin in the magnetosphere of a highly magnetized neutron star. We carried out a multiwavelength campaign with the FAST radio telescope and NICER X-ray observatory to investigate any possible X-ray emission temporally coincident with the bright radio bursts. The observations took place on 2021 March 4, 9 and 19. We detected five bright radio bursts with FAST, four of which were strictly simultaneous with the NICER observations. The peak flux density of the radio bursts ranged between 28 and 105 mJy, the burst fluence between 27 and 170 mJy ms, and the burst durations between 1.7 and 12.3 ms. The radio bursts from FRB 180301 exhibited a complex time domain structure, and subpulses were detected in individual bursts, with no significant circular polarization. The linear degree of polarization in the L band reduced significantly compared to the 2019 observations. We do not detect any X-ray emission in excess of the background during the 5, 10, 100 ms, 1 and 100 s time intervals at/around the radio-burst barycenter-corrected arrival times, at a > 5σ confidence level. The 5σ upper limits on the X-ray (a) persistent flux is <7.64 × 10−12 erg cm−2 s−1, equivalent to L X < 2.50 × 1045 erg s−1 and (b) 5 ms fluence is <2 × 10−11 erg cm−2, at the radio burst regions. Using the 5 ms X-ray fluence upper limit, we can estimate the radio efficiency η R/X ≡ L radio/L X−ray ≳ 10−8. The derived lower limit on η R/X is consistent with both magnetospheric models and synchrotron maser models involving relativistic shocks.
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition

The sensor-based human activity recognition (HAR) in mobile application scenarios is often confronted with variation in sensing modalities and deficiencies in annotated samples. To address these two challenging problems, we devised a graph-inspired deep learning approach that uses data from human-body mounted wearable sensors. As a step toward a complete HAR solution, the proposed method was further used to build a deep transfer learning model. Specifically, we present a multi-layer residual structure involving graph convolutional neural network (ResGCNN) toward the sensor-based HAR tasks, namely the HAR-ResGCNN approach. Experimental results on the PAMAP2 and mHealth data sets demonstrate that our ResGCNN is effective at capturing the characteristics of actions with comparable results compared to other sensor-based HAR models (with an average accuracy of 98.18% and 99.07%, respectively). More importantly, the parameter-based transfer learning experiments using the ResGCNN model show excellent transferability and small sample learning ability, which is a promising solution in sensor-based HAR applications.