Miracle-dz/UniFAR-FTUs huggingface dataset:
A Case for Transparent Reliability in DRAM Systems

Mass-produced commodity DRAM is the preferred choice of main memory for a broad range of computing systems due to its favorable cost-per-bit. However, today’s systems have diverse system-specic needs (e.g., performance, energy, reliability) that are dicult to address using one-size-ts-all generalpurpose DRAM. Unfortunately, although system designers can theoretically adapt commodity DRAM chips to meet their particular design goals (e.g., by exploiting slack in access timings to improve performance, or implementing system-level RowHammer mitigations), we observe that designers today lack the necessary insight into commodity DRAM chips’ reliability characteristics to implement these techniques in practice. In this work, we make a case for DRAM manufacturers to provide increased transparency into simple device characteristics (e.g., internal row address mapping, cell array organization) that aect consumer-visible reliability. Doing so has negligible impact on manufacturers given that these characteristics can be reverse-engineered using known techniques; however, it has signicant benet for system designers, who can then make informed decisions to beer adapt commodity DRAM to meet modern systems’ needs while preserving its cost advantages. To support our argument, we study four ways that system designers can adapt commodity DRAM chips to system-specic design goals: (1) improving DRAM reliability; (2) reducing DRAM refresh overheads; (3) reducing DRAM access latency; and (4) defending against RowHammer aacks. We observe that adopting solutions for any of the four goals requires system designers to make assumptions about a DRAM chip’s reliability characteristics. ese assumptions discourage system designers from using such solutions in practice due to the diculty of both making and relying upon the assumption. We identify DRAM standards as the root of the problem: current standards rigidly enforce a xed operating point with no specications for how a system designer might explore alternative operating points. To overcome this problem, we introduce a two-step approach that reevaluates DRAM standards with a focus on transparency of reliability characteristics so that system designers are encouraged to make the most of commodity DRAM technology for both current and future DRAM chips.
Measuring the Salad Bowl: Superdiversity on Twitter

Superdiversity refers to large cultural diversity in a population due to immigration. In this paper we introduce a superdiversity index based on the changes in the emotional content of words used by a multi-cultural community, compared to the standard language. To compute our index we use Twitter data and we develop an algorithm to extend a dictionary for lexicon based sentiment analysis. We validate our index by comparing it with oﬃcial immigration statistics available from the European Commission’s Joint Research Center, through the D4I data challenge. We show that, in general, our measure correlates with immigration rates, at various geographical resolutions. Our method produces very good results across languages, being tested here both on English and Italian tweets. We argue that our index has predictive power in regions where exact data on immigration is not available, paving the way for a nowcasting model of immigration rates.
Learning Functional Distributional Semantics with Visual Data

Functional Distributional Semantics is a recently proposed framework for learning distributional semantics that provides linguistic interpretability. It models the meaning of a word as a binary classifier rather than a numerical vector. In this work, we propose a method to train a Functional Distributional Semantics model with grounded visual data. We train it on the Visual Genome dataset, which is closer to the kind of data encountered in human language acquisition than a large text corpus. On four external evaluation datasets, our model outperforms previous work on learning semantics from Visual Genome.
A Unifying Framework for Combining Complementary Strengths of Humans and ML toward Better Predictive Decision-Making

Hybrid human-ML systems are increasingly in charge of consequential decisions in a wide range of domains. A growing body of work has advanced our understanding of these systems by providing empirical and theoretical analyses. However, existing empirical results are mixed, and theoretical proposals are often incompatible with each other. Our goal in this work is to bring much-needed organization to this ﬁeld by oﬀering a unifying framework for understanding conditions under which combining complementary strengths of human and ML leads to higher quality decisions than those produced by them individually—a state to which we refer to as human-ML complementarity . We focus speciﬁcally on the context of human-ML predictive decision-making systems and investigate optimal ways of combining human and ML-based predictive decisions, accounting for the underlying causes of variation in their judgments. Within this scope, we present two crucial contributions. First, drawing upon prior literature in human psychology, machine learning, and human-computer interaction, we introduce a taxonomy characterizing a wide variety of criteria across which human and machine decision-making diﬀer. Building on our taxonomy, our second contribution presents a unifying optimization-based framework for formalizing how human and ML predictive decisions should be aggregated optimally. We show that our proposed framework encompasses several existing models of human-ML complementarity as special cases. Last but not least, the exploratory analysis of our framework oﬀers a critical piece of insight for future work in this area: the mechanism by which we combine human-ML judgments should be informed by the underlying causes of their diverging decisions.
Sharper Utility Bounds for Differentially Private Models: Smooth and Non-smooth

In this paper, by introducing Generalized Bernstein condition, we propose the first O(√p over n∈ ) high probability excess population risk bound for differentially private algorithms under the assumptions G-Lipschitz, L-smooth, and Polyak-Łojasiewicz condition, based on gradient perturbation method. If we replace the properties G-Lipschitz and L-smooth by α-Hölder smoothness (which can be used in non-smooth setting), the high probability bound comes to O(n-α over 1+2α) w.r. t n, which cannot achieve O (1/n) when α ∈(0,1]. To solve this problem, we propose a variant of gradient perturbation method, max1,g -Normalized Gradient Perturbation (m-NGP). We further show that by normalization, the high probability excess population risk bound under assumptions α-Hölder smooth and Polyak-Łojasiewicz condition can achieve O (√p over n∈), which is the first O (1/n) high probability excess population risk bound w.r. t n for differentially private algorithms under non-smooth conditions. Moreover, experimental results show that m-NGP improves the performance of the differentially private model over real datasets.
High average power ultrafast laser technologies for driving future advanced accelerators

Large scale laser facilities are needed to advance the energy frontier in high energy physics and accelerator physics. Laser plasma accelerators are core to advanced accelerator concepts aimed at reaching TeV electron electron colliders. In these facilities, intense laser pulses drive plasmas and are used to accelerate electrons to high energies in remarkably short distances. A laser plasma accelerator could in principle reach high energies with an accelerating length that is 1000 times shorter than in conventional RF based accelerators. Notionally, laser driven particle beam energies could scale beyond state of the art conventional accelerators. LPAs have produced multi GeV electron beams in about 20 cm with relative energy spread of about 2 percent, supported by highly developed laser technology. This validates key elements of the US DOE strategy for such accelerators to enable future colliders but extending best results to date to a TeV collider will require lasers with higher average power. While the per pulse energies envisioned for laser driven colliders are achievable with current lasers, low laser repetition rates limit potential collider luminosity. Applications will require rates of kHz to tens of kHz at Joules of energy and high efficiency, and a collider would require about 100 such stages, a leap from current Hz class LPAs. This represents a challenging 1000 fold increase in laser repetition rates beyond current state of the art. This whitepaper describes current research and outlook for candidate laser systems as well as the accompanying broadband and high damage threshold optics needed for driving future advanced accelerators.
Tweets2Stance: Users stance detection exploiting Zero-Shot Learning Algorithms on Tweets

In the last years there has been a growing attention towards predicting the political orientation of active social media users, being this of great help to study political forecasts, opinion dynamics modeling and users polarization. Existing approaches, mainly targeting Twitter users, rely on content-based analysis or are based on a mixture of content, network and communication analysis. The recent research perspective exploits the fact that a user's political affinity mainly depends on his/her positions on major political and social issues, thus shifting the focus on detecting the stance of users through user-generated content shared on social networks. The work herein described focuses on a completely unsupervised stance detection framework that predicts the user's stance about specific social-political statements by exploiting content-based analysis of its Twitter timeline. The ground-truth user's stance may come from Voting Advice Applications, online tools that help citizens to identify their political leanings by comparing their political preferences with party political stances. Starting from the knowledge of the agreement level of six parties on 20 different statements, the objective of the study is to predict the stance of a Party p in regard to each statement s exploiting what the Twitter Party account wrote on Twitter. To this end we propose Tweets2Stance (T2S), a novel and totally unsupervised stance detector framework which relies on the zero-shot learning technique to quickly and accurately operate on non-labeled data. Interestingly, T2S can be applied to any social media user for any context of interest, not limited to the political one. Results obtained from multiple experiments show that, although the general maximum F1 value is 0.4, T2S can correctly predict the stance with a general minimum MAE of 1.13, which is a great achievement considering the task complexity.
Probing Heavy Sterile Neutrinos at Ultrahigh Energy Neutrino Telescopes via the Dipole Portal

The question of how heavy a sterile neutrino can be probed in experiments leads us to investigate the Primakoﬀ production of heavy sterile neutrinos up to PeV masses from ultrahigh-energy neutrinos via the magnetic dipole portal. Despite the suppression from the small magnetic moment, the transition is signiﬁcantly enhanced by tiny t -channel momentum transfers, similar to the resonant production of pions and axions in an external electromagnetic ﬁeld. Based on the current IceCube measurement of astrophysical neutrinos up to PeV energies, strong constraints can already be derived on the transition magnetic moments of sterile neutrinos up to TeV masses. Moreover, we investigate the sensitivity of future tau neutrino telescopes, which are designed for EeV cosmogenic neutrino detection. We ﬁnd that sterile neutrino masses as large as 30 TeV can be probed at tau neutrino telescopes such as GRAND, POEMMA, and Trinity.
Following Closely: A Robust Monocular Person Following System for Mobile Robot

Monocular person following (MPF) is a capability that supports many useful applications of a mobile robot. However, existing MPF solutions are not completely satisfactory. Firstly, they often fail to track the target at a close distance either because they are based on a visual servo or they need the observation of the full body by the robot. Secondly, their target Re-IDentification (Re-ID) abilities are weak in cases of target appearance change and highly similar appearance of distracting people. To remove the assumption of full-body observation, we propose a width-based tracking module, which relies on the target width, which can be observed even at a close distance. For handling issues related to appearance variation, we use a global CNN (convolutional neural network) descriptor to represent the target and a ridge regression model to learn a target appearance model online. We adopt a sampling strategy for online classifier learning, in which both long-term and short-term samples are involved. We evaluate our method in two datasets including a public person following dataset and a custom-built one with challenging target appearance and target distance. Our method achieves state-of-the-art (SOTA) results on both datasets. For the benefit of the community, we make public the dataset and the source code.
Learning to Scaffold: Optimizing Model Explanations for Teaching

Modern machine learning models are opaque, and as a result there is a burgeoning academic subfield on methods that explain these models' behavior. However, what is the precise goal of providing such explanations, and how can we demonstrate that explanations achieve this goal? Some research argues that explanations should help teach a student (either human or machine) to simulate the model being explained, and that the quality of explanations can be measured by the simulation accuracy of students on unexplained examples. In this work, leveraging meta-learning techniques, we extend this idea to improve the quality of the explanations themselves, specifically by optimizing explanations such that student models more effectively learn to simulate the original model. We train models on three natural language processing and computer vision tasks, and find that students trained with explanations extracted with our framework are able to simulate the teacher significantly more effectively than ones produced with previous methods. Through human annotations and a user study, we further find that these learned explanations more closely align with how humans would explain the required decisions in these tasks. Our code is available at https://github.com/coderpat/learning-scaffold
Data Driven Modeling of Turbocharger Turbine using Koopman Operator

: A turbocharger plays an essential part in reducing emissions and increasing the fuel eﬃciency of road vehicles. The pulsating ﬂow of exhaust gases, along with high heat exchange from the turbocharger casing, makes developing control-oriented models diﬃcult. Several researchers have used maps provided by manufacturers to solve this problem. These maps often fail to incorporate any heat transfer eﬀects and are unsuitable for wide operating regions. Also, with the availability of more and better sensor data, there is a need for a method that can exploit this to obtain a better predictive model. Koopman approaches rely on the observation that one can lift the nonlinear dynamics of the turbine into an inﬁnite-dimensional function space over which dynamics are linear. The objective of this paper is to develop a model to predict the transient and steady-state behavior of the turbine using the Koopman operator which can be helpful for control design and analysis. Our approach is as follows. We use experimental data from a Cummins heavy-duty diesel engine to develop a turbine model using Extended Dynamic Mode Decomposition, which approximates the action of the Koopman operator on a ﬁnite-dimensional subspace of the space of observables. The results demonstrate superior performance compared to a tuned nonlinear autoregressive network with an exogenous input model widely used in the literature. The performance of these two models is analyzed based on their ability to predict turbine transient and steady-state behavior.
The Equivariance Criterion in a Linear Model for Fixed-X Cases

In this article, we explored the usage of equivariance criterion in linear model with ﬁxed-X for the estimation and extended the model to allow multiple populations, which, in turn, leads to a larger transformation group. The minimum risk equivariant estimators of the coeﬃcient vector and the covariance matrix were derived, which was consistent with earlier works. This article serves as an early exploration of equivariance criterion in linear model.
Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks

Cross-modal encoders for vision-language (VL) tasks are often pretrained with carefully curated vision-language datasets. While these datasets reach an order of 10 million samples, the labor cost is prohibitive to scale further. Conversely, unimodal encoders are pretrained with simpler annotations that are less cost-prohibitive, achieving scales of hundreds of millions to billions. As a result, unimodal encoders have achieved state-of-art (SOTA) on many downstream tasks. However, challenges remain when applying to VL tasks. The pretraining data is not optimal for cross-modal architectures and requires heavy computational resources. In addition, unimodal architectures lack cross-modal interactions that have demonstrated significant benefits for VL tasks. Therefore, how to best leverage pretrained unimodal encoders for VL tasks is still an area of active research. In this work, we propose a method to leverage unimodal vision and text encoders for VL tasks that augment existing VL approaches while conserving computational complexity. Specifically, we propose Multimodal Adaptive Distillation (MAD), which adaptively distills useful knowledge from pretrained encoders to cross-modal VL encoders. Second, to better capture nuanced impacts on VL task performance, we introduce an evaluation protocol that includes Visual Commonsense Reasoning (VCR), Visual Entailment (SNLI-VE), and Visual Question Answering (VQA), across a variety of data constraints and conditions of domain shift. Experiments demonstrate that MAD leads to consistent gains in the low-shot, domain-shifted, and fully-supervised conditions on VCR, SNLI-VE, and VQA, achieving SOTA performance on VCR compared to other single models pretrained with image-text data. Finally, MAD outperforms concurrent works utilizing pretrained vision encoder from CLIP. Code will be made available.
Dimensional regularization, Wilsonian RG, and the naturalness and hierarchy problem

While it is usually stated that dimensional regularization (DR) has no direct physical interpretation, consensus has recently grown on the idea that it might be endowed with special physical properties that would provide the mechanism that solves the natural-ness/hierarchy problem. Comparing direct Wilsonian calculations with the corresponding DR ones, we ﬁnd that DR indeed has a well-deﬁned physical meaning, and we point out its limitations. In particular, our results show that DR cannot provide the solution to the naturalness/hierarchy problem. The absence of too large corrections to the Higgs boson mass is due to a secretly realized ﬁne-tuning, rather than special physical properties of DR. We also investigate these issues within the Wilsonian RG framework and, by com-parison with the usual perturbative RG analysis, we show that several popular proposals for the resolution of the problem, commonly considered as physical mechanisms free of ﬁne-tuning, again secretly implement the tuning.
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires

Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9’s symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect.
Frequency estimation under non-Markovian spatially correlated quantum noise

We study the estimation precision attainable by entanglement-enhanced Ramsey interferometry in the presence of spatiotemporally correlated non-classical noise. Our analysis relies on an exact expression of the reduced density matrix of the qubit probes under general zero-mean Gaussian stationary dephasing, which is established through cumulant-expansion techniques and may be of independent interest in the context of non-Markovian open dynamics. By continuing and expanding our previous work (Beaudoin et al 2018 Phys. Rev. A 98 020102(R)), we analyze the effects of a non-collective coupling regime between the qubit probes and their environment, focusing on two limiting scenarios where the couplings may take only two or a continuum of possible values. In the paradigmatic case of spin–boson dephasing noise from a thermal environment, we find that it is in principle possible to suppress, on average, the effect of spatial correlations by randomizing the location of the probes, as long as enough configurations are sampled where noise correlations are negative. As a result, superclassical precision scaling is asymptotically restored for initial entangled states, including experimentally accessible one-axis spin-squeezed states.
Matrix-Scaled Consensus

This paper proposes matrix-scaled consensus algorithm, which generalizes the scaled consensus algorithm in [1]. In (scalar) scaled consensus algorithms, the agents’ states do not converge to a common value, but to different points along a straight line in the state space, which depends on the scaling factors and the initial states of the agents. In the matrix-scaled consensus algorithm, a positive/negative definite matrix weight is assigned to each agent. Each agent updates its state based on the product of the sum of relative matrix scaled states and the sign of the matrix weight. Under the proposed algorithm, each agent asymptotically converges to a final point differing with a common consensus point by the inverse of its own scaling matrix. Thus, the final states of the agents are not restricted to a straight line but are extended to an open subspace of the state-space. Convergence analysis of matrix-scaled consensus for single and double-integrator agents are studied in detail. Simulation results are given to support the analysis.