Reviewerly/exHarmony huggingface dataset:
PCA and t-SNE analysis in the study of QAOA entangled and non-entangled mixing operators

In this paper, we employ PCA and t-SNE analysis to gain deeper insights into the behavior of entangled and non-entangled mixing operators within the Quantum Approximate Optimization Algorithm (QAOA) at varying depths. Our study utilizes a dataset of parameters generated for max-cut problems using the Stochastic Hill Climbing with Random Restarts optimization method in QAOA. Specifically, we examine the $RZ$, $RX$, and $RY$ parameters within QAOA models at depths of $1L$, $2L$, and $3L$, both with and without an entanglement stage inside the mixing operator. The results reveal distinct behaviors when we process the final parameters of each set of experiments with PCA and t-SNE, where in particular, entangled QAOA models with $2L$ and $3L$ present an increase in the amount of information that can be preserved in the mapping. Furthermore, certain entangled QAOA graphs exhibit clustering effects in both PCA and t-SNE. Overall, the mapping results clearly demonstrate a discernible difference between entangled and non-entangled models, quantified numerically through explained variance in PCA and Kullback-Leibler divergence (after optimization) in t-SNE, where some of these differences are also visually evident in the mapping data produced by both methods.
Students’ Reaction on Operating Rhetorical Strategies in Promoting Linguistic Skills and Cultural Diversity at Islamic Multicultural Classrooms

This research was motivated by some multicultural classroom students’ reactions to practicing writing argumentative text including the fact that Indonesia's adult literacy rate of 95% is not accompanied by the ability to recognize and adopt composed facts, which on average is very little. This research was formerly aimed to depict how multicultural classroom students react to deploying rhetorical strategies to write and advance their linguistic skills as well as cultural diversity The research approach adopted in this research was qualitative. The types of data sources in this study are documents in the form of various thesis manuscripts from a multicultural classroom of Masters’s Study programs at UIN SATU Tulungagung. The data collection technique applied to the main data source in the form of archives or written documents is to rely on the note-taking technique and administrating one online questionnaire survey to thirty-two students as well as attending an interview. In the thesis of multicultural classroom deployed seven strategies counting definition, comparison, cause-and-effect, problem-solution, means-end, listing, partition strategies were applied by students in argumentative text units which they compiled at various levels, both at the sentence unit level, paragraph unit, and essay unit; several facets of appealing the students experience in writing argumentative text also appeared promoting linguistic and cultural diverse in composing argumentative text counting 5 linguistics facets and 2 cultural diverse such as argumentative state/disputatiousness, probing and assortment criticism, adopting composing typical, recognized arrangement, pondering on preceding insight, instructors’ concentrating on awareness development, indigenous upbringing; experience of deploying rhetorical strategies in order to enhance linguistic skill counting conceptual and procedural knowledge to the same degree credibility, logic and emotion mainly deploying cognitive, social, search and metacognitive strategies.
A Review of Assured Data Deletion Security Techniques in Cloud Storage

Cloud computing is an interesting technology that allows customers to have convenient, on-demand network connectivity based on their needs with minimal maintenance and contact between cloud providers. The issue of security has arisen as a serious concern, particularly in the case of cloud computing, where data is stored and accessible via the Internet from a third-party storage system. It is critical to ensure that data is only accessible to the appropriate individuals and that it is not stored in third-party locations. Because third-party services frequently make backup copies of uploaded data for security reasons, removing the data the owner submits does not guarantee the removal of the data from the cloud. Cloud data storage has grown in popularity, and the problem of ensured deletion has been solved. Several schemes to overcome the assured deletion problem have been proposed over the last few years. The proposed solutions have addressed the scaling overhead, trusted third parties, delays, single points of failure, and other inefficiencies. Customers had the option of receiving verifiable proof of deletion from cloud service providers. This article focuses on the issue of how cloud data storage clients may be confident that the deleted data from the cloud cannot be recovered. Furthermore, it discusses the practice of secure deletion. Moreover, the paper explores currently used methods to achieve the security of assuring the deletion of data faced by cloud entities such as cloud service providers, data owners, and cloud users. After that, the paper analyzes techniques to find the pros and cons of assured deletion and the problems that were solved by these techniques. Finally, the paper identifies some future directions for the development of assured deletion of cloud storage.
Minimax optimal testing by classification

This paper considers an ML inspired approach to hypothesis testing known as classifier/classification-accuracy testing ($\mathsf{CAT}$). In $\mathsf{CAT}$, one first trains a classifier by feeding it labeled synthetic samples generated by the null and alternative distributions, which is then used to predict labels of the actual data samples. This method is widely used in practice when the null and alternative are only specified via simulators (as in many scientific experiments). We study goodness-of-fit, two-sample ($\mathsf{TS}$) and likelihood-free hypothesis testing ($\mathsf{LFHT}$), and show that $\mathsf{CAT}$ achieves (near-)minimax optimal sample complexity in both the dependence on the total-variation ($\mathsf{TV}$) separation $\epsilon$ and the probability of error $\delta$ in a variety of non-parametric settings, including discrete distributions, $d$-dimensional distributions with a smooth density, and the Gaussian sequence model. In particular, we close the high probability sample complexity of $\mathsf{LFHT}$ for each class. As another highlight, we recover the minimax optimal complexity of $\mathsf{TS}$ over discrete distributions, which was recently established by Diakonikolas et al. (2021). The corresponding $\mathsf{CAT}$ simply compares empirical frequencies in the first half of the data, and rejects the null when the classification accuracy on the second half is better than random.
Beyond Normal: On the Evaluation of Mutual Information Estimators

Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology. However, mutual information estimators are typically evaluated on simple families of probability distributions, namely multivariate normal distribution and selected distributions with one-dimensional random variables. In this paper, we show how to construct a diverse family of distributions with known ground-truth mutual information and propose a language-independent benchmarking platform for mutual information estimators. We discuss the general applicability and limitations of classical and neural estimators in settings involving high dimensions, sparse interactions, long-tailed distributions, and high mutual information. Finally, we provide guidelines for practitioners on how to select appropriate estimator adapted to the difficulty of problem considered and issues one needs to consider when applying an estimator to a new data set.
The factorization of $X^n-a$ and $f(X^n)$ over $\mathbb F_q$

The factorizations of the polynomial $X^n-1$ and the cyclotomic polynomial $\Phi_n$ over a finite field $\mathbb F_q$ have been studied for a very long time. Explicit factorizations have been given for the case that $\mathrm{rad}(n)\mid q^w-1$ where $w=1$, $w$ is prime or $w$ is the product of two primes. For arbitrary $a\in \mathbb F_q^\ast$ the factorization of the polynomial $X^n-a$ is needed for the construction of constacyclic codes. Its factorization has been determined for the case $\mathrm{rad}(n)\mid q-1$ and for the case that there exist at most three distinct prime factors of $n$ and $\mathrm{rad}(n)\mid q^w-1$ for a prime $w$. Both polynomials $X^n-1$ and $X^n-a$ are compositions of the form $f(X^n)$ for a monic irreducible polynomial $f\in \mathbb F_q[X]$. The factorization of the composition $f(X^n)$ is known for the case $\gcd(n, \mathrm{ord}(f)\cdot \mathrm{deg}(f))=1$ and $\mathrm{rad}(n)\mid q^w-1$ for $w=1$ or $w$ prime. However, there does not exist a closed formula for the explicit factorization of either $X^n-1$, the cyclotomic polynomial $\Phi_n$, the binomial $X^n-a$ or the composition $f(X^n)$. Without loss of generality we can assume that $\gcd(n,q)=1$. Our main theorem, Theorem 18, is a closed formula for the factorization of $X^n-a$ over $\mathbb F_q$ for any $a\in \mathbb F_q^\ast$ and any positive integer $n$ such that $\gcd(n,q)=1$. From our main theorem we derive one closed formula each for the factorization of $X^n-1$ and of the $n$-th cyclotomic polynomial $\Phi_n$ for any positive integer $n$ such that $\gcd(n,q)=1$ (Theorem 2.5 and Theorem 2.6). Furthermore, our main theorem yields a closed formula for the factorization of the composition $f(X^n)$ for any irreducible polynomial $f\in \mathbb F_q[X]$, $f\neq X$, and any positive integer $n$ such that $\gcd(n,q)=1$ (Theorem 27).
Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness

In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false positive and false negative error rate imbalances, identifying statistically significant disparities between groups which are present even when adjusting for group-level differences in base rates. We describe a novel IJDI-Scan approach which can efficiently identify the intersectional subpopulations, defined across multiple observed attributes of the data, with the most significant IJDI. To evaluate IJDI-Scan's performance, we conduct experiments on both simulated and real-world data, including recidivism risk assessment and credit scoring. Further, we implement and evaluate approaches to mitigating IJDI for the detected subpopulations in these domains.
New Cross-Core Cache-Agnostic and Prefetcher-based Side-Channels and Covert-Channels

In this paper, we reveal the existence of a new class of prefetcher, the XPT prefetcher, in the modern Intel processors which has never been officially documented. It speculatively issues a load, bypassing last-level cache (LLC) lookups, when it predicts that a load request will result in an LLC miss. We demonstrate that XPT prefetcher is shared among different cores, which enables an attacker to build cross-core side-channel and covert-channel attacks. We propose PrefetchX, a cross-core attack mechanism, to leak users' sensitive data and activities. We empirically demonstrate that PrefetchX can be used to extract private keys of real-world RSA applications. Furthermore, we show that PrefetchX can enable side-channel attacks that can monitor keystrokes and network traffic patterns of users. Our two cross-core covert-channel attacks also see a low error rate and a 1.7MB/s maximum channel capacity. Due to the cache-independent feature of PrefetchX, current cache-based mitigations are not effective against our attacks. Overall, our work uncovers a significant vulnerability in the XPT prefetcher, which can be exploited to compromise the confidentiality of sensitive information in both crypto and non-crypto-related applications among processor cores.
When quantum memory is useful for dense coding

We discuss dense coding with $n$ copies of a specific preshared state between the sender and the receiver when the encoding operation is limited to the application of group representation. In this problem, if the receiver keeps the quantum system of a part of preshared states, the receiver needs quantum memory. Depending on available encoding unitary operations, we investigate what preshared state offers an advantage of using quantum memory on the receiver's side.
Deep graph kernel point processes

Point process models are widely used for continuous asynchronous event data, where each data point includes time and additional information called "marks", which can be locations, nodes, or event types. This paper presents a novel point process model for discrete event data over graphs, where the event interaction occurs within a latent graph structure. Our model builds upon Hawkes's classic influence kernel-based formulation in the original self-exciting point processes work to capture the influence of historical events on future events' occurrence. The key idea is to represent the influence kernel by Graph Neural Networks (GNN) to capture the underlying graph structure while harvesting the strong representation power of GNNs. Compared with prior works focusing on directly modeling the conditional intensity function using neural networks, our kernel presentation herds the repeated event influence patterns more effectively by combining statistical and deep models, achieving better model estimation/learning efficiency and superior predictive performance. Our work significantly extends the existing deep spatio-temporal kernel for point process data, which is inapplicable to our setting due to the fundamental difference in the nature of the observation space being Euclidean rather than a graph. We present comprehensive experiments on synthetic and real-world data to show the superior performance of the proposed approach against the state-of-the-art in predicting future events and uncovering the relational structure among data.
Mitigating Speculation-based Attacks through Configurable Hardware/Software Co-design

New speculation-based attacks that affect large numbers of modern systems are disclosed regularly. Currently, CPU vendors regularly fall back to heavy-handed mitigations like using barriers or enforcing strict programming guidelines resulting in significant performance overhead. What is missing is a solution that allows for efficient mitigation and is flexible enough to address both current and future speculation vulnerabilities, without additional hardware changes. In this work, we present SpecControl, a novel hardware/software co-design, that enables new levels of security while reducing the performance overhead that has been demonstrated by state-of-the-art methodologies. SpecControl introduces a communication interface that allows compilers and application developers to inform the hardware about true branch dependencies, confidential control-flow instructions, and fine-grained instruction constraints in order to apply restrictions only when necessary. We evaluate SpecControl against known speculative execution attacks and in addition, present a new speculative fetch attack variant on the Pattern History Table (PHT) in branch predictors that shows how similar previously reported vulnerabilities are more dangerous by enabling unprivileged attacks, especially with the state-of-the-art branch predictors. SpecControl provides stronger security guarantees compared to the existing defenses while reducing the performance overhead of two state-of-the-art defenses from 51% and 43% to just 23%.
Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction

Cooperative multi-agent reinforcement learning (MARL) for navigation enables agents to cooperate to achieve their navigation goals. Using emergent communication, agents learn a communication protocol to coordinate and share information that is needed to achieve their navigation tasks. In emergent communication, symbols with no pre-specified usage rules are exchanged, in which the meaning and syntax emerge through training. Learning a navigation policy along with a communication protocol in a MARL environment is highly complex due to the huge state space to be explored. To cope with this complexity, this work proposes a novel neural network architecture, for jointly learning an adaptive state space abstraction and a communication protocol among agents participating in navigation tasks. The goal is to come up with an adaptive abstractor that significantly reduces the size of the state space to be explored, without degradation in the policy performance. Simulation results show that the proposed method reaches a better policy, in terms of achievable rewards, resulting in fewer training iterations compared to the case where raw states or fixed state abstraction are used. Moreover, it is shown that a communication protocol emerges during training which enables the agents to learn better policies within fewer training iterations.
Masked Diffusion Models Are Fast Distribution Learners

Diffusion model has emerged as the \emph{de-facto} model for image generation, yet the heavy training overhead hinders its broader adoption in the research community. We observe that diffusion models are commonly trained to learn all fine-grained visual information from scratch. This paradigm may cause unnecessary training costs hence requiring in-depth investigation. In this work, we show that it suffices to train a strong diffusion model by first pre-training the model to learn some primer distribution that loosely characterizes the unknown real image distribution. Then the pre-trained model can be fine-tuned for various generation tasks efficiently. In the pre-training stage, we propose to mask a high proportion (e.g., up to 90\%) of input images to approximately represent the primer distribution and introduce a masked denoising score matching objective to train a model to denoise visible areas. In subsequent fine-tuning stage, we efficiently train diffusion model without masking. Utilizing the two-stage training framework, we achieves significant training acceleration and a new FID score record of 6.27 on CelebA-HQ $256 \times 256$ for ViT-based diffusion models. The generalizability of a pre-trained model further helps building models that perform better than ones trained from scratch on different downstream datasets. For instance, a diffusion model pre-trained on VGGFace2 attains a 46\% quality improvement when fine-tuned on a different dataset that contains only 3000 images. Our code is available at \url{https://github.com/jiachenlei/maskdm}.
New Binary Self-Dual Cyclic Codes with Square-Root-Like Minimum Distances

The construction of self-dual codes over small fields such that their minimum distances are as large as possible is a long-standing challenging problem in the coding theory. In 2009, a family of binary self-dual cyclic codes with lengths $n_i$ and minimum distances $d_i \geq \frac{1}{2} \sqrt{n_i}$, $n_i$ goes to the infinity for $i=1,2, \ldots$, was constructed. In this paper, we construct a family of (repeated-root) binary self-dual cyclic codes with lengths $n$ and minimum distances at least $\sqrt{n}-2$. New families of lengths $n=q^m-1$, $m=3, 5, \ldots$, self-dual codes over ${\bf F}_q$, $q \equiv 1$ $mod$ $4$, with their minimum distances larger than or equal to $\sqrt{\frac{q}{2}}\sqrt{n}-q$ are also constructed.
Explicit Syntactic Guidance for Neural Text Generation

Most existing text generation models follow the sequence-to-sequence paradigm. Generative Grammar suggests that humans generate natural language texts by learning language grammar. We propose a syntax-guided generation schema, which generates the sequence guided by a constituency parse tree in a top-down direction. The decoding process can be decomposed into two parts: (1) predicting the infilling texts for each constituent in the lexicalized syntax context given the source sentence; (2) mapping and expanding each constituent to construct the next-level syntax context. Accordingly, we propose a structural beam search method to find possible syntax structures hierarchically. Experiments on paraphrase generation and machine translation show that the proposed method outperforms autoregressive baselines, while also demonstrating effectiveness in terms of interpretability, controllability, and diversity.
EMoG: Synthesizing Emotive Co-speech 3D Gesture with Diffusion Model

Although previous co-speech gesture generation methods are able to synthesize motions in line with speech content, it is still not enough to handle diverse and complicated motion distribution. The key challenges are: 1) the one-to-many nature between the speech content and gestures; 2) the correlation modeling between the body joints. In this paper, we present a novel framework (EMoG) to tackle the above challenges with denoising diffusion models: 1) To alleviate the one-to-many problem, we incorporate emotion clues to guide the generation process, making the generation much easier; 2) To model joint correlation, we propose to decompose the difficult gesture generation into two sub-problems: joint correlation modeling and temporal dynamics modeling. Then, the two sub-problems are explicitly tackled with our proposed Joint Correlation-aware transFormer (JCFormer). Through extensive evaluations, we demonstrate that our proposed method surpasses previous state-of-the-art approaches, offering substantial superiority in gesture synthesis.
Query Encoder Distillation via Embedding Alignment is a Strong Baseline Method to Boost Dense Retriever Online Efficiency

The information retrieval community has made significant progress in improving the efficiency of Dual Encoder (DE) dense passage retrieval systems, making them suitable for latency-sensitive settings. However, many proposed procedures are often too complex or resource-intensive, which makes it difficult for practitioners to adopt them or identify sources of empirical gains. Therefore, in this work, we propose a trivially simple recipe to serve as a baseline method for boosting the efficiency of DE retrievers leveraging an asymmetric architecture. Our results demonstrate that even a 2-layer, BERT-based query encoder can still retain 92.5% of the full DE performance on the BEIR benchmark via unsupervised distillation and proper student initialization. We hope that our findings will encourage the community to re-evaluate the trade-offs between method complexity and performance improvements.
3D Keypoint Estimation Using Implicit Representation Learning

In this paper, we tackle the challenging problem of 3D keypoint estimation of general objects using a novel implicit representation. Previous works have demonstrated promising results for keypoint prediction through direct coordinate regression or heatmap-based inference. However, these methods are commonly studied for specific subjects, such as human bodies and faces, which possess fixed keypoint structures. They also suffer in several practical scenarios where explicit or complete geometry is not given, including images and partial point clouds. Inspired by the recent success of advanced implicit representation in reconstruction tasks, we explore the idea of using an implicit field to represent keypoints. Specifically, our key idea is employing spheres to represent 3D keypoints, thereby enabling the learnability of the corresponding signed distance field. Explicit keypoints can be extracted subsequently by our algorithm based on the Hough transform. Quantitative and qualitative evaluations also show the superiority of our representation in terms of prediction accuracy.