Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models

Likelihood-based, or explicit, deep generative models use neural networks to construct flexible high-dimensional densities. This formulation directly contradicts the manifold hypothesis, which states that observed data lies on a low-dimensional manifold embedded in high-dimensional ambient space. In this paper we investigate the pathologies of maximum-likelihood training in the presence of this dimensionality mismatch. We formally prove that degenerate optima are achieved wherein the manifold itself is learned but not the distribution on it, a phenomenon we call manifold overfitting. We propose a class of two-step procedures consisting of a dimensionality reduction step followed by maximum-likelihood density estimation, and prove that they recover the data-generating distribution in the nonparametric regime, thus avoiding manifold overfitting. We also show that these procedures enable density estimation on the manifolds learned by implicit models, such as generative adversarial networks, hence addressing a major shortcoming of these models. Several recently proposed methods are instances of our two-step procedures; we thus unify, extend, and theoretically justify a large class of models.
LaMemo: Language Modeling with Look-Ahead Memory

Although Transformers with fully connected self-attentions are powerful to model long-term dependencies, they are struggling to scale to long texts with thousands of words in language modeling. One of the solutions is to equip the model with a recurrence memory. However, existing approaches directly reuse hidden states from the previous segment that encodes contexts in a uni-directional way. As a result, this prohibits the memory to dynamically interact with the current context that provides up-to-date information for token prediction. To remedy this issue, we propose Look-Ahead Memory (LaMemo) that enhances the recurrence memory by incrementally attending to the right-side tokens and interpolating with the old memory states to maintain long-term information in the history. LaMemo embraces bi-directional attention and segment recurrence with an additional computation overhead only linearly proportional to the memory length. Experiments on widely used language modeling benchmarks demonstrate its superiority over the baselines equipped with different types of memory mechanisms.
Automatic Fake News Detection: Are current models “fact-checking” or“gut-checking”?

Automatic fake news detection models are ostensibly based on logic, where the truth of a claim made in a headline can be determined by supporting or refuting evidence found in a resulting web query. These models are believed to be reasoning in some way; however, it has been shown that these same results, or better, can be achieved without considering the claim at all – only the evidence. This implies that other signals are contained within the examined evidence, and could be based on manipulable factors such as emotion, sentiment, or part-of-speech (POS) frequencies, which are vulnerable to adversarial inputs. We neutralize some of these signals through multiple forms of both neural and non-neural pre-processing and style transfer, and find that this flattening of extraneous indicators can induce the models to actually require both claims and evidence to perform well. We conclude with the construction of a model using emotion vectors built off a lexicon and passed through an “emotional attention” mechanism to appropriately weight certain emotions. We provide quantifiable results that prove our hypothesis that manipulable features are being used for fact-checking.
Model-agnostic Multi-Domain Learning with Domain-Specific Adapters for Action Recognition

In this paper, we propose a multi-domain learning model for action recognition. The proposed method inserts domain-specific adapters between layers of domain-independent layers of a backbone network. Unlike a multi-head network that switches classification heads only, our model switches not only the heads, but also the adapters for facilitating to learn feature representations universal to multiple domains. Unlike prior works, the proposed method is model-agnostic and doesn't assume model structures unlike prior works. Experimental results on three popular action recognition datasets (HMDB51, UCF101, and Kinetics-400) demonstrate that the proposed method is more effective than a multi-head architecture and more efficient than separately training models for each domain.
Nonrelativistic limit of the Euler‐HMPN approximation models arising in radiation hydrodynamics

In this paper, we are concerned with the nonrelativistic limit of a class of computable approximation models for radiation hydrodynamics. The models consist of the compressible Euler equations coupled with moment closure approximations to the radiative transfer equation. They are first‐order partial differential equations with source terms. As hyperbolic relaxation systems, they are showed to satisfy the structural stability condition proposed by the second author. Based on this, we verify the nonrelativistic limit by combining an energy method with a formal asymptotic analysis.
COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval

Large-scale single-stream pre-training has shown dramatic performance in image-text retrieval. Regrettably, it faces low inference efficiency due to heavy attention layers. Recently, two-stream methods like CLIP and ALIGN with high inference efficiency have also shown promising performance, however, they only consider instance-level alignment between the two streams (thus there is still room for improvement). To overcome these limitations, we propose a novel COllaborative Two-Stream vision-language pretraining model termed COTS for image-text retrieval by enhancing cross-modal interaction. In addition to instance-level alignment via momentum contrastive learning, we leverage two extra levels of cross-modal interactions in our COTS: (1) Token-level interaction - a masked vision-language modeling (MVLM) learning objective is devised without using a cross-stream network module, where variational autoencoder is imposed on the visual encoder to generate visual tokens for each image. (2) Task-level interaction - a KL-alignment learning objective is devised between text-to-image and image-to-text retrieval tasks, where the probability distribution per task is computed with the negative queues in momentum contrastive learning. Under a fair comparison setting, our COTS achieves the highest performance among all two-stream methods and comparable performance (but with 10,800× faster in inference) w.r. t. the latest single-stream methods. Importantly, our COTS is also applicable to text-to-video retrieval, yielding new state-of-the-art on the widely-used MSR-VTT dataset.
Towards Porting Operating Systems with Program Synthesis

The end of Moore’s Law has ushered in a diversity of hardware not seen in decades. Operating system (OS) (and system software) portability is accordingly becoming increasingly critical. Simultaneously, there has been tremendous progress in program synthesis. We set out to explore the feasibility of using modern program synthesis to generate the machine-dependent parts of an operating system. Our ultimate goal is to generate new ports automatically from descriptions of new machines. One of the issues involved is writing specifications, both for machine-dependent operating system functionality and for instruction set architectures. We designed two domain-specific languages: Alewife for machine-independent specifications of machine-dependent operating system functionality and Cassiopea for describing instruction set architecture semantics. Automated porting also requires an implementation. We developed a toolchain that, given an Alewife specification and a Cassiopea machine description, specializes the machine-independent specification to the target instruction set architecture and synthesizes an implementation in assembly language with a customized symbolic execution engine. Using this approach, we demonstrate the successful synthesis of a total of 140 OS components from two pre-existing OSes for four real hardware platforms. We also developed several optimization methods for OS-related assembly synthesis to improve scalability. The effectiveness of our languages and ability to synthesize code for all 140 specifications is evidence of the feasibility of program synthesis for machine-dependent OS code. However, many research challenges remain; we also discuss the benefits and limitations of our synthesis-based approach to automated OS porting.
Characterizing metastable states with the help of machine learning

Present-day atomistic simulations generate long trajectories of ever more complex systems. Analyzing these data, discovering metastable states, and uncovering their nature are becoming increasingly challenging. In this paper, we first use the variational approach to conformation dynamics to discover the slowest dynamical modes of the simulations. This allows the different metastable states of the system to be located and organized hierarchically. The physical descriptors that characterize metastable states are discovered by means of a machine learning method. We show in the cases of two proteins, chignolin and bovine pancreatic trypsin inhibitor, how such analysis can be effortlessly performed in a matter of seconds. Another strength of our approach is that it can be applied to the analysis of both unbiased and biased simulations.
A deep first-order system least squares method for solving elliptic PDEs

We propose a First-Order System Least Squares (FOSLS) method based on deep-learning for numerically solving second-order elliptic PDEs. The method we propose is capable of dealing with either variational and non-variational problems, and because of its meshless nature, it can also deal with problems posed in high-dimensional domains. We prove the Γ-convergence of the neural network approximation towards the solution of the continuous problem, and extend the convergence proof to some well-known related methods. Finally, we present several numerical examples illustrating the performance of our discretization.
On Variants of Root Normalised Order-aware Divergence and a Divergence based on Kendall's Tau

This paper reports on a follow-up study of the work reported in Sakai, which explored suitable evaluation measures for ordinal quantification tasks. More specifically, the present study defines and evaluates, in addition to the quantification measures considered earlier, a few variants of an ordinal quantification measure called Root Normalised Order-aware Divergence (RNOD), as well as a measure which we call Divergence based on Kendall's $\tau$ (DNKT). The RNOD variants represent alternative design choices based on the idea of Sakai's Distance-Weighted sum of squares (DW), while DNKT is designed to ensure that the system's estimated distribution over classes is faithful to the target priorities over classes. As this Priority Preserving Property (PPP) of DNKT may be useful in some applications, we also consider combining some of the existing quantification measures with DNKT. Our experiments with eight ordinal quantification data sets suggest that the variants of RNOD do not offer any benefit over the original RNOD at least in terms of system ranking consistency, i.e., robustness of the system ranking to the choice of test data. Of all ordinal quantification measures considered in this study (including Normalised Match Distance, a.k.a. Earth Mover's Distance), RNOD is the most robust measure overall. Hence the design choice of RNOD is a good one from this viewpoint. Also, DNKT is the worst performer in terms of system ranking consistency. Hence, if DNKT seems appropriate for a task, sample size design should take its statistical instability into account.
ALMA Lensing Cluster Survey: ALMA-Herschel Joint Study of Lensed Dusty Star-forming Galaxies across z ≃ 0.5 – 6

We present an ALMA-Herschel joint analysis of sources detected by the ALMA Lensing Cluster Survey (ALCS) at 1.15 mm. Herschel/PACS and SPIRE data at 100–500 μm are deblended for 180 ALMA sources in 33 lensing cluster fields that are detected either securely (141 sources; in our main sample) or tentatively at S/N ≥ 4 with cross-matched HST/Spitzer counterparts, down to a delensed 1.15 mm flux density of ∼0.02 mJy. We performed far-infrared spectral energy distribution modeling and derived the physical properties of dusty star formation for 125 sources (109 independently) that are detected at >2σ in at least one Herschel band. A total of 27 secure ALCS sources are not detected in any Herschel bands, including 17 optical/near-IR-dark sources that likely reside at z = 4.2 ± 1.2. The 16th, 50th, and 84th percentiles of the redshift distribution are 1.15, 2.08, and 3.59, respectively, for ALCS sources in the main sample, suggesting an increasing fraction of z ≃ 1 − 2 galaxies among fainter millimeter sources (f 1150 ∼ 0.1 mJy). With a median lensing magnification factor of μ=2.6−0.8+2.6 , ALCS sources in the main sample exhibit a median intrinsic star formation rate of 94−54+84 M ⊙ yr−1, lower than that of conventional submillimeter galaxies at similar redshifts by a factor of ∼3. Our study suggests weak or no redshift evolution of dust temperature with L IR < 1012 L ⊙ galaxies within our sample at z ≃ 0 − 2. At L IR > 1012 L ⊙, the dust temperatures show no evolution across z ≃ 1–4 while being lower than those in the local universe. For the highest-redshift source in our sample (z = 6.07), we can rule out an extreme dust temperature (>80 K) that was reported for MACS0416 Y1 at z = 8.31.
RIS-Assisted Visible Light Communication Systems: A Tutorial

Recent intensive and extensive development of the fifth-generation (5G) of cellular networks has led to their deployment throughout much of the world. As part of this implementation, one of the challenges that must be addressed is the skip-zone problem, which occurs when objects such as trees, people, animals, and vehicles obstruct the transmission of signals. In free-space optical (FSO) and radio frequency (RF) systems, dead zones are most often caused by buildings and trees, while in visible light communications (VLC), obstructions are caused by individuals moving around a room or objects placed in the room. A signal obstruction can significantly reduce the signal-to-noise ratio in RF and indoor VLC systems, whereas in FSO systems, where the transmitted signals are directional, the obstruction can completely disrupt data transmission. Therefore, the skip-zone dilemma must be resolved to ensure the smooth and efficient operation of 5G and beyond networks. By placing a relay between a transmitter and a receiver, the effects of obstacles can be mitigated. As a result, the signal from the transmitter will reach the receiver. In recent years, reconfigurable intelligent surfaces (RISs) that are more efficient than relays have become widely accepted as a method of mitigating skip-zones and providing reconfigurable radio environments. However, there have been limited studies of RISs for optical wireless communication (OWC) systems. Through the RIS technology, OWC and RF communication channels can be reconfigured. This paper aims to provide a comprehensive tutorial on indoor VLC systems utilizing RIS technology. The article discusses the basics of VLC and RISs and reintroduces RISs for OWC systems, focusing on RIS-assisted indoor VLC systems. We also provide a comprehensive overview of optical RISs and examine the differences between optical RISs, RF-RISs, and optical relays. Furthermore, we discuss in detail how RISs can be used to overcome line-of-sight blockages and the device orientation issue in VLC systems while revealing key challenges such as RIS element orientation design, RIS elements to access point/user assignment design, and RIS array positioning design problems that need to be studied. Moreover, we discuss and propose several research problems on integrating optical RISs with other emerging technologies, including non-orthogonal multiple access, multiple-input multiple-output systems, physical layer security, and simultaneous lightwave and power transfer in VLC systems. Finally, we highlight other important research directions that can further improve the performance of RIS-assisted VLC systems.
Comets in Context: Comparing Comet Compositions with Protosolar Nebula Models

Comets provide a valuable window into the chemical and physical conditions at the time of their formation in the young solar system. We seek insights into where and when these objects formed by comparing the range of abundances observed for nine molecules and their average values across a sample of 29 comets to the predicted midplane ice abundances from models of the protosolar nebula. Our fiducial model, where ices are inherited from the interstellar medium, can account for the observed mixing ratio ranges of each molecule considered, but no single location or time reproduces the abundances of all molecules simultaneously. This suggests that each comet consists of material processed under a range of conditions. In contrast, a model where the initial composition of disk material is “reset,” wiping out any previous chemical history, cannot account for the complete range of abundances observed in comets. Using toy models that combine material processed under different thermal conditions, we find that a combination of warm (CO-poor) and cold (CO-rich) material is required to account for both the average properties of the Jupiter-family and Oort cloud comets, and the individual comets we consider. This could occur by the transport (either radial or vertical) of ice-coated dust grains in the early solar system. Comparison of the models to the average Jupiter-family and Oort cloud comet compositions suggests the two families formed in overlapping regions of the disk, in agreement with the findings of A’Hearn et al. and with the predictions of the Nice model.
A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations

We propose a novel federated learning paradigm to model data variability among heterogeneous clients in multi-centric studies. Our method is expressed through a hierarchical Bayesian latent variable model, where client-specific parameters are assumed to be realization from a global distribution at the master level, which is in turn estimated to account for data bias and variability across clients. We show that our framework can be effectively optimized through expectation maximization (EM) over latent master's distribution and clients' parameters. We also introduce formal differential privacy (DP) guarantees compatibly with our EM optimization scheme. We tested our method on the analysis of multi-modal medical imaging data and clinical scores from distributed clinical datasets of patients affected by Alzheimer's disease. We demonstrate that our method is robust when data is distributed either in iid and non-iid manners, even when local parameters perturbation is included to provide DP guarantees. Our approach allows to quantify the variability of data, views and centers, while guaranteeing high-quality data reconstruction as compared to the state-of-the-art autoencoding models and federated learning schemes.The code is available at https://gitlab.inria. fr/epione/federated-multi-views-ppca
Fragmentation of the $^4$He$_2$ dimer by relativistic highly charged projectiles in collisions with small kinetic energy release

We study theoretically the fragmentation of the helium dimer, 4 He 2 , into singly charged ions in collisions with relativistic highly-charged projectiles. We discuss the main mechanisms driving this process with the focus on the fragmentation caused by direct ionization of both atomic sites of the dimer in a single collision with the projectile. This direct mechanism dominates the He 2 → He + + He + breakup events with the kinetic energies of the emerging ionic fragments below 4–5 eV. We explore the energy and angular distributions of the He + ions produced in collisions with 1 and 7 GeV/u U 92+ projectiles and show that their shape is signiﬁcantly aﬀected by relativistic and higher-order eﬀects in the interaction between the projectile and the dimer. We also show that the shape of the energy spectrum is quite sensitive to the binding energy of the He 2 dimer which can be exploited for its precise determination. The contribution of the direct mechanism to the total cross section for the He 2 fragmentation by 1 and 7 GeV/u U 92+ was calculated to be 3 . 65 Mb and 2 . 4 Mb, respectively, representing roughly half of this cross section.
On graphic degree sequences and matching numbers

The recently introduced Degree Preserving Growth model (Nature Physics, DOI:10.1038/s41567-021-01417-7) uses matchings to insert new vertices of prescribed degrees into the current graph of an ever-growing graph sequence. In this paper we are presenting lower bounds on the matching numbers of the graphs in the growth sequence, based on their degree sequence only. We are interested in bounds on maximal/maximum matchings in some realizations (potential matching number), and also on common bounds on all realizations (forcible matching number). Particularly, we prove that whether a degree sequence can be extended with a new degree $2\delta$ is equivalent to the existence of a realization of the original degree sequence with a matching of size $\delta$ that covers the largest $2\delta$ degrees. Second, we estimate the minimum size of both the maximal and the maximum matchings, as determined by the degree sequence, independently of the graphical realization. Furthermore, we also estimate the maximum value of the matching number over all possible realizations for the degree sequence. Along this line we answer a question raised by Biedl, Demaine et. al. (DOI:10.1016/j.disc.2004.05.003). We briefly discuss an application to prime gap sequences.
A More Eﬃcient and Egalitarian Mechanism for School Choice

How should students be assigned to schools? Two mechanisms have been suggested and implemented around the world: deferred acceptance (DA) and top trading cycles (TTC). These two mechanisms are widely considered excellent choices owing to their outstanding stability and incentive properties. We show theoretically and empirically that both mechanisms perform poorly with regard to two key desiderata such as eﬃciency and equality, even in large markets. In contrast, the rank-minimizing mechanism is signiﬁcantly more eﬃcient and egalitarian. It is also Pareto optimal for the students, unlike DA, and generates less justiﬁed envy than TTC.
ResT V2: Simpler, Faster and Stronger

This paper proposes ResTv2, a simpler, faster, and stronger multi-scale vision Transformer for visual recognition. ResTv2 simplifies the EMSA structure in ResTv1 (i.e., eliminating the multi-head interaction part) and employs an upsample operation to reconstruct the lost medium- and high-frequency information caused by the downsampling operation. In addition, we explore different techniques for better apply ResTv2 backbones to downstream tasks. We found that although combining EMSAv2 and window attention can greatly reduce the theoretical matrix multiply FLOPs, it may significantly decrease the computation density, thus causing lower actual speed. We comprehensively validate ResTv2 on ImageNet classification, COCO detection, and ADE20K semantic segmentation. Experimental results show that the proposed ResTv2 can outperform the recently state-of-the-art backbones by a large margin, demonstrating the potential of ResTv2 as solid backbones. The code and models will be made publicly available at \url{https://github.com/wofmanaf/ResT}