Miracle-dz/UniFAR-FTUs huggingface dataset:
Protecting File Activities via Deception for ARM TrustZone

A TrustZone TEE often invokes an external filesystem. While filedata can be encrypted, the revealed file activities can leak secrets. To hide the file activities from the filesystem and its OS, we propose Enigma, a deception-based defense injecting sybil file activities as the cover of the actual file activities. Enigma contributes three new designs. (1) To make the deception credible, the TEE generates sybil calls by replaying file calls from the TEE code under protection. (2) To make sybil activities cheap, the TEE requests the OS to run K filesystem images simultaneously. Concealing the disk, the TEE backs only one image with the actual disk while backing other images by only storing their metadata. (3) To protect filesystem image identities, the TEE shuffles the images frequently, preventing the OS from observing any image for long. Enigma works with unmodified filesystems shipped withLinux. On a low-cost Arm SoC with EXT4 and F2FS, our system can concurrently run as many as 50 filesystem images with 1% of disk overhead per additional image. Compared to common obfuscation for hiding addresses in a flat space, Enigma hides file activities with richer semantics. Its cost is lower by one order of magnitude while achieving the same level of probabilistic security guarantees.
Covariant entanglement wedge cross-section, balanced partial entanglement and gravitational anomalies

The balanced partial entanglement (BPE) was observed to give the reflected entropy and the entanglement wedge cross-section (EWCS) for various mixed states in different theories [1,2]. It can be calculated in different purifications, and is conjectured to be independent from purifications. In this paper we calculate the BPE and the EWCS in generic covariant scenarios in two-dimensional CFTs with and without gravitational anomalies, and find that they coincide with the reflected entropy. In covariant configurations we determine the partition for the purifying system with the help of the gravitational anomalies, and we extend our discussion to topological massive gravity (TMG). We give the first prescription to evaluate the entropy quantity associated to the EWCS beyond Einstein gravity, i.e. the correction to the EWCS from the Chern-Simons term in TMG. Apart from the gravity theory and geometry, further input from the mixed state should be taken into account.
Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization

At first sight it may seem straightforward to use recurrent layers in Deep Reinforcement Learning algorithms to enable agents to make use of memory in the setting of partially observable environments. Starting from widely used Proximal Policy Optimization (PPO), we highlight vital details that one must get right when adding recurrence to achieve a correct and efficient implementation, namely: properly shaping the neural net's forward pass, arranging the training data, correspondingly selecting hidden states for sequence beginnings and masking paddings for loss computation. We further explore the limitations of recurrent PPO by benchmarking the contributed novel environments Mortar Mayhem and Searing Spotlights that challenge the agent's memory beyond solely capacity and distraction tasks. Remarkably, we can demonstrate a transition to strong generalization in Mortar Mayhem when scaling the number of training seeds, while the agent does not succeed on Searing Spotlights, which seems to be a tough challenge for memory-based agents.
GL-RG: Global-Local Representation Granularity for Video Captioning

Video captioning is a challenging task as it needs to accurately transform visual understanding into natural language description. To date, state-of-the-art methods inadequately model global-local representation across video frames for caption generation, leaving plenty of room for improvement. In this work, we approach the video captioning task from a new perspective and propose a GL-RG framework for video captioning, namely a Global-Local Representation Granularity. Our GL-RG demonstrates three advantages over the prior efforts: 1) we explicitly exploit extensive visual representations from different video ranges to improve linguistic expression; 2) we devise a novel global-local encoder to produce rich semantic vocabulary to obtain a descriptive granularity of video contents across frames; 3) we develop an incremental training strategy which organizes model learning in an incremental fashion to incur an optimal captioning behavior. Experimental results on the challenging MSR-VTT and MSVD datasets show that our DL-RG outperforms recent state-of-the-art methods by a significant margin. Code is available at https://github.com/ylqi/GL-RG.
Infra-red thermography as a tool to evaluate the influence of liner characteristics and over-milking

The present paper consists of a brief introduction to infra-red thermography and the results from an experiment dealing with infra-red thermography as a tool to monitor circulatory impairments associated with machine milking. Thermography focuses, collects and transforms the infra-red range of the electromagnetic spectrum that is emitted from any body in a heat-dependent fashion. Thermography furthermore images a pictorial summary of the heat gradients generated and can thereby visualize the thermal patterns of the skin resulting in useful mapping of the underlying circulation and metabolism. Eight Danish Holstein cows were milked experimentally in a combined group and split-udder design. All cows were milked with a very thin experimental liner on their right rear teat and a standard liner on their left rear teat. Four animals were milked without over-milking and four animals were milked excessively for 5 min to simulate over-milking. In order to estimate teat integrity, the teats were challenged by being submersed in ethanol 20 min post-milking. Rear teats were subject to infra-red thermography. The results from thermography indicate a general drop in teat temperature during preparation. Teat temperature increased during milking and continued to increase until an ethanol challenge 20 min post-milking. The challenge induced a significant drop in teat temperature. During the following 10 min post-challenge, teat temperatures approached pre-challenge rather then pre-milking temperatures. Teat temperatures were dependent on type of liner. Mid-teat temperatures post-challenge relative to pre-preparation were dependent on over-milking. Thermography was considered a useful method to indirectly and non-invasively evaluate teat tissue integrity.
Logical Reasoning with Span Predictions: Span-level Logical Atoms for Interpretable and Robust NLI Models

Current Natural Language Inference (NLI) models achieve impressive results, sometimes outperforming humans when evaluating on in-distribution test sets. However, as these models are known to learn from annotation arte-facts and dataset biases, it is unclear to what extent the models are learning the task of NLI instead of learning from shallow heuristics in their training data. We address this issue by introducing a logical reasoning framework for NLI, creating highly transparent model decisions that are based on logical rules. Unlike prior work, we show that the improved inter-pretability can be achieved without decreasing the predictive accuracy. We almost fully retain performance on SNLI while identifying the exact hypothesis spans that are responsible for each model prediction. Using the e-SNLI human explanations, we also verify that our model makes sensible decisions at a span level, despite not using any span-level labels during training. We can further improve model performance and the span-level decisions by using the e-SNLI explanations during training. Finally, our model outperforms its base-line in a reduced data setting. When training with only 100 examples, in-distribution performance improves by 18%, while out-of-distribution performance improves on SNLI-hard, MNLI-mismatched, MNLI-matched and SICK by 11%, 26%, 22%, and 21% respectively.
The Geometry of Multilingual Language Model Representations

We assess how multilingual language models maintain a shared multilingual representation space while still encoding language-sensitive information in each language. Using XLM-R as a case study, we show that languages occupy similar linear subspaces after mean-centering, evaluated based on causal effects on language modeling performance and direct comparisons between subspaces for 88 languages. The subspace means differ along language-sensitive axes that are relatively stable throughout middle layers, and these axes encode information such as token vocabularies. Shifting representations by language means is sufficient to induce token predictions in different languages. However, we also identify stable language-neutral axes that encode information such as token positions and part-of-speech. We visualize representations projected onto language-sensitive and language-neutral axes, identifying language family and part-of-speech clusters, along with spirals, toruses, and curves representing token position information. These results demonstrate that multilingual language models encode information along orthogonal language-sensitive and language-neutral axes, allowing the models to extract a variety of features for downstream tasks and cross-lingual transfer learning.
Contextual Information-Directed Sampling

Information-directed sampling (IDS) has recently demonstrated its potential as a data-efﬁcient reinforcement learning algorithm (Lu et al., 2021). However, it is still unclear what is the right form of information ratio to optimize when contextual information is available. We investigate the IDS design through two contextual bandit problems: contextual bandits with graph feedback and sparse linear contextual bandits. We provably demonstrate the advantage of contextual IDS over conditional IDS and emphasize the importance of considering the context distribution. The main message is that an intelligent agent should invest more on the actions that are beneﬁcial for the future unseen contexts while the conditional IDS can be myopic. We further propose a computationally-efﬁcient version of contextual IDS based on Actor-Critic and evaluate it empirically on a neural network contextual bandit.
CEP3: Community Event Prediction with Neural Point Process on Graph

Many real world applications can be formulated as event forecasting on Continuous Time Dynamic Graphs (CTDGs) where the occurrence of a timed event between two entities is represented as an edge along with its occurrence timestamp in the graphs.However, most previous works approach the problem in compromised settings, either formulating it as a link prediction task on the graph given the event time or a time prediction problem given which event will happen next. In this paper, we propose a novel model combining Graph Neural Networks and Marked Temporal Point Process (MTPP) that jointly forecasts multiple link events and their timestamps on communities over a CTDG. Moreover, to scale our model to large graphs, we factorize the jointly event prediction problem into three easier conditional probability modeling problems.To evaluate the effectiveness of our model and the rationale behind such a decomposition, we establish a set of benchmarks and evaluation metrics for this event forecasting task. Our experiments demonstrate the superior performance of our model in terms of both model accuracy and training efficiency.
Diversity vs. Recognizability: Human-like generalization in one-shot generative models

Robust generalization to new concepts has long remained a distinctive feature of human intelligence. However, recent progress in deep generative models has now led to neural architectures capable of synthesizing novel instances of unknown visual concepts from a single training example. Yet, a more precise comparison between these models and humans is not possible because existing performance metrics for generative models (i.e., FID, IS, likelihood) are not appropriate for the one-shot generation scenario. Here, we propose a new framework to evaluate one-shot generative models along two axes: sample recognizability vs. diversity (i.e., intra-class variability). Using this framework, we perform a systematic evaluation of representative one-shot generative models on the Omniglot handwritten dataset. We first show that GAN-like and VAE-like models fall on opposite ends of the diversity-recognizability space. Extensive analyses of the effect of key model parameters further revealed that spatial attention and context integration have a linear contribution to the diversity-recognizability trade-off. In contrast, disentanglement transports the model along a parabolic curve that could be used to maximize recognizability. Using the diversity-recognizability framework, we were able to identify models and parameters that closely approximate human data.
Verifying the Blade Element Momentum Method in unsteady, radially varied, axisymmetric loading using a vortex ring model

Although the Blade Element Momentum method has been derived for the steady conditions, it is used for unsteady conditions by using corrections of engineering dynamic inflow models. Its applicability in these cases is not yet fully verified. In this paper, the validity of the assumptions of quasi-steady state and annuli independence of the blade element momentum theory for unsteady, radially varied, axi-symmetric load cases is investigated. Firstly, a free wake model that combines a vortex ring model with a semi-infinite cylindrical vortex tube was developed and applied to an actuator disc in three load cases: (i) steady uniform and radially varied, (ii) two types of unsteady uniform load and (iii) unsteady radially varied load. Results from the three cases were compared with Momentum Theory and also with two widely used engineering dynamic inflow models—the Pitt-Peters and the Oye for the unsteady load cases. For unsteady load, the free wake vortex ring model predicts different hysteresis loops of the velocity at the disc or local annuli, and different aerodynamic work from the engineering dynamic inflow models. Given that the free wake vortex ring model is more physically representative, the results indicate that the engineering dynamic inflow models should be improved for unsteady loaded rotor, especially for radially varied unsteady loads. © 2016 The Authors. Wind Energy Published by John Wiley & Sons, Ltd.
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models

Vision-language pre-training (VLP) has shown impressive performance on a wide range of cross-modal tasks, where VLP models without reliance on object detectors are becoming the mainstream due to their superior computation efficiency and competitive performance. However, the removal of object detectors also deprives the capability of VLP models in explicit object modeling, which is essential to various position-sensitive vision-language (VL) tasks, such as referring expression comprehension and visual commonsense reasoning. To address the challenge, we introduce PEVL that enhances the pre-training and prompt tuning of VLP models with explicit object position modeling. Specifically, PEVL reformulates discretized object positions and language in a unified language modeling framework, which facilitates explicit VL alignment during pre-training, and also enables flexible prompt tuning for various downstream tasks. We show that PEVL enables state-of-the-art performance of detector-free VLP models on position-sensitive tasks such as referring expression comprehension and phrase grounding, and also improves the performance on position-insensitive tasks with grounded inputs. We make the data and code for this paper publicly available at https://github.com/thunlp/PEVL.
What Do Compressed Multilingual Machine Translation Models Forget?

Recently, very large pre-trained models achieve state-of-the-art results in various natural language processing (NLP) tasks, but their size makes it more challenging to apply them in resource-constrained environments. Compression techniques allow to drastically reduce the size of the models and therefore their inference time with negligible impact on top-tier metrics. However, the general performance averaged across multiple tasks and/or languages may hide a drastic performance drop on under-represented features, which could result in the amplification of biases encoded by the models. In this work, we assess the impact of compression methods on Multilingual Neural Machine Translation models (MNMT) for various language groups, gender, and semantic biases by extensive analysis of compressed models on different machine translation benchmarks, i.e. FLORES-101, MT-Gender, and DiBiMT. We show that the performance of under-represented languages drops significantly, while the average BLEU metric only slightly decreases. Interestingly, the removal of noisy memorization with compression leads to a significant improvement for some medium-resource languages. Finally, we demonstrate that compression amplifies intrinsic gender and semantic biases, even in high-resource languages. Code: https://github.com/alirezamshi/bias-compressedMT
The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains

Recent model pruning methods have demonstrated the ability to remove redundant parameters without sacrificing model performance. Common methods remove redundant parameters according to the parameter sensitivity, a gradient-based measure reflecting the contribution of the parameters. In this paper, however, we argue that redundant parameters can be trained to make beneficial contributions. We first highlight the large sensitivity (contribution) gap among high-sensitivity and low-sensitivity parameters and show that the model generalization performance can be significantly improved after balancing the contribution of all parameters. Our goal is to balance the sensitivity of all parameters and encourage all of them to contribute equally. We propose a general task-agnostic method, namely intra-distillation, appended to the regular training loss to balance parameter sensitivity. Moreover, we also design a novel adaptive learning method to control the strength of intra-distillation loss for faster convergence. Our experiments show the strong effectiveness of our methods on machine translation, natural language understanding, and zero-shot cross-lingual transfer across up to 48 languages, e.g., a gain of 3.54 BLEU on average across 8 language pairs from the IWSLT’14 dataset.
Exploiting Array Geometry for Reduced-Subspace Channel Estimation in RIS-Aided Communications

A reconfigurable intelligent surface (RIS) can be used to improve the channel gain between a base station (BS) and user equipment (UE), but only if its $N$ reflecting elements are configured properly. This requires accurate estimation of the cascaded channel from the UE to the BS through each RIS element. If the channel structure is not exploited, pilot sequences of length $N$ must be used, which is a major practical challenge since $N$ is typically at the order of hundreds. To address this problem without requiring user-specific channel statistics, we propose a novel estimator, called reduced-subspace least squares (RS-LS) estimator, that only uses knowledge of the array geometry. The RIS phase-shift pattern is optimized to minimize the mean-square error of the channel estimates. The RS- LS estimator largely outperforms the conventional least- squares estimator, and can be utilized with a much shorter pilot length since it exploits the fact that the array geometry confines the possible channel realizations to a reduced-rank subspace.
Testing the conservative character of particle simulations: I. Canonical and noncanonical guiding center model in Boozer coordinates

The guiding center (GC) Lagrangian in Boozer coordinates for toroidally confined plasmas can be cast into canonical form by eliminating terms containing the covariant component [Formula: see text] of the magnetic field vector with respect to the poloidal flux function [Formula: see text]. In an unperturbed plasma, [Formula: see text] can be eliminated via exact coordinate transformations, but, in general, one relies on approximations, assuming that the effect of [Formula: see text] is small. Here, we are interested in the question whether Hamiltonian conservation laws are still satisfied when [Formula: see text] is retained in the presence of fluctuations. Considering fast ions in the presence of a shear Alfvén wave field with fixed amplitude, fixed frequency, and a single toroidal mode number n, we show that simulations using the code ORBIT with and without [Formula: see text] yield practically the same resonant and nonresonant GC orbits. The numerical results are consistent with theoretical analyses (presented in the appendix), which show that the unabridged GC Lagrangian with [Formula: see text] retained yields equations of motion that possess two key properties of Hamiltonian flows: (i) phase space conservation and (ii) energy conservation. As counter-examples, we also show cases where energy conservation (ii) or both conservation laws (i) and (ii) are broken by omitting certain small terms. When testing the conservative character of the simulation code, it is found to be beneficial to apply perturbations that do not resemble normal (eigen) modes of the plasma. The deviations are enhanced and, thus, more easily spotted when one inspects wave-particle interactions using nonnormal modes.
Discourse-Aware Soft Prompting for Text Generation

Current efficient fine-tuning methods(e.g., adapters, prefix-tuning, etc.) have optimized conditional text generation via training a small set of extra parameters of the neural language model, while freezing the rest for efficiency. While showing strong performance on some generation tasks, they don’t generalize across all generation tasks. We show that soft-prompt based conditional text generation can be improved with simple and efficient methods that simulate modeling the discourse structure of human written text.We investigate two design choices: First, we apply hierarchical blocking on the prefix parameters to simulate a higher-level discourse structure of human written text. Second, we apply attention sparsity on the prefix parameters at different layers of the network and learn sparse transformations on the softmax-function. We show that structured design of prefix parameters yields more coherent, faithful and relevant generations than the baseline prefix-tuning on all generation tasks.
$\clw$-hypercontractions and their model

We revisit the study of ω -hypercontractions corresponding to a single weight sequence ω = { ω k } k ≥ 0 introduced by Olofsson in [22] and ﬁnd an analogue of Nagy-Foias characteristic function in this setting. Explicit construction of characteristic functions is obtained and it is shown to be a complete unitary invariant. By considering a multi-weight sequence W and W -hypercontractions we extend Olofsson’s work [22] in the multi-variable setting. Model for W -hypercontractions is obtained by ﬁnding their dilations on certain weighted Bergman spaces over the polydisc corresponding to the multi-weight sequence W . This recovers and provides a diﬀerent proof of the earlier work of Curto and Vasilescu [13, 14] for γ -contractive multi-operators through a particular choice of multi-weight sequence.
Mechanism Design with Predictions

Improving algorithms via predictions is a very active research topic in recent years. This paper initiates the systematic study of mechanism design in this model. In a number of well-studied mechanism design settings, we make use of imperfect predictions to design mechanisms that perform much better than traditional mechanisms if the predictions are accurate (consistency), while always retaining worst-case guarantees even with very imprecise predictions (robustness). Furthermore, we refer to the largest prediction error sufficient to give a good performance as the error tolerance of a mechanism, and observe that an intrinsic tradeoff among consistency, robustness and error tolerance is common for mechanism design with predictions.